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Demand Side Management Using Hybrid Bacterial Foraging and Genetic Algorithm Optimization Techniques
Abstract
Today, energy is the most valuable resource, new methods and techniques are being discovered to fulfill the demand of energy. However, energy demand growth causes a serious energy crisis, especially when demand is comparatively high and creates the peak load. This problem can be handled by integrating Demand Side Management (DSM) with traditional Smart Grid (SG) through two way communication between utility and customers. The main objective of DSM is peak load reduction where SG targets cost minimization and user comfort maximization. In this study, our emphasis is on cost minimization and load management by shifting the load from peak hours toward the off peak hours. In this underlying study, we adapt hybridization of two optimization approaches, Bacterial Foraging (BFA) and Genetic Algorithm (GA). Simulation results verify that the adapted approach reduces the total cost and peak average ratio by shifting the load on off peak hours with very little difference between minimum and maximum 95% confidence interval.
... A variety of approaches are used to solve the load shifting problem, such as particle swarm optimization-based algorithms [18], reinforcement learning [19], ant lion optimization algorithm [20], linear programming [21], and cuckoo search with grasshopper optimization algorithms [22]. The genetic algorithm (GA) is also considered in several works to solve the load shifting problem [23]- [25]. In [23], a demand-side management (DSM) approach with GA is used to give the best solution based on optimizing the load shaping in DSM. ...
... Awais et al. [24] proposed a similar approach, but with a greater focus on minimizing the peak to average ratio and the overall electricity consumption cost. Reference [25] adapted a hybridization of two optimization approaches, bacterial foraging (BFA) and GA, to reach the best solution. However, these research works have significant limitations, including neglecting the influence of load shifting and local generation on network operation [18], [19], [21]- [25], different appliances [19]- [21], and consumers gain from participating in the load shifting [23], [25]. ...
... Reference [25] adapted a hybridization of two optimization approaches, bacterial foraging (BFA) and GA, to reach the best solution. However, these research works have significant limitations, including neglecting the influence of load shifting and local generation on network operation [18], [19], [21]- [25], different appliances [19]- [21], and consumers gain from participating in the load shifting [23], [25]. Moreover, in [26], Bashir et al. used several machine learning algorithms, such as support vector machines, K-nearest neighbor, logistic regression, naive bayes, neural networks, and decision tree classifier, to predict the smart grid stability. ...
The residential sector electricity demand has been increasing over the years, leading to an increasing effort of the power network components, namely during the peak demand periods. This demand increasing together with the increasing levels of renewable-based energy generation and the need to ensure the electricity service quality, namely in terms of the voltage profile, is challenging the distribution network operation. Demand response can play an important role in facing these challenges, bringing several benefits, both for the network operation and for the consumer (e.g., increase network components lifetime and consumers bill reduction). The present research work proposes a genetic algorithm-based model to use the consumers’ load flexibility with demand response event participation. The proposed method optimally shifts residential loads to enable the consumers’ participation in demand response while respecting consumers’ preferences and constraints. A realistic low voltage distribution network with 236 buses is used to illustrate the application of the proposed model. The results show considerable energy cost savings for consumers and an improvement in voltage profile.
... The BFOA is hybrid with GA and is applied in [13]. In their work, they used RTP pricing scheme is taken into account to improve the load of the customer, UC and EC. ...
... In reality, some appliances may take less time to complete their operations. EC minimization and reduction in WT Consumer's threshold limit is not focused DR programs [13] Minimize power consumption Implements the DR program peak demand hours not considered BPSO [16] Reduces peak hours demands Peak demand is reduced Reduction in the bill Electric cost is not considered HSA [17] Reduces operational cost UC is not considered DSM model is presented using GA [18] Reduces operational cost, PAR Time complexity is completely ignored ...
... In the aforementioned literature, complete benefits have not been taken from a smart grid. EC and PAR are minimized by many researchers and some researchers focus on UC and load shifting to off-peak hours from on-peak hours as in [13,33]. However, aforementioned parameters are not catered by any related work simultaneously. ...
Nowadays, automated appliances are exponentially increasing. Therefore, there is a need for a scheme to accomplish the electricity demand of automated appliances. Recently, many Demand Side Management (DSM) schemes have been explored to alleviate Electricity Cost (EC) and Peak to Average Ratio (PAR). In this paper, energy consumption problem in a residential area is considered. To solve this problem, a heuristic based DSM technique is proposed to minimize EC and PAR with affordable user’s Waiting Time (WT). In heuristic techniques: Bacterial Foraging Optimization Algorithm (BFOA) and Flower Pollination Algorithm (FPA) are implemented.
... The BFOA is used in paper [13], which is a hybrid with GA. In this paper, author used RTP scheme to optimize the user's load and the cost by keeping in mind about user's comfort, cost minimization and reduction in PAR. ...
... By using scheduler we can schedule the home appliances with which energy consumption is reduced in DSM. Many of the researchers try to minimize the price of electricity which is our main problem by shifting the load towards off-peak hours [28] using GA and [13] BFOA. PAR reduction, minimization of EC, UC maximization and minimize the power consumption are the most common objectives of electricity management in SG. ...
... Cost, minimization and appliances; utility uplifting, and not considering the privacy of user MOEA [11] Cost minimization and reducing the WT Consumer exceeds the threshold limit is not focused DR programs [13] Reduce the overall power consumption Implement the DR program Successfully in peak demand hours BPSO [16] Reducing the excess demand from peak hours along with reduce in the bill Reduction in peak demand and saving utility bills are not considered, FPA [17] side lobe level minimization and null placement ignore interferences in undesired direction HSA [18] Have to reduce operational cost ...
Nowadays, different schemes and ways are proposed to meet the user's load requirement of energy towards the Demand Side (DS) in order to encapsulate the energy resources. However, this Load Demand (LD) increases day by day. This increase in LD is causing serious energy crises to the utility and DS. As the usage of energy increases with the increase in user's demand respectively, the peak is increased in these hours which affect the customer's in term of high-cost prices. This issue is tackled using some schemes and their proper integration. Two-way communication is done by the utility through Smart Grid (SG) between utility and customers. Customers that show some good behavior and helps the utility to control this LD, can perform a key role here. In this paper, our main focus is to control the Customer Side Management (CSM) by reducing the peak generation from on-peak hours. In our scenario, we focus on saving the cost expenditure of users by giving them comfort and shifting the load of appliances from high LD hours to low LD hours. In this study, we adopt the optimization algorithms, like Bacterial Foraging Optimization Algorithm (BFOA), Flower Pollination Algorithm (FPA) and proposed our Hybrid Bacterial Flower Pollination Algorithm (HBFPA) to optimize the solution of our problem using the famous electricity scheme named as Critical Peak Pricing(CPP) with three different Operational Time intervals (OTIs). Simulations and results show that our scheme reduces the cost and peak to the average ratio by proper shifting the appliances from highly load demanding hours to the low demanding hours with the negligibly small difference between the maximum and minimum 90% of confidence interval.
... This bi-directional communication is useful for energy optimization, load balancing, electricity cost reduction and minimizing PAR [2], [3], [4]. Load balancing is basically efficient management of energy consumption by balancing load in on-peak hours and off-peak hours [13], [14]. User tries to minimize electricity cost by shifting load from on-peak hour to off-peak hours. ...
... We have used equation 1 to calculate electricity cost for 24 hours, equation 2 is used to calculate load and as shown in equation 3 PAR is calculated using this equation. These equations are used by the authors in [13] for electricity cost, load, and PAR calculations. ...
... While, probabilistic nature of GA does not guarantee optimality. GA performs best for larger population, while BFA performs best for small population [13]. Execution time of GA is less as compare to other meta-heuristic techniques [14]. ...
In the past few years, a number of optimization techniques have been designed for Home Energy Management System (HEMS). In this paper, we evaluated the performance of two heuristic algorithms, i.e., Harmony Search Algorithm (HSA) and Tabu Search (TS) for optimization in residential area. These algorithms are used for efficient scheduling of Smart Appliances (SA) in Smart Homes (SH). Evaluated results show that TS performed better than HSA in achieving our defined goals of cost reduction, improving User Comfort (UC) level and minimization of Peak to Average Ratio (PAR). However, there remains a trade-off between electricity cost and waiting time.
... DSM functions can be classified as follows; Load Management and Demand Response [7]. Load [5,6] with the management of load in such a manner that it distributes the demand evenly as well as satisfies the consumers' demands [8]. ...
... The objective function to reduce the peak-to-average ratio is given by (7). ...
The evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is formulated using the multiple knapsack technique. Serial and parallel scheduling algorithms of home appliances namely MKSI (Multiple knapsacks with serial implementation) and MKPI (Multiple knapsacks with parallel implementation) are proposed to reduce electricity cost and PAR. Price-based demand response techniques are incorporated to shift appliances from peak hours to off-peak hours to optimize energy consumption. The proposed algorithms are tested on real-time datasets and evaluated based on time of use pricing tariff and critical peak pricing. The performance of both the algorithms is compared with the unscheduled scenario and existing algorithm. Simulations show that both proposed algorithms are efficient methods for home energy management to minimize PAR and electricity bills of consumers. The proposed MKSI algorithm achieves cost reduction of 20.26% and 42.53% for TOU and CPP, respectively as compared to the unscheduled scenario while PAR is reduced by 45.07% and 39.51% for TOU and CPP, respectively. The proposed MKPI algorithm achieves 22.33% and 46.36% cost reduction compared to the unscheduled case for TOU and CPP while the PAR ratio is reduced by 46.47% and 41.16% for TOU and CPP respectively.
... The suggested algorithm arranges devices to bring the load curve of the consumption schedule closer to the load curve of the desired load curve [35][36][37][38]: ...
... The off-peak power limit (W L 1 ) and on-peak power limit W L 2 are calculated so that fair load distribution at the customer's end can be realized via a house load powermanagement scheduler [35][36][37][38]. ...
Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Additionally, due to environmental, technical and economic constraints, it is challenging to meet demand at certain hours, such as peak hours. Therefore, it is necessary to manage network consumption to modify the peak load and tackle power system constraints. One way to achieve this goal is to use a demand response program. The home energy management system (HEMS), based on advanced internet of things (IoT) technology, has attracted the special attention of engineers in the smart grid (SG) field and has the tasks of demand-side management (DSM) and helping to control equality between demand and electricity supply. The main performance of the HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic called the bald eagle search optimization algorithm (BESOA) to discover the optimal scheduling of home appliances. Furthermore, the HEMS architecture is programmed based on MATLAB and ThingSpeak modules. The HEMS uses the BESOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the PAR, and increase user comfort. The results show the suggested system’s ability to obtain optimal home energy management, decreasing the energy cost, microgrid emission cost, and PAR (peak to average ratio).
... However, a question on what basis the control action must be performed should be answered, e.g. various strategies are implemented such as: to reduce the cost of energy for individual consumer [5], to perform peak shaving, or to minimize carbon emission [27], to maintain battery life [28], etc. DSM strategies with optimization processes such as Heuristic Optimization [5], Genetic Algorithm Optimization, Hybrid Bacterial Foraging [29], Whale Optimization [30], and Fuzzy Logics [31] are implemented to find the optimal point for timely control action to maintain user satisfaction at the same time. However, these techniques require high computational speed and programming skill, which is a lot to ask for as simple energy management systems such as found in rural locations. ...
... As the SoC of ESS is dynamic though out the day, a model-based analysis must be performed. The heuristic-based searching algorithm like genetic, particle swarm, whale optimization is commonly used to find the best point or position in these type of research sectors [29,30]. However, these algorithms are suitable to be used for a large search area, whereas in the proposed case area, an exhaustive search shows a higher advantage over other algorithms as our search portion is smaller. ...
Microgrid (MG) is one of the practical and best concepts to provide energy access to rural communities, where electric grid extension is not techno-economically feasible. Since the trend of load consumption is not uniform with a low load factor in a rural area, the required rating of the system becomes very high. Similarly, the generation is fixed for these MGs, whereas the load increases continuously over time. Such a system faces supply deficit issues triggering a high number of interruptions that may cause frequent blackouts. Hence, rolling blackout and load clipping techniques are preferred during the peak load period in most of the rural MGs. These issues lead to an unreliable power supply and low satisfaction level of the user. This paper presents the load prioritization technique to guarantee the continuous supply for the essential loads within the rural community. A day-ahead energy allocation technique is mathematically formulated and optimized to maximize the total hours of energy served. This technique maximized the hours of energy served to the load with higher priority followed by the load with lower priorities. From this study, it is found that the proposed strategy helps to improve the hours of energy served in the overall system, by improving the state of charge (SoC) level of the battery system. The result shows that the user satisfaction level has been improved by 5% through 100% of continuity for the essential loads.
... According to the International Energy Outlook (IEO2016), energy demand is expected to keep rising over the next two decades up to 56% [119]. In other side, about 37% of the global energy consumption is only due to the residential sector. ...
... Experimental results demonstrate that Peak-load has been decreased by 22% compared with unscheduled case and the load-curve is balanced by user preference for appliances usage. In [119], a hybrid algorithm (GA-BF) was used to adjust the electricity bills of consumer and PAR using the load shifting technique. Authors also succeeded in achieving a favourable trade-off between minimizing the energy cost and the PAR value. ...
Due to the rapidly developing of electric power system across the world in response to technical, economic and environmental developments, modern power systems often operate proximate to their maximal limits, engendering voltage instability risks in electric grid. On the other hand, excessive penetration of renewable energy sources into electrical grids may lead to many problems and operational limit violations, such as over and under voltages, active power losses and overloading of transmission lines, power plants failure, voltage instability risks and users discomfort. These problems happen when the system exceeds maximal operational capability (MOC) limit. In this thesis, firstly, various meta-heuristic optimization techniques have been developed and implemented to deal with different power system problems, such as single and multi-objective optimal reactive power dispatch problem. Since the characteristics of optimal reactive power dispatch (ORPD) in nature non-linear and non-convex and are consisting of mix of discrete and continuous variables; some non-conventional optimization techniques are developed and adopted to deal with discrete ORPD problem in large-scale electric grids.
Technology advancement for green energy and its integration to the electric power system (EPS) has gathered substantial interest in the last couple of decades. Incorporating such resources has proven to reduce power losses and improve the reliability of electrical network. However, hyper-production or hypo-production of these resources in electric grid has imposed additional operational and control issues in voltage-regulation, system stability, and feasibility of solutions. Renewables incorporation into electric grid has led to significant changes in the types of consumption as well as dramatic and direct changes in the needs of optimal planning and operation of electric grids. To assess the suitability of the nonclassical optimization techniques for modern power systems, stochastic optimal power flow (OPF) problem with uncertain reserves from renewable energy sources was studied under different scenarios, including valve pointe effect and gas emission. Simulation results demonstrated that with hybrid generation system we could benefit with 2.4 % per hour cost reduction compared to traditional grid that based only on thermal generations as power sources. In addition, a new application of slim mould algorithm for practical optimal power flow with integration of renewables was conducted on Algerian electricity grid (DZA114-220/60 kV). Different cases were studied, where the feasibility of solutions and all control variables are deeply discussed. Hence, hybrid generation system is more effective and viable than classical system.
In smart grid, efficient load management can help balance, reduce the burdensome on the electric network, and minimize operational electricity-cost. Robust optimization is a method that is used increasingly in the scheduling of household loads through demand side control. To this end, demand side management (DSM) scheme based on Meta-heuristic optimization techniques is proposed for home energy management system. The main objective behind this study is to adjust the peak demand and offering the total energy required at minimum cost with high quality. More precisely, in order to make consumers aware of their effective and essential contributions to helping the operator system during emergency cases (requests during peak hours). Simulation results showed that the proposed DSM scheme based meta-heuristic algorithms enrolling higher reduction in the total energy cost up to 26% compared to the base case and peak average ratio is curtailed by 55 %. In overall, the obtained results demonstrate significant improvement in energy quality, electric power system security, and notable reduction of peak demand in offering total energy required at minimum cost.
... Several methods have been introduced for DR, in which authors have utilized home energy management systems based on a load shifting strategy to minimize the electricity cost and peak to average ratio (PAR) through hybrid bacterial foraging and genetic algorithm [10]. Besides, the authors evaluate a strategy for three various pricing schemes. ...
... (1) all objects marked as unvisited (2) do (3) an unvisited object p selected randomly (4) mark p as visited (5) if the Eps-neighborhood of p include at least MinPts objects (6) a new cluster C created, and add p to C; (7) let N be the set of objects in the Eps-neighborhood of p; (8) for each point p in N (9) if p is unvisited (10) mark p as visited; (11) if the Eps-neighborhood of p include at least MinPts points, add mentioned points to N; (12) if p is not yet a member of any cluster, add p to C (13) end for (14) output C; (15) else mark p as noise; (16)until no object is unvisited ...
In recent years, modern appliances with high electricity demand have played a significant role in residential energy consumption. Despite the positive impact of these appliances on the quality of life, they suffer from major drawbacks, such as serious environmental concerns and high electricity bills. This paper introduces a consolidated framework of load management to alleviate those drawbacks. Initially, benefiting from a demonstrative analysis of home energy consumption data, controllable and responsive appliances in smart home are identified. Then, the energy consumption pattern is reduced and shifted using flexible load models and better utilization of existing energy storage systems. This can be achieved through data mining approaches, i.e., density-based spatial clustering of application with noise (DBSCAN) method. In this technique, no sensor for detection or measurement instruments will be required, whose deployment incur cost to the system or increase security risk for consumers. In the following, one scheduling of using controllable appliances, which is formulated by convex optimization, is considered for the demand response (DR) program, provided that this plan doesn't affect customers’ priority and convenience. In the last stage, the deployment of energy storage systems, such as plug-in hybrid electric vehicles (PHEVs) and battery energy storage systems (BESS), is introduced to lower the energy cost and improve the performance of the proposed DR model. Simulation results of this demand response are compared with conventional k-clustering methods to confirm the economic superiority of the DBSCAN clustering technique using the data of a residential unit during three different scenarios.
... According to International Energy Outlook (IEO2016), Energy Demand is expected to increase up to 56% between 2015 and 2040 (Khalid et al. 2016). Moreover, about 37% of the world global energy consumption is only due to the residential sector, which has attracted considerable attention in the research community to deal with this problem. ...
... Experimental results demonstrate that Peak-load has been decreased by 22% compared with unscheduled case and the load-curve is balanced by user preference for appliances usage. In (Khalid et al. 2016), a hybrid algorithm (GA-BF) was used to adjust the electricity bills of consumer and PAR using the load shifting technique. Authors also succeeded in achieving a favorable trade-off between minimizing the energy cost and the PAR value, but this hybrid optimizer was not significantly curtailed the waiting time of end users. ...
With arrival of advanced technologies, automated appliances in residential sector are still in unlimited growth. Therefore, the design of new management schemes becomes necessary to be achieved for the electricity demand in an effort to ensure safety of domestic installations. To this end, the Demand Side Management (DSM) is one of suggested solution which played a significant role in micro-grid and Smart Grid (SG) systems. DSM program allows end-users to communicate with the grid operator so they can contribute in making decisions and assist the utilities to reduce the peak power demand through peak periods. This can be done by managing loads in a smart way, while keeping up customer loyalty. Nowadays, several DSM programs are proposed in the literature, almost all of them are focused on the domestic sector energy management system (EMS). In this original work, four heuristics optimization algorithms are
proposed for energy scheduling in smart home, which are: bat algorithm (BA), grey wolf optimizer (GWO), moth flam optimization (MFO), algorithm, and Harris hawks optimization (HHO) algorithm. The proposed model used in this experiment is based on two different electricity pricing schemes: Critical-Peak-Price (CPP) and Real-Time-Price (RTP). In addition, two operational time intervals (OTI) (60 min and 12 min) were considered to evaluate the consumer's demand and
behavior of the suggested scheme. Simulation results show that the suggested model schedules the appliances in an optimal way, resulting in electricity-cost and peaks reductions without compromising users' comfort. Hence, results confirm the superiority of HHO algorithm in comparison with other optimization techniques.
... The overall objective of load management is to schedule a load during high demand to low demand intervals. This can be done by the combination of the GA algorithm and bacterial foraging (BF) in [10]. Hybrid techniques [10] minimizes the electricity bill and PAR using the load management shifting. ...
... This can be done by the combination of the GA algorithm and bacterial foraging (BF) in [10]. Hybrid techniques [10] minimizes the electricity bill and PAR using the load management shifting. In GA and BF algorithm optimization method, there is a compromise between customers bills and PAR. ...
Electricity is the basic demand of consumers. With the passage of time, the demand for electricity is increasing day by day. Smart grid (SG) is evolved to satisfy the demand of consumers. To manage electricity load from peak hours to low peak hours, consumer needs to control their appliances by home energy management system (HEMS). HEMS schedule the appliances according to customers need.
Energy management using demand-side management (DSM) techniques play an
important role in SG domain. Smart meters (SM) and energy management controllers (EMC) are the important components of the SG. Intelligent energy optimization techniques play a vital role in the reduction of the electricity bill via scheduling home appliances. Through appliance’s scheduling, the consumer gets a feasible cost for consumed electricity. DSM provides the facility for consumers to schedule their appliances for the reduction of power price and rebate in peak loads. HEMS is allowed to remotely shut down their appliances in emergency conditions through direct load control programs. Meta-heuristic algorithms have been used for the optimization of the user energy consumption in an efficient way. Electricity load forecasting plays a vital role in improving the use of energy through customers to make decisions efficiently. The accuracy of load prediction is a challenging task because of randomness and noise disturbance. In this thesis, efficient algorithms are proposed to control the load in residential units. Our proposed schemes are used to minimize the user comfort delay time. Customers waiting time is inversely proportional to the total cost and peak to average ratio (PAR). The aim of the current research is to manage the power of the residential units in an optimized way and predict the exact load. Simulation results show the minimum user waiting time, however, the total cost is compromised due to the high demand of the load and predict the exact load for users. In the end, our proposed schemes show better result through simulation results.
In this thesis, we proposed new schemes which are used to lower the electricity price, PAR and user discomfort in electricity consumption side. The proposed schemes performed better than existing benchmark schemes. The proposed schemes used real-time price (RTP) signal for calculating the electricity cost and PAR. Simulation results also show that the proposed algorithms have met the objective of DSM. For prediction, the proposed scheme is performed better than benchmark schemes and predict the exact electricity load.
... The overall objective of load management is to schedule a load during high demand to low demand intervals. This can be done by the combination of the GA algorithm and bacterial foraging (BF) in [10]. Hybrid technique [10] minimizes the electricity bill and PAR using the load management shifting. ...
... This can be done by the combination of the GA algorithm and bacterial foraging (BF) in [10]. Hybrid technique [10] minimizes the electricity bill and PAR using the load management shifting. Hybrid scheme uses the RTP signal for reducing the electricity bills and PAR. ...
Energy management using demand side management (DSM) techniques plays a key role in smart grid (SG) domain. Smart meters and energy management controllers are the important components of the SG. A lot of research has been done on energy management system (EMS) for scheduling the appliances. The aim of current research is to organize the power of the residential units in an optimized way. Intelligent energy optimization techniques play a vital role in reduction of the electricity bill via scheduling home appliances. Through appliance’s scheduling, consumer gets feasible cost in response to the consumed electricity. The utility provides the facility for consumers to schedule their appliances for the reduction of electricity bill and peak demand reduction. The utility company is allowed to remotely shut down their appliances in emergency conditions through direct load control programs. A lot of research has been done on energy management system (EMS) for scheduling the appliances. In this work, an efficient EMS is proposed for controlling the load in residential units. Meta-heuristic algorithms have been used for the optimization of the user energy consumption schedules in an efficient way. Our proposed scheme is used to minimize the user waiting time. User waiting time is inversely proportional to the total cost and peak to average ratio (PAR). Simulation result shows the minimum user waiting time, however, the total cost is compromised due to the high demand of the load. In the end, our proposed scheme will be validated through simulations.
... In [15] and [16] authors proposed a HEMS to reduce electricity cost and waiting time. In [15], the optimation techniques effectively reduce the cost and PAR; however, a compromise between cost and UC exists. ...
... In [15], the optimation techniques effectively reduce the cost and PAR; however, a compromise between cost and UC exists. In [16], authors proposed a hybrid scheme of BFA and GA considering RTP pricing signal. Hybrid scheme outperforms both existing techniques in cost reduction and minimizing the average waiting time of the appliances; however, a compromise between electricity cost and PAR exists. ...
With the advent of the smart grid, it has become possible to improve the energy systems. To optimize the energy consumption pattern of the appliances, home energy management system is proposed for smart homes. Energy management in smart homes is a challenging task, therefore, the concept of demand-side management was introduced. For the effective scheduling of smart appliance, we propose a metaheuristic optimization technique. The proposed technique is hybrid of two existing techniques: Tabu Search (TS) and Bacterial Foraging Algorithm (BFA). The aim of the proposed technique is to reduce energy consumption so that user electricity bill reduces. Also, improves user comfort in term of average waiting time. For electricity bill calculation and appliance scheduling, time of use price tariff is used. Simulation results demonstrate that proposed scheme outperformed existing schemes in cost reduction and the average waiting time minimization.However, TS outruns other scheduling schemes in peak to average ratio reduction.
... Proposed [19] a hybrid approach of BFA and genetic algorithm (GA) for cost minimization and load management by shifting all the load form on-peak to off-peak hours. They used RTP scheme in their hybrid model whose results after simulations verify that the model reduces total cost of electricity and peak average ratio. ...
... In which trade off occurs between cost and PAR, and cost and user comfort, which are not fully considered by most of the researchers. As authors in [11], [12], [14], [16], [17], [19], [20] and [21] never considered comfort. Few researcher ignored PAR in [10], [11], [14], [16] and [18] due to which a burden was created on utilities due generation of peaks on off peak hours. ...
With the advent of Smart Grid (SG), it provides
the consumers with the opportunity to schedule their power
consumption load efficiently in such a way that it reduces their
energy cost while also minimizing their Peak to Average Ratio
(PAR) in the process. We in this paper target the appliances
to schedule in such a way that it increases User Comfort
(UC) and decreases electricity consumption load which benefits
both consumer and utility. In this paper, we proposed hybrid
of Bacterial Forging Algorithm (BFA) and Tabu Search (TS)
Algorithm using different Operational time Interval (OTI) to
schedule appliances while balancing User Comfort which is the
main objective of the Demand Side Management (DSM). This
paper tries to reduce both waiting time and electricity cost
simultaneously in the new hybrid Bacterial Foraging Tabu Search
(BFTS) technique. Real time pricing (RTP) scheme was used to
get the total cost of electricity consumed. We compared the results
of proposed hybrid scheme with Bacterial Forging (BFA) and
Tabu Search (TS) Algorithm using different Operational time
Interval (OTI). The result shows effectiveness of using hybrid
Bacterial Foraging Tabu Search (BFTS) technique for Demand
Side Management (DSM).
... Authors just designed the technique in the paper but did not apply on SG for its performance exploration. According to [5], DSM is introduced to handle the issue of energy demands plus to utilize the energy efficiently. Authors proposed the hybrid technique and compared their proposed technique with GA, BFA and unscheduled appliances. ...
... Algorithm 1 EWA 1: procedure START 2: Initialization: Generates counter of t = 1; Set P as population of NP individual earthworm which is randomly distributed in search space; numbers of kept earthworm are set as nKEW, maximum generation MaxGn, α as similarity factor, proportional aspect β , constant γ = 0.9. 3: Evaluation of Fitness: each earthworm is evaluated individually according to its position 4: While till best solution is not achieved or t <MaxGen 5: All the earthworms in population are then sorted according to their fitness values 6: for i = 1 to NP (all earthworms) do 7: Generate offspring xi1 through Reproduction 1 8: Generate offspring through Reproduction 2 9: Do crossover 10: if i <nKEW then 11: set the number of particular parents (N) and the produced off springs (M); Select the N parents using method i.e. roulette wheel selection; Generate the M offspring; Calculating x i2 according to offspring M generated 12: else Our next task is to compare EWA with BFA [5]. For BFA, we have used same appliances classification and parameters as mentioned in Table 3, Table 4 and Table 5. ...
Smart grid based energy management system promises an efficient consumption of electricity. For optimized energy consumption, a bio inspired meta-heuristic algorithms: Earth Worm Algorithm (EWA) and Bacterial Foraging Algorithm (BFA) are presented in this paper. In this work, we targeted residential area. Our aim is to reduce the electricity cost and Peak to Average Ratio (PAR). We have used the Critical Peak Pricing (CPP) scheme for calculating electricity bill. Through simulations, we have compared the results of EWA, BFA and unscheduled appliances. After implementing our techniques, EWA based energy management controller gives more efficient results than BFA in term of cost, while for PAR reduction, BFA performs better than EWA.
... In contrast, fossil fuels are used in the transportation system. Thereby the transportation sector is responsible for 24 percent of worldwide greenhouse gas emissions [2][3][4]. ...
Consumers regulate power use through two-way transmission between the source and the customer via Smart
... In order to address these issues, hybrid meta-heuristic optimizers are used along with traditional optimizers. Hybrid optimizers such as a hybrid multi-objective PSO and non-inferior solution sorting genetic algorithm (NSGA-II) algorithm [23,7] that is the combination of GA and Artificial Bee Colony, [39], a mutation-based ant colony optimization, [35] a hybrid GA and PSO, [19] a hybrid Bacterial Foraging and GA Optimization Techniques, etc., are a few methods that is used to resolve DSM issues. Despite the fact that these hybrid techniques are extremely complex, their effectiveness in resolving intricate issues can be improved. ...
In Smart Grid, demand side management (DSM) is frequently utilized due to its dependable features and advantages for lowering the cost of electricity. A demand-shifting-based DSM strategy is proposed in this paper. All players in a hierarchical day-ahead smart grid framework, including customers, demand response (DR) aggregator, and utility system, aim to boost their respective profits. It is quite difficult to meet the needs of all players at once. A multi-objective problem is developed based on these criteria and combining the concept of demand-shifting-based DSM for a day-ahead energy market. In order to resolve the complex problem, a new Ameliorated Class Topper Optimization (ACTO) algorithm is proposed in this paper. Exploration, exploitation, and local optimal avoidance capabilities are validated using twenty-nine benchmark functions. To demonstrate the effectiveness of the proposed ACTO and DSM scheme, the results acquired are also compared with some existing well-known techniques for the current smart grid framework. The proposed algorithm is also investigated on a wind-integrated power system to validate the results. The simulation outcomes demonstrate that the proposed approach simultaneously offers a significant incentive to each player in the day-ahead electricity market by optimizing generation and load profile.
... The IPSO is better than the traditional PSO and Tabu Search (TS) in the two-level energy optimization scheduling strategy. Reference [17] proposed a BPSO method for scheduling household energy management systems with distributed power sources. This method can effectively reduce economic costs, energy consumption, and environmental pollution. ...
This paper proposes an improved Bacterial Foraging Optimization for economically optimal dispatching of the microgrid. Three optimized steps are presented to solve the slow convergence, poor precision, and low efficiency of traditional Bacterial Foraging Optimization. First, the self-adaptive step size equation in the chemotaxis process is present, and the particle swarm velocity equation is used to improve the convergence speed and precision of the algorithm. Second, the crisscross algorithm is used to enrich the replication population and improve the global search performance of the algorithm in the replication process. Finally, the dynamic probability and sine-cosine algorithm are used to solve the problem of easy loss of high-quality individuals in dispersal. Quantitative analysis and experiments demonstrated the superiority of the algorithm in the benchmark function. In addition, this study built a multi-objective microgrid dynamic economic dispatch model and dealt with the uncertainty of wind and solar using the Monte Carlo method in the model. Experiments show that this model can effectively reduce the operating cost of the microgrid, improve economic benefits, and reduce environmental pollution. The economic cost is reduced by 3.79% compared to the widely used PSO, and the economic cost is reduced by 5.23% compared to the traditional BFO.
... The off-peak power limit ( 1 ) and on-peak power limit 2 are calculated, so that fair load distribution at customer's end can be realized via house load power management scheduler. ∈ load is calculated for a single chromosome [40][41][42] (Figure 3). ...
The steady increase in the energy demand and the growing carbon footprint has forced electricity‐based utilities to shift from their use of non‐renewable energy sources to renewable energy sources. Furthermore, there has been an increase in the integration of renewable energy sources in the electric grid. Hence, one needs to manage the energy consumption needs of the consumers, more effectively. Consumers can connect all the devices and houses to the internet by using Internet of Things (IoT) technology. In this study, the researchers have developed and proposed a novel 2‐stage hybrid method that schedules the power consumption of the houses possessing a distributed energy generation and storage system. Stage 1 modeled the non‐identical Home Energy Management Systems (HEMSs) that can contain the DGS like WT and PV. The HEMS organise the controllable appliances after taking into consideration the user preferences, electricity prices and the amount of energy produced /stored. The set of optimal consumption schedules for every HEMS was estimated using a BPSO and BSA. On the other hand, Stage 2 includes a Multi‐Agent‐System (MAS) based on the IoT. The system comprises two portions: software and hardware. The hardware comprises the Base Station Unit (BSU) and many Terminal Units (TUs).
... Electricity is a great and significant favor of science to human beings, which makes their life easier [1], [2]. It is used in various sectors such as medical, agriculture, transportation, industrial, commercial and residential. ...
Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid’s safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.
... For attaining good results in terms of cost and PAR minimization, Genetic Algorithm (GA), Moth-Flame Optimization algorithm (MFO) and a hybrid version of the GA and MFO, namely, Time-constrained Genetic Moth-Flame Optimization (TG-MFO) algorithms are used in [11]. In [12], BFA, GA and a hybrid version of these two algorithms, are used for energy optimization. These algorithms focus mainly on cost and PAR minimization and load management on consumer side. ...
Energy is the soul of each new invention on this Bio-sphere. The upturn in this advancement and technological growth, energy resources are getting scarce. As the energy resources are limited and can not be increased in the same proportion as with the exponential rising demand, that is why, we have to manage our energy consumption smartly. An optimal integration of the Renewable energy sources (RESs) for this purpose is the need of the day. This paper proposes a bio-inspired algorithm, namely, the Lion’s Algorithm (LA), for an efficient Energy Management System (EMS) in industrial areas, along with a beneficial utilization of RES and energy storing units (ESUs). Different objectives, like, Total Energy Cost (TEC), Peak to Average power Ratio (PAR), Hourly Load (HL) and maximization of end-user comfort (the reduction in waiting time) are analyzed and observed. LA algorithm is specially designed to achieve these objectives up-to maximum optimal limits. The MATLAB simulation results illustrate that, our proposed algorithm reduced the cost up to 42.66% and PAR 35.94% compared to un-scheduled and scheduled with other state-of-the-art algorithms, with an average waiting time of only 0.216 h (12.96 s).
... They reduced the PAR and electricity cost to a greater extent but the waiting time results are not good in the case of the MFO algorithm, while in the case of GA algorithm they reduced the waiting time as well as PAR, but the electricity cost increased in the simulation results. Similarly in [10], the author used a hybrid technique, BFA and GA, for energy optimization. When two or more algorithms are merged into a single one, it is called a hybrid algorithm. ...
As the world population and its dependency on energy is growing exponentially day by
day, the existing energy generating resources are not enough to fulfill their needs. In the conventional grid system, most of the generated energy is wasted because of improper demand side management (DSM). This leads to a difficulty in keeping the equilibrium between the user need and electric power production. To overcome these difficulties, smart grid (SG) is introduced, which is composed of the integration of two-way
communication between the user and utility. To utilize the existing energy resources in a better way, SG is the best option since a large portion of the generated energy is consumed by the educational institutes. Such institutes also need un-interrupted power supply at the lowest cost. Therefore, in this paper, we have taken a university campus load. We have not only applied two bio-inspired heuristic algorithms for energy scheduling—namely, the Firefly Algorithm (FA) and the Lion Algorithm (LA)—but also proposed a hybrid version, FLA, for more optimal results. Our main objectives are a reduction in both, that is, the cost of energy and the waiting time of consumers or end users. For this purpose, in our proposed model, we have divided all appliances into two categories—shiftable appliances and non-shiftable appliances. Shiftable appliances are feasible to be used in any of the time slots and can be planned according to the day-ahead pricing
signal (DAP), provided by the utility, while non-shiftable appliances can be used for a specified duration and cannot be planned with the respective DAP signal. So, we have scheduled shiftable appliances only. We have also used renewable energy sources (RES) for achieving maximum end user benefits. The simulation results show that our proposed hybrid algorithm, FLA, has reduced the cost excellently. We have also taken into consideration the consumers’ waiting times, due to scheduling of appliances.
... A hybrid technique uses two optimization approaches of bacterial foraging algorithm (BFA) and GA [18] with the objective of reducing cost and peak to average ratio (PAR). PAR is one of the important performance metrics used to evaluate how peak electricity consumption affects the system. ...
There will be a dearth of electrical energy in the world in the future due to exponential increase in electrical energy demand of rapidly growing world population. With the development of Internet of Things (IoT), more smart appliances will be integrated into homes in smart cities that actively participate in the electricity market by demand response programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, the energy management strategy using a price-based demand response program is developed for IoT-enabled residential buildings. We propose a new EMS for smart homes for IoT-enabled residential building smart devices by scheduling to minimize cost of electricity, alleviate peak-to-average ratio, correct power factor, automatic protective appliances, and maximize user comfort. In this method, every home appliance is interfaced with an IoT entity (a data acquisition module) with a specific IP address, which results in a wide wireless system of devices. There are two components of the proposed system: software and hardware. The hardware is composed of a base station unit (BSU) and many terminal units (TUs). The software comprises Wi-Fi network programming as well as system protocol. In this study, a message queue telemetry transportation (MQTT) broker was installed on the boards of BSU and TU. In this paper, we present a low-cost platform for the monitoring and helping decision making about different areas in a neighboring community for efficient management and maintenance, using information and communication technologies. The findings of the experiments demonstrated the feasibility and viability of the proposed method for energy management in various modes. The proposed method increases effective energy utilization, which in turn increases the sustainability of IoT-enabled homes in smart cities. The proposed strategy automatically responds to power factor correction, to protective home appliances, and to price-based demand response programs to combat the major problem of the demand response programs, which is the limitation of consumer’s knowledge to respond upon receiving demand response signals. The schedule controller proposed in this paper achieved an energy saving of 6.347 kWh real power per day, this paper achieved saving 7.282 kWh apparent power per day, and the proposed algorithm in our paper saved $2.3228388 per day.
... In [14], the authors used a hybrid version of the genetic algorithm and moth-flame optimization algorithm and proposed the time-constrained genetic moth-flame optimization (TG-MFO) algorithm for achieving better results in terms of cost and PAR reduction. Likewise, in [15], BFA and the genetic algorithm (GA) are used to make a hybrid algorithm for energy optimization. The hybrid algorithm is a merged form of two or more algorithms. ...
Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting time.
... In this paper we have considered DSM, which helps in load balancing between users and utility [3]. In DSM strategies, demand response (DR) is the mechanism in which utility tries to manage consumer demand with a condition, such that users must reduce their consumption at critical times [4]. Utilities give different incentives, in the form of reduced electricity pricing, to the user due to this load reduction at peak hours. ...
Industries are consuming more than 27% of the total generated energy in the world, out of which 50% is used by different machines for processing, producing, and assembling various goods. Energy shortage is a major issue of this biosphere. To overcome energy scarcity, a challenging task is to have optimal use of existing energy resources. An efficient and effective mechanism is essential to optimally schedule the load units to achieve three objectives: minimization of the consumed energy cost, peak-to-average power ratio, and consumer waiting time due to scheduling of the load. To achieve the aforementioned objectives, two bio-inspired heuristic techniques—Grasshopper-Optimization Algorithm and Cuckoo Search Optimization Algorithm—are analyzed and simulated for efficient energy use in an industry. We considered a woolen mill as a case study, and applied our algorithms on its different load units according to their routine functionality. Then we scheduled these load units by proposing an efficient energy management system (EMS). We assumed automatic operating machines and day-ahead pricing schemes in our EMS.
... The users can get maximum benefits in terms of a decrease in electricity consumption charges, reduction in the peak-to-average ratio (PAR) and avoiding energy blackouts due to two features of DSM, namely the demand response (DR) and load management (LM) [11]. DR programs play a significant role in a smart grid operation by scheduling household appliances from away from hightariff time slots to low-tariff time slots according to time-based electricity tariffs, which are explained in References [9,12]. ...
The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.
... A hybrid technique using two optimization approaches of Bacterial Foraging Algorithm (BFA) and Genetic Algorithm (GA) [39] with the objective of reducing cost and PAR. PAR is one of the important performance metric used to evaluate how peak electricity consumption affects the system. ...
Energy consumption in residential sector is the 25% of all the sectors. Maintaining user comfort and energy optimization are the major tasks of Home Energy Management System. Appliances of Heating, Ventilation and Air Conditioning (HVAC) and lighting devices constitute up to 64% and 4% of energy consumption respectively in residential buildings. Different techniques like Demand Response (DR) and pricing tariffs like Time Of Use (TOU) has been used to make user participate in the energy consumption reduction. In the literature review, many techniques have been discussed which use Fuzzy Logic System integrated with other techniques for energy consumption minimization. However, user comfort is often sacrificed in these techniques.
In this thesis, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an input parameter in order to maintain the thermostat set-point according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption simulation. When defining FIS, number of rules in the rule base plays an important role in the correct working. With the increase in number of rules, task of defining them in FIS becomes time consuming and chances
of manual errors increase. In this research, we have also proposed the automatic rule base generation using combinatorial method. Proposed techniques
are evaluated using Mamdani FIS and Sugeno FIS. The input parameters of
proposed FIS are indoor temperature, outdoor temperature, occupancy, price
rate, initialized set points and humidity whereas energy consumption is the
output of the system. Performance metrics used for the evaluation of the
MATLAB simulation results are energy consumption, Peak-to-Average Ratio (PAR), cost, efficiency gain and user comfort. In addition, a model has
been proposed which will quantify the user comfort with respect to different energy consumption levels. Simulation result validates that proposed
technique reduces energy consumption by 28%.
... The presented DSM proved its potential to save more energy as compared to other DSM techniques. Hybridization of two optimization techniques is proposed in [22] for cost reduction and load management. Results showed that the presented approach successfully reduced the total cost and PAR. ...
Smart Gird is a technology that has brought many advantages with its evolution. Smart Grid is indispensable as it will lead us towards environmentally sustainable economic growth. Home energy management in Smart Grid is a hot research topic now a days. It aims at reducing the energy cost of users, gaining energy self-reliance and decreasing Greenhouse gas emissions. Renewable energy technologies nowadays are best suitable for off grid services without having to build extensive and complicated infrastructure. With the advent of Smart Grid (SG), the occupants have the opportunity to integrate with renewable energy sources (RESs) and to actively take part in demand side Management (DSM). This review paper is comprehensive study of various optimization techniques and their implementation with respect to electricity cost diminution, load balancing, power consumption and user's comfort maximization etc. for Home Energy Management in Smart Grid. This paper summarizes recent trends of energy usage from hybrid renewable energy integrated sources. It discusses several methodologies and techniques for hybrid renewable energy system optimization.
... Hybrid bacterial foraging and genetic (HBG) algorithm-based DSM for smart homes was proposed by the authors in [13]. They focused on peak load reduction, cost minimization, user comfort maximization and load shifting. ...
Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. Therefore, to have an optimum usage of the existing energy resources on the consumer side, new techniques and algorithms are being discovered and used in the energy optimization process in the smart grid (SG). In SG, because of the possibility of bi-directional power flow and communication between the utility and consumers, an active and optimized energy scheduling technique is essential, which minimizes the end-user electricity bill, reduces the peak-to-average power ratio (PAR) and reduces the frequency of interruptions. Because of the varying nature of the power consumption patterns of consumers, optimized scheduling of energy consumption is a challenging task. For the maximum benefit of both the utility and consumers, to decide whether to store, buy or sale extra energy, such active environmental features must also be taken into consideration. This paper presents two bio-inspired energy optimization techniques; the grasshopper optimization algorithm (GOA) and bacterial foraging algorithm (BFA), for power scheduling in a single office. It is clear from the simulation results that the consumer electricity bill can be reduced by more than 34.69% and 37.47%, while PAR has a reduction of 56.20% and 20.87% with GOA and BFA scheduling, respectively, as compared to unscheduled energy consumption with the day-ahead pricing (DAP) scheme.
... A hybrid technique using two optimization approaches of Bacterial Foraging Algorithm (BFA) and genetic algorithm [40] with the objective of reducing cost and PAR has been proposed. PAR is an important performance metric used to evaluate the effect of peak electricity consumption on the system. ...
Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.
... Formulas are given in equation (1) and (2). For fitness evaluation Rosenbrock function is used [14]. N p (size of search space), N c (chemotactic steps), N r (reproduction steps), N e (elimination and dispersal steps), N s (swimming steps). ...
In this work, two meta-heuristic bio-inspired algorithms and our proposed hybrid technique (Bacterial foraging optimization algorithm (BFA) and BAT algorithm (BA) (HBB)) are proposed for optimizing and scheduling the appliances of residential consumers. BFA, BA and our proposed technique HBB are used for scheduling the appliances in order to find the optimal solution. Appliances have different power ratings and power consumption patterns. Three different operational time intervals of 5, 30 and 60 minutes are taken in this work and their comparison is carried out. Eighteen appliances are considered and they are classified into three categories: interruptible, non-interruptible and base load appliances. Single home scenario is considered in this work. Results show that proposed technique has significantly reduced electricity cost and peak-to-average ratio. Consumers have not only supposed to pay less electricity bill, however, utilities also have to bear less stress especially in on-peak hours.
... This paper is an extension of [28], in which the focus of the study is to employ DSM in HEMS for load management to reduce electricity cost, PAR and user discomfort. To minimize user discomfort, we have introduced the concept of coordination among home appliances to handle the interrupts in an emergency situation. ...
IIn this paper, we propose a home energy management system which employs load shifting strategy of demand side management to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to minimize electricity cost and peak to average ratio while maintaining user comfort through coordination among home appliances. In order to meet the load demand of electricity consumers, we schedule the load in day-ahead and real-time basis. We propose a fitness criterion for proposed hybrid technique which helps in balancing the load during On-peak and Off-peak hours. Moreover, for real-time rescheduling, we present the concept of coordination among home appliances. This helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of appliance. For this purpose, we formulate our realtime rescheduling problem as knapsack problem and solve it through dynamic programming. This study also evaluates the behavior of the proposed technique for three pricing schemes including: time of use, real-time pricing and critical peak pricing. Simulation results illustrate the significance of the proposed optimization technique with 95% confidence interval.
... The results are compared between case studies of Netherlands and Burundi. The two techniques GA and bacterial foraging algorithm (BFA) are hybridized in [15], this hybrid approach satisfies the objectives. These objectives are cost minimization and load curtailment from on peak hours to off peak hours by maximizing the user comfort. ...
From previous years, the research on usage of renewable energy sources (RES), specially photo voltaic (PV) arrays. This paper is based on home energy management system (HEMS). We propose a grid connected microgrid to fulfill the load demand of residential area. We have consider fifteen homes with six appliance for each home, the appliances are taken as the base load. For bill calculation, real time pricing (RTP) tariff is used. Ant colony optimization (ACO) is used for the scheduling of appliances. To fulfill the load demand; Wind turbine (WT), PV, micro turbine (MT), fuel cell (FC) and diesel generator (DG) are used. Energy storage devices are used with generators to store excessive energy. Also, we propose penalty and incentive (PI) mechanism to reduce the overall cost. Objectives of the paper are cost and peak to average ratio (PAR). The simulation results show better performance with our optimization technique rather than without any technique.
In order to develop and execute a demand response (DR) system for a household energy management system, an effective and adaptable energy management architecture is provided in this study. Several issues related to the current home energy management system (HEMS) are among those that do not give their consumers a choice to assure user comfort (UC) or a long-term answer to lowered carbon emissions. Our research suggests a programmable heuristic-based energy management controller (HPEMC) to manage a residential building in order to minimize power costs, reduce carbon emissions, increase UC, and lower the peak-to-average ratio (PAR). In this study, the demand-responsive appliance scheduling problem is solved using an energy management system to reduce the cost and a PAR. Numerous case studies have been used to demonstrate the viability of the suggested method. The simulation results confirmed the effectiveness of the proposed method and that it is capable of running a hybrid microgrid in various modes. The findings indicate that the proposed schedule controller saved 25.98% of energy.
Demand-side management, a new development in smart grid technology, has
enabled communication between energy suppliers and consumers. Demand side
energy management (DSM) reduces the cost of energy acquisition and the associated
penalties by continuously monitoring energy use and managing appliance schedules.
Demand response (DR), distributed energy resources (DER), and energy efciency
(EE) are three categories of DSM activities that are growing in popularity as a result
of technological advancements in smart grids. During the last century, the energy
demand has grown signifcantly in tandem with the increase in the global population.
This is related to the expansion of business, industry, agriculture, and the increasing
use of electric vehicles. Because of the sharp increase in global energy consumption,
it is currently extremely difcult to manage problems such as the characterization of
home appliances, integration of intermittent renewable energy sources, load categorization, various constraints, dynamic pricing, and consumer categorization. To address
these issues, it is critical to examine demand-side management (DSM), which has the
potential to be a practical solution in all energy demand sectors, including residential,
commercial, industrial, and agricultural. This paper has provided a detailed analysis
of the diferent challenges associated with DSM, including technical, economic, and
regulatory challenges, and has proposed a range of potential solutions to overcome
these challenges. The PRISMA reviewing methodology is adopted based on relevant
literature to focus on the issues identifed as barriers to improving DSM functioning.
The optimization techniques used in the literature to address the problem of energy
management were discussed, and the hybrid techniques have shown a better performance due to their faster convergence speed. Gaps in future research and prospective
paths have been briefy discussed to provide a comprehensive understanding of the
current DSM implementation and the potential benefts it can ofer for an energy management system. This comprehensive review of DSM will assist all researchers in this
feld in improving energy management strategies and reducing the efects of system
uncertainties, variances, and restrictions.
The study of power system planning, security and control is crucial for determining optimal power flow (OPF), a highly non-linear complicated issue. The functioning of the power system remains complex when Renewable energy sources are connected to the grid along traditional generators. This review paper describes various techniques applicable to the power system networks accessible in the literature. The conventional optimisation algorithms have been reintroduced, while the power systems have been heavily predisposed over the last few years. The most crucial technical, adaptive and economic tool in this context is OPF. In this paper, we compare OPF approaches related to objective functions. Aside from computational power, the case study network and publication dates for these algorithms are described. Finally, we will address some of the basic challenges that arise from the new OPF approach to the modern grid. Mainly, optimisation techniques are compared in terms of reliability, accurateness, faster convergence and applicability for single and multiple objective functions. Initially, the paper discusses types of networks such as AC or DC, constraints and their objectives on OPF, mathematical and different optimisation techniques reviewed. The heuristic and meta-heuristic algorithms, like genetic algorithm and Jaya algorithm, were compared for various operational parameters. In this work, OPF with single- or multi-objective functions such as voltage stability, voltage deviation, emission, cost and losses, consisting of conventional and renewable energy resources were considered. Additionally, Pseudo-code algorithms were presented for some recently evolved algorithms to ease the understanding of the readers.
Flexible demand management for residential load scheduling, which considers constraints, such as load operating time window and order between them, is a key aspect in demand response. This paper aims to address constraints imposed on the operation schedule of appliances while also participating in demand response events. An innovative crossover method of genetic algorithms is proposed, implemented, and validated. The proposed solution considers distributed generation, dynamic pricing, and load shifting to minimize energy costs, reducing the electricity bill. A case study using real household workload data is presented, where four appliances are scheduled for five days, and three different scenarios are explored. The implemented genetic algorithm achieved up to 15% in bill reduction, in different scenarios, when compared to business as usual.
Revolutions in human activities and lifestyles result in a transition from conventional to intelligent residential building infrastructure. Conventional heating, ventilation, and air conditioning (HVAC), refrigerator and lighting system challenges are addressed without taking into account building heat gains, outdoor illuminance, and temperature. Based on these parameters, a mathematical model for cost estimation of residential building energy consumption, considering indoor heat gains, outdoor temperature, outdoor illuminance, and TOU price has been developed. A total of 46 swarm intelligence based optimization algorithms are used to optimize different building parameters. These swarm intelligence algorithms (SIA) are compared using the convergence curves, statistical and box-plot analysis and the Bald Eagle search (BES) algorithm is found to be the best algorithm among all 46 SIAs. The mean energy consumption costs of the best algorithm, BES and the worst algorithm, fireworks algorithm (FA) are found to be Rs. 8.85 and Rs. 12.98, respectively. In addition, economic analysis has been conducted for the proposed study and it is compared with the existing models with building energy management systems (BEMS) and conventional model (without BEMS). It is observed that, based on this analysis, the cost savings achieved by the proposed study are nearer to 34% and 57% as compared to existing and conventional models.
Wireless sensor networks’ energy consumption is the major challenge to be handled. Clustering is one of the techniques majorly used for reducing energy consumption. During the course of time, many methodologies are being proposed and the existing ones are hybrid. Still, the energy can be reduced more. The scope of optimization is always there. Existing approaches either reduce energy consumption or work on routing or only on data gathering capabilities. But the technique proposed increases the lifetime of the wireless sensor network (WSN) by reducing energy consumption and improves routing efficiency. This paper proposes an approach based upon Fractal Clustering to improve the lifetime of the sensor nodes. The proposed approach named Enhanced Energy Efficient Fuzzy-based Fractal Clustering (EEFFC) algorithm optimizes the performance of WSN. First, fractal clustering is used on sensor nodes to find the location of the sensors. Then, a fuzzy inference system (FIS) is applied to results produced by fractal clustering. Applying FIS on cluster heads generated will optimize the results. As a result, the cost of data transmission will reduce, and hence, the lifetime of the network will improve. FIS generates multi-level clustering, which will result in a better routing path for sensor nodes. Hence, routing will also be improved. MATLAB 2020 is the simulation tool. The results of the simulation depict that EEFFC shows optimized results and it works better than LEACH, LEACH-SF, TEEN and DEEC. The energy consumption is being reduced by reducing the listening time of a node and by reducing the communication distance, for which clustering is optimized. The energy consumption has been reduced by 2% as compared to the algorithms it is compared with. Also, the node’s time of death has been delayed by 3% in total.
In Smart Grid system, the consumers are provided with the opportunity to schedule their home appliances in response to variations in electricity price over time. This paper presents the optimal scheduling of resident appliances. This optimal scheduling is formulated as an optimization problem and is solved by applying improved Genetic Algorithm (GA). In this improved GA, the selection of chromosomes is carried out using entropy method, blended crossover and mutation is performed using correlation coefficient. The simulation is performed for a single resident load profile for a set of appliances using Python programming. The result shows the reduction in the electricity cost to that of the original. The number of iterations taken by the improved Genetic Algorithm (GA) is comparatively lesser than the standard GA and the execution time is reduced by 2.76 s. Thus the result proves the effectiveness of the proposed improvisation in Genetic Algorithm for optimal load scheduling.
With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.
This manuscript proposes a hybrid method for system modeling and optimal allocation of low cost micro‐grid. The proposed hybrid method is the joint execution of the enhanced elephant herding optimization algorithm (EHOA) and adaptive neuro fuzzy inference system (ANFIS) named as EHO‐ANFIS. By using MG inputs, such as solar photovoltaic, wind turbine (WT), micro turbine (MT), fuel cell (FC), and battery energy storage system. EHO optimizes micro‐grid configuration in minimal fuel costs based on needed load requirement. Here, learning phase of ANFIS is utilized for predicting the load requirement. EHOA reduces operation and maintenance costs, emission cost on the basis of the predicted load requirement. The proposed method is executed in MATLAB/Simulink site and the robustness of the proposed method is compared with different existing methods. In the proposed method, the maximal generated power of photovoltaic represents 6 kW, wind turbine indicates 7.8 kW, micro turbine denotes 11.8 kW, FC implies 6.8 kW, and battery refers 3 kW. By utilizing genetic algorithm, the generated power of photovoltaic signifies 7 kW, WT implicates 6 kW, MT implicates 4 kW, FC refers 7 kW, and battery implies 14 kW. The proposed method has minimal cost effective depending on its load demand. The computational time of the proposed technique under 100, 250, 500, and 1000 trails is 5600, 14 000, 28 000, and 56 000 s.
There are many objectives in a power system for better operation of generator units which needs to be satisfied both in terms of economic and technical constraints. The objectives like operating and running cost, reliability, emissions, multi-area unit commitment, losses, voltage stability, reactive power etc., are mostly considered in a large power system network with multiple generating units. In order to solve such large units in less operating time with accuracy, meta-heuristic methods are found to be feasible. Hence, this paper discusses different linear and non-linear and meta-heuristic methods applicable to power systems available in the literature. The review is done based on the method adopted, applicability and the technique adopted. A comparison is done among most of the optimization techniques in terms of accuracy, quicker in getting convergence, reliability, and its applicability in terms of single or multi-objective.
The smart grid plays a vital role in decreasing electricity cost via Demand Side Management (DSM). Smart homes, being a part of the smart grid, contribute greatly for minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the Peak to Average Ratio (PAR) and electricity cost with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time where user waiting time is considered to be minimum for residential consumers with multiple homes. Hence, in contribution 1, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart electricity storage system is also taken into account for more efficient operation of the HEM System. Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is instigated in a smart building which is comprised of thirty smart homes (apartments). Critical Peak Pricing (CPP) and Real-Time Pricing (RTP) signals are examined in terms of electricity cost assessment for both a single smart home and a smart building. In addition, feasible regions are presented for multiple and single smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results prove the effectiveness of our proposed scheme for multiple and single smart homes concerning electricity cost and PAR minimization. Moreover, there subsists a tradeoff between electricity cost and user waiting.
With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, DSM is modeled as an optimization problem and the solution is obtained by applying metaheuristic techniques with different pricing schemes. In contribution 2, an optimization technique, the Hybrid Gray Wolf Differential Evolution (HGWDE) is proposed by merging the Enhanced Differential Evolution (EDE) and Gray Wolf Optimization (GWO) schemes using the same RTP and CPP tariffs. Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility.
However, there is a trade-off between User Comfort (UC) and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the PAR is reduced up to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced up to 12.81%, 12.012% and 12.95%, respectively, for 15-min, 30-min and 60-min operational time intervals (OTI). On the other hand, the PAR and electricity bill are reduced up to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.
Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources. Microgrid generates power for electricity consumers and operates in both islanded and grid-connected modes more efficiently and economically. In contribution 3, we propose optimization schemes for reducing electricity cost and minimizing PAR with maximum UC in a smart home. We consider a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through Multiple Knapsack (MKP) then it is solved by existing heuristic techniques: GWO, binary particle swarm optimization (BPSO), GA and Wind Driven Optimization (WDO). Furthermore, we also propose three hybrid schemes for electricity cost and PAR reduction: (1) hybrid of GA and WDO named as WDGA; (2) hybrid of WDO and GWO named as WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system has also integrated to make our proposed schemes more cost-efficient and reliable to ensure stable grid operations. Finally, simulations have been performed to verify our proposed schemes. Results show that our proposed schemes efficiently minimize the electricity cost and PAR. Moreover, our proposed techniques: WDGA, WDGWO and WBPSO outperform the existing heuristic techniques.
The advancements in smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In contribution 4, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a Home Energy Management Controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the electricity proving utility with the load profile of the home. The smart meter is connected to power grid having an advanced metering infrastructure which is responsible for two way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising UC. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and RTP information. Simulation results show that proposed algorithms reduce the PAR by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.
Minimizing the electricity consumption and cost is one of the most demanding needs of today. As with the rapid increase in demand, there is a great need to design new solutions for effective energy management. With the advent of new Information Communication Technologies (ICT) traditional electricity grids, meters, buildings and appliances became Smart Grids (SGs), Smart Meters (SMs), Smart Buildings (SBs) and Smart Appliances (SAs). The SBs consists of a large number of SAs. These smart appliances are constantly sharing, their data with SGs, SMs and SBs. So a huge amount of data is generated every day. This data requires complex computations, faster retrievals and larger storage facilities [1]. Keeping this in view, a new energy management system is designed with the help of Cloud and Fog computing. As the Cloud Computing (CC) provides large number of data computation and permanent storage facilities however, it has limitations in fast data retrieval and causes response delays. On the other hand, Fog Computing (FC) offers faster information retrieval with less response delays with only limitation of temporary storage. The proposed system architecture integrates the qualities of both CC and FC by combining their services. To manage the load between different Virtual Machines (VMs) on Fog servers a new load balancing algorithm Modified Shortest Job First (MSJF) is the proposed. The performance of proposed algorithm is evaluated through different performance parameters. e.g. Processing Time (PT), Response Time (RT) and cost. To validate the performance of proposed scheme simulations are carried out in the Cloud Analyst tool. From the results it is assumed that the proposed technique can not outperforms the Round Robin (RR) and Throttled algorithms, due to its limitations in network delays and RT.
Minimizing the electricity consumption and cost is one of the most demanding needs of today. As with the rapid increase in demand, there is a great need to design new solutions for effective energy management. With the advent of new Information Communication Technologies(ICT) traditional electricity grids, meters, buildings and appliances be-came Smart Grids (SGs), Smart Meters (SMs), Smart Buildings (SBs)and Smart Appliances (SAs). The SBs consists of a large number of SAs.These smart appliances are constantly sharing, their data with SGs, SMs and SBs. So a huge amount of data is generated every day. This data requires complex computations, faster retrievals and larger storage facilities [1]. Keeping this in view, a new energy management system is designed with the help of Cloud and Fog computing. As the Cloud Computing (CC) provides large number of data computation and permanent storage facilities however, it has limitations in fast data retrieval and causes esponse delays. On the other hand, Fog Computing (FC) offers faster information retrieval with less response delays with only limitation of temporary storage. The proposed system architecture integrates the qualities of both CC and FC by combining their services. To manage the load between different Virtual Machines (VMs) on Fog servers anew load balancing algorithm Modified Shortest Job First (MSJF) is the proposed. The performance of proposed algorithm is evaluated through different performance parameters. e.g. Processing Time (PT), ResponseTime (RT) and cost. To validate the performance of proposed scheme simulations are carried out in the Cloud Analyst tool. From the results it is assumed that the proposed technique can not outperforms the RoundRobin (RR) and Throttled algorithms, due to its limitations in network delays and RT.
With the advent of the smart grid, it has become possible to improve the energy systems. To optimize the energy consumption pattern of the appliances, home energy management system is proposed for smart homes. Energy management in smart homes is a challenging task, therefore, the concept of demand-side management was introduced. For the effective scheduling of smart appliance, we propose a metaheuristic optimization technique. The proposed technique is hybrid of two existing techniques: Tabu Search (TS) and Bacterial Foraging Algorithm (BFA). The aim of the proposed technique is to reduce energy consumption so that user electricity bill reduces. Also, improves user comfort in term of average waiting time. For electricity bill calculation and appliance scheduling, time of use price tariff is used. Simulation results demonstrate that proposed scheme outperformed existing schemes in cost reduction and the average waiting time minimization. However, TS outruns other scheduling schemes in peak to average ratio reduction.
The smart grid plays a vital role in decreasing electricity cost through Demand Side
Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity
consumption cost via scheduling home appliances. However, user waiting time increases due to
the scheduling of home appliances. This scheduling problem is the motivation to find an optimal
solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user
waiting time. There are many studies on Home Energy Management (HEM) for cost minimization
and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple
parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time
was minimum for residential consumers with multiple homes. Hence, in this work, we propose an
efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently
developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA),
which can be used for electricity cost and peak load alleviation with minimum user waiting time.
The integration of a smart Electricity Storage System (ESS) is also taken into account for more
efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the
real-time electricity consumption pattern for every residence, i.e., every home has its own living
pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes
(apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in
terms of electricity cost estimation for both a single smart home and a smart building. In addition,
feasible regions are presented for single and multiple smart homes, which show the relationship
among the electricity cost, electricity consumption and user waiting time. Experimental results
demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms
of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost
and user waiting.
With the tendency to move toward automated home energy management systems, the cooperative capacity of the smart grid “SG” became more and more significant. Distributed generation “DG”, in addition to the novel patterns of electricity production, enabled the two-way energy flow. The concern of this paper is on introducing an automated control technique for optimizing the operational performance of the DG units within the residential applications. This problem is formulated in a constrained-nonlinear optimization structure based on a detailed economic system. Genetic algorithm “GA” technique is used to solve this problem by defining the optimal settings of the DG units. Availability of two-way communication and the use of smart meters will enable online implementation and facilitate data collection within a fully automated system. Based on certain tariffs and realistic load curves, many scenarios of operation are analyzed and assessed. The results show that using optimization technique in cooperative with advanced smart meters can enhance the economic situation of the overall grid significantly. Copyright
This paper proposes a new demand response scheduling framework for an array of households, which are grouped into different categories based on socio-economic factors, such as the number of occupants, family decomposition and employment status. Each of the households is equipped with a variety of appliances. The model takes the preferences of participating households into account and aims to minimize the overall production cost and, in parallel, to lower the individual electricity bills. In the existing literature, customers submit binary values for each time period to indicate their operational preferences. However, turning the appliances "on" or "off" does not capture the associated discomfort levels, as each appliance provides a different service and leads to a different level of satisfaction. The proposed model employs integer values to indicate household preferences and models the scheduling problem as a multi-objective mixed integer programming. The main thrust of the framework is that the multi-level preference modeling of appliances increases their "flexibility"; hence, the job scheduling can be done at a lower cost. The model is evaluated by using the real data provided by the Department of Energy & Climate Change, UK. In the computational experiments, we examine the relation between the satisfaction of consumers based on the appliance usage preferences and the electricity costs by exploring the Pareto front of the related objective functions. The results show that the proposed model leads to significant savings in electricity cost, while maintaining a good level of customer satisfaction.
The plug-in electric vehicle (PEV) has attracted more and more attention because of the energy crisis and environmental pollution, which is also the main shiftable load of the residential users' demand side management (DSM) system in the future smart grid (SG). In this paper, we employ game theory to provide an autonomous energy management system among residential users considering selling energy back to the utility company by discharging the PEV's battery. By assuming all users are equipped with smart meters to execute automatic energy consumption scheduling (ECS) and the energy company can adopt adequate pricing tariffs relating to time and level of energy usage, we formulate an energy management game, where the players are the residential users and the strategies are their daily schedules of household appliance use. We will show that the Nash equilibrium of the formulated energy management game can guarantee the global optimization in terms of minimizing the energy costs, where the depreciation cost of PEV's battery because of discharging and selling energy back is also considered. Simulation results verify that the proposed game-theoretic approach can reduce the total energy cost and individual daily electricity payment. Moreover, since plug-in electric bicycles (PEBs) are currently widely used in China, simulation results of residential users owing household appliances and bidirectional energy trading of PEBs are also provided and discussed.
In the operation of a smart grid (SG), day-ahead load forecasting (DLF) is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN), predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN))-based model for SGs is able to capture the non-linearity(ies) in the history load curve with 97.11% accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38.50%.
In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modeled in a simple way which considers appliance class, user preferences and weather conditions. The Wind Driven Optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, Min-max Regret based Knapsack Problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with fast convergence rate, as compared to the existing techniques
Taking advantage of two-way communication infrastructure and bidirectional energy trading between utility companies and customers in the future smart grid (SG), autonomous energy management programs become crucial to the demand-side management (DSM). Most of the existing autonomous energy management schemes are for the scenario with a single utility company or the scenario with one-way energy trading. In this paper, an autonomous household energy management system with multiple utility companies and multiple residential customers is studied by considering the bidirectional energy trading. To minimize the overall costs of both the utility companies and the residential customers, the energy management system is formulated as a double cooperative game. That is, the interaction among the residential users is formulated as a cooperative game, where the players are the customers and the strategies are the daily schedules of their household appliances; and the interaction among the utility companies is also formulated as a cooperative game, where the players are the suppliers and the strategies are the proportions of the daily total energy they provide for the customers. Without loss of generality, the bidirectional energy trading in the double cooperative game is formulated by allowing plug-in electric vehicles (PEVs) to discharge and sell energy back. Two distributed algorithms will be provided to realize the global optimal performance in terms of minimizing the energy costs, which can be guaranteed at the Nash equilibriums of the formulated cooperative games. Finally, simulation results illustrated that the proposed double cooperative game can benefit both the utility companies and residential users significantly.
This paper analyzes a consumption scheduling mechanism for domestic appliances within a home area network. The aim of the proposed scheduling is to minimize the total energy price paid by the consumer and to reduce power peaks in order to achieve a balanced daily load schedule. An exact and computationally efficient mixed-integer linear programming (MILP) formulation of the problem is presented. This model is verified by several problem instances. Realistic scenarios based on the real price tariffs commercially available in the Czech Republic are calculated. The results obtained by solving the optimization problem are compared with a simulation of the ripple control service currently used by many domestic consumers in the Czech Republic
Building energy use associated with non-domestic buildings accounts for approximately 19% of the total UK CO2 emissions. Energy consumption in a non-domestic building is a complex issue due to a wide variety of uses and energy services and therefore the energy demand of individual buildings need to be understood. A pilot study was undertaken to analyse the relationship between the electrical energy demand profiles and user activities for a university building. To gain insight into how the building is used, operated and managed on a daily basis, an online questionnaire was distributed to staff and students as well as interviews conducted with key management personnel. Analysis was performed on the half-hourly electrical demand data for the case-study building to identify key trends and patterns in energy use. The shape and magnitude of energy demand profiles shows a significant trend which does not seem to be strongly connected to occupancy patterns. It was found that the building was mostly controlled by a building management system (BMS) where building users have minimal access to the controls. However, it was interesting to find that the detailed information on the occupancy patterns could help the management team to redesign control strategies for optimum energy performance of the building.
This paper focuses on the problem of energy imbalance
management in a microgrid. The problem is investigated
from the power market perspective. Unlike the traditional power
grid, a microgrid can obtain extra energy from a renewable
energy source (RES) such as a solar panel or a wind turbine.
However, the stochastic input from the RES brings difficulty
in balancing the energy. In this study, a novel pricing scheme
is proposed that provides robustness against the intermittent
power input. The proposed scheme considers possible uncertainty
in the marginal benefit and the marginal cost of the power
market. It uses all available information on the power supply,
power demand, and imbalanced energy. The parameters of the
scheme are evaluated using an H∞ performance index. It turns
out that the parameters can be obtained by solving a linear
matrix inequality problem, which is efficiently solvable due to its
convexity. Simulation examples are given to show its excellent
performance in
Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.
This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm. Keywords: Genetic Algorithms, Search, Parallel Algorithms 1 Introduction Genetic Algorithms are a family of computational models inspired by evolution. These algorithms encode a potential solution to a specific problem on a simple chromosome-like data structure and apply recombination operators to these structures so as to preserve critical information. Genetic algorithms are often viewed as function optimizers, although the range of problems to which genetic algorithms have been applied is quite broad. An implementation of a genetic algorithm begins with a pop...
In this paper we propose novel and more realistic analytical models for the determination of the peak demand under four power demand control scenarios. Each scenario considers a finite number of appliances installed in a residential area, with diverse power demands and different arrival rates of power requests. We develop recursive formulas for the efficient calculation of the peak demand under each scenario, which take into account the finite population of the appliances. Moreover, we associate each scenario with a proper real-time pricing process in order to derive the social welfare. The proposed analysis is validated through simulations. Moreover, the performance evaluation of the proposed formulas reveals that the absence of the assumption of finite number of appliances could lead to serious peak-demand over-estimations.
Introduction of real-time pricing models for the price of electricity may lead to a reduction in both economic and environmental burden compared with existing models. Thanks to the timely response to price changes during the day the user can achieve a reduction in the payments for energy. However, recent studies show that lack of knowledge of users and their unwillingness to adapt their habits to changing energy prices is the biggest obstacle to successful implementation of the model. In our work, we propose to solve this problem by introducing automatic optimal energy consumption scheduling framework. This article presents the authors extension in the past created MILP model for the optimization of household appliances. The model is extended by rules of behavior that allows preserving logical links in the course of periodically triggered optimization (so called receding horizon). We also present and test the software simulator, in which the designed model is implemented. The application simulates the operation of Building Energy Manager, which based on user preferences, prediction electricity prices and other important parameters controls the operation of appliances in the house.
Home energy management system technology can provide a smart and efficient way of optimizing energy usage in residential buildings. This paper presents a home energy management system algorithm that monitors and controls household appliances based on time-of-use (TOU) energy pricing models while accounting for multiple inhabitants sharing a home and its appliances. This algorithm helps to manage and schedule usage by prioritizing multiple users with preferred usage patterns. Two different scenarios will be implemented to develop and test the influence of a multiple-users and load priority (MULP) algorithm on reducing energy consumption, energy cost and carbon footprint. In the first scenario, TOU pricing and different demand limits are used, while the second scenario focuses on the TOU pricing with different demand limits combined with the MULP model. Simulation results show that the combination of the MULP model and the TOU pricing leads to significant reductions in user payments and total energy consumption. Copyright © 2015 John Wiley & Sons, Ltd.
Demand side management encourages the users in a smart grid to shift their electricity consumption in response to varying electricity prices. In this paper, we propose a distributed framework for the demand response based on cost minimization. Each user in the system will find an optimal start time and operating mode for the appliances in response to the varying electricity prices. We model the cost function for each user and the constraints for the appliances. We then propose an approximate greedy iterative algorithm that can be employed by each user to schedule appliances. In the proposed algorithm, each user requires only the knowledge of the price of the electricity, which depends on the aggregated load of other users, instead of the load profiles of individual users. In order for the users to coordinate with each other, we introduce a penalty term in the cost function, which penalizes large changes in the scheduling between successive iterations. Numerical simulations show that our optimization method will result in lower cost for the consumers, lower generation costs for the utility companies, lower peak load, and lower load fluctuations.
Demand response (DR) for residential and small commercial buildings is
estimated to account for as much as 65% of the total energy savings potential
of DR, and previous work shows that a fully automated Energy Management System
(EMS) is a necessary prerequisite to DR in these areas. In this paper, we
propose a novel EMS formulation for DR problems in these sectors. Specifically,
we formulate a fully automated EMS's rescheduling problem as a reinforcement
learning (RL) problem (referred to as the device based RL problem), and show
that this RL problem decomposes over devices under reasonable assumptions.
Compared with existing formulations, our new formulation (1) does not require
explicitly modeling the user's dissatisfaction on job rescheduling, (2) enables
the EMS to self-initiate jobs, (3) allows the user to initiate more flexible
requests and (4) has a computational complexity linear in the number of
devices. We also propose several new performance metrics for RL algorithms
applied to the device based RL problem, and demonstrate the simulation results
of applying Q-learning, one of the most popular and classical RL algorithms, to
a representative example.
Various forms of demand side management (DSM) programs are being deployed by utility companies for load flattening amongst the residential power users. These programs are tailored to offer monetary incentives to electricity customers so that they voluntarily consume electricity in an efficient way. Thus, DSM presents households with numerous opportunities to lower their electricity bills. However, systems that combine the various DSM strategies with a view to maximizing energy management benefits have not received sufficient attention. This study therefore proposes an intelligent energy management framework that can be used to implement both energy storage and appliance scheduling schemes. By adopting appliance scheduling, customers can realize cost savings by appropriately scheduling their power consumption during the low peak hours. More savings could further be achieved through smart electricity storage. Power storage allows electricity consumers to purchase power during off-peak hours when electricity prices are low and satisfy their demands when prices are high by discharging the batteries. For optimal cost savings, the customers must constantly monitor the price fluctuations in order to determine when to switch between the utility grid and the electricity storage devices. However, with a high penetration of consumer owned storage devices, the charging of the batteries must be properly coordinated and appropriately scheduled to avoid creating new peaks. This paper therefore proposes an autonomous smart charging framework that ensures both the stability of the power grid and customer savings.
Grid computing is a form of distributed computing that co-ordinates and provides the facility of resource sharing over various geographical locations. Resource scheduling in Grid computing is a complex task due to the heterogeneous and dynamic nature of the resources. Bacterial foraging has recently emerged as a global optimization algorithm for distributed optimization and control. This paper proposes the use of the bacterial foraging optimization technique for Grid resource scheduling. A novel bacterial foraging based hyper-heuristic resource scheduling algorithm has been designed to effectively schedule the jobs on available resources in a Grid environment. The performance of the proposed algorithm has been evaluated with the existing common heuristics based scheduling algorithms through the GridSim toolkit. The experimental results show that the proposed algorithm outperforms the existing algorithms by minimizing cost and makespan of user applications submitted to the Grid.
This book represents a complete guide to the planning and implementation of effective demand-side management (DSM) programs. The book provides guidelines for planning a DSM program designed for the power market of the 1990s. It also covers implementing strategies which allow the most productive use of both fuel resources and capital. For utility managers, this excellent reference provides expert guidance for every component of the DSM program, including load management programs, forecasting, pricing, and promoting of efficient end-use technologies. It provides new insights into utility incentive and rebate programs, and how to best take advantage of cost-saving benefits. This book contains 15 chapters.
We present the electricity use in the commercial sector in subtropical Hong Kong during the 31 year period from 1970 to 2000. The average annual growth rate was 7.4% during the past three decades. Commercial buildings, especially shopping centres, are major electricity end uses. A survey of four fully air conditioned shopping centres built during the 1990s was conducted to establish some energy use characteristics pertinent to shopping centres in subtropical climates. It was found that air conditioning and electric lighting were the major electricity end uses, accounting for about 85% of the total building energy use. Electricity use per unit gross floor area ranged from 391 to 454 kW h/m2, with an average of 430 kW h/m2. Simple regression analysis also revealed that electricity use showed a strong correlation with the mean monthly outdoor dry bulb temperature. This paper presents the work and discusses the energy efficiency implications.
Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
Demand-side management. Vols. 1-5. EPRI
- C W Gellings
- J H Chamberlin
Gellings, C. W., & Chamberlin, J. H. (1988). Demand-side management.
Vols. 1-5. EPRI, Palo Alto, CA.
Office of Technology Assessment Adapted From Battelle-Columbus Division and Synergic Resources Corp., Demand Side Management
- U S Congress
U.S. Congress, Office of Technology Assessment 1993, Adapted From
Battelle-Columbus Division and Synergic Resources Corp., Demand Side
Management, Volume 3: Technology Alternatives and Market Implementation Methods EPRIIAVEM-3597 (Palo Alto CA: Eclectic Power
Research Institute, 1984)
Biomimicry of BFA for distributed optimization and control
- K M Passino
Passino, K. M. (2002). Biomimicry of BFA for distributed optimization
and control. Control Systems, IEEE, 22(3), 52-67.
Adapted From Battelle-Columbus Division and Synergic Resources Corp
- U S Congress
U.S. Congress, Office of Technology Assessment 1993, Adapted From
Battelle-Columbus Division and Synergic Resources Corp., Demand Side
Management, Volume 3: Technology Alternatives and Market Implementation Methods EPRIIAVEM-3597 (Palo Alto CA: Eclectic Power
Research Institute, 1984)