Conference Paper

Towards Multiple Knapsack Problem Approach for Home Energy Management in Smart Grid

Authors:
  • COMSATS University Islamabad-Wah Campus
Conference Paper

Towards Multiple Knapsack Problem Approach for Home Energy Management in Smart Grid

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Abstract

The energy demand of residential end users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. In this paper, we describe a scheme to solve multiple knapsack problems (MKP) using heuristic algorithms. Keeping total energy consumption of each household appliance under certain threshold with maximum benefit is regarded as knapsack problem. Here, we design multiple knapsack problems for each hour of a day to schedule different numbers of appliance. To avoid peak hours, load is shifted in low and mid peak hours. Different algorithms are used to schedule household appliances. We use ant colony optimization (ACO) that is one of the meta-heuristic techniques to solve multiple knapsack problems which enables fast convergence rate for scheduling of appliances. Results show that propose scheme is an efficient method for home energy management to maximize user comfort and minimize electricity bills.

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... For this purposed model, we assume waiting time of each shiftable appliance not greater than 5, if operation start time of an appliance is greater than our assumption then utility pays penalty. if C(t)is high peak hour then 17: calculate ϕa using (28) 18: else 19: start an appliance 20: end if 21: local update pheromone for each ant refer [21] 22: ...
... choose best solution so far 23: global update pheromone for each ant refer [21] 24: ...
... In [20], linear programming is used to designed the optimization function. Refer to [21], we modified its algorithm for our designed scenario. Algorithm. ...
Conference Paper
Full-text available
In this paper, we introduce a generic architecture for demand side management (DSM) and use combined model of time of use tariff and inclined block rates. The problem formulation is carried via multiple knapsack and its solution is obtained via ant colony optimization (ACO). Simulation results show that the designed model for energy management achieves our objectives; it is proven as a cost-effective solution to increase sustainability of smart grid. The ACO based energy management controller performs more efficiently than energy management controller without ACO based scheduling in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.
... In this work, the main objectives are to reduce cost, optimize the communication setup to maximize the comfort level of end-user and reduce overall scheduling time. Here, the scheduling problem is formulated using Multiple Knapsack Problem (MKP) criterion [25]. It is a resource allocation problem that consists of 'm' resources (capacities), set of 'n' objects, 'j' number of knapsacks and map our scheduling problem in MKP as follows: ...
... The output of solar and wind powers with respect to solar input radiation power and wind availability can be calculated through (25) and (26), ...
... 25 shows an intelligent plugging of time flexible loads in poor homes. ...
Thesis
Full-text available
Smart community setups nowadays are subjected to complicated issues such as instability, intermittent integration of the load at the demand side and lack of intelligent two-way communication process. These issues need to be addressed in terms of a balanced power demand dispatch (DD) in the real-time or day-ahead duplex signal regime under multi-microgrids. This paper offers an intelligent multi-agent-based approach that works between different levels of communication and their respective layers for a community-based system to optimize the power in community-based multi-microgrids model. This will further enhance user personal comfort. Constraints relative to cost minimization also have a relation with this model. A three-level structure with various layers of autonomous agents take intelligent decisions based on prioritized particle swarm optimization (P-PSO), prioritized plug and play (PPnP), and knapsack; considering DD as the main driver of the system to handle price and power consumption uncertainties. Distinct smart home models, depending upon their living habits, are keenly observed providing their power infrastructure and personal comfort. Load appliances considered as load agents are individually contemplated for maximum proficiency. Furthermore, two-way communication between utility and consumers lower downs the risk of inefficiency of the system where anyone seems unsatisfied with the other.
... In this paper [15], authors present scheme to solve multiple knapsack problems using heuristic algorithms. To obtain maximum benefits, a threshold is defined so that total energy consumption of appliances remain under the threshold. ...
... The main drawback of this technique is that it may suffer from early convergence. Local and global pheromone update equations from [15] are: Local pheromone update equation is: ...
Conference Paper
This paper, provides comparative assessment of performance for home energy management (HEM) controller which categories the household appliances into three different categories 1) Fixed appliances 2) Interrupt able appliances and 3) Non-interrupt able appliances on the bases of their load profiles and user preference. It is designed on the bases of two bio-inspired algorithms, genetic algorithm (GA), bacterial foraging algorithm (BFA) and two nature-inspired algorithms binary particle swarm optimization algorithm (BPSO) and ant colony optimization algorithm (ACO). Demand side management system (DSM) is also inaugurate. Real time pricing (RTP) model is used for energy price calculation. The objectives of minimize electricity cost consumption and peak to average (PAR) ratio are achieve successfully, as simulations validates. Simulations perform for aforemention heuristic algorithms, ACO perform best among all four algorithms. Average cost for schedule algorithms GA, BFA, BPSO and ACO are 95.58%, 81%, 90.4% and 76.48% respectively.
... In [33], linear programming is used to designed the optimization function. Refer to [34], we modified its algorithm for our designed scenario. Algorithm. ...
... Our proposed model is applicable for single and multiple homes in residential areas. Major modifications and possible outcomes in ACO algorithm in contrast to [34] are given in table. 4.3 ...
Thesis
Full-text available
Smart grid (SG) is evolutionary idea in which all components of conventional power grid are modernized with the advance integration of information technology, sensors and autonomous system. The bi-directional flow of information and power in SG with optimal integration of renewable energy sources encourage customers to participate in energy management schemes and demand response. Meanwhile, innovation components of grid such as transmission, demand side management and demand response are develop as modernized applications with lots of benefits as well as challenges in SG. Therefore, we explore potential solution to the interesting and challenging problems of SG. In this dissertation, we comparatively evaluate the performance of home energy management controller which is designed on the basis of heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO). In this regard, we introduce a generic architecture for demand side management (DSM) which integrates residential area domain with smart area domain via wide area network. In addition, problem formulation is carried via multiple knapsack problems. For energy pricing, combined model of time of use tariff and inclined block rates is used. Simulation results show that all designed models for energy management act significantly to achieve our objectives and proven as a cost-effective solution to increase sustainability of SG. GA based energy management controller performs more efficiently than BPSO based energy management controller and ACO based energy management controller in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization and its execution time is also less than other two models.
... In this work, the main objectives are to reduce cost, optimize the communication setup to maximize the comfort level of end-user and reduce overall scheduling time. Here, the scheduling problem is formulated using Multiple Knapsack Problem (MKP) criterion [25]. It is a resource allocation problem that consists of 'm' resources (capacities), set of 'n' objects, 'j' number of knapsacks and map our scheduling problem in MKP as follows: ...
... In cases of emergency, control is handed over to the consumer. The consumer is then referred to the need constraints table N nec (i), is later shown in equation (25). If the number of consumers is increased, the decision would be taken on a need constraint table. ...
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Full-text available
Smart community setups nowadays are subjected to complicated issues such as instability, intermittent integration of the load at the demand side, and lack of intelligent two-way communication process. These issues need to be addressed in terms of a balanced power demand dispatch (DD) in the real-time or day-ahead duplex signal regime under multi-microgrids. This paper offers an intelligent multi-agent-based approach that works between different levels of communication and their respective layers for a community-based system to optimize the power in community-based multi-microgrids model. This will further enhance user personal comfort. Constraints relative to cost minimization also have a relation with this model. A three-level structure with various layers of autonomous agents take intelligent decisions based on prioritized particle swarm optimization (P-PSO), prioritized plug and play (PPnP), and knapsack; considering DD as the main driver of the system to address objectives like price and power consumption uncertainties. Distinct smart home models, depending upon their living habits, are keenly observed providing their power infrastructure and personal comfort. Load appliances considered as load agents are individually contemplated for maximum proficiency. Furthermore, two-way communication between utility and consumers lowers down the risk of the inefficiency of the system.
... In [33], linear programming is used to designed the optimization function. Refer to [34], we modified its algorithm for our designed scenario. Algorithm. ...
... Our proposed model is applicable for single and multiple homes in residential areas. Major modifications and possible outcomes in ACO algorithm in contrast to [34] are given in Table 5 Algorithm 3. Improved Algorithm of ACO-EMC 1: Initialize all parameters (˛a,ˇa, a , a ) 2: ...
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In this paper, we comparatively evaluate the performance of home energy management controller which is designed on the basis of heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO). In this regard, we introduce a generic architecture for demand side management (DSM) which integrates residential area domain with smart area domain via wide area network. In addition, problem formulation is carried via multiple knapsack problem. For energy pricing, combined model of time of use tariff and inclined block rates is used. Simulation results show that all designed models for energy management act significantly to achieve our objections and proven as a cost-effective solution to increase sustainability of smart grid. GA based energy management controller performs more efficiently than BPSO based energy management controller and ACO based energy management controller in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.
... As results, ACO minimized the throughput and the number of request failure and maximized the computation power [10] . The different scheduling problem solved in [44] by knapsack and optimized by ant colony optimization (ACO). In [45] , resource allocation in the cloud is solved by the KnapGA genetic backpack algorithm. ...
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Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.
... One of the disastrous condition in the existing power grid is system outage, occurs due to unbalance between aggregated power demand and generation capacity from all the resources in a specific region. To solve this issue, the concept of the knapsack problem is applied in our model to limit the pattern of utilized energy within the gross generation capacity of the plant (Rahim et al., 2015). Let P T be the maximum value of power generated and P 4 is the net power consumed by all the users as ...
... Simulation results show the efficacy of the presented approach. A knapsack approach is presented in [4] for Home Energy Management in Smart Grid. Ant Colony Optimization (ACO) is used to determine multiple knapsack problem. ...
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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.
... The authors in [41], found the optimal orders of running tasks based on deadline and minimum cost using knapsack with dynamic programming. The different scheduling problem solved in [42] by knapsackbased ACO to find the best solution as a mapping between multiple knapsacks and load scheduling. In [43], a resource scheduling in cloud solved by the combination of the knapsack and GA with the fitness function include utilization of CPU, network throughput, and input/output rate of the disk. ...
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In today’s world, the internet of things (IoT) is developing rapidly. Wireless sensor network (WSN) as an infrastructure of IoT has limitations in the processing power, storage, and delay for data transfer to cloud. The large volume of generated data and their transmission between WSNs and cloud are serious challenges. Fog computing (FC) as an extension of cloud to the edge of the network reduces latency and traffic; thus, it is very useful in IoT applications such as healthcare applications, wearables, intelligent transportation systems, and smart cities. Resource allocation and task scheduling are the NP-hard issues in FC. Each application includes several modules that require resources to run. Fog devices (FDs) have the ability to run resource management algorithms because of their proximity to sensors and cloud as well as the proper processing power. In this paper, we review the scheduling strategies and parameters as well as providing a greedy knapsack-based scheduling (GKS) algorithm for allocating resources appropriately to modules in fog network. Our proposed method was simulated in iFogsim as a standard simulator for FC. The results show that the energy consumption, execution cost, and sensor lifetime in GKS are better than those of the first-come-first-served (FCFS), concurrent, and delay-priority algorithms.
... In order to reduce the PAR and billing price, load should be schedules in different time slots. We used Knap technique (Rahim S.-et-al , 2015)to formulate the scheduling of load at different times. Hence forecasting the boundary about demand vs utilization of energy for consumers to reduce the PAR. ...
Preprint
Stability and protection of the electrical power systems are always of primary concern. Stability can be affected mostly by increase in the load demand. Power grids are overloaded in peak hours so more power generation units are required to cope the demand. Increase in power generation is not an optimal solution. With the enlargement in Smart grid (SG), it becomes easier to correlate the consumer demand and available power. The most significant featutre of smart grid is demand response (DR) which is used to match the demand of available electrical energy and shift the peak load into off peak hours to improve the economics of energy and stability of grid stations. Presently we used Genetic algorithm (GA) to schedule the load via real time pricing signal (RTP). Load is categorized depending on their energy requirement, operational constraint and duty cycle. We conclude that GA provides optimal solution for scheduling of house hold appliances by curtailing overall utilized energy cost and peak to average ratio hence improving the load profile.
... Due to the nature of 0-1 KP it can be scaled up to multiple knapsacks, which then becomes the 0/1 Multiple Knapsack Problem (0-1 MKP), which is an NP-hard problem. 0-1 MKP permits more complex problem formulations than 0-1 KP, and has applications in radar technology [11], Cloud systems [1] [5], multiprocessor scheduling [4], and energy management [18]. The formal definition of 0-1 MKP [17, p.13] follows: Definition 2.2.1. ...
Thesis
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Today’s markets are heavily concentrated on Cloud Computing, where trends project an increasing amount of services living therein. Because these data centers, named Clouds, provide ability to dynamically scale the resources assigned to a service. It is a technology which allows products to remain competitive in a constantly changing market. Consumers profit from this solution, as it is much easier to maintain the quality of a given service (QoS) by allocating more resources within a Cloud, on the contrary it lies within the Cloud provider’s interest to fit as many services possible within their Cloud. This poses the question of how resources integral to facilitation of a service are meant to be allocated to enable efficient utilization of resources within the Cloud. Services consisting of several Virtual machines are placed on as few physical machines as possible using a 3-dimensional Multiple Subset-sum problem model approach. The virtual machine placement case considered is Infrastructure as a Service (IaaS), and its implementation is facilitated by Java Constraint Programming Library (JaCoP) developed by Prof. Krzysztof Kuchcinski and PhD Radosław Szymanek. Results from this work deem constraint programming a suitable approach for enhancing an existing virtual machine deployment process in an IaaS Cloud.
... Kumaraguruparan et al. [7] apply MKP problem in task scheduling. Rahim et al. [8] apply MKP problem in home energy management. Li et al. [9] apply MKP problem in graph theory in bipartite graphs. ...
... They considered small capacities of sub problems and obtained the best values of parameters include deadline in unpredictable tasks, the network congestion and the inaccurate task size. The different scheduling problem solved in [24] by optimized knapsack. They managed the smart grid using knapsack problem and consider the start timing objective. ...
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Thesis
Full-text available
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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%.
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The advancement of renewable energy technologies has seen the emergence of customer owned grid tied wind and solar microgrids. These microgrids offer an opportunity to energy users to lower their energy costs as well as enabling the power suppliers to regulate the utility grid. However, the integration of the renewable energy based sources into the smart grid increases the complexity of the main grid. The success of this scheme will be heavily reliant on accurate real-time information exchange between the microgrid, the main grid, and the consumers. The communication between these agents will be critical in implementation of intelligent decisions by the smart grid. The microgrids will be required to relay energy forecasts information to the utility grid. Similarly, customers will be expected to submit energy demand schedules, to actively monitor energy price signals, to participate in energy bids, and to respond to energy management signals in real time. This kind of grid-user interaction will be overwhelming and could result in consumer apathy. There is therefore a need to develop smart systems that will autonomously execute all these tasks without the prompting of the customers. This paper presents one such approach. In this study, we proposed a demand side energy management for a grid connected household with a locally generated photovoltaic energy. To ensure efficient household energy management, smart scheduling of electrical appliances has also been presented.
Article
A key component of the smart grid is the ability to enable dynamic residential pricing to incentivize the customer and the overall community to utilize energy more uniformly. However, the complications involved require that automated strategies be provided to the customer to achieve this goal. This paper presents a solution to the problem of optimally scheduling a set of residential appliances under day-ahead variable peak pricing in order to minimize the customer's energy bill (and also, simultaneously spread out energy usage). We map the problem to a well known problem in computer science - the multiple knapsack problem - which enables cheap and efficient solutions to the scheduling problem. Results show that this method is effective in meeting its goals.
Conference Paper
The electricity grid is undergoing a major renovation and becoming a smart grid by integrating the advances in Information and Communication Technologies (ICT). Current applications in energy generation, power distribution and its consumption need improvement in several ways, such as, making efficient use of green energy, increasing automation in distribution and enabling residential energy management. The existing grid does not provide sufficient mechanisms to manage the residential electricity consumption. However, interconnecting consumer devices with the home area networks, and at the same time, communicating with the utility networks through a home gateway facilitate residential energy management in smart grids. Residential energy management uses utility-driven price signals which vary depending on the time of the day. This is called as Time Of Use (TOU) pricing. In TOU pricing, electricity consumption during peak hours costs more than electricity consumption during off-peak hours. TOU prices reflect the variation in the actual cost of power during one day. Utilities run bas plants to supply power for the base load. In peak hours, demands of the consumers rise, and utilities bring peaker plants online to supply additional power. Peaker plants have higher operating costs and higher GreenHouse Gas (GHG) emission rates than base plants. Therefore, reducing peak load decreases the expenses for energy generation and it decreases the GHG emissions. Wireless sensor networks can play a key role in reducing the demand of the consumers in peak hours. In this paper, we employ TOU-aware energy management in a smart home with wireless sensor home area network and analyze the impact of this schemes on the peak load. We show that our scheme decreases the use of the appliances in peak hours and reduces the energy bills for consumers.
Article
The authors have proposed a method of reducing the energy consumption in residential buildings by providing household members with information on energy consumptions in their own houses. An on-line interactive “energy-consumption information system” that displays power consumptions of, at most, 18 different appliances, power and city-gas consumption of the whole house and room temperature, for the purpose of motivating energy-saving activities has been constructed and the effectiveness of the system investigated by installing it in 10 residential buildings. The experiment showed that energy-saving consciousness was raised and energy consumption was in fact reduced by the energy-saving activities of the household members. In this paper, the system is described in detail and the effectiveness of reducing energy-consumption of the whole house and for space heating will be discussed. Also the energy-saving activities in a certain household are shown by using load duration curves.
Article
This paper presents a novel co-operative agents approach, ant colony search algorithm (ACSA)-based scheme, for solving a short-term generation scheduling problem of thermal power systems. The main purpose of this paper is to investigate the applicability of an alternative intelligent search method in power system optimisation, particularly in short-term generation scheduling problems. The ACSA is derived from the theoretical biology of the topic of ant trail formation and foraging methods. A set of co-operating agents, ants, co-operate to find a good solution for the short-term generation scheduling problem of thermal units. In the ACSA, the state transition rule, global and local updating rules are also introduced to ensure the optimal solution. Once all the ants have completed their tours, a global pheromone-updating rule is then applied and the process is iterated until the stop condition is satisfied. The effectiveness of the proposed scheme has been demonstrated on the daily generation scheduling problem of model power systems.
Article
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.
Article
Conventional cost-based load management ignores the rate structure offered to customers. The resulting cost savings may cause revenue loss. In a deregulated power industry where utilities absorb the ultimate consequence of their decision making, reexamination of load management must be conducted. In this paper, profit-based load management is introduced to examine generic direct load control scheduling. Based upon the cost/market price function, the approach aims to increase the profit of utilities. Instead of determining the amount of energy to be deferred or to be paid back, the algorithm controls the number of groups power customer/load type to maximize the profit. In addition to the advantage of better physical feel on how the control devices should operate, the linear programming algorithm provides a relatively inexpensive and powerful approach to the scheduling problem
An Optimal Power Scheduling Method for Demand Response in Home Energy Management System A Genetic Evolutionary Task Scheduling Method for Energy Efficiency in Smart Homes
  • Z Zhao
  • W Lee
  • Y Shin
  • K B Song
Z. Zhao, W.C Lee,Y. Shin and K.B. Song, An Optimal Power Scheduling Method for Demand Response in Home Energy Management System, IEEE Transactions on Smart Grid, Vol. 4, No. 3, pp. 1391 1400, 2013. [12] H. Miao, X. Huang and G. Chen, A Genetic Evolutionary Task Scheduling Method for Energy Efficiency in Smart Homes, International Journal of Electrical Engineering, Vol. 7, No. 5, pp. 1827 6660, 2012.
A Knapsack Problem Approach for Achieving Efficient Energy Consumption in Smart Grid for End-user Life Style
  • O A Sianaki
  • O Hussian
  • A R Tabesh
O.A. Sianaki, O. Hussian and A.R. Tabesh,A Knapsack Problem Approach for Achieving Efficient Energy Consumption in Smart Grid for End-user Life Style,IEEE conference, 2010.