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Cost and Comfort Based Optimization of Residential Load in Smart Grid
Abstract
In smart grid, several optimization techniques are developed for residential load scheduling purpose. Preliminary all the conventional techniques aimed at minimizing the electricity consumption cost. This paper mainly focuses on minimization of electricity cost and maximiza-tion of user comfort along with the reduction of peak power consumption. We develop a multi-residential load scheduling algorithm based on two heuristic optimization techniques: genetic algorithm and binary particle swarm optimization. The day-ahead pricing mechanism is used for this scheduling problem. The simulation results validate that the proposed model has achieved substantial savings in electricity bills with maximum user comfort. Moreover, results also show the reduction in peak power consumption. We analyzed that user comfort has significant effect on electricity consumption cost.
... In the existing literature, verity of approaches has been proposed for resolving entity matching problem, such as genetic algorithms and genetic programming. Many heursitics algorithms have also been used in literature for optimization of various problems which show very goof results [5,6,7]. Moreover, various types of distance measures have been used to effectively identify the entities that are related to each other. ...
... Due to the above problems, various techniques have been developed by the research community. However, these approaches prove to be insufficient in resolving the entity matching problems [7]. The use of Genetic algorithm has been very successful in resolving the entity matching problems; however, one of the main limitations of Genetic algorithms is the selection of best and suitable fitness functions [8]. ...
... Form the existing research work presented by Mishra et al. [7], in the implementation phase; a pre-generated distance matrix was used. In the case of our proposed approach, pre-generated distance matrices can also be used. ...
In the current scenario of the World Wide Web and other information and communication technology domain, a huge amount of data is generated daily. This huge amount of data plays an important role in the sustainability of different business and organizations. This data belongs to different entities. Entity matching is the process of linking and mapping data to the respective entities that it belongs. Entity matching and author name disambiguation is a well-known problem in different areas, such as databases and Artificial intelligence. For instance, entity matching, and author name disambiguation problems often arise in digital libraries such as DBLP, Arnet Miner, and Google Scholar. The authors and scholars can change their addresses, affiliations, and some authors have the same names. In such scenarios mapping the huge number of records to their respective entities becomes a difficult and challenging task. In the literature, a variety of techniques and approaches has been providing for entity matching problems. Some of these approaches use machine learning algorithms, text mining, and information retrieval-based approaches. However, these approaches were not successful in completely resolving the entity matching problem. In this work, we proposed a teacher learning-based optimization algorithm (TLBO) for entity matching and author name disambiguation. We select four different similarity distance measure and eight different validity indexes for mapping records to their respective entities in bibliographic databases. For the experimental purpose, we use sixteen different publicly available datasets along with DBLP and Arnet Miner datasets. The results achieved by most of the validity indices and TLBO algorithm achieved an average F-measure score of 85% using distance measures. Our proposed approaches outperformed the existing techniques that were based on a genetic algorithm in most cases.
... We need to tackle this situation by applying different techniques to overcome the complexity of the system [12,13]. To achieve the best result different optimization tools and techniques has designed for many years such as PSO, DSM, GA, etc [14][15][16][17][18][19][20][21]. purpose of these techniques which are used to find out the best solution for minimizing of consumption of energy, electricity cost, and PAR. ...
... It provides efficient results in cost reduction and user discomfort. In [20] they schedule a load of energy author to develop a HEMS system. To minimize bill rate DR technique is used. ...
Electric energy is the basic need for human survival on this earth as these needs increase with the rapid increase in population. It's become a challenge to manage home energy with the current situation. Smart grid provided different techniques to overcome these challenges to meet the need. This paper presents the result of the different optimization techniques that give the best performance in reducing cost, PAR, and user discomfort. Based on results the best result techniques are also combined to make a hybrid model for more accuracy. This paper not only describes optimization techniques but also the limitations and features of these techniques.
... SG consists of several elements such as AMI, energy storage system, towers, transmission lines, Control Center (CC) and excellent communication networks [1,2]. IoTenabled SGs connect various SG components including smart meters, controllers, DAUs, PMUs, PDCs, fault isolators over the Internet, to enable ubiquitous connectivity. ...
... In this work, the author deployed sensors at different positions of transmission lines and also considered far away positions from the substation. Power Line Sensor Network (PLSN) is proposed for the transmission line to minimize the sensor and communication costs [1]. In [8], the authors discussed the performance of the linear network model and concluded that the model is ineffective in delivering data promptly. ...
... Optimizing residential DR is a typical example of a green computing problem [27]. The aim is to manage the required demand to match the available energy resources [28,29]. The challenge is made more difficult by the varying loads, energy costs, RESs, and mobile storage energy consumption. ...
Energy management strategies are crucial to efficiently scheduling appliances and preventing peak generation due to increased energy demand. It is essential to manage the demand and supply of energy based on the consumer’s consumption patterns using various heuristic optimization techniques. Additionally, the end-user is more concerned with minimizing electricity costs and reducing peak-to-average ratios (PARs). This work proposes an arithmetic Harris hawks optimization (AHHO) as a new approach for improving the Harris hawks algorithm to optimize residential demand response (DR) load management in a smart grid. Our method employs arithmetic and lightweight flight operators based on the Lévy flight distribution to generate diverse design solutions and improve the HHO’s exploration capabilities. We consider 15 smart appliances and categorize them based on how much energy they use, computing the electricity price using real-time pricing (RTP) and critical peak pricing (CPP). While maintaining user satisfaction within operational and power limits, the objective is to decrease energy costs and PAR. We evaluate the effectiveness of our proposed AHHO approach against nine cutting-edge algorithms using both RTP and CPP schemes. The findings demonstrate that our suggested approach performs better than the other algorithms because it achieved cost savings of 42.10% and 30% under RTP and CPP schemes, respectively. Meanwhile, it also reduced PAR by 55.17% and 50% under RTP and CPP schemes, respectively.
... In [24], different DSM techniques have been discussed which include but are not limited to direct load control, load preference, peak clipping, valley filling, and differential tariff. Authors in [25][26][27][28] successfully apply optimization algorithms with the common objective of cost minimization and peak load reduction. Similarly, in [29], mixed-integer linear programming is used with a multiobjective strategy to minimize the cost and peak energy demand. ...
Appliance scheduling on the user’s defined preferences plays a pivotal role in smart home energy management systems. In this context, energy management controllers are largely used to satisfy the users’ demand preferences within their financial budget constraints. This work proposes a robust technique based on demand-side energy management for efficiently monitoring and controlling the domestic loads. A nature-inspired crescive consumer satisfaction algorithm (NCSA) is proposed for the optimal scheduling pattern based on time and device preferences. The proposed algorithm maximizes user satisfaction at the preset user budget by producing optimum appliance scheduling patterns. It considers household appliances input data such as time of use, power ratings, and the absolute maximum satisfaction for optimal scheduling. The proposed algorithm is evaluated on three budget schemes, and the simulation results reveal that the proposed algorithm achieves a better satisfaction index at a lower cost per unit satisfaction. Results also show that the proposed algorithm has a good convergence rate and is generalizable to any random budget scenario.
... Different intelligent methods such as evolutionary programming, GA, particle swarm optimization, sine cosine algorithm, exist for optimization of task in power systems [31]- [33], and applied for single machine infinite bus and multimachine design parameters. Problems arising in PSS design are well met using these techniques; however, in the case of optimization of a complex and high dimensional objective function, there is a need for some more efficient approach. ...
Voltage instability in a power system produces low-frequency oscillations (LFOs), causing adverse effects in power distribution. Intelligent control schemes can overcome the limitations of fixed-parameter structures in power system stabilizers (PSS). Flexible alternating current transmission system (FACTS) control along with some supplementary control have remarkable potential in damping the oscillations. This paper proposes an adaptive neurofuzzy based recurrent wavelet control (ANRWC) scheme to enhance the power system stability. The proposed scheme utilizes recurrent Gaussian as antecedent part’s membership function and recurrent wavelet function in consequent parts. Our scheme uses gradient descent, adadelta, adaptive moment estimation (ADAM) and proximal gradient descent algorithms for optimization in which parameters of the scheme are updated using a back-propagation algorithm. A multi-machine power system is used for testing the controller. We evaluate the proposed control scheme in comparison to conventional lead-lag control and an adaptive neurofuzzy takagi sugeno kang (ANFTSK) control scheme. For comparison, we calculate the performance indices (PIs) for different controllers. Both quantitative and qualitative evaluations assert the effectiveness of the proposed control as compared to other schemes.
... The results showed the optimal load scheduling and consumption of many appliances in a residence are managed in a unified way. Authors of [86][87][88][89] have used several heuristics optimization techniques for load scheduling problems to minimize user bills and enhance user satisfaction. They proposed their hybrid techniques by eliminating the limitation of these techniques and compared their results with base techniques to show the efficacy of their techniques. ...
High consumption and ever-increasing demand for electricity at commercial, residential, and industrial levels have attracted the research community to look for new technologies for the future grid. A smart grid is an advanced technology-enabled electrical grid system with the incorporation of information and communication technology. The smart grid also enables two-way power flow, and enhanced metering infrastructure capable of self-healing, resilient to attacks, and can forecast future uncertainties. This paper surveys various smart grid frameworks, social, economic, and environmental impacts, energy trading, and integration of renewable energy sources over the years 2015 to 2021. Energy storage systems, plugin electric vehicles, and a grid to vehicle energy trading are explored which can potentially minimize the need for extra generators. This study shows that the integration of renewable energy sources, plug-in electric vehicles, and energy storage systems provide long-term economic and environmental benefits and have identified challenges, opportunities, and future directions. This study is expected to serve as a potential source of guidance for the researchers to explore various aspects of the smart grid over the last 06 years which shows them the future directions. This survey will also serve as a guiding source for transmission and distribution systems operators while showing them the right path to transform their traditional grids into smart grids.
... In there, edges are removed one by one from a complete graph [7]. This process can be optimized by using heuristic based optimization algorithms, for instance many optimization techniques have been proposed in literature for engineering application in recent years [9]- [11]. ...
When analyzing different networks, it may be important to discover communities inside them. Community detection techniques are useful for social media algorithms to discover people with common interests and keep them tightly connected. Community detection can be used in machine learning to detect groups with similar properties and extract groups for various reasons. Our approach is based on modularity, which tries to maximize the difference between the actual number of edges in a community and the expected number of edges in the community.The algorithm starts with a weighted network of N nodes. In the first phase, the algorithm assigns a different community to each node of the network. Then for each node, it considered the neighbours and evaluate the gain of modularity by removing the particular node from the current community and placing in the neighbour's community.
... FACTS tools are static power electronics devices to promote stability, controllability and power transferability of a system. FACTS controllers are proficient in changing the parameters of a system effectively and also improve the power system's stability [17,18,19]. The principal purpose of FACTS controllers is to regulate the flow of power, reliable loading for transmission lines near the thermal margins and preventing outages. ...
Flexible AC Transmission System controller performs effective damping of low-frequency oscillations when provided with suitable supplementary damping control like a Static synchronous series compen-sator. In this context, the paper proposes an adaptive neuro-fuzzy recurrent wavelet control for smart cities that uses the recurrent Gaussian membership function and recurrent wavelet neural network in antecedent and consequent parts respectively. The paper applies the gradient descent optimization with a back-propagation algorithm to update the parameters of suggested adaptive neuro-fuzzy recurrent wavelet control. Simulations are performed on two test systems, both showing the effectiveness of the recommended control scheme as compared to traditional lead-lag control and artificial neuro-fuzzy Takagi Sugeno Kang control. Calculations of performance index for various fault scenarios lead to the conclusion that the control scheme can damp oscillations effectively and enhances the power system's transient stability.
... The idea of smart homes involves various types of energy production & storage devices, information & communication infrastructures, and control mechanism to adjust the energy consumption pattern automatically. An essential device in a smart electricity grid environment known as Home energy management system (HEMS), which allows the users to participate in the load shifting plan where they can shift their load in an off-peak hour [12,13,14]. Having new developments and highly demand of pricing schemes and smart-loads, residential users find it hard to schedule these loads manually. ...
Robust optimization is a technique that is used increasingly used in home energy management systems , where it is applied in the scheduling of household loads through demand side control. In a smart grid, efficient load management can help balance and reduce the burden on the national power grid and also minimize local operational electricity cost. In this work, interruptible loads and ther-mostatically controlled loads are analyzed to obtain optimal schedules in the presence of uncertainty. Firstly, the uncertain parameters are represented as different intervals, and then in order to control the degree of conservatism, these parameters are divided into various robustness levels. The conventional scheduling problem is transformed into a deterministic scheduling problem by translating the intervals and robustness levels into constraints. We then apply Harris' hawk optimization together with integer linear programming to further optimize the load scheduling. Cost and trade-off schemes are considered to analyze the financial consequences of several robustness levels. Results show that the proposed method is adaptable to user requirements and robust to the uncertainties.
... The idea of smart homes involves various types of energy production & storage devices, information & communication infrastructures, and control mechanism to adjust the energy consumption pattern automatically. An essential device in a smart electricity grid environment known as Home energy management system [19,20,21]. Having new developments and highly demand of pricing schemes and smart-loads, residential users find it hard to schedule these loads manually. ...
... SG consists of several elements such as AMI, energy storage system, towers, transmission lines, Control Center (CC) and excellent communication networks [1]. IoT-enabled SGs connect various SG components including smart meters, controllers, DAUs, PMUs, PDCs, fault isolators over the Internet, to enable ubiquitous connectivity. ...
... The idea of smart homes in-ORCID(s): volves various types of energy production & storage devices, information & communication infrastructures, and control mechanism to adjust the energy consumption pattern automatically. An essential device in a smart electricity grid environment known as Home energy management system (HEMS), which allows the users to participate in the load shifting plan where they can shift their load in an off-peak hour [12,13,14]. Having new developments and highly demand of pricing schemes and smart-loads, residential users find it hard to schedule these loads manually. ...
... FACTS tools are static power electronics devices to promote stability, controllability and power transferability of a system. FACTS controllers are proficient in changing the parameters of a system effectively and also improve the power system's stability [17,18,19]. The principal purpose of FACTS controllers is to regulate the flow of power, reliable loading for transmission lines near the thermal margins and preventing outages. ...
... The idea of smart homes in-ORCID(s): volves various types of energy production & storage devices, information & communication infrastructures, and control mechanism to adjust the energy consumption pattern automatically. An essential device in a smart electricity grid environment known as Home energy management system (HEMS), which allows the users to participate in the load shifting plan where they can shift their load in an off-peak hour [12,13,14]. Having new developments and highly demand of pricing schemes and smart-loads, residential users find it hard to schedule these loads manually. ...
... There are many optimization techniques used in literature for different engineering purposes which have performed quiet well and showed their efficacy. For instance [9]- [11], [11]- [15], [15]- [19] are few works to mention where therse these techniques have performed quiet well on different engineering problems. ...
Deaf people use sign language as the only reliable source of communication with each other as well as with normal people. These communicating signs are made up of the shape of the hand and movement. In Pakistan, deaf people use Pakistan sign language (PSL) as a means of communication with people. In scientific literature, many studies have been done on PSL recognition and classification. Most of these work focused on colored-based hands while some others are sensors and Kinect-based approaches. These techniques are costly and also avoid user-friendliness. In this paper, a technique is proposed for the recognition of thirty-six static alphabets of PSL using bare hands. The dataset is obtained from the sign language videos. At a later step, four vision-based features are extracted i.e. local binary patterns, a histogram of oriented gradients, edge-oriented histogram, and speeded up robust features. The extracted features are individually classified using Multiple kernel learning (MKL) in support vector machine (SVM). We employed a one-to-all approach for the implementation of basic binary SVM into the multi-class SVM. A voting scheme is adopted for the final recognition of PSL. The performance of the proposed technique is measured in terms of accuracy, precision, recall, and F-score.
... The idea of smart homes involves various types of energy production & storage devices, information & communication infrastructures, and control mechanism to adjust the energy consumption pattern automatically. An essential device in a smart electricity grid environment known as Home energy management system (HEMS), which allows the users to participate in the load shifting plan where they can shift their load in an off-peak hour [12,13,14]. Having new developments and highly demand of pricing schemes and smart-loads, residential users find it hard to schedule these loads manually. ...
... FACTS tools are static power electronics devices to promote stability, controllability and power transferability of a system. FACTS controllers are proficient in changing the parameters of a system effectively and also improve the power system's stability [17,18,19]. The principal purpose of FACTS controllers is to regulate the flow of power, reliable loading for transmission lines near the thermal margins and preventing outages. ...
A smart city is a dynamic and sustainable urban system that provides a great quality of service to its residents by optimally managing its resources. In smart cities, low-frequency oscillations are a serious concern to the power system as they adversely affect system's stability. Moreover, power system stabilizers are inefficient due to their fixed-parameter architecture. Flexible AC Transmission System controller performs effective damping of low-frequency oscillations when provided with suitable supplementary damping control like a Static synchronous series compensator. In this context, the paper proposes an adaptive neuro-fuzzy recurrent wavelet control for smart cities that uses the recurrent Gaussian membership function and recurrent wavelet neural network in antecedent and consequent parts respectively. The paper applies the gradient descent optimization with a back-propagation algorithm to update the parameters of suggested adaptive neuro-fuzzy recurrent wavelet control. Simulations are performed on two test systems, both showing the effectiveness of the recommended control scheme as compared to traditional lead-lag control and artificial neuro-fuzzy Takagi Sugeno Kang control. Calculations of performance index for various fault scenarios lead to the conclusion that the control scheme can damp oscillations effectively and enhances the power system's transient stability.
... SG consists of several elements such as AMI, energy storage system, towers, transmission lines, Control Center (CC) and excellent communication networks [1][2][3]. IoT-enabled SGs connect various SG components including smart meters, controllers, DAUs, PMUs, PDCs, fault isolators over the Internet, to enable ubiquitous connectivity. All these components are connected with each other to support the future grid network. ...
Smart Grid (SG) faces several challenges to efficiently transfer the power generated to power consumers. So, a robust monitoring tool is required to monitor the transmission lines in order to ensure the security of the resource. This power transmission monitoring is a good example of an ultra-reliable low latency application of 5G with the aim to provide quality of service and quality of experience. The primary objective of this study is to design a wireless network for real-time monitoring of transmission lines to take preventive measures. In this paper, we present an Internet of Things enabled real-time transmission line monitoring system comprising wireless, wired, and cellular technologies. The objective is to minimize the time delay at the minimum installation cost of the network. In our proposed model, all the sensors are powered by Renewable Energy Resources (RES) like wind and solar energy, etc. The placement problem is formulated to determine the location of cellular-enabled transmission towers. Moreover, feasible regions are also calculated to show the relationship between time delay and energy consumption. Results show that the proposed model provides efficient solutions and takes less time for data transmission and is more energy-efficient.
... This approach can rank the appliances in a house area. To flatten the voltage curve, an interruptible load is scheduled and reshaped by using binary particle swarm optimization in [36], [37]. The voltage profile and the requirements of the users are considered in interruptible load reduction. ...
A smart grid (SG) is an emerging technology that provides electricity in a cost-efficient and eco-friendly way. SG combined with distributed energy resources (DERs) plays a crucial role in extending the existing grid's capacity while mitigating carbon emissions. The potential sources of DERs include solar, wind, and tidal energy. Usually, these DERs are located far away from the grid and not necessarily tied to the grid system. However, the energy trading capabilities of a grid-tied DERs are getting attention, both from academia and industry. This bonding of grid-tied DERs helps to decrease the loss of surplus energy, build an energy storage capacity, and other operational charges. Energy-consuming flexible home tasks can be optimized coordinately with the operations of DERs to minimize the economic cost and CO 2 emissions. In this work, our problem is multi-objective and we aim to reduce both electricity price and CO 2 emission. We proposed a multi-objective self-adaptive multi-population based Jaya algorithm (PMO-SAMP-Jaya) to schedule the operations of flexible home tasks. Different pricing schemes have been applied to uncover the correlation between CO 2 emission, economic cost, and pricing schemes. We assume a smart building, including 30 smart homes with PV and energy storage system (ESS) as DERs. Promising results have shown the effectiveness of our proposed scheme. INDEX TERMS Smart grid, distributed energy resources (DERs), demand side management, environmental scheduling, renewable energy, meta-heuristic optimization.
... A variety of similarity measures are presented in the literature to accurately find the solution to entity matching problem in bibliographic databases. The problem can be formulated as an optimization problem, and Heuristic techniques can efficiently and effectively solve the optimization problem [6,7,8,9]. These techniques have proved their effectiveness in solving the optimization problems at a low computational cost. ...
... SG consists of several elements such as AMI, energy storage system, towers, transmission lines, Control Center (CC) and excellent communication networks [1][2][3]. IoT-enabled SGs connect various SG components including smart meters, controllers, DAUs, PMUs, PDCs, fault isolators over the Internet, to enable ubiquitous connectivity. All these components are connected with each other to support the future grid network. ...
Smart Grid (SG) faces several challenges to efficiently transfer the power generated to power consumers. So, a robust monitoring tool is required to monitor the transmission lines in order to ensure security of the resource. This power transmission monitoring is a good example of ultra reliable low latency application of 5G with the aim to provide quality of service and quality of experience. The primary objective of this study is to design a wireless network for real-time monitoring of transmission lines to take the preventive measures. In this paper, we
present an Internet of Things enabled real-time transmission line monitoring system comprising of wireless, wired, and cellular technologies. The objective is to minimize the time delay at minimum installation cost of the
network. In our proposed model, all the sensors are powered by Renewable Energy Resources (RES) like wind and solar energy, etc. The placement problem is formulated to determine the location of cellular enabled transmission towers. Moreover, feasible regions are also calculated to show the relationship between time delay and energy consumption. Results show that proposed model provides efficient solutions and takes less time for data transmission and is more energy efficient.
... Several works are devoted to community detection based on multiobjective optimization. For example, meta-heuristics algorithms can effectively solve multi-objective optimization problems with less computational efforts [14][15][16][17][18]. Ji et al. [19] proposed combining ACO with an evolutionary algorithm based on decomposition. ...
Discovering communities is one of the important features of complex networks, as it reveals the structural features within such networks. Community detection is an optimization problem, and there have been significant efforts devoted to detecting communities with dense intra-links. However, single-objective optimization approaches are inadequate for complex networks. In this work, we propose the Multi-Layer Ant Colony Optimization (MLACO) to detect communities in complex networks. This algorithm takes Ratio Cut (RC) and Kernel K-means (KKM) as an objective function and attempts to find the optimal solution. The findings from our evaluation of MLACO using both synthetic and real-world complex networks demonstrate that it outperforms other competing approaches, in terms of normalized mutual information (NMI) and modularity (Q). Moreover, we also evaluate our algorithm for small-scale and large-scale networks showing the utility of our proposed approach.
... This approach can rank the appliances in a house area. To flatten the voltage curve, an interruptible load is scheduled and reshaped by using binary particle swarm optimization in [36], [37]. The voltage profile and the requirements of the users are considered in interruptible load reduction. ...
A smart grid (SG) is an emerging technology that provides electricity in a cost-efficient and eco-friendly way. SG combined with distributed energy resources (DERs) plays a crucial role in extending the existing grid’s capacity while mitigating carbon emissions. The potential sources of DERs include solar, wind, and tidal energy. Usually, these DERs are located far away from the grid and not necessarily tied to the grid system. However, the energy trading capabilities of a grid-tied DERs are getting attention, both from academia and industry. This bonding of grid-tied DERs helps to decrease the loss of surplus energy, build an energy storage capacity, and other operational charges. Energy-consuming flexible home tasks can be optimized coordinately with the operations of DERs to minimize the economic cost and CO2 emissions. In this work, our problem is multi-objective and we aim to reduce both electricity price and CO2 emission. We
proposed a multi-objective self-adaptive multi-population based Jaya algorithm (multi-objective SAMPJaya) to schedule the operations of flexible home tasks. Different pricing schemes have been applied to uncover the correlation between CO2 emission, economic cost, and pricing schemes. We assume a smart building, including 30 smart homes with PV and energy storage system (ESS) as DERs. Promising results have shown the effectiveness of our proposed scheme.
... • Ratio Cut (RC): Inter-communities link densities sum is RC [19]. It ensure sparse inter connections and since we dealing with unsigned graph so RC is calculated as: Many optimization algorithms are introduced to detect communities in network like ant colony optimization algorithm [20,21], genetic algorithm [22], cuckoo optimization algorithm [23], particle swarm optimization [24,25], evolutionary strategy [26], label propagation [27] and stimulated annealing [28]. Heuristics optimization techniques shows good results to solve optimization problems [29,30]. ...
Discovering communities is one of the important feature of complex networks to be considered as it reveal its structural features. Community detection modeled as optimization problem and huge efforts have been devoted to detecting communities with dense intra links. In recent years, many algorithms detected communities by optimizing single objective. Single objective criteria does not illustrate information about true structure of network and provide inadequate solutions for complex networks. A multi-layer ant colony optimization with chance constraint is proposed to detect communities in complex networks. Further, this algorithm will optimize multi-objectives to detect communities. This algorithm takes Ratio Cut (RC) and Kernel K-Means (KKM) as an objective function and tries to minimize these objectives. The concept of Pareto optimal set is used to update pheromone value. Through local pheromone updating the weak dominated solutions are forgotten. Exploration space will be improved using this way of pheromone updating. Therefore in optimal space, new solution will be searched and found by algorithm. Complexity of algorithm and construction of ant solution is improved by introducing a multi-layer model in Ant Colony Optimization (ACO). By creating optimal stopping criteria iterations of algorithm are reduced. To show the effectiveness of proposed algorithm experiments are performed on real world complex networks. Comparison are made with recent similar approaches in terms of normalized mutual information and modularity.
... Heuristic algorithms have shown their effectiveness to solve the optimization problems, like GA [25], [26], BPSO [27], [28] and ACO [29]. A lot of work done in community detection through particle swarm optimization algorithm. ...
Cloud computing develop quickly, which has powerful computing ability and allows authorized users to store and transmit data on cloud servers. Therefore, the security of applications in the cloud environment becomes an inevitable challenge. Therefore, many scholars at home and abroad have studied how to improve the security in cloud environment. The emergence of attribute-based encryption (ABE) has made it a better choice to improve cloud security. Subsequently, since ciphertext attribute-based encryption (CP-ABE) is more suitable for cloud storage than key attribute-based encryption (KP-ABE). Therefore, in view of the efficiency and security of CP-ABE, the CP-ABE’s access structure and revocation mechanism are studied in depth. Finally, we summarizes the weaknesses of the existing ABE schemes and points out the future research directions. So the paper provides a reference for further research.
In Smart Grid (SG), integration of renewable energy sources such as solar and wind is a challenging task due to their intermittent nature. Most of the existing Demand Side Management (DSM) techniques are based on Day Ahead Pricing (DAP) or Time of Use (TOU) pricing which can deviate from Real Time Pricing (RTP) due to unpredictable energy consumption trends and electricity prices. This paper presents opportunistic scheduling algorithms in a RTP environment based on Optimal Stoping Rule (OSR). We classify different users and assign priorities based on energy demand. In order to minimize the electricity bill and appliance waiting time cost, we modify the First Come First Serve (FCFS) scheduling algorithm. Regarding comfort maximization, Priority Enable Early Deadline First (PEEDF) scheduling algorithm is proposed which schedules the appliances based on minimum length of operation time and priority constraints. Simulation results validate the effectiveness of the proposed algorithms in terms of electricity cost reduction and user comfort maximization.
One of the most challenging problems associated with operation of Smart Micro-Grids is the optimal energy management of residential buildings with respect to multiple and often conflicting objectives. In this paper, a multi-objective mixed integer nonlinear programming model is developed for optimal energy use in a smart home, considering a meaningful balance between energy saving and a comfortable lifestyle. Thorough incorporation of a mixed objective function, under different system constraints and user preferences, the proposed algorithm could not only reduce the domestic energy usage and utility bills, but also ensured an optimal task scheduling and a thermal comfort zone for the inhabitants. To verify the efficiency and robustness of the proposed algorithm, a number of simulations were performed under different scenarios using real data; and the obtained results were compared in terms of total energy consumption cost, users’ convenience rates and thermal comfort level.
In smart grid, residential consumers adopt different load scheduling methods to manage their power consumptions with specific objectives. The conventional load scheduling methods aim to maximize the consumption payoff or minimize the consumption cost. In this paper, we introduce a novel concept of cost efficiency-based residential load scheduling framework to improve the economical efficiency of the residential electricity consumption. The cost efficiency is defined as the ratio of consumer's total consumption benefit to its total electricity payment during a certain period. We develop a cost-efficient load scheduling algorithm for the demand-side's day-ahead bidding process and real-time pricing mechanism by using a fractional programing approach. Results show that the proposed scheduling algorithm can effectively reflect and affect user's consumption behavior and achieve the optimal cost-efficient energy consumption profile. For practical consideration, we also take into account the service fee and distributed energy resources (DERs) in our framework, and analyze their impacts on the cost efficiency. Simulation results confirm that the proposed algorithm significantly improves consumer's cost efficiency. It is shown that a higher service fee will decrease the cost efficiency, while the integration of DERs can effectively improve the cost efficiency.
For electricity consumers, there are power loads which need to be processed in a predefined time interval. The electricity price could vary between peak and off-peak time. In that case, the intelligent task scheduling module in a smart home can minimize the entire energy expense if the task control module could schedule the electrical equipments’ start times, which are determined by their power consumptions and operation time constraints. In Smart Grid environments, this Advanced Metering Infrastructure (AMI) could automatically schedule the operation time of each equipment to minimize the residential overall power consumption while satisfying the equipment’s operation constraint such as the equipment needs to be started at a time between two predefined time instants, and the power system is not overloaded at any time instant. In this research, the paper formulates the situation as an optimization problem and proposes a Genetic Algorithm (GA) based algorithm to find the optimum schedule arrangement for all the tasks in a smart home to reduce the energy cost. The performance of the GA based method is evaluated with the previous research works such as SA based method and greedy search method. The simulation results show that the GA based scheduling algorithm can efficiently and optimally minimize customers’ electricity cost.
Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.
h i g h l i g h t s DSM scheme that could maximize satisfaction within user's budget is presented. Load-satisfaction algorithm using the scheme is developed based on GA. We quantified user satisfaction based on certain rules. We develop cost per unit satisfaction index, k($) that relates budget to satisfaction. The proposed algorithm is effective in offering the maximum satisfaction. a b s t r a c t This paper presents a demand side load management technique that is capable of controlling loads within the residential building in such a way that the user satisfaction is maximized at minimum cost. Load-satisfaction algorithm was developed based on three postulations that allow satisfaction to be quantified. The input data required by the algorithm includes the power ratings of the electrical devices, its time of use, kW h electrical consumption as well as the satisfaction of the user on each electrical appliance at every hour of the day. From the data, the algorithm is able to generate an energy usage pattern, which would give the user maximum satisfaction at a predetermined user-budget. A cost per unit satisfaction index (k) which relates the expenditure of a user to the satisfaction achievable is also derived. To test the applicability of the proposed load-satisfaction management technique, three budget scenarios of $0.25/day, $0.5/day and $1.00/day are performed. The result of each of the scenarios using the proposed techniques is compared to cases in which the electrical appliances are randomly used. The results obtained revealed that the proposed algorithm offered the maximum satisfaction and minimum cost per unit satisfaction for all the scenarios.
In this paper, we study a multi-residential electricity load scheduling problem with multi-class appliances in smart grid. Compared with the previous works in which only limited types of appliances are considered or only single residence grids are considered, we model the grid system more practically with jointly considering multi-residence and multi-class appliance. We formulate an optimization problem to maximize the sum of the overall satisfaction levels of residences which is defined as the sum of utilities of the residential customers minus the total cost for energy consumption. Then, we provide an electricity load scheduling algorithm by using a PL-Generalized Benders Algorithm which operates in a distributed manner while protecting the private information of the residences. By applying the algorithm, we can obtain the near-optimal load scheduling for each residence, which is shown to be very close to the optimal scheduling, and also obtain the lower and upper bounds on the optimal sum of the overall satisfaction levels of all residences, which are shown to be very tight.
An interval number optimization method is proposed in this paper to tackle the household load scheduling problem with uncertain hot water demand and ambient temperature. The household loads considered include residential thermostatically controlled loads such as electric water heater and air conditioner, and interruptible loads such as clothes washer and pool pump. The uncertain-but-bounded parameters are modelled as interval numbers, based on which the uncertain load scheduling problem is formulated and transformed. A binary particle swarm optimization combined with integer linear programming is introduced to solve the transformed problem. Two schemes, named cost scheme and trade-off scheme, are contrastively discussed to study the economic impacts of different tolerance degrees for constraint violation. Simulation results demonstrate that the proposed method is flexible to different consumer demands and robust to the uncertainties.
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.
The potential to schedule portion of the electricity demand in smart energy systems is clear as a significant opportunity to enhance the efficiency of the grids. Demand response is one of the new developments in the field of electricity which is meant to engage consumers in improving the energy consumption pattern. We used Teaching & Learning based Optimization (TLBO) and Shuffled Frog Leaping (SFL) algorithms to propose an optimization model for consumption scheduling in smart grid when payment costs of different periods are reduced. This study conducted on four types residential consumers obtained in the summer for some residential houses located in the centre of Tehran city in Iran: first with time of use pricing, second with real-time pricing, third one with critical peak pricing, and the last consumer had no tariff for pricing. The results demonstrate that the adoption of demand response programs can reduce total payment costs and determine a more efficient use of optimization techniques.
Power companies are unable to withstand the consumer power requirement due to growing population, industries and buildings. The use of automated electrical appliances have increased exponentially in day to day activity. To maintain a possible balance between the supply and demand the power companies are introducing the demand side management approach. As a result, consumers are adopted for load shifting or scheduling their loads into off-peak hours to reduce the electricity bill. When all the consumers are trying to run the scheduled electrical appliances at the same time then the usage of energy in the off peak hour curve is marginally high. However, service providers are in need of a load balancing mechanism to avoid over or under utilization of the power grid. In the existing works, threshold limit is applied for a home to maintain the balanced load and if the consumer exceeds it then the additional charges are applied in the bill. To overcome the above mentioned drawbacks there is a need to increase the power usage with minimum cost and reducing the waiting time. For this purpose, in this paper we implement multi-objective evolutionary algorithm, which results in the cost reduction for energy usage and minimize the waiting time for appliance execution. The result reveals that if the consumer exceeds the threshold limit, the scheduled running electrical appliances temporarily stops to maintain the energy usage under threshold level for cost benefit and resumes the stopped appliances later. Further, the proposed technique minimizes the overall electricity bill and waiting time for the execution of electrical appliances.
In this paper, we utilize the GA method to optimize the start time units of all the OAAs to achieve our objectives. Since the start time unit is the only variable in our scheme and the constraint parameters are set in the beginning, we assume that the total fitness function is (14). In the selection process, we adopt a roulette selection method in which the individual with a better fitness value has a higher probability to be selected for further processing. In general, the time complexity of the GA process can be represented as O(generation number*(mutation complexity + crossover complexity + selection complexity)). Assume the maximal generation number, the size of the population, and the number of individuals are denoted by g, N, and na, respectively; therefore, the time complexity of our scheme is O(gNna). In this case, the time cost increases as the three parameters become larger, and, usually, the time cost of GA optimization does not satisfy people. However, in our approach, the power scheduling process is implemented at the beginning of the day; therefore, after time parameters are determined, there is enough time for power scheduling, and the algorithm running time problem is not so important. We think a time cost of a few seconds is acceptable. In this paper, the population size is 200; the probability of crossover and the probability of mutation are 90% and 2%, respectively. Finally, when the generation number reaches 1,000, the evolution process will finish.
Generally speaking, the relationship between electricity cost and DTRave is a tradeoff. In other words, as the value of DTRave increases, electricity cost decreases. However, the minimum electricity cost value would emerge at a position at which the DTRave value is about 50%, which is not definite, due to the random POE. From the result shown in Fig. 6, at the position that DTRave equals 0, it implies that the major consideration is minimizing the delay time; thus, in this case, ω1=0, ω2=1. However, when the minimum electricity cost is reached, ω1=1, ω2=0.
This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer's electricity bill whilst minimizing the daily volume of curtailed energy, and therefore considering the user's comfort level. To avoid shifting a large portion of consumer demand toward the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer's demand goes beyond a prescribed power threshold. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller in minimizing the household's daily electricity bill while {preserving} comfort level, as well as preventing creation of new least-price peaks.
With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.
Demand response is a key feature of the smart grid. The addition of bidirectional communication to today's power grid can provide real-time pricing (RTP) to customers via smart meters. A growing number of appliance companies have started to design and produce smart appliances which embed intelligent control modules to implement residential demand response based on RTP. However, most of the current residential load scheduling schemes are centralized and based on either day-ahead pricing (DAP) or predicted price, which can deviate significantly from the RTP. In this paper, we propose an opportunistic scheduling scheme based on the optimal stopping rule as a real-time distributed scheduling algorithm for smart appliances' automation control. It determines the best time for appliances' operation to balance electricity bill reduction and inconvenience resulting from the operation delay. It is shown that our scheme is a distributed threshold policy when no constraint is considered. When a total power constraint exists, the proposed scheduling algorithm can be implemented in either a centralized or distributed fashion. Our scheme has low complexity and can be easily implemented. Simulation results validate proposed scheduling scheme shifts the operation to off-peak times and consequently leads to significant electricity bill saving with reasonable waiting time.
This paper mainly focuses on demand side management and demand response, including drivers and benefits, shiftable load scheduling methods and peak shaving techniques. Demand side management techniques found in literature are overviewed and a novel electricity demand control technique using real-time pricing is proposed. Currently users have no means to change their power consumption to benefit the whole system. The proposed method consists of modern system identification and control that would enable user side load control. This would potentially balance demand side with supply side more effectively and would also reduce peak demand and make the whole system more efficient.
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: Load Management, Load Profiling, Load Shifting, Residential and Industrial Consumer, Energy Audit, Reliability, Urban, Semi-urban And Rural Setting
- I K Maharjan
Maharjan, I.K.: Demand Side Management: Load Management, Load Profiling,
Load Shifting, Residential and Industrial Consumer, Energy Audit, Reliability,
Urban, Semi-urban And Rural Setting. LAP Lambert Academic Publ. (2010)