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A Social Spider Optimization Based Home Energy Management System
Abstract
Home energy management within the traditional grid is difficult, so Smart Grid (SG) is introduced by upgrading the traditional grid, i.e., adding the Information Technology (IT) and Sensors Network (SN) to traditional grids. SG manages the Demand of electricity and help in solving the electricity load management problem. Demand Side response has two parts monitoring the electricity and notify consumers about its pricing scheme and bill, this can be done using smart meters. In smart metering system homes are integrated with Energy Management Controller (EMC) which uses Demand Side Management (DSM) systems based on a optimization technique. In this paper a system is proposed which manages the load by shifting from peak hours to off peak hours, reduce electricity bill, reduce waiting time and reduce Peak to Average Ratio (PAR). For simulations we use classification consist of 3 classes of appliances, Time of Use (ToU) as our pricing signal and Social Spider Optimization (SSO) our technique. The simulations results show the achievements of the system.
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In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.
This paper targets the process of optimizing the operation of a PV-battery backup system under intermittent grid electricity supply. A predictive scheduling layer is developed as a part of a complete load management process. The main objective of the study is to ensure a permanent power supply for a high energy consuming residential application. The control algorithm plans the activation of predictable loads 24 h ahead through compromising between a decrease in the resulting discomfort levels and the conservation of a high autonomy of the system. The strength of the developed control lies in ensuring the complete coordination between all the components of the installation: the grid, PV panels, battery storage, and the load demand. No loss of power supply is allowed during the day and realistic and technical constraints are applied. The demand side management program is formulated as a multi-objective optimization algorithm solved using the Non-dominated Sorting Genetic Algorithm (NSGA-II) technique. A fuzzy logic decision maker is developed for an automatic trade-off process implementing the residents’ preferences. The simulation results show excellent performance and flexibility of the proposed algorithm. The benefits of the load management are proved to have a great impact on the backup installation sizing, which leads to notable reduction of its price.
In this study, a stochastic programming model is proposed to optimize the performance of a smart micro-grid in a short term to minimize operating costs and emissions with renewable sources. In order to achieve an accurate model, the use of a probability density function to predict the wind speed and solar irradiance is proposed. On the other hand, in order to resolve the power produced from the wind and the solar renewable uncertainty of sources, the use of demand response programs with the participation of residential, commercial and industrial consumers is proposed. In this paper, we recommend the use of incentive-based payments as price offer packages in order to implement demand response programs. Results of the simulation are considered in three different cases for the optimization of operational costs and emissions with/without the involvement of demand response. The multi-objective particle swarm optimization method is utilized to solve this problem. In order to validate the proposed model, it is employed on a sample smart micro-grid, and the obtained numerical results clearly indicate the impact of demand side management on reducing the effect of uncertainty induced by the predicted power generation using wind turbines and solar cells.
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.
This paper details a proposed demand response (DR) application to optimize the operation of appliances in an indeterminate environment in a home energy management system (HEMS). An indeterminate environment results from forecasted errors of electricity prices and system loads, so a probabilistic analysis of the system performance is of significant interest. Herein, a chance constrained, optimization-based model is formulated to accommodate these uncertainties. The resulting DR application can be easily embedded in resource limited electric devices. To reduce the computational cost, both improved particle swarm optimization (PSO) and a two-point estimate method are presented to solve the chance constrained problem. The improved PSO is used to provide the optimum solution, while the probabilistic assessment of uncertainties is estimated using a two-point estimate method. Numerical comparisons were made to justify the effectiveness of the method. The simulated results obtained using the models indicate that the proposed method can significantly reduce the computational burden while maintaining a high level of accuracy.
This paper studies the power scheduling problem for residential consumers in smart grid. In general, the consumers have two types of electric appliances. The first type of appliances have flexible starting time and work continuously with a fixed power. The second type of appliances work with a flexible power in a predefined working time. The consumers can adjust the starting time of the first type of appliances or reduce the power consumption of the second type of appliances to reduce the payments. However, this will also incur discomfort to the consumers. Assuming the electricity price is announced by the service provider ahead of time, we propose a power scheduling strategy for the residential consumers to achieve a desired trade-off between the payments and the discomfort. The power scheduling is formulated as an optimization problem including integer and continuous variables. An optimal scheduling strategy is obtained by solving the optimization problem. Simulation results demonstrate that the scheduling strategy can achieve a desired tradeoff between the payments and the discomfort.
Smart grid technology is a collection of existing and up-and-coming technologies working together to improve the distribution of electric energy. It provides the providers and consumer of power real time information on power production and consumption. Cyber technology is being utilized in electric smart grid system from the generation of the power to its distribution to consumers. Nowadays it has become relatively easy for consumers to be monitored through wired and wireless means using these cyber technologies on how they effectively and efficiently manage power provided to them. However with the introduction of cyber technologies in electric grid systems, there is an added risk to its implementation and operation. This paper will be looking at the security risk associated with power production and transmission, the communication protocols used in the smart grid and the security risk for consumers.
In this paper, we focus on the problems of load scheduling and power trading in systems with high penetration of renewable energy resources (RERs). We adopt approximate dynamic programming to schedule the operation of different types of appliances including must-run and controllable appliances. We assume that users can sell their excess power generation to other users or to the utility company. Since it is more profitable for users to trade energy with other users locally, users with excess generation compete with each other to sell their respective extra power to their neighbors. A game theoretic approach is adopted to model the interaction between users with excess generation. In our system model, each user aims to obtain a larger share of the market and to maximize its revenue by appropriately selecting its offered price and generation. In addition to yielding a higher revenue, consuming the excess generation locally reduces the reverse power flow, which impacts the stability of the system. Simulation results show that our proposed algorithm reduces the energy expenses of the users. The proposed algorithm also facilitates the utilization of RERs by encouraging users to consume excess generation locally rather than injecting it back into the power grid.
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.
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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.
We propose a consumption scheduling mechanism for home and neighbourhood area load demand management in smart grid using integer linear programming (ILP) and game theory. The proposed mechanism is able to schedule both the optimal power and the optimal operation time for power-shiftable appliances and time-shiftable appliances respectively according to the power consumption patterns of all the individual appliances. Simulation results for both the centralised and the distributed management scenarios have been presented to demonstrate and verify the effectiveness of the proposed technique.