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Implementing Critical Peak Pricing in Home Energy Management using Biography based Optimization and Genetic Algorithm in Smart Grid
Abstract
In domestic area, demand of an electricity has been growing with the increase of energy consumed by appliances. So, there must be a mechanism for scheduling the appliances and reducing a power consumption in Home Energy Management System (HEMS). In this regard, we integrate two heuristic techniques Genetic Algorithm (GA) and Biography Based Optimization (BBO) in HEMS by using smart grid. Our discussion and simulations results clearly shows the effect on cost minimization, peak to average reduction and load reduction from on-peak to off-peak hours. We have used a Critical Peak Pricing (CPP) model for electricity bill calculation. Both GA and BBO outperforms for the reduction of cost Peak to Average Ratio (PAR) and load, by achieving user comfort maximization.
... The results shows that it successfully achieved the desired objectives by reducing the EC and minimization of PAR, whereas in terms of UC, they achieved average UC. In [2], authors proposed a DSM model using bioinspired techniques GA and BBO. The proposed model achieved both EC minimization and minimization of PAR. ...
... Many optimization algorithms have been pro-posed to achieve the aforementioned objectives. In [2][10] [11][12] [17], GA, BBO, HSA and GBPSO are used for PAR reduction. However , UC is not considered by the authors. ...
Home Energy Management Systems (HEMS) have been widely used for energy management in smart homes. Energy management in a smart home is a challenging task, which require efficient scheduling of appliances. The main focus of HEMS is to schedule the operation of appliances in such a way that it gives us optimized performance in terms of Peak to Average Ratio (PAR), Electric Cost (EC) minimization, execution time and User Comfort (UC). The Time of Use (ToU) pricing scheme is used in this paper. We used Genetic Algorithm (GA), Biogeography-based optimization (BBO) and our proposed hybrid Genetic Biogeography-based Optimization (GBBO), techniques to schedule appliances in single home and for multiple homes. Simulations are carried out using eight different appliances. The results show that GA and GBBO execute better in case of PAR reduction and EC minimization. GBBO outperforms in terms of user comfort. We calculated the UC in terms of waiting time.
... The results shows that it successfully achieved the desired objectives by reducing the EC and minimization of PAR, whereas in terms of UC, they achieved average UC. In [2], authors proposed a DSM model using bioinspired techniques GA and BBO. The proposed model achieved both EC minimization and minimization of PAR. ...
... Many optimization algorithms have been pro-posed to achieve the aforementioned objectives. In [2][10] [11][12] [17], GA, BBO, HSA and GBPSO are used for PAR reduction. However , UC is not considered by the authors. ...
... The results shows that it successfully achieved the desired objectives by reducing the EC and minimization of PAR, whereas in terms of UC, they achieved average UC. In [2], authors proposed a DSM model using bioinspired techniques GA and BBO. The proposed model achieved both EC minimization and minimization of PAR. ...
... Many optimization algorithms have been pro-posed to achieve the aforementioned objectives. In [2][10] [11][12] [17], GA, BBO, HSA and GBPSO are used for PAR reduction. However , UC is not considered by the authors. ...
Home Energy Management Systems
(HEMS) have been widely used for energy management
in smart homes. Energy management in a smart home
is a challenging task, which require efficient scheduling
of appliances. The main focus of HEMS is to schedule
the operation of appliances in such a way that it
gives us optimized performance in terms of Peak to
Average Ratio (PAR), Electric Cost (EC) minimization,
execution time and User Comfort (UC). The Time of
Use (ToU) pricing scheme is used in this paper. We
used Genetic Algorithm (GA), Biogeography-based
optimization (BBO) and our proposed hybrid Genetic
Biogeography-based Optimization (GBBO), techniques
to schedule appliances in single home and for multiple
homes. Simulations are carried out using eight different
appliances. The results show that GA and GBBO execute
better in case of PAR reduction and EC minimization.
GBBO outperforms in terms of user comfort. We
calculated the UC in terms of waiting time.
The field of smart meter data analytic is a relatively young field that grew because of the wealth of data generated from the use of smart meters. This data has been used for several sustainability applications including power management and planning. This review paper aims at presenting a unifying taxonomy to classify the various domains of smart meter analytic and their underlying functions and techniques. The aim is to better understand the current research trends, approaches, and opportunities. The paper reviews the functions, applications, and techniques after examining a huge body of knowledge within the three main domains of the field, namely forecasting, knowledge discovery, and power management. In the forecasting domain, the functions are classified based on the scale and horizon. Other domains are divided into relevant functions, algorithms, and general techniques. The review is performed from the perspective of data science; emphasizing the data tasks such as data wrangling, analytics algorithms, and evaluation methods for each domain. A review of the various algorithms and techniques within each function is presented, and the paper concludes with a discussion of issues and research opportunities for smart meter data within the machine learning and data science fields.
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.
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.
Today's electricity system is undergoing a transformation from a model of centralized electricity generation, to a more decentralized paradigm, where a large number of small energy prosumers (i.e. both producers and consumers) generate energy and may participate in the energy market. In these markets, energy is usually traded at a time prior to the time of delivery in an exchange, based on forecasts of the production and the consumption, and the cost for the prosumers is related to the accuracy of these forecasts, through the application of penalties when an imbalance appears. In this paper we study the problem of orchestrating the energy prosumers into virtual clusters, in order to participate in the market as a single entity and to reduce the total energy cost, through the reduction of the total relative forecasting inaccuracies. Using a real dataset of 33 prosumers located in Greece, we study different clustering algorithms, including spectral, genetic and an adaptive algorithm. The performance evaluation results show that significant cost reduction may be achieved, through the association of the prosumers into groups.
Distribution networks are currently undergoing fundamental changes due to the rise of smart solutions such as for instance Demand Response (DR), which increases network complexity and challenges the adequacy of traditional planning practices. This calls for the use of suitable planning methodologies. However, the planning problem may be too cumbersome for most commercial software tools, or may lead to complex bespoke optimisation models that may not be easy to use by network planners. In this light, this work proposes a recursive function that can be used in practical algorithms for planning of smart distribution networks. The recursive function can emulate business-as-usual planning practices and further optimise them, including DR options as potential substitutes for network reinforcement. Several case studies based on real UK networks highlight the robustness and flexibility of the proposed algorithms to address different problems, including uncertainty analysis and risk management. The results clearly show that the proposed tool leads to increased economic and social benefits, particularly when optimising investment strategies considering smart DR solutions. The model is herein distributed open-source as accompanying material to this paper with the aim of encouraging new practices required for smart distribution network planning beyond the traditional academic applications.
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.
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.
This paper presents a heuristic approach to Active Demand Side Management in the Off-Grid systems with a set of specific requirements. The tests were performed on the smart house platform developed at VSB – Technical University of Ostrava campus, Czech Republic in order to accomplish the effective design, testing, operation and analysis of the proposed system. These results consequently consist of a prerequisite for the development of new leading-edge power distribution systems in the Off-Grid environment and for improvement of the efficiency, security and reliability of the existing systems.
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.
In future smart grids, the electricity suppliers can modify the customers' load consumption pattern by implementing appropriate DSM (demand side management) programs using smart meters. Most of the existing studies on DSM, only consider one utility company in the supplier side. In this paper, the possibility of existing more than one supplier in the smart grid is addressed by modeling the DSM problem as two non-cooperative games: the supplier side game, and the customer side game. In the first game, the suppliers' profit maximization problem is formulated by applying supply function bidding mechanism. In the proposed mechanism, the electricity suppliers submit their bids to the DSM center, where the electricity price is computed and is sent to the customers. In the second game, the customers aim to determine optimal load profile to maximize their daily payoff. The existence and uniqueness of the Nash equilibrium in the mentioned games are explored and a computationally tractable distributed algorithm is designed to determine the equilibrium. Simulations are performed for a smart grid system with 3 suppliers and 1000 customers. Simulation results demonstrate the superior performance of the proposed mechanism in reducing the peak load and increasing the suppliers' profit and the customers' payoff.
Biogeography-based optimization (BBO) is a powerful population-based algorithm inspired by biogeography and has been extensively applied to many science and engineering problems. However, its direct-copying-based migration and random mutation operators make BBO possess local exploitation ability but lack global exploration ability. To remedy the defect and enhance the performance of BBO, an enhanced BBO variant, called POLBBO, is developed in this paper. In POLBBO, a proposed efficient operator named polyphyletic migration operator can formally utilize as many as four individuals' features to construct a new solution vector. This operator cannot only generate new features from more promising areas in the search space, but also effectively increase the population diversity. On the other hand, an orthogonal learning (OL) strategy based on orthogonal experimental design is employed. The OL strategy can quickly discover more useful information from the search experiences and efficiently utilize the information to construct a more promising solution, and thereby provide a systematic and elaborate reasoning method to guide the search directions of POLBBO. The proposed POLBBO is verified on a set of 24 benchmark functions with diverse complexities, and is compared with the basic BBO, five state-of-the-art BBO variants, five existing OL-based algorithms, and nine other evolutionary algorithms. The experimental results and comparisons demonstrate that the polyphyletic migration operator and the OL strategy can work together well and enhance the performance of BBO significantly in terms of the quality of the final solutions and the convergence rate.
We propose a consumption scheduling mechanism for home area load management in smart grid using integer linear programming (ILP) technique. The aim of the proposed scheduling is to minimise the peak hourly load in order to achieve an optimal (balanced) daily load schedule. 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 based on home and neighbourhood area scenarios have been presented to demonstrate the effectiveness of the proposed technique.