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Efficient Power Scheduling in Smart Homes using Meta Heuristic Hybrid Grey Wolf Differential Evolution Optimization Technique
Abstract
With the emergence of automated environment,
energy demand by consumer is increasing day by day. More than
80% of total electricity is being consumed in residential sector.
In this paper, a heuristic optimization technique is proposed for
the efficient utilization of energy sources to balance load between
demand and supply sides. An optimization technique is proposed
which is a hybrid of Enhanced differential evolution (EDE)
algorithm and Gray wolf optimization (GWO). The proposed
scheme is named as hybrid gray wolf differential evolution
(HGWDE) . It is applied for home energy management (HEM)
with the objective function of cost minimization and reducing
peak to average ratio (PAR). Load shifting is performed from
on peak hours to off peak hours on basis of user preference
and real time pricing (RTP) tariff defined by utility. However,
there is a trade off between user comfort and above mentioned
parameters. To validate the performance of proposed algorithm,
simulations have been carried out in MATLAB. Results illustrate
that PAR and electricity bill have been reduced to 53.02%, and
12.81% respectively.
Recently, Home Energy Management (HEM) controllers have been widely used for residential load management in a smart grid. Generally, residential load management aims at reducing the electricity bills and curtailing the Peak-to-Average Ratio (PAR). In this thesis, we design a HEM controller on the bases of four heuristic algorithms: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO). Moreover, we proposed the hybrid algorithm which is Genetic BPSO (GBPSO). All the selected algorithms are tested with the consideration of essential home appliances in Real Time Pricing (RTP) environment. Simulation results show that each algorithm in the HEM controller reduces the electricity cost and curtails the PAR. GA based HEM controller performs relatively better in term of PAR reduction; it curtails approximately 34% PAR. Similarly, BPSO based HEM controller performs relatively better in term of cost reduction it reduces approximately 36% cost. Moreover, GBPSO based HEM controller performs better than the other algorithms based HEM controllers in terms of both cost reduction and PAR curtailment.
Many new demand response strategies are emerging for energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable appliances to minimize smart appliance energy costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable costs are negligible.
In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modeled in a simple way which considers appliance class, user preferences and weather conditions. The Wind Driven Optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, Min-max Regret based Knapsack Problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with fast convergence rate, as compared to the existing techniques
In this paper, the Ant Colony Optimization (ACO) based Optimal Power Flow (OPF) analysis implemented using MATLAB® is applied for the Iraqi Super High (SHV) grid, which consists of (11) generation and (13) load bus connected to each other with 400-kV power transmission lines. The results obtained with the proposed approach are presented and compared favorably with results of other approaches, like the Linear Programming (LP) method. All data used this analysis is taken from the Iraqi Operation and Control Office, which belongs to the ministry of electricity.
Smart grid is an emerging technology which is considered as an ultimate solution to meet the increasing power demand challenges. Modern communication technologies has enabled the successful implementation of smart grid, which aims at provision of demand side management mechanisms, such as demand response. In this paper, we propose residential load scheduling model for demand side management. It is assumed that electric prices are announced on day-ahead basis. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem, and an optimal schedule is achieved by solving the minimization problem. Simulation results validate that teacher learning based optimization performs better as compared to genetic algorithm, showing comparable results with linear programming with less computational efforts. TLBO is able to obtain the desired trade-off between consumer electric bill and user discomfort.
A smart grid is a modernized form of the traditional grid. Smart grid benefits both, consumer and energy services provider. Demand side management is one of the key component of smart grid to fulfill consumersi electricity demands in an efficient manner. It helps consumers to manage their load in an effective way to reduce their electricity bill. In this paper, we design a home energy management controller based on three heuristic techniques: teaching learning based optimization, binary particle swarm optimization and enhanced differential evaluation. The major objective of designing this controller is to minimize consumers electricity bill while maximizing consumers satisfaction. Simulation results show that TLBO achieved maximum user satisfaction at minimum cost and peak to average ratio. A trade-off analysis between user satisfaction and energy consumption cost is demonstrated in simulation results.
The Differential Evolution (DE) algorithm, introduced by Storn and Price in 1995, has become one of the most efficacious population-based optimization approaches. In this algorithm, use is made of the significant concepts of mutation, crossover, and selection. The tuning control parameters are population size, mutation scaling factor, and crossover rate. Over the last decade, several variants of DE have been presented to improve its performance aspects. In the present paper, we further enhance DE. The population size and mutation scaling factor are taken alone in the tuning process; the crossover rate is treated implicitly in the crossover stage. Five forms for crossover are suggested for the first 100 iterations of the computational algorithm. After this learning period, we pick the form which yields the best value of the objective function in the greatest number of iterations (among the 100). Our algorithm is tested on a total of 47 benchmark functions: 27 traditional functions and 20 special functions chosen from CEC2005 and CEC2013. The results are assessed in terms of the mean and standard deviation of the error, success rate, and average number of function evaluations over successful runs. Convergence characteristics are also investigated. Comparison is made with the original DE and Success-History based Adaptive DE (SHADE) as a state-of-the-art DE algorithm, and the results demonstrate the superiority of the proposed approach for the majority of the functions considered.
This paper presents a load shifting algorithm for optimizing consumption patterns by taking preferences of customers and electricity costs into account. A load shifting cost component is introduced for recording inconvenience experienced by customers while load shifting. Load shifting cost reflects the reluctance of customers for their inconvenience where the customers are unique in terms of their preferences and costs. The proposed algorithm would allow demand side management to be conducted in a decentralized manner, where no problem of load synchronization exists. Multi-agent system based simulation studies were carried out on three types of customers namely, residential, commercial, and industrial customers to verify this algorithm. The simulation results show that substantial load levelling is achieved by the proposed algorithm. This study is further extended to examine the effects of incentives that encourage customers to participate in load shifting. However, this does not lead to a better load levelling perhaps due to the small sample sized simulation systems. Instead, incentives could be used as a short-term measure for customers to know that how the mechanism would work for them. (C) 2014 AIP Publishing LLC.
Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.