Enhanced Velocity Differential Evolutionary Particle Swarm Optimization for Optimal Scheduling of a Distributed Energy Resources With Uncertain Scenarios

Abstract

In the MicroGrid environment, the high penetration of uncertain energy sources such as solar Photovoltaics (PVs), Energy Storage Systems (ESSs), Demand Response (DR) programs, Vehicles to Grid (V2G or G2V) and Electricity Markets make the Energy Resource Management (ERM) problem highly complex. All such complexities should be addressed while maximizing income and minimizing the total operating costs of aggregators that accumulate all types of available energy resources from the MicroGrid system. Due to the presence of mixed-integer, discrete variables and non-linear network constraints, it is sometimes very difficult to solve such problem using traditional optimization methods. This paper proposes a new metaheuristic optimization technique entitled the ''Enhanced Velocity Differential Evolutionary Particle Swarm Optimization'' (EVDEPSO) algorithm to tackle the ERM problem. Its key feature is the updation of the Velocity by the terms named as Enhanced Velocity, Cooperation and Stochastic Uni-Random Distribution and position by the term Deceleration Factor. The performance of the proposed method is measured by a case study comprises of 100 scenarios of a 25-bus MicroGrid with high penetration of aforementioned energy sources. IEEE Computational Intelligence Society organized the competition on the above mentioned problem, in which EVDEPSO secured a second rank. The results of EVDEPSO are compared with the competition participated optimization algorithms. It also compared with well-known optimization algorithms such as Variable Neighborhood Search and Differential Evolutionary Particle Swarm Optimization. The comparison results show that the performance of EVDEPSO is superior in terms of the Ranking Index (R.I) and Average Ranking Index (A.R.I) as compared to the aforementioned algorithms. For effective comparative analysis of algorithms, standard statistical test named as One-Way ANOVA and Tukey Test is used. The results of this test also prove the effectiveness of EVDEPSO algorithm as compared to all tested algorithms. INDEX TERMS Enhanced velocity differential evolutionary particle swarm optimization, distributed energy resource management, smart grid, electric vehicles, demand response, electricity market, energy storage. SYMBOLS SYMBOLS Description DiG Conventional DG units Sup External power suppliers Solar Solar Generation (PV) units ST Energy Storage Systems (ESSs) Ev Electric vehicles(Ev) Ms Electricity Markets LD Load SE Scenario The associate editor coordinating the review of this manuscript and approving it for publication was Sheng Huang.

A Mixed-Binary Linear Programming Model for Optimal Energy Management of Smart Buildings

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A DE-LS Metaheuristic Algorithm for Hybrid Flow-Shop Scheduling Problem considering Multiple Requirements of Customers

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