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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.

This paper introduces a new protection system for solar photovoltaic generator (SPVG)-connected networks. The system is a combination of voltage-restrained overcurrent relays (VROCRs) and directional overcurrent relays (DOCRs). The DOCRs are implemented to sense high fault current on the grid side, and VROCRs are deployed to sense low fault current supplied by the SPVG. Furthermore, a novel challenge for the optimal coordination of DOCRs-DOCRs and DOCRs-VROCRs is formulated. Due to the inclusion of additional constraints of VROCR, the relay coordination problem becomes more complicated. To solve this complex problem, a hybrid Harmony Search Algorithm-Bollinger Bands (HSA-BB) method is proposed. Also, the lower and upper bands in BB are dynamically adjusted with the generation number to assist the HSA in the exploration and exploitation stages. The proposed method is implemented on three different SPVG-connected networks. To exhibit the effectiveness of the proposed method, the obtained results are compared with the genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search algorithm (CSA), HSA and hybrid GA-nonlinear programming (GA-NLP) method. Also, the superiority of the proposed method is evaluated using descriptive and nonparametric statistical tests.

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Within the MicroGrid environment, the Energy Resource Management (ERM) problem becomes highly complex due to the uncertainty related to the Renewable Generation (RG) such as Photovoltaic power generation (PV), Electric Vehicle (EV) trip with Grid to Vehicle (G2V) or Vehicle to Grid (V2G), Energy Market price and load demand with Demand Response (DR) programs. Each of these issues should be tackled while optimizing revenues and reducing the running costs of Virtual Power Player (VPP) that collects each of these types of energy resources from the MicroGrid. This article presents a new hybrid optimization algorithm called "Hybrid Levy Particle Swarm Variable Neighborhood Search Optimization" (HL_PS_VNSO) to solve the ERM problem. Its key aspect is the hybridization of the Particle Swarm Optimization (PSO) and the Variable Neighborhood Search Optimization (VNS) algorithm with the enhanced step length using Levy Flight. The effectiveness of the proposed approach is measured by a 25-bus MicroGrid with 500 uncertain scenarios of the aforementioned uncertainty. The results of HL_PS_VNSO are compared with the most advanced optimization algorithms. The findings show that HL_PS_VNSO's results are superior for the Average Ranking Index (A.R.I) and Ranking Index (R.I). For effective comparative analysis of algorithms, the traditional statistical method called One-way ANOVA Tukey Analysis is used. The results from this analysis also prove the superiority of HL_PS_VNSO over the most advanced optimization algorithms.

The precise coordination of Directional Overcurrent Relays (DOCRs) is required to identify fault timely, effectively and isolate them from the network to avoid possible outages in a power system. The DOCRs coordination is an optimization problem including highly nonlinear constraints. In this paper, Cuckoo Search Algorithm (CSA) is implemented to solve coordination problem of DOCRs on two different case studies. The parameters of CSA are effectively tuned to obtain global best solution for the DOCRs coordination problem. The obtained results using the proposed method are compared with Genetic Algorithm (GA) and hybrid GA-Nonlinear programming (GA-NLP) methods. The result shows that the effective modification of CSA parameters can obtain feasible and superior solution for this complex problem.

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