Metaheuristic Optimization Algorithm for Day-Ahead Energy Resource Management (ERM) in Microgrid Environment of Power System

Distribution system operators (DSOs) have difficulty in scheduling distributed energy resources owing to the increasing power demand and penetration of renewable energy. The goal of this study is to determine the charging/discharging of PV energy-integrated energy storage system (PV-ESS), EV charging price, and demand response (DR) incentive values considering voltage management. To achieve the optimal energy operation for a distribution network, this study proposes an evolutionary game theory (EGT)-based new scheduling strategy, considering voltage management for a multi-agent system (MAS). The EGT, which is a decision-making strategy, is used by agents to cooperate and derive the best scheduling with their own behavior pattern functions to minimize the system operating cost. Photovoltaicenergy storage systems, electric vehicles charging power, and loads can perform charging/discharging scheduling, electric vehicle charging planning, and demand response participation, respectively. Under DSO supervision, a reward that stabilizes the voltage profile of the power distribution system is also implemented during the cooperation process. The proposed energy scheduling strategy combines an EGTbased decision-making with particle swarm optimization (PSO) to solve the optimization problem and determine the payoff function through self-evolutionary improvement. The effectiveness of the EGT-PSO has been analyzed for an IEEE 33-bus distribution system, and the results demonstrate that the proposed scheduling strategy not only achieves the most economical decision among agents but also manages the voltage profile.INDEX TERMS evolutionary game theory, distribution system operator, multi-agent system, optimal energy scheduling strategy, voltage management. NOMENCLATURE A. SETS

Section: B Literature Reviewmentioning

confidence: 99%

“…Step 7: Memorize the best scheduling for each agent that stabilize the average voltage given by (25).…”

Section: Optimal Solution Frameworkmentioning

confidence: 99%

An Evolutionary Game Theory-Based Optimal Scheduling Strategy for Multiagent Distribution Network Operation Considering Voltage Management

Lee

Kim

2022

“…Equation 2represents the income obtained from market transactions [11], where bids and offers are permitted in two markets with unique features, namely local and wholesale markets. The aim of the aggregator is to reduce operating costs (O.C) while maximizing the income [21]. So objective function becomes minimization to get the maximum profit [4] as shown in equation 3.…”

Section: Day-ahead Energy Resource Management (Erm) With Uncertaimentioning

confidence: 99%

“…Equation (20) states that, charge and discharge processes in energy storage unit ST cannot be simultaneous in time slot t: Charge and Discharge limit of Energy Storage unit ST in time slot t is given by equations (21) and 22:…”

Section: Day-ahead Energy Resource Management (Erm) With Uncertaimentioning

confidence: 99%

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

Dabhi

Pandya

2020

Self Cite

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.

“…Hybridization of GA and SA is suggested in [33] for the optimal scheduling of the sources of energy in the islanded and grid-connected modes of a MicroGrid. In [34] author developed the hybridization of VNS and DEEPSO with cross-entropy characteristic for the optimal scheduling in a MicroGrid. The GECAD group in collaboration with Delft University organized the competition [35] on ERM problem in a microgrid with 100 uncertain scenarios at IEEE-WCCI/CEC conference, 2018.…”

mentioning

confidence: 99%

Uncertain Scenario Based MicroGrid Optimization via Hybrid Levy Particle Swarm Variable Neighborhood Search Optimization (HL_PS_VNSO)

Dabhi

Pandya

2020

Self Cite

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.