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

Abstract

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 Selection of Optimal Structure for Stand-Alone Micro-Grid Based on Modeling and Optimization of Distributed Generators

“…The power output of BG should be between max and min boundaries, as follow [18]： 𝑃 𝐵𝐺 𝑚𝑖𝑛 ≤ 𝑃 𝐵𝐺 (𝑡) ≤ 𝑃 𝐵𝐺 𝑚𝑎𝑥 (30) where 𝑃 𝐵𝐺 𝑚𝑖𝑛 and 𝑃 𝐵𝐺 𝑚𝑎𝑥 are the minimum and maximum output power of BG.…”

“…The traditional AI algorithms were employed in the early studies and improved in recent years, some studies made comparisons of traditional versions and the improved. The improved particle swarm optimization (IPSO) [30], [31], modified ABC algorithm [32] and improved GA algorithm [33], [34] were proved that have better convergence and excellent dynamic performance than those original visions. For instance, Bao et al [35] proposed an IPSO algorithm with two improvements for solving the coordinated scheduling of day-ahead cooling load and electricity in MG. One improvement was added the mandatory correction to enhance the algorithm performance.…”

Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets

“…Concerning the application of metaheuristics, the no free lunch theorem [22] of optimization also proves that designing an algorithm that consistently provides effective and robust solutions for all types of complex non-linear problems is not possible. Recently, hybridization of evolutionary algorithms has attracted attention for solving energy market problems, due to their ability to handle non-linearity and uncertainty [20,23,24]. Typically, a bi-level optimization problem is inherently highly nonlinear [25], non-convex, non-differentiable, discontinued and NP-hard [26].…”

Energy Resource Scheduling Optimization for Smart Power Distribution Grids - Hour-Ahead Horizon

“…In this paper we deal with energy resources management (ERM). This problem is considered to be one of the most challenging optimization in energy systems, since it is considered to be a problem of high dimensionality and with high number of restrictions [5]- [7]. In this way, platforms have been developed to study this crucial problem in intelligent network operations [8], [9].…”

“…On the other hand, evolutionary computing (EC) has been widely applied in the energy field [18], because of the problem's complexity, where it was suggested because of its great efficiency and scalability. It can be expressed that evolutionary computing is inspired by the different evolutionary mechanisms of the nature, where several evolutionary algorithms have been used in the solution of some optimization problems, for example, electricity markets [19], [20], demand prediction [21], [22], intervention planning [23], [24], energy resources management [5]- [7]. Despite the wide use of EC and CI techniques applied to address the challenging dayahead problem, due to high number of variables such as distributed energy resources and electric vehicles, we adopt a mathematical deterministic approach.…”