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SMART MICROGRIDS OPERATION CONSIDERING EXPERT KNOWLEDGE AND ENSEMBLED BASED METAHEURISTIC OPTIMIZATION ALGORITHMS
Keywords:
operation in uncertain environments, energy metaheuristic optimization, smart microgrid, CBBO-Cauchy-DEEPSO, ensemble heuristic algorithmDOI:
https://doi.org/10.17654/0972087123001Abstract
The number of distributed energy resources (DER) and controllable and non-controllable loads have increased during the last decade; this poses a challenge to smart microgrids (SMGs) operators. The challenges appear as network constraints, excessive processing time for operation planning, no guarantee of optimal solution results of the formulation of a non-convex problem, and a limited number of evaluations for real-time or day-ahead planning, among others. To perform a good number of scenario evaluations and to provide near optimal solutions, metaheuristics appears as a possible strategy to meet simplified formulations and reduce processing time. This work proposes a heuristic ensemble algorithm using a chaotic mapping strategy and the biogeographic-based optimization - Cauchy - differential evolutionary particle swarm optimization algorithm (CBBO-Cauchy-DEEPSO) to solve a stochastic control model for proper SMGs operation. The objective is to control the power generation dispatch and consumption for different DERs. DERs in this network include energy storage systems (ESS), electric vehicles (EVs) operating in vehicle-to-grid (V2G) mode, dispatchable units, one non-dispatchable PV unit and loads with demand response/control capability. The stochasticity problem is the result of different uncertainty sources such as sun irradiance, EVs travel trips, load variations, and market prices. The CBBO-Cauchy-DEEPSO heuristic ensemble algorithm shows a high performance in the genetic and evolutionary computation conference - GECCO 2020 competition with encrypted code. The algorithm was tested alone during the competition with no special tweaks for initialization and evaluation; nevertheless, this document shows the results of a hybrid strategy that uses expert knowledge from system operators and the CBBO-Cauchy-DEEPSO strategy to enhance the original algorithm performance. In this way, this paper presents results, discussion, comparison, and recommendations regarding the proposed algorithm.
Received: September 7, 2022
Accepted: October 28, 2022
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