Abstract

Increased use of renewable energies in smart microgrids (SMGs) present new technical challenges to system operation. SMGs must be self-sufficient and operate independently; however, when more elements are integrated into SMGs, as distributed energy resources (DER), traditional explicit mathematical formulations will demand too much data from the network and become intractable. In contrast, tools based on optimization with metaheuristics can provide near optimal solutions in acceptable times. Considering this, this paper presents the variable neighborhood search differential evolutionary particle swarm optimization (VNS-DEEPSO) algorithm to solve multi-objective stochastic control models, as SMGs system operation. The goal is to control DER while maximizing profit. In this work, DER considered the bidirectional communication between energy storage systems (ESS) and electric vehicles (EVs). They can charge/discharge power and buy/sell energy in the electricity markets. Also, they have elements such as traditional generators (e.g., reciprocating engines) and loads, with demand response/control capability. Sources of uncertainty are associated with weather conditions, planned EV trips, load forecasting and the market prices. The VNS-DEEPSO algorithm was the winner of the IEEE Congress on Evolutionary Computation/The Genetic and Evolutionary Computation Conference (IEEE-CEC/GECCO 2019) smart grid competition (with encrypted code) and also won the IEEE World Congress on Computational Intelligence (IEEE-WCCI) 2018 smart grid competition (these competitions were developed by the group GECAD, based at the Polytechnic Institute of Porto, in collaboration with Delft University and Adelaide University). In the IEEE-CEC/GECCO 2019, the relative error improved between 32% and 152% in comparison with other algorithms.

Highlights

  • Decarbonization is a very important industrial and societal trend that is driving the adoption of renewable generation technologies and transportation electrification

  • An algorithm that has good solutions for a framework for the treatment of problems in microgrids with high complexity due to aspects of uncertainty related to the weather conditions, load forecast, travel electric vehicles (EVs) ’and market prices, proposed in [5,7]; The novel VNS-DEEPSO algorithm is compared to the best fit of other algorithms at the international level and this one demonstrates superior performance

  • To show the performance of the VNS-DEEPSO algorithm, this section presents the ranking of the teams that attended the IEEE-WCCI 2018 and CEC/GECCO 2019 competitions in the programming category of Smart Grids [5,7,23]

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Summary

Introduction

Decarbonization is a very important industrial and societal trend that is driving the adoption of renewable generation technologies and transportation electrification. Problems related to the real world demand greater awareness of the efficient use of resources, the search for new optimal solutions results in this work with the following contributions: An algorithm that get good solutions for a framework (encrypted test bed by GECAD group) in the operation of smart microgrids with of energy storage systems (ESS), electric vehicles (EVs), loads with demand response, electricity markets, and renewables energies, proposed in [5,7]; 3. An algorithm that has good solutions for a framework (encrypted test bed by GECAD group) for the treatment of problems in microgrids with high complexity due to aspects of uncertainty related to the weather conditions, load forecast, travel EVs ’and market prices, proposed in [5,7]; The novel VNS-DEEPSO algorithm is compared to the best fit of other algorithms at the international level and this one demonstrates superior performance.

State of Art
Structure of the Algorithm
Encoding of the Fitness Function
Mathematical
Objective Function
Assumptions of the Model
Optimization Using VNS-DEEPSO Algorithm
DEEPSO Algorithm
VNS Algorithm
New Versions of the VNS-DEEPSO Algorithm
Heuristic Rules Based on System Operation
Results and Discussion
Scheduling
Discharge charge: to
Conclusions
Full Text
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