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Abstract
In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO), multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance of these methods with the use of multi-dimensional signaling is also compared with this technique, which has previously been shown to boost metaheuristics performance for single-objective problems. Hence, multi-dimensional signaling is adapted and implemented here for the proposed multi-objective problem. In addition, parallel computing is used to mitigate the methods’ computational execution time. To validate the proposed techniques, a realistic case study for a chosen area of the northern region of Portugal is considered, namely part of Vila Real distribution grid (233-bus). It is assumed that this grid is managed by an energy aggregator entity, with reasonable amount of electric vehicles (EVs), several distributed generation (DG), customers with demand response (DR) contracts and energy storage systems (ESS). The considered case study characteristics took into account several reported research works with projections for 2020 and 2050. The findings strongly suggest that the signaling method clearly improves the results and the Pareto front region quality.
Highlights
The increasing concern over global climate changes and air pollution has motivated policy makers to promote renewable energy sources [1]
All the non-dominated solutions (NDS) found by multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II), had violations, which was caused by the voltage and line limit violations and by insufficient generation
It is possible to conclude that the signaling method use contributed to a better performance of the three metaheuristics addressed in this paper
Summary
The increasing concern over global climate changes and air pollution has motivated policy makers to promote renewable energy sources [1]. The multi-objective model published in [15] does not include the power flow equations, electric vehicles (EVs), demand response (DR) and energy storage systems (ESS). This work proposes a multi-objective operational scheduling for charging and discharging of EVs in a smart distribution network in the day-ahead context. The multi-objective optimization considers both costs and emissions but the work seems limited since it does not formulate or consider demand response, wind or photovoltaic (PV) generation and ESS, which are vital smart grids features. In [15], a multi-objective energy management for a micro-grid using both intelligent techniques and linear programming is presented to minimize the operation costs and the environment impacts. A multi-objective function is used to maximize the profit corresponding to the difference between the income and operating costs and a function to minimize the CO2 emissions. This paper is organized as follows: after this introductory part, Section 2 presents the multi-objective ERM mathematical model, Section 3 presents the technical solutions employed in this paper, Section 4 discusses the case study, and Section 5 presents our conclusions
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