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Advisor(s)
Abstract(s)
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.
Description
Keywords
Electric vehicle (EV) Emissions Energy resources management (ERM) Multi-objective optimization Virtual power player (VPP) Smart grid