Abstract
The integration of different energy resources from traditional power systems presents new challenges for real-time implementation and operation. In the last decade, a way has been sought to optimize the operation of small microgrids (SMGs) that have a great variety of energy sources (PV (photovoltaic) prosumers, Genset CHP (combined heat and power), etc.) with uncertainty in energy production that results in different market prices. For this reason, metaheuristic methods have been used to optimize the decision-making process for multiple players in local and external markets. Players in this network include nine agents: three consumers, three prosumers (consumers with PV capabilities), and three CHP generators. This article deploys metaheuristic algorithms with the objective of maximizing power market transactions and clearing price. Since metaheuristic optimization algorithms do not guarantee global optima, an exhaustive search is deployed to find global optima points. The exhaustive search algorithm is implemented using a parallel computing architecture to reach feasible results in a short amount of time. The global optimal result is used as an indicator to evaluate the performance of the different metaheuristic algorithms. The paper presents results, discussion, comparison, and recommendations regarding the proposed set of algorithms and performance tests.
Highlights
Introduction and State of ArtThese days, there are multiples changes in the structure of transmission and distribution of electric energy that allows the integration of new technologies notions in generation, storage, electric mobility [1,2], and energy metering [3]
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Some of the strategies included hybrid metaheuristic techniques such as Harris’s hawks optimization (HHO)-DEEPSO, I-GWO, and WOA. It results in better average optimal points, since their implementation did fall in local optima
Summary
These days, there are multiples changes in the structure of transmission and distribution of electric energy that allows the integration of new technologies notions in generation, storage, electric mobility [1,2], and energy metering [3]. As a result of these system changes, electricity dependence and energy transactions have increased. The Local Energy Markets (LEM) is an opportunity for small grid actors to actively take part in the bidding process. LEM allows local transactions that empower consumers, producers, and prosumers in the goal of creating energy balances [4]. The larger the number of actors, the harder the synchronization, control, and optimal market operation. To replicate and achieve optimal local energy market responses, it is necessary to guarantee coordination among the different market participants and appropriate tools for proper decision making [4,5]
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