RESUMEN
The advent of multi-Microgrid (MG) energy systems necessitates the optimization of management strategies to curtail operational costs. This paper introduces an innovative MG energy management strategy that integrates Chaotic Local Search (CLS) with Particle Swarm Optimization (PSO) to fulfill this requirement. Our approach leverages PSO for extensive global exploration and subsequently employs CLS to refine local searches, thereby ensuring the attainment of optimal global outcomes. To further enhance performance, we have crafted a PSO algorithmic framework underpinned by chaotic local search principles, aimed at circumventing regions of local optima. The study presents a comprehensive MG energy system model that encompasses a photovoltaic generation unit, battery energy storage, and a micro gas turbine. The experimental data corroborates that our proposed algorithm secures optimal solutions within a range of 48.2-51.7, outperforming others in achieving these optimal resolutions. When juxtaposed with Scenario 1, there is a significant reduction in both operational and primary energy conversion costs by 24.22 % and 31.39 %, respectively. In comparison to Scenario 2, these figures are reduced by an additional 3.08 % and 6.05 %, respectively. The research findings underscore the strategy's exceptional performance in optimization tasks, as illustrated by the simulation outcomes. The methodology's application to a micro-energy network substantiates its practical relevance. Collectively, this research offers a holistic solution for the optimization of MG energy systems, effectively merging theoretical progress with tangible practical applications.
RESUMEN
The required load in a typical microgrid structure fluctuates from hour to hour. Based on the peaks and troughs of the load demand curve, the power system utilities preset different price of electricity at various periods of the same day which is referred to as time-of-use (TOU) pricing policy. An optimal economic strategy called demand side management (DSM) reduces load during peak hours by shifting the elastic loads and compensates the shifted load by increasing the demand during valley hours. This recreates the total demand pattern on the demand price elasticity relation. The novel benefaction of this research suggests a DSM approach based on a hybrid intelligence technique to attain a compromised solution between minimum generation costs and pollutants emitted by DERs of an LV grid-connected microgrid system. Several grid involvement strategies, power market pricing types, and demand response mechanisms are examined in five separate instances for the microgrid system. The outcomes achieved in each example show that the recommended DSM technique may be applied and is appropriate in terms of cost reduction. When 30-40% of customers participated in the DSM curriculum, the generation cost was reduced by 10-13%. Environment constrained economic dispatch provided a superior compromise amongst lowest generation cost and emission for different microgrid load models. Statistical analysis and comparison of the results from previously published literatures validates the superiority of the proposed approach incorporated with DSM policy.