Abstract:
The utility’s utilization of communication technology and renewable energy sources has paved the path for selfsustaining
microgrids (MGs). However, the intermittency of solar and wind energies raises concerns about
meeting demand effectively. To ensure optimal performance of distributed MGs, an efficient energy management
system (EMS) is crucial to tackle this uncertainty. Historically, MGs have primarily achieved operational cost
reduction through optimal functioning. Integrating demand response (DR) into the EMS could further enhance
operational efficiency and peak reduction. This research work addresses this challenge by incorporating DR
programs into grid-connected MGs’ energy management. Stochastic programming is employed to account for the
unpredictable solar and wind behaviours. Flexible price elasticity is used to calculate price elasticity coefficients,
portraying customer responses effectively. The implemented research work compares the Dragon Fly Algorithm
with other heuristic approaches, resulting in a 12.42 % reduction in overall operating costs and the efficacy of the
proposed algorithm is shown.. Using the Analytic Hierarchy Process (AHP), the User Satisfaction Index is
assessed, revealing that the CPP demand response initiative tops the satisfaction scale with a score of 0.92881..
Moreover, this research offers an exhaustive evaluation of techno-economic markers for each scenario, systematically
ranked using the proposed AHP methodology..
