Abstract
The green innovations in the energy sector are smart solutions to meet the excessive power requirements through renewable energy resources (RERs). These resources have forwarded the revolutionary relief in control of carbon dioxide gaseous emissions from traditional energy resources. The use of RERs in a heuristic manner is necessary to meet the demand side management in microgrids (MGs). The pricing scheme limitations hinder the profit maximization of MG and their customers. In addition, recent pricing schemes lack mechanistic underpinning. Therefore, a dynamic electricity pricing scheme through linear regression is designed for RERs to maximize the profit of load customers (changeable and unchangeable) in MG. The demand response optimization problem is solved through the particle swarm optimization (PSO) technique. The proposed dynamic electricity pricing scheme is evaluated under two different scenarios. The simulation results verified that the proposed dynamic electricity pricing scheme sustained the profit margins and comforts for changeable and unchangeable load customers as compared to fixed electricity pricing schemes in both scenarios. Hence, the proposed dynamic electricity pricing scheme can readily be used for real microgrids (MGs) to grasp the goal for cleaner energy production.
Highlights
The maximization of profit for changeable and unchangeable load customers is objectively aimed for optimization of demand response (DR) in MG through the dynamic electricity pricing scheme
The dynamic electricity pricing scheme is designed by using regression analysis to utilize the power generated at the supply side efficiently
The particle swarm optimization (PSO) algorithm is selected as the metaheuristic technique to optimize the DR
Summary
The lack of pricing signal design was observed in this study In another write-up, Yi et al [21] scheduled the appliances in real time at small residential DR by using an optimal stopping scheme. [27] introduced time- or price-based self-scheduling models in the day-ahead energy markets to increase the profit of DR aggregators. Energies 2022, 15, 1385 profit increment of MG aggregator and, the interaction with electricity customers In this model, the problem existed in the DR price that was fixed instead of dynamic and dependent upon the real operational conditions. Had negative influence on changeable load customers operating at small scales [17] Keeping these facts in view, the present study aims to solve the above mentioned problems, and novel features are mentioned below:.
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