Dynamic Price-Based Demand Response through Linear Regression for Microgrids with Renewable Energy Resources

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.

Coordinated optimization of source‐grid‐load‐storage for wind power grid‐connected and mobile energy storage characteristics of electric vehicles

“…In [18], a price-based demand response model based on artificial neural networks is designed to improve the effectiveness of energy management strategies to address price uncertainty. In [19], a linear regression dynamic tariff scheme is constructed to enhance the comfort and economy of load users. In [20], a price-sensitive demand response model considering wind energy consumption is used to improve the flexibility and economy of unit operation.…”

A novel automated demand response control using fuzzy logic for islanded battery‐operated rural microgrids

“…In the literature, there are several approaches applied for the performance improvement of demand response (DR) and demand side management (DSM)programs where most of the research works uses the pricing mechanism. In [1] the dynamic electricity pricing mechanism is proposed using the particle swarm optimization algorithm. A hybrid demand response scheme is introduced in [3] for the optimal operation of the microgrid in terms of cost where maximize the microgrid operator benefit and reduce the overall operation cost.…”

“…Microgrid systems with distributed generation have been successful in electrifying far-flung areas [1,2]. The operation of low-voltage microgrids is critical as it has a massive impact on the performance due to limited power backup and abrupt variation in the overall load [2,3].…”

“…In the literature, there are several approaches applied for the performance improvement of demand response (DR) and demand side management (DSM)programs where most of the research works uses the pricing mechanism. In [1] the dynamic electricity pricing mechanism is proposed using the particle IET Renew. Power Gener.…”

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

“…However, this model only focused on the flexible customers. Furthermore, both flexible and inflexible customers are considered in [87]. The customers are encouraged to join the DR program giving them flexible profits as an incentive for following the DR.…”