Investigation in economic analysis of microgrids based on renewable energy uncertainty and demand response in the electricity market. (15th June 2021)

• Record Type: Journal Article
• Authors: Huang, Shoujun; Abedinia, Oveis
• Is Part Of: Energy. Volume 225(2021)
• Publication Date: 2021-06-15
• Subjects: Energy storage systems -- Demand side management -- Electricity market -- Optimization algorithm -- Electric vehicle; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.120247
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Owing to the increasing utilization of renewable resources such as wind turbines (WT), photovoltaic (PV) into a microgrid (MG), optimal planning has become important to satisfy the energy demand due to inherent uncertainties. This paper proposes a new model of planning based on renewable energy uncertainty and demand response and electric vehicles (EVs) in order to minimize the electricity market's total cost. Considering uncertainty challenges, energy storage system (ESS) and demand response programs based on time-of-use (TOU) are employed as a solution for managing the power flow in MG to warranty the essential load supporting and voltage stability and satisfy electrical and heat demands. Moreover, in this paper, the influence of price-based demand response (DR) for industrial, commercial, and residential loads is taken into account. Finally, the proposed problem is modeled as an optimization problem while the related decision variables are adjusted by a modified version of virus colony search (VCS) algorithm based on chaos theory in order to increase the exploitation and exploration terms. The proposed approach is tested on an MG system with several scenarios through analyzing the effect of DR programs based on the total cost reduction. As shown in the simulation results, DR highly reduced total cost (20–26% related to the case without DR), in which voltage dip (maximum 1.4%) and power deviation (maximum 1.2%) were enhanced. Highlights: Probabilistic uncertainty model for RESs, ESS units, price, & load signals. Presenting a new algorithm to enhance local and global search. Integrating a WP, PVs, CGT, ESS, and DRPs to virtual power plant. Proposing a bi-level scheduling model.