Improvement of Regional-Market Management Considering Reserve and Emergency Demand Response Program

Signal Processing and Renewable Energy

مقاله 5، دوره 5، شماره 2، شهریور 2021، صفحه 61-82   

نویسندگان

Seyyed Ebrahim Hosseini¹؛ Mojtaba Najafi^{* 2}؛ Ali Akhavein³

¹Department of Electrical Engineering, Bushehr Branch, Islamic Azad University, Bushehr, Iran

²Department of Electrical Engineering, Bushehr Branch, Islamic Azad University, Bushehr, Iran

³Department of electrical engineering, South Tehran Branch, Islamic Azad University

چکیده

The emergency demand response program (EDRP) is a type of program that can be utilized as a tool for controlling the price of electricity when there is a lack of reliability in the distribution system. In this study, a formulation is proposed for determining the optimum amount of demands in the EDRP according to the viewpoints of the regional market manager (RMM) aimed at reducing the EDRP costs and smoothening the load curve based on the logarithmic model and the matrix of demand elasticity. The probability that the aggregators should present their available reserves to the RMM in response to the received incentives has also been included in the proposed formulations. The market manager then prioritizes the available reserves using the reserve-margin factor (RMF). Three algorithms including co-evolutionary particle swarm optimization (C-PSO), co-evolutionary teaching learning-based optimization (C-TLBO) and co-evolutionary improved teaching learning-based optimization (C-ITLBO) are used for reducing the EDRP costs. The results show that the proposed formulations are effective in improving the economic performance of the regional market and the load curve. Furthermore, the results indicate the superiority of the C-ITLBO algorithm in terms of the total cost, incentive rate and peak shaving in comparison with C-PSO and C-TLBO algorithms.

کلیدواژه‌ها

Emergency demand response program؛ Electricity market؛ logarithmic model؛ Reserve margin factor؛ Co-evolutionary improved teaching learning-based optimization