CVaR-based retail electricity pricing in day-ahead scheduling of microgrids. (15th July 2021)

• Record Type: Journal Article
• Authors: Ghasemi, Ahmad; Jamshidi Monfared, Houman; Loni, Abdolah; Marzband, Mousa
• Is Part Of: Energy. Volume 227(2021)
• Publication Date: 2021-07-15
• Subjects: Demand response (DR) -- Retail electricity pricing (REP) -- Conditional value-at-risk (CVaR) -- Energy management (EM) -- Micro-grid (MG); Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.120529
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: One of the most important methods for implementing the demand response (DR) schemes is to allocate the time-varying prices to the power demand by consumers. Indeed, the effective implementation of DR programs passes from a successful pricing way of retail electricity. However, the massive influence of renewable energy sources with unpredictable generation in today's power systems such as Micro-grids has made any planning encounter serious challenges. Therefore, in this paper, a new retail electricity pricing method has been proposed to reduce the effects of risk resulting from the uncertain generation of renewable energy sources and the wholesale electricity market's hourly estimated prices. To this end, a CVaR (Conditional Value at Risk) optimization framework is used to determine the next day's energy management planning of Micro-grid and retail electricity prices. Simulation results demonstrate that the use of risk-averse conditions, in comparison with non-risk conditions, results in lessening the standard deviation of optimal retail prices and the expected cost. In more detail, the standard deviation of optimal retail prices and the expected cost is decreased by 29.94% and 24.63%, respectively. Moreover, the results show a 5.92% reduction in the peak value of demand. Highlights: The uncertainties of power system disrupt the retail electricity pricing process. Effective retail electricity pricing is key factor of a successful demand response program. A risk-based optimization framework is proposed to determine retail electricity prices. The expected cost and peak value of demand decreased by 24.63% and 5.92% respectively. The proposed pricing method results in more efficient demand response program.