Two Stage Stochastic Optimization Scheduling of Power System Considering Thermal Power Units and Energy Storage Peak Shaving Pricing Strategies

Abstract

The escalating grid-connected capacity of renewable energy sources, predominantly wind and photovoltaic (PV) power, along with its inherent volatility and anti-peaking attributes, exacerbates the peaking demands on the power system. Consequently, this trend necessitates enhanced flexibility in power system peaking capabilities. In response to this challenge, this paper introduces an optimal scheduling methodology grounded in a two-stage stochastic model tailored for power systems, which incorporates thermal-storage peaking pricing. Initially, a hierarchical decision-making framework, employing the group decision hierarchy analysis method, is devised to formulate a peaking pricing strategy for thermal power and energy storage. Subsequently, acknowledging the variability of wind power, multiple scenarios are generated and consolidated through the application of k-means clustering. A two-stage stochastic optimization approach is then utilized for day-ahead pre-dispatch of thermal power and storage units, and intraday dispatch adjustments are made to accommodate the uncertainties associated with renewable energy sources by leveraging flexible resources, thereby ensuring power balance, and maximizing wind power integration. The efficacy of the proposed approach is validated through simulations conducted on the enhanced IEEE39-bus system. The numerical results show that the flexibility of the system can be effectively improved, and the wind power and photovoltaic accommodation rate is increased by 98.11 and 99.42%, respectively.