A Two-Stage Dispatch Mechanism for Virtual Power Plant Utilizing the CVaR Theory in the Electricity Spot Market

Abstract

The electricity spot market is now being implemented in China. Demand response, as a kind of flexible resource, is also being studied and explored for the constructed power market. Among the many demand response applications, the virtual power plant (VPP) as an aggregator of distributed energy resources (DERs), receives ever-increasing attention. However, the participation manner and related impacts of the VPP to the electricity spot market are still unknown within the current power market rules. Under this background, obeying the present trading rules of China’s electricity spot market, a two-stage dispatching model with optimized bidding and operating strategy in the day-ahead (DA) and real-time (RT) market for the VPP is proposed. In the designed model, the conditional risk value (CVaR) is adopted to address the risk encountered by the uncertainty of the electricity spot market price. The impact of the user-side over-deviated revenue mechanism (UORM) of the China spot market on the income of the VPP in the DA and RT market is also analyzed. For a full evaluation, different coefficients for the influence of DA and RT risk, UORM, and energy storage system (ESS) are tested to investigate their respective impacts on the revenue of the VPP. The simulation cases prove that the proposed method is helpful for the VPP to optimize DERs’ output in the electricity spot market according to its own risk preference.

Operation Strategy of Electricity Retailers Based on Energy Storage System to Improve Comprehensive Profitability in China’s Electricity Spot Market

“…In paper [5], the authors focused on the Turkish electricity spot market, and they established a multistage risk model combined with the price prediction model, which can dynamically manage portfolios in order to reduce risks. In literature [6,7], in the two-stage dispatching control of the DA market and RT market, power was dispatched through VPP and micro-grid, and a risk preference model utilizing the CVaR method was established. In paper [8], for the DA market and regulatory market, the energy hub operator uses the stochastic mixed integer linear programming (MILP) method and takes into account the CVaR method in order to optimize scheduling of electric energy and natural gas procurement.…”

“…Due to the adjustment of peak-valley price policies in recent years [7], the difference in peak-valley prices has decreased in most regions, which results in the economic decline of peak shifting modes. According to the current construction cost of ESD, it is economical when the peak-valley price difference is more than 0.7 CNY/kWh.…”

“…According to the analysis in Section 3.1, synthetic optimization considers three parts of electricity purchase costs, which includes electricity cost of the DA market, electricity cost of the RT market and deviation assessment penalty for reducing electricity retailer costs. The optimization goal is shown in Equation (7).…”

Optimal Control of a Virtual Power Plant by Maximizing Conditional Value-at-Risk

“…X. Kong et al [24] used the robust stochastic method to analyze uncertain factors and solve the optimal scheduling of multi-energy VPPs. For the uncertainty of electricity pricing, a risk-averse stochastic stage [25,26] was introduced by using CVAR. R. Lima et al [27] considered both risk-neutral and risk-averse formulations that depend on CVAR to solve the optimal operation of a VPP.…”

Comparison of Electricity Spot Price Modelling and Risk Management Applications

“…For example, Ref. [34] model the risks for a virtual power plant, Ref. [35] examine the supplier selection problem for large customers, or [36] risk averse natural gas generator operation in a micro-grid.…”