Study On Short-term Trading And Optimal Operation Strategy For CVPP Considering Uncertainties

Problems such as energy shortage and environment pollution are turning out to be increasingly severe, which require the development of renewable energy sources (RES). As the most important type of secondary energy, production and consumption patterns of electricity are undergoing fundamental changes. On the side of distribution networks, appearance of distributed generation (DG) and demand response (DR) resources make future distribution networks characterized by energy resources diversity, bidirectional power flow and so on. Thus the operation mode of the grid becomes more complicated, dispatching mode and management idea are needed to transform to adapt the requirement of smart grid. As an effective way of connecting distributed resources with grid, virtual power plant(VPP) can participate into the market trading and grid management in the form of a special power plant on base of RES aggregation and optimization, which reflects the feature of intelligence and interaction of power system on the side of distribution networks.Taking the VPP technology and electricity market mechanism as breakthrough point, this paper studies market trading and internal optimization strategy of commercial VPP(CVPP), whose aims are effective control and management of distribution networks resources as well as influence reducement caused by the uncertainty of RES when ensuring the optimal profits of CVPP.First, with the aim that maximization of bidding profits in day-ahead market and rewards or punishment in balancing market under the background of energy market, a stochastic programming model of CVPP’s trading is constructed in which multi-scenario method is used to deal with day-ahead market price and wind power uncertainty. Based on the model, the influence that the uncertainty of market price and wind power prediction reduces when time approaches is considered, and traditional two-stage stochastic programming model is improved into a multi-stage stochastic programming model.Then, optimal bidding profits in the combined market consisting of energy market and spinning reserve market is set as the objective, the robust optimization model of CVPP combined market trading is constructed, in which the wind power uncertainty is dealt with by robust optimization, note that resources could choose to trade in different markets according to its features.Case study validates the effectiveness of the proposed model and the results comply with the trading rules of the market. The first case indicates that the multi-stage stochastic programming considering certainty increase of stochastic variables could raise the whole certainty gain of CVPP. The second case indicates that the total profits CVPP gained from trading in the combined market are greatly influenced by robust coefficients and error coefficients of wind power prediction. Therefore, operators could choose suitable trading strategy according to their preference with risk. The above researches could provide reference for market trade strategy of CVPP.