Stochastic trading of storage systems in short term electricity markets considering intraday demand response market

Abstract

In recent years, the renewable energy sources are used in the power system for reducing environmental pollution. One of these renewable energy sources is wind farm that is used widely. However, they increase the uncertainty of the power system. To solve this problem, plenty researches have proposed several methods that have some disadvantages. This paper tries to decline the effects of uncertainty of wind turbines by using of the energy storage systems and optimal demand response programs. A stochastic planning of energy storage systems and wind generation are presented in the short term electricity markets considering the trading day-head and intraday market of demand response. In order to formulate an accurate model of various components of system, all needed constraints are considered. In this problem, wind turbines, energy storage systems, and demand response aggregator work coordinately that is called virtual power plant. For better comparison, two states consist of coordinated and uncoordinated working of all components are considered and assessed. The various scenarios for electricity market price, adjustment market price, imbalance energy price, and the generated power of wind turbine are considered. These scenarios are generated with hybrid neural network and coati optimization algorithm. Also, the iterative fast progressive Kantorovich method is used for scenario reduction. The results show the better response of the proposed method in optimal operation of virtual power plant.