Stochastic risk-constrained short-term scheduling of industrial cogeneration systems in the presence of demand response programs

This paper presents a stochastic programming framework for solving the scheduling problem faced by an industrial customer with cogeneration facilities, conventional power production system, and heat only units. The power and heat demands of the customer are supplied considering demand response (DR) programs. In the proposed DR program, the responsive load can vary in different time intervals. In the paper, the heat-power dual dependency characteristic in different types of CHP units is taken into account. In addition, a heat buffer tank, with the ability of heat storage, has been incorporated in the proposed framework. The impact of the market and load uncertainties on the scheduling problem is characterized through a stochastic programming formulation. Autoregressive integrated moving average (ARIMA) technique is used to generate the electricity price and the customer demand scenarios. The daily and weekly seasonalities of demand and market prices are taken into account in the scenario generation procedure. The conditional value-at-risk (CVaR) methodology is implemented in order to limit the risk of expected profit due to market price and load forecast volatilities.