![]() Among various RESs, wind generation has become the most popular RES (Alamdari et al, 2012, Leung and Yang, 2012). Nowadays, due to shortage of fossil fuels and environmental concerns, using renewable energy resources (RESs) has attracted attentions as one of the most efficient and reliable approaches to meet the electricity demand growth, reduce energy cost and improve system reliability. The numerical results demonstrate effectiveness of the proposed DR model in improving the wind generation profit in the day-ahead market. The results show that as risk aversion behavior increases, more incentive payment is needed to convince the customer to reduce his/her demand. To determine the day-ahead and balancing market prices, a probabilistic two-step market clearing optimization is solved considering uncertainties of wind power and behavior of electricity customers. To this end, the wind power producer can either use real time balancing market or DR to cope with the energy submitted in the day-ahead market and the delivered energy. Then, the DR is used to maximize wind power profit in the day-ahead market. The proposed model is designed to maximize the individual customer’s welfare under incentive-based demand response (DR) programs. Based on the utility function, an economic demand model is developed as a function of customer risk aversion behavior to consider the effect of incentive payment on electricity consumption. This function interprets how a rational consumer would make consumption decisions. The utility function which measures customer’s preferences is an important concept in microeconomics. In this paper, behavior of electricity customers is modeled using utility function. ![]()
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