Optimal Power Flow Based Demand Response Offer Price Optimization

In the deregulated electricity market, demand side participation has become a very important solution to real-time energy balance. Demand response programs are offered by the utility companies to reduce the load on the customer side with monetary incentives when the power demand is high. It is a popular tool used by electricity utilities to manage the supply load and reduce the operating cost. In each time period, the utility predicts the load up front and gets prepared to supply the forecasted load. If the actual demand turns out to exceed the prediction, the utility sends a price signal to customers and pay the demand response cost in exchange for load reduction. A considerable amount of papers have discussed the demand response program in the sense of improving the reliability of power systems [3][10][14][15][16], designing appropriate incentive rates structures for interruptible load programs [4][7][11][13], and hedging against risks [1][6][8][12]. This paper discusses the optimal execution strategy the utility can apply so that the demand response operating cost is minimized. We study the offer price optimization problem with AC power flow constraints imposed. The goal is to find the offer prices such that the total cost of satisfying the required demand is minimized under the distribution grid setup. We first show that the location of where the demand response comes from has a significant effect on the total cost due to transmission losses. Therefore, modeling the power flow constraints in the offer bid optimization problem is important and can significantly decrease the costs. The optimal power flow problem is non-convex in general. We formulate convex relaxations of the offer bid optimization with power flow constraints and develop heuristics for the uncertain supply (or demand-response) function. We propose an efficient heuristics that use a linear DC approximation for the case the supply function is uncertain. We present computational experiments that demonstrate the cost savings when we consider the power flow constraints in the offer bid optimization as compared to optimal bid computation without the power flow constraints.