Demand response minimizing the impact on the consumers' utility considering forecast error on renewable energy resource

In the smart grid, which is a new generation grid integrated with bidirectional communication and advanced information technology, it is supposed that the number of energy suppliers will increase because many renewable resources will be connected to the grid. The increase in the number of suppliers that use renewable energy resources introduces new challenges in scheduling and dispatching controllable resources to control the load of the power grid in the system. By using the pricing method that maximizes social welfare, consumers/suppliers can determine the optimal power consumption/generation that maximizes own welfare. However, when a forecast error of renewable energy output occurs, the gap between the total power consumption and generation causes unless consumers decrease power consumption from the optimal consumption. Decreasing consumers' power consumption might lower their utility. In this paper, we define impact on consumers' utility and propose two demand response methods which aim to minimize the impact. The first method, called One-Time Demand Response, minimizes the impact on consumers' utility of a certain time slot by using a Lagrange multiplier. In the second method, called Foresight Demand Response, all consumers can forecast the electricity market price of the next time slot by using linear regression, and consumers do not reduce power consumption when the incentive of a certain time slot is cheaper than the forecast electricity market price of the next time slot. From the performance evaluation, we confirm the impact on consumers' utility in One-Time Demand Response and Foresight Demand Response. We also confirm that both two proposals can increase consumers' welfare compared to not carrying out demand response.