Abstract
The development of the smart grid is driving an explosion of interest in demand response programs in the power and energy industry. The term “demand response” is usually used to describe programs that result in changes in electricity usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time. To do so, smart usage of major home appliances is necessary. This paper compares optimal and heuristic demand response (DR) algorithms through a computational experiment strategy. The optimal DR algorithm is obtained based on the objective of minimizing the cost for household electricity consumption. The heuristic DR algorithm is based on the dynamic price information during a day. The computational experiment approach combines building energy consumption simulation and dynamic electricity price together for different DR algorithm evaluation. The paper examines the characteristics of the two different DR strategies and how they are affected by dynamic price tariffs, seasons, and weathers.
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