Optimal Day-Ahead Power Procurement With Renewable Energy and Demand Response

This study proposes the demand-side power procurement problem to optimally reduce consumer's energy cost. The motivation stems from pressing issues on an increase of energy cost in an industrial section. From an energy consumer's perspective, there exists an opportunity to reduce energy cost by adjusting purchase and consumption of energy in response to time-varying electricity price while utilizing renewable energy, which is called demand response. In this case, energy storage can be used to mitigate fluctuation of intermittent renewable supply and volatile electricity price. Although it is anticipated to serve a significant amount of energy consumption from renewable energy and to avoid peak electricity price, variability and uncertainty in power demand, renewable supply, and electricity price make it challenging to determine an optimal power procurement. The main objective of this study is to suggest a decision-making methodology that enables energy consumers to optimally determine power procurement against time varying and stochastic electricity price and renewable supply. Specifically, this study formulates an optimal day-ahead power procurement as a two-stage stochastic mixed-integer program and proposes a solution approach based on Benders decomposition. The proposed methodology can be successfully applied to energy-intensive industries, such as data centers.