A Dynamic Programming Model For Simulating Demand Response and Renewable Energy

Abstract

The purpose of this thesis is to address unsustainable growth in electricity demand. This thesis examines industries and particular firms to identify energy consuming processes to manipulate for real-world demand response. Using dynamic programming to mathematically model temperature control demand response identifies the optimal policy for cost-minimization. Leveraging this model and statistical models of solar power and electricity pricing simulates potential future energy consumption patterns for this particular process. This thesis illustrates that pursuing demand response integrated with renewable energy, in the specific case of temperature control and solar power, produces a combined cost-minimizing strategy with a lower volatility than that of either independent process.