Impact of residential customer classification on demand response results under high renewable penetration

The large scale deployment of renewable generation is generally seen as the most promising option for displacing fossil fuel generators, especially coal-fired power plants. A key challenge in integrating Renewable Energy Resources (RERs) is to find approaches that ensure long term sustainability and economic profit. The appropriate design of Demand Response (DR) could control both the increase in peak demand and volatility of prices. An essential part of good DR design, especially in the residential sector, is understanding diverse customer behavior. In this paper, an Incentive Based Demand Response (IBDR) is proposed segregating the elasticity of household appliances. The proposed elasticity approach provides a more accurate model of both the load shift and load reduction potential in the residential sector. Customer classification is explored to understand the diversity of customer behavior. In the presented case study, the retirement of seven coal-fired power plants and expansion of RERs is simulated using data from the reduced WECC 240-bus system. Results show although renewable expansion could lead to benefit loss for utilities and abrupt changes in market prices, the proposed IBDR program could minimize such impacts.