Risk-constrained framework for residential storage space heating load management

• A decision support tool for customers to optimize their space heating load.
• The proposed framework considers the risk-aversion behavior of customers.
• The DR framework is optimized using Genetic Algorithm.
• The proposed framework can easily be implemented at the household level.

Residential demand response based on real time pricing provides a strong incentive for customers to reduce their energy payment. However, acting under an environment with time-varying prices will expose them to uncertain energy bills. This paper presents a risk-constrained framework for residential customers for scheduling the electric storage space heating load. The proposed decision framework attempts to accomplish desired settlement between expected cost minimization and cost deviation without altering the user's thermal comfort. The price and load uncertainty are captured by a scenario based stochastic programming approach. The optimization model is solved using Genetic Algorithm and implemented using a moving-window procedure. The simulation results demonstrate that the proposed framework for scheduling the storage space heating load provides a method to selectively hedge against the price and load uncertainty risk. The optimal framework will result in an improved interaction between the electrical aggregator and its customers under the smart grid paradigm.