Multi-Temporal Risk Minimization Of Adaptive Load Management In Electricity Spot Markets

Adaptive load management (ALM) is a new way to balance power supply and demand by capturing the economic value each market participant sees, and finds the optimum by iterating the information between various market layers and by adjusting their transactions accordingly. Load serving entities (LSEs) play a role as a mediator between the supply and the demand in this framework. In this paper, we discuss the aspect of the risk minimization of an LSE by purchasing electricity at a volatile and risky spot market price, while meeting the end-users' energy needs represented by a state space model and constraints of an optimization problem. We propose a new concept that looks not only into the risk at the independent time steps in a single spot market but also into the correlated risks between different time steps and different spot markets, using Markowitz optimization. The proposed concept is also novel in the sense that it deploys loads with different physical characteristics (or storage with different time constants) for the spot markets with different time scales and intervals. We show through a simulation a possible benefit and drawback of this LSE's risk minimization framework compared to cost minimization without risk management. We conclude that there is a clear tradeoff between minimizing the risk of uncertainty and maximizing the profit of an LSE. Also, risk in different time scales should be managed in different ways with the right information exchange and technology infrastructure to fully utilize the adaptability of the loads to the volatile price signal.