Real-Time Electricity Retail Pricing Dual Optimization With Context-Based Fuzzy Optimal Algorithm

Abstract

Despite its bright prospect to promote affordable energy, one of the main concerns of real-time retail pricing for electricity is price volatility that would create potential bill shocks especially for low-income consumers. This would demotivate consumers to participate in real-time pricing scheme or to act as responsive participants to reduce peak demand. As a motivation to address this concern, this article proposes dual optimization method with fuzzy optimal algorithm to solve the optimization problem presented in the real-time pricing model with the aims to reduce price volatility and to lower the minimum price further. The fuzzy optimal algorithm applies a context-based fuzzy inferencing and a gradient adjustment to arrive at the optimal retail price for every time interval. Context-based fuzzy inferencing allows fuzzy value redefinition so that the desired level of precision is preserved. Gradient adjustment simplifies the derivation of the optimal retail price via a series of iterations. Simulation results reveal that price fluctuations are able to be reduced which would create stability and avoid undesired price spikes. At the same time, the results confirm that further reduction in the minimum retail price can be achieved which would improve welfare benefits while maintaining the desired level of consumer satisfaction.