A simulation study for residential electricity user behavior under dynamic variable pricing with demand charge
研究一种基于同时需求收费的动态可变电价方案，通过多属性效用函数和模型预测控制模拟居民用电行为，并与实际负荷数据验证，探讨其对削峰和可再生能源整合的影响。
Attempting to increase energy efficiency and improve system load factors in an electricity distribution system, Demand Response (DR) has long been proposed and implemented as a form of load management. Various pricing structures incentivizing consumers to shift energy consumption from on-peak to off-peak periods are evident in this field. Most DR methods currently used in practice belong to static variable pricing (e.g., Time of Use, Critical Peak Pricing) and the impact of such tariffs has been well established. However, dynamic variable pricing in general is less studied and much less practiced in the field, due to the lack of understanding of consumer behavior in response to price uncertainty. In this article, we study a novel dynamic variable pricing scheme that uses the coincident demand charge to reduce load consumption during peak events. We employ a multi-attribute utility function and model predictive control to simulate consumer behavior of utility maximization in home energy consumption. We use a conditional Markov chain to model and predict the system peak. Effects of the proposed residential electricity rate based on coincident demand charge are compared with other pricing schemes through simulation validated with real-world residential load profiles. Finally, we extend the simulations to study the impact of integrating renewable solar production in a DR program.