Demand Side Management in Smart Grids Using a Repeated Game Framework

Abstract

Demand side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. To provide incentives for consumers to shift their consumption to off-peak times, the utility company charges consumers differential pricing for using power at different times of the day. Consumers take into account these differential prices when deciding when and how much power to consume daily. Importantly, while consumers enjoy lower billing costs when shifting their power usage to off-peak times, they also incur discomfort costs due to the altering of their power consumption patterns. Existing works propose stationary strategies for the myopic consumers to minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested, foresighted consumers as a repeated energy scheduling game and prove that the stationary strategies are suboptimal in terms of long-term total billing and discomfort costs. Subsequently, we propose a novel framework for determining optimal nonstationary DSM strategies, in which consumers can choose different daily power consumption patterns depending on their preferences, routines, and needs. As a direct consequence of the nonstationary DSM policy, different subsets of consumers are allowed to use power in peak times at a low price. The subset of consumers that are selected daily to have their joint discomfort and billing costs minimized is determined based on the consumers' power consumption preferences as well as on the past history of which consumers have shifted their usage previously. Importantly, we show that the proposed strategies are incentive-compatible. Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50% compared to existing DSM strategies.

Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling

“…Recent DSM studies [2][3][4][5][6][7][8], have presented some techniques for appliance energy consumption scheduling, peak demand reduction (PDR), and Peak-to-Average-Ratio (PAR) demand reduction with some level of consumer preferences.…”

Evaluation of Demand-Side Management over Pricing Competition of Multiple Suppliers Having Heterogeneous Energy Sources

“…The common goal of these studies is to find optimal strategies for consumers or suppliers by considering the impact of pricing mechanisms [3,12,[17][18][19][20][21][22][23][24]. In [10], the authors suggested a DSM strategy that uses a day-ahead load shifting technique to overcome both peak load demand problems.…”

“…In [10], the authors suggested a DSM strategy that uses a day-ahead load shifting technique to overcome both peak load demand problems. The work of Song [24] proposed an optimal critical peak pricing scheme for DSM by considering the interactions of consumers where there is a single utility company. In this work, a repeated game framework for nonstationary DSM was proposed to minimize the long-term total cost and discomfort cost of consumers.…”

A layered approach for enabling demand side management in smart grid

“…DSM has been proved useful for the residential sector, commercial sector, and industry sector [13]- [16]. It has been realized in practice in recent years, supporting many appliances, such as space heating [17], water heaters [18], and electric vehicles [19].…”

“…In [14]- [16], the DR aggregators were mentioned as the intermediary, but the revenue of it was not considered. In [13], the electricity bill of consumers was emphasized, but the associated discomfort level was not taken into account. In [12] and [20], only conventional generation was considered.…”