Non-stationary Demand Side Management Method for Smart Grids

ABSTRACT Demand side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. The consumers choose their power consumption patterns according to different prices charged at different times of the day. Importantly, consumers incur discomfort costs from altering their power consumption patterns. Existing works propose stationary strategies for consumers that myopically minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested consumers as a repeated energy scheduling game which foresightedly minimizes their long-term total costs. We then propose a novel methodology for determining optimal nonstationary DSM strategies in which consumers can choose different daily power consumption patterns depending on their preferences and routines, as well as on their past history of actions. We prove that the existing stationary strategies are suboptimal in terms of long-term total billing and discomfort costs and that the proposed strategies are optimal and incentivecompatible (strategy-proof). 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

Cooperation and Storage Tradeoffs in Power-Grids with Renewable Energy Resources

One of the most important challenges in smart grid systems is the integration of renewable energy resources into its design. In this work, two different techniques to mitigate the time varying and intermittent nature of renewable energy generation are considered. The first one is the use of storage, which smooths out the fluctuations in the renewable energy generation across time. The second technique is the concept of distributed generation combined with cooperation by exchanging energy among the distributed sources. This technique averages out the variation in energy production across space. This paper analyzes the trade-off between these two techniques. The problem is formulated as a stochastic optimization problem with the objective of minimizing the time average cost of energy exchange within the grid. First, an analytical model of the optimal cost is provided by investigating the steady state of the system for some specific scenarios. Then, an algorithm to solve the cost minimization problem using the technique of Lyapunov optimization is developed and results for the performance of the algorithm are provided. These results show that in the presence of limited storage devices, the grid can benefit greatly from cooperation, whereas in the presence of large storage capacity, cooperation does not yield much benefit. Further, it is observed that most of the gains from cooperation can be obtained by exchanging energy only among a few energy harvesting sources