Dynamic Pricing in Smart Grids under Thresholding Policies

Our ever-increasing reliance on electricity coupled with inefficient consumption has resulted in several economical and environmental threats. To curb these threats, smart grids are emerging. These improved power systems could potentially reduce the peak consumption and better match demand to supply, to produce both economical and environmental advantages. In this work, we consider two pricing problems in the smart grid, under a dynamic pricing model, where consumers follow threshold policies to schedule their power consumption. The first problem is to set the prices during the different time periods such that the peak demand is minimized. The second problem is to set the prices such that the power demand matches the supply. Firstly, we propose generic heuristics called GREEDY and SLIDING-WINDOW that are able to solve the two studied problems in addition to any other optimization problem, under the same model. Secondly, we provide theoretical analysis for the uniform-pricing approach in the context of peak-demand minimization. In addition, we propose optimal algorithms for the two optimization problems that can be used when the maximum deadline period of the power jobs is relatively small. Moreover, we conduct several experiments to evaluate the proposed algorithms and the uniform pricing approach on real data. Our experimental results showed that our proposed heuristics have a relatively low approximation ratio, and have the potential to provide a significant energy saving in many cases compared to the Time-of-Use (ToU) pricing. Furthermore, the experiments showed that while the uniform pricing has an acceptable approximation ratio in the average case, it leads to energy loss compared to the ToU pricing. Finally, the experiments demonstrated a tradeoff between optimality and speed.