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Distributed and renewable-energy resources are likely to play an important role in the future energy landscape as consumers and enterprise energy users reduce their reliance on the main electricity grid as their source of electricity. Environmental or ambient sensing of parameters such as temperature and humidity, and amount of sunlight and wind, can be used to predict electricity demand from users and supply from renewable sources, respectively. In this paper, we describe a Smart Grid Cyber-Physical System (SG-CPS) comprising sensors that transmit real-time streams of sensed information to predictors of demand and supply of electricity and an optimization-based decision maker that uses these predictions together with real-time grid electricity prices and historical information to determine the quantity and timing of grid electricity purchases throughout the day and night. We investigate two forms of the optimization-based decision maker, one that uses linear programming and another that uses multi-stage stochastic programming. Our results show that sensing-driven predictions combined with the optimization-based purchasing decision maker hosted on the SG-CPS platform can cope well with uncertainties in demand, supply, and electricity prices and make grid electricity purchasing decisions that successfully keep both the occurrence of electricity shortfalls and the cost of grid electricity purchases low. We then examine the computational and memory requirements of the aforementioned prediction and optimization algorithms and find that they are within the capabilities of modern embedded system microprocessors and, hence, are amenable for deployment in typical households and communities.
Tham et al. (Thu,) studied this question.