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Under the real-time electricity pricing environment in smart grid, building owners are faced with the indoor temperature control problem to minimize the daily electricity cost. Taking the heating scenario as an example, an intuitive strategy is to maintain the building's indoor temperature always at the lower bound of the predetermined comfort range. However, this strategy may not always achieve the lowest electricity bill, especially with the significant fluctuation of electricity prices. On the other hand, the cost minimization problem can be optimally solved one day ahead in a temporally- coupled manner, but the challenge lies in that the building owner needs to acquire the accurate information of electricity prices and outdoor temperatures of the next day, which may not be available. In this paper, we equivalently decouple the cost minimization problem into subproblems at each hour. Each subproblem can be temporally decoupled and optimally solved, only requiring the next-hour electricity price. Besides, the temporally-decoupled algorithm explicitly indicates when to take advantage of pre-heating/cooling for electricity cost reduction. It is demonstrated with the real data that our proposed algorithm could result in considerable economic savings compared with the intuitive strategy, paving the way towards practically applicable cost-effective smart buildings.
Deng et al. (Thu,) studied this question.