Ordinal Optimization: Soft Computing for Hard Problems (International Series on Discrete Event Dynamic Systems)
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Buildings contribute a significant part in the energy consumption and CO2 emission in many countries. Building energy saving has thus become a hot research topic recently. The technology advances in power co-generation, on-site generation, and storage devices bring us the opportunity to reduce the cost and CO2 emission while meeting the demand in buildings. A fundamental difficulty to schedule this multi-energy system, besides other difficulties, is the discrete and large search space. In this paper, the multi-energy scheduling problem is modeled as a nonlinear programming problem with integer variables. A method is developed to solve this problem in two steps, which uses ordinal optimization to address the discrete and large search space and uses linear programming to solve the remaining sub-problems. The performance of this method is theoretically quantified, and compared with enumeration and a priority-and-rule-based scheduling policy. Numerical results show that our method provides a good tradeoff between the solution quality and the computational time comparing with the other two methods. We hope this work brings more insight on multi-energy scheduling problem in general.