Multi-Objective Optimal Operation of Building Energy Management Systems with Thermal and Battery Energy Storage in the Presence of Load Uncertainty

This paper presents the optimal operation of a building energy management system (BEMS), with combined heat and power (CHP) generation, thermal storage (TES), battery (BES), subject to load demand uncertainty. The main objective is reduce total operating cost (TOC) CO2 emission (TCOE). First, we develop two models forecasting, one for weekday other weekend, using artificial neural networks, long short-term memory, convolutional networks. Then, incorporate predicted uncertainty planning dispatch BEMS. TES aims store waste from generation CHP discharge absorption chiller supply cooling load. BES spinning reserve (SR) play an important role in handling demand. BEMS, uncertainty, formulated as linear program. We can efficiently solve program provide solution that satisfies constraints. Thereafter, determine size BES, based on economics environmental operation. proposed BEMS compared previous without SR. Furthermore, propose multi-objective operation, where normalization TOC TCOE introduced, function defined combination normalized TCOE. numerical results reveal trade-off relationship between In particular, when minimum, becomes maximum. On hand, provides method select point, well analyze flow