A general multi-agent control approach for building energy system optimization

Abstract

8Penetration of advanced building control techniques into the market has been slow since buildings are unique and site-9 specific controller design is costly. In addition, for medium-to large-sized commercial buildings, HVAC system 10 configurations can be very complex making centralized control infeasible. This paper presents a general multi-agent control 11 methodology that can be applied to building energy system optimization in a "plug-and-play" manner. A multi-agent 12 framework is developed to automate the controller design process and reduce the building-specific engineering efforts. To 13 support distributed decision making, two alternative consensus-based distributed optimization algorithms are adapted and 14implemented within the framework. The overall multi-agent control approach was tested in simulation with two case studies: 15 optimization of a chilled water cooling plant and optimal control of a direct-expansion (DX) air-conditioning system serving 16 a multi-zone building. In both cases, the multi-agent controller was able to find near-optimal solutions and significant energy 17 savings were achieved. 18Keyword: Multi-agent control; Building energy system optimization; Distributed optimization; HVAC component 19 coordination 20 INTRODUCTION 21More than 40% of the primary energy usage in the United States is related to energy consumption in buildings [1] and if 22 buildings are not operated properly, a significant amount of energy is wasted. The energy savings opportunities for optimal 23 building controls are becoming widely recognized leading to growing research efforts in the past few years. However, the 24 deployment of advanced controls in buildings has been progressing very slowly due to several reasons: (1) buildings are 25 unique in terms of both building construction and heating, ventilation and air-conditioning (HVAC) system configuration, 26 which makes building-specific controller design costly; (2) optimal control of complex building energy systems is difficult 27

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