This paper proposes an optimal risk-constrained energy management strategy for commercial buildings in a commercial campus with islanding capabilities.The goal is to minimize the total operation and maintenance costs,...This paper proposes an optimal risk-constrained energy management strategy for commercial buildings in a commercial campus with islanding capabilities.The goal is to minimize the total operation and maintenance costs,while maximizing comprehensive comfort levels for the occupants.A two-stage riskconstrained,scenario-based stochastic optimization approach is adopted to handle various uncertainties associated with the energy management process,such as power generation of rooftop solar panels,arrival state-of-charges,and arrival/departure time of plug-in electric vehicles,intermittent load demand,and uncertain grid-connection conditions.A conditional-valueat-risk method is introduced to provide a risk-averse energy management strategy.To face the challenge of both reducing the computational burden and maintaining the accuracy of the stochastic programming,an advanced scenario reduction method is leveraged.Extensive simulation results validate the effectiveness of the proposed energy management strategy for minimizing total operating and maintenance costs of commercial buildings with islanding capabilities,while maximizing comprehensive comfort levels of the occupants.展开更多
基金supported by State Grid Corporation of China(SGCC)under project“Hybrid Energy Storage Management Platform for Integrated Energy System”(No.SGGR0000DLJS1800932).
文摘This paper proposes an optimal risk-constrained energy management strategy for commercial buildings in a commercial campus with islanding capabilities.The goal is to minimize the total operation and maintenance costs,while maximizing comprehensive comfort levels for the occupants.A two-stage riskconstrained,scenario-based stochastic optimization approach is adopted to handle various uncertainties associated with the energy management process,such as power generation of rooftop solar panels,arrival state-of-charges,and arrival/departure time of plug-in electric vehicles,intermittent load demand,and uncertain grid-connection conditions.A conditional-valueat-risk method is introduced to provide a risk-averse energy management strategy.To face the challenge of both reducing the computational burden and maintaining the accuracy of the stochastic programming,an advanced scenario reduction method is leveraged.Extensive simulation results validate the effectiveness of the proposed energy management strategy for minimizing total operating and maintenance costs of commercial buildings with islanding capabilities,while maximizing comprehensive comfort levels of the occupants.
