Flexible operation of active distribution network using integrated smart buildings with heating, ventilation and air-conditioning systems

Abstract Aiming to utilize the flexibility of smart buildings for flexible operation of active distribution network, a combined modeling and optimal scheduling method for the active distribution network with integrated smart buildings is proposed in this paper. Based on the heat storage characteristics of a building, the energy consumption prediction model of the building considering different heating zones with different orientations is developed using the Resistor-Capacitor thermal network model. Then, different optimal control methods of the heating, ventilation and air-conditioning system in the building are developed. The energy consumption management of the heating, ventilation and air-conditioning system is achieved by adjusting the room temperature within the suitable temperature comfort range. In order to further consider the impact of the integration of smart buildings on the economic and security operation of the active distribution network, the optimal scheduling method of the active distribution network with integrated smart buildings is developed considering the load factor of the aggregation of the smart buildings. Finally, the optimal scheduling results of the aggregation of the smart buildings under different heating, ventilation and air-conditioning control methods in the winter heating scenario are analyzed. In addition, based on the branch flow model, the optimal power flow model of active distribution network with on-load tap changer is constructed by piecewise linearization and second-order cone relaxation to achieve flexible and optimal operation of the active distribution network. Thus, the impacts of the optimal schedules of the aggregation of smart buildings on the economic and security operation of the active distribution network are further evaluated. Numerical studies demonstrate that the proposed optimal scheduling method can make full use of the demand response potential of the smart buildings and further contribute to the operating costs reduction of the smart buildings. Meanwhile, the optimization of the active distribution network with the load factor of the aggregation of buildings can reduce the power loss and increase the minimum voltage magnitude of the active distribution network utilizing the flexibility of the smart buildings.

[1]  Steven H. Low,et al.  Branch Flow Model: Relaxations and Convexification—Part II , 2012 .

[2]  Enrico Fabrizio,et al.  Assessment of building cooling energy need through a quasi-steady state model : Simplified correlation for gain-loss mismatch , 2007 .

[3]  G. Rizzoni,et al.  A highly resolved modeling technique to simulate residential power demand , 2013 .

[4]  J. Torriti,et al.  Demand response experience in Europe: Policies, programmes and implementation , 2010 .

[5]  Li Tianyan.  Regulation Technology of Air-conditioning Load in Commercial Buildings for Balance of Power Grid Peak and Valley , 2015 .

[6]  M. E. Baran,et al.  Optimal capacitor placement on radial distribution systems , 1989 .

[7]  Hongbin Sun,et al.  Integrated energy systems , 2016 .

[8]  J. Braun,et al.  Load Control Using Building Thermal Mass , 2003 .

[9]  Peng Li,et al.  An enhanced SOCP-based method for feeder load balancing using the multi-terminal soft open point in active distribution networks , 2017 .

[10]  Dionysia Kolokotsa,et al.  The role of smart grids in the building sector , 2016 .

[11]  S. Roaf,et al.  Standards for Thermal Comfort: Indoor air temperature standards for the 21st century , 1995 .

[12]  Tao Jiang,et al.  Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system , 2017 .

[13]  Erling D. Andersen,et al.  On implementing a primal-dual interior-point method for conic quadratic optimization , 2003, Math. Program..

[14]  Hongjie Jia,et al.  Optimal day-ahead scheduling of integrated urban energy systems , 2016 .

[15]  Mahdi Shahbakhti,et al.  Optimal exergy control of building HVAC system , 2015 .

[16]  Mark Jennings,et al.  A review of urban energy system models: Approaches, challenges and opportunities , 2012 .

[17]  Lazaros G. Papageorgiou,et al.  Economic and environmental scheduling of smart homes with microgrid: DER operation and electrical tasks , 2016 .

[18]  Xiandong Xu,et al.  Hierarchical microgrid energy management in an office building , 2017 .

[19]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .