Hierarchical microgrid energy management in an office building

A two-stage hierarchical Microgrid energy management method in an office building is proposed, which considers uncertainties from renewable generation, electric load demand, outdoor temperature and solar radiation. In stage 1, a day-ahead optimal economic dispatch method is proposed to minimize the daily Microgrid operating cost, with the virtual energy storage system being dispatched as a flexible resource. In stage 2, a two-layer intra-hour adjustment methodology is proposed to smooth the power exchanges at the point of common coupling by coordinating the virtual energy storage system and the electric vehicles at two different time scales. A Vehicle-to-Building control strategy was developed to dispatch the electric vehicles as a flexible resource. Numerical studies demonstrated that the proposed method is able to reduce the daily operating cost at the day-ahead dispatch stage and smooth the fluctuations of the electric power exchanges at the intra-hour adjustment stage.

[1]  Zhao Yang Dong,et al.  Operational Planning of Electric Vehicles for Balancing Wind Power and Load Fluctuations in a Microgrid , 2017, IEEE Transactions on Sustainable Energy.

[2]  P. Varaiya,et al.  Bringing Wind Energy to Market , 2012, IEEE Transactions on Power Systems.

[3]  Paras Mandal,et al.  A novel hybrid approach using wavelet, firefly algorithm, and fuzzy ARTMAP for day-ahead electricity price forecasting , 2013, IEEE Transactions on Power Systems.

[4]  Bo Guo,et al.  Optimal operation of a smart residential microgrid based on model predictive control by considering uncertainties and storage impacts , 2015 .

[5]  Jianzhong Wu,et al.  Planning of Fast EV Charging Stations on a Round Freeway , 2016, IEEE Transactions on Sustainable Energy.

[6]  Kwok-wai Mui,et al.  Energy policy for integrating the building environmental performance model of an air conditioned building in a subtropical climate , 2006 .

[7]  Standard Ashrae.  Thermal Environmental Conditions for Human Occupancy , 1992 .

[8]  Jianzhong Wu,et al.  Active power regulation for large-scale wind farms through an efficient power plant model of electric vehicles , 2017 .

[9]  Johan Driesen,et al.  Electric Vehicle Charging in an Office Building Microgrid With Distributed Energy Resources , 2014, IEEE Transactions on Sustainable Energy.

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

[11]  Liwei Tian,et al.  Evaluation on energy and thermal performance for office building envelope in different climate zones of China , 2015 .

[12]  Mohammad Moradzadeh,et al.  A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management , 2016 .

[13]  Recep Yumrutaş,et al.  Experimental investigation for total equivalent temperature difference (TETD) values of building walls and flat roofs , 2009 .

[14]  Luca A. Tagliafico,et al.  Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach , 2014 .

[15]  Lingfeng Wang,et al.  Integration of plug-in hybrid electric vehicles into energy and comfort management for smart building , 2012 .

[16]  Eric Wai Ming Lee,et al.  An analysis of a medium size grid-connected building integrated photovoltaic (BIPV) system using measured data , 2013 .

[17]  Jianzhong Wu,et al.  Primary Frequency Response From Electric Vehicles in the Great Britain Power System , 2013, IEEE Transactions on Smart Grid.

[18]  Petru-Daniel Morosan,et al.  Building temperature regulation using a distributed model predictive control , 2010 .

[19]  W. Beckman,et al.  Solar Engineering of Thermal Processes , 1985 .

[20]  P. Fanger.  Moderate Thermal Environments Determination of the PMV and PPD Indices and Specification of the Conditions for Thermal Comfort , 1984 .

[21]  Recep Yumrutaş,et al.  Validation of periodic solution for computing CLTD (cooling load temperature difference) values for building walls and flat roofs , 2015 .

[22]  Hongjie Jia,et al.  Hierarchical management for integrated community energy systems , 2015 .

[23]  Yang Zhao,et al.  MPC-based optimal scheduling of grid-connected low energy buildings with thermal energy storages , 2015 .

[24]  Tianzhen Hong,et al.  The human dimensions of energy use in buildings: A review , 2018 .

[25]  Jianhui Wang,et al.  Coordinated control for large-scale EV charging facilities and energy storage devices participating in frequency regulation , 2014 .

[26]  Rubiyah Yusof,et al.  Review of HVAC scheduling techniques for buildings towards energy-efficient and cost-effective operations , 2013 .

[27]  Meral Özel,et al.  Optimum location and distribution of insulation layers on building walls with various orientations , 2007 .

[28]  Tao Zhang,et al.  Model predictive control-based operation management for a residential microgrid with considering forecast uncertainties and demand response strategies , 2016 .

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

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

[31]  Lazaros G. Papageorgiou,et al.  Efficient energy consumption and operation management in a smart building with microgrid , 2013 .

[32]  Hemanshu R. Pota,et al.  Energy management for a commercial building microgrid with stationary and mobile battery storage , 2016 .

[33]  Yi Ding,et al.  A Convex Model of Risk-Based Unit Commitment for Day-Ahead Market Clearing Considering Wind Power Uncertainty , 2015, IEEE Transactions on Power Systems.

[34]  H. P. Garg,et al.  Energy conservation in a cinema hall under hot and dry condition , 1996 .

[35]  Ardeshir Mahdavi,et al.  Occupants' operation of lighting and shading systems in office buildings , 2008 .

[36]  S. P. Shankar,et al.  Integrated energy systems , 1982 .

[37]  Qing-Shan Jia,et al.  Energy-Efficient Buildings Facilitated by Microgrid , 2010, IEEE Transactions on Smart Grid.

[38]  Alex Q. Huang,et al.  Model predictive control-based power dispatch for distribution system considering plug-in electric vehicle uncertainty , 2014 .

[39]  Yongjun Sun,et al.  Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming , 2015 .

[40]  Bo Qu,et al.  Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system , 2016 .

[41]  Yunfei Mu,et al.  A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles ☆ , 2014 .

[42]  Hongwei Mao,et al.  Sustainable Building in China - A Green Leap Forward? , 2013 .

[43]  C. Cristofari,et al.  Innovative alternative solar thermal solutions for housing in conservation-area sites listed as national heritage assets , 2015 .

[44]  Myoung-Souk Yeo,et al.  Application of artificial neural network to predict the optimal start time for heating system in building , 2003 .

[45]  Ju Bin Song,et al.  Optimal charging and discharging for multiple PHEVs with demand side management in vehicle-to-building , 2012, Journal of Communications and Networks.