A hierarchical coordinated demand response control for buildings with improved performances at building group

Demand response control is one of the common means used for building peak demand limiting. Most of the existing demand response controls focused on single building’s performance optimization, and thus may cause new undesirable peak demands at building group, imposing stress on the grid power balance and limiting the economic savings. A few latest studies have demonstrated the potential benefits of demand response coordination, but the proposed methods cannot be applied in large scales. The main reason is that, for demand response coordination of multiple buildings, associated computational load and coordination complexity, increasing exponentially with building number, are challenges to be solved. This study, therefore, proposes a hierarchical demand response control to optimize operations of a large scale of buildings for group-level peak demand reduction. The hierarchical control first considers the building group as a ‘virtual’ building and searches the optimal performance that can be achieved at building group using genetic algorithm. To realize such optimal performance, it then coordinates each single building’s operation using non-linear programming. For validations, the proposed method has been applied on a case building group, and the study results show that the hierarchical control can overcome the challenges of excessive computational load and complexity. Moreover, in comparison with conventional independent control, it can achieve better performances in aspects of peak demand reduction and economic savings. This study provides a coordinated control for application in large scales, which can improve the effectiveness and efficiency in relieving the grid stress, and reduce the end-users’ electricity bills.

[1]  José R. Vázquez-Canteli,et al.  Reinforcement learning for demand response: A review of algorithms and modeling techniques , 2019, Applied Energy.

[2]  Mohammed H. Albadi,et al.  Demand Response in Electricity Markets: An Overview , 2007, 2007 IEEE Power Engineering Society General Meeting.

[3]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[4]  Shengwei Wang,et al.  Dynamic simulation of a building central chilling system and evaluation of EMCS on-line control strategies , 1998 .

[5]  Pei Huang,et al.  A clustering based grouping method of nearly zero energy buildings for performance improvements , 2019, Applied Energy.

[6]  Pei Huang,et al.  A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty , 2019 .

[7]  Joseph Virgone,et al.  Energetic efficiency of room wall containing PCM wallboard: A full-scale experimental investigation , 2008 .

[8]  Leslie K. Norford,et al.  Optimal use of thermal energy storage resources in commercial buildings through price-based demand response considering distribution network operation , 2017 .

[9]  Dian-ce Gao,et al.  A GA-based coordinated demand response control for building group level peak demand limiting with benefits to grid power balance , 2016 .

[10]  Xinhua Xu,et al.  Building-group-level performance evaluations of net zero energy buildings with non-collaborative controls , 2018 .

[11]  Zhengwei Li,et al.  Performance evaluation of conventional demand response at building-group-level under different electricity pricings , 2016 .

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

[13]  Yongjun Sun,et al.  A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level , 2018, Energy.

[14]  Yongjun Sun,et al.  Life-cycle cost benefit analysis and optimal design of small scale active storage system for building demand limiting , 2014 .

[15]  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 .

[16]  Zhenjun Ma,et al.  An optimal control strategy for complex building central chilled water systems for practical and real-time applications , 2009 .

[17]  Yongjun Sun,et al.  A robust demand response control of commercial buildings for smart grid under load prediction uncertainty , 2015 .

[18]  Frédéric Kuznik,et al.  A review on phase change materials integrated in building walls , 2011 .

[19]  Pei Huang,et al.  A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level , 2019, Energy.

[20]  Shengwei Wang,et al.  Evaluation of a fast power demand response strategy using active and passive building cold storages for smart grid applications , 2015 .

[21]  Yongjun Sun,et al.  Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements , 2018 .

[22]  Sebastian Herkel,et al.  Load shifting using the heating and cooling system of an office building: Quantitative potential evaluation for different flexibility and storage options , 2017 .

[23]  Shengwei Wang,et al.  A demand limiting strategy for maximizing monthly cost savings of commercial buildings , 2010 .

[24]  Robert F. Boehm,et al.  Measurements and simulations for peak electrical load reduction in cooling dominated climate , 2012 .

[25]  Yongjun Sun,et al.  A top-down control method of nZEBs for performance optimization at nZEB-cluster-level , 2018, Energy.

[26]  Fu Xiao,et al.  An interactive building power demand management strategy for facilitating smart grid optimization , 2014 .

[27]  Giorgio Rizzoni,et al.  Role of residential demand response in modern electricity markets , 2014 .

[28]  Fu Xiao,et al.  Peak load shifting control using different cold thermal energy storage facilities in commercial buildings: A review , 2013 .

[29]  Constantine Kontokosta,et al.  Pattern recognition in building energy performance over time using energy benchmarking data , 2018, Applied Energy.

[30]  Jaume Salom,et al.  Understanding net zero energy buildings: Evaluation of load matching and grid interaction indicators , 2011 .

[31]  Moncef Krarti,et al.  Optimal control of building storage systems using both ice storage and thermal mass – Part I: Simulation environment , 2012 .