Integrated optimal scheduling and predictive control for energy management of an urban complex considering building thermal dynamics

Abstract In this paper, an integrated optimal scheduling and predictive control scheme with a hierarchical structure is proposed for energy management of an urban complex (UC). The proposed scheme consists of a scheduling layer optimizing the energy usage of the UC and a control layer controlling the heating, ventilation, and air conditioning (HVAC) in each individual building. In the control layer, a detailed physical model of the individual building with HVAC system is developed to predict its energy consumption while considering the thermal dynamics of the building envelope with multiple layers of construction material. In the scheduling layer, a multi-objective optimal scheduling is formulated based on the predictive energy consumption of the buildings to reduce the peak-valley load difference and minimize the operating cost of the UC. Finally, the optimal control schedules are obtained and issued to the individual HVACs. Numerical results show that the proposed method can reduce the operating cost and reduce the peak-valley load difference for the UC. Meanwhile, the HVACs can be controlled in an optimal way within the limits of indoor temperature.

[1]  Tao Jiang,et al.  Scheduling distributed energy resources and smart buildings of a microgrid via multi‐time scale and model predictive control method , 2018, IET Renewable Power Generation.

[2]  Bourhan Tashtoush,et al.  Dynamic model of an HVAC system for control analysis , 2005 .

[3]  Mahdi Shahbakhti,et al.  Building Efficiency and Sustainability in the Tropics ( SinBerBEST ) Title Handling model uncertainty in model predictive control for energy efficient buildings Permalink , 2014 .

[4]  Yingying Chen,et al.  Optimal Dispatch of Air Conditioner Loads in Southern China Region by Direct Load Control , 2016, IEEE Transactions on Smart Grid.

[5]  Sanjib Ganguly,et al.  Multi-objective planning of electrical distribution systems using dynamic programming , 2013 .

[6]  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.

[7]  Shengwei Wang,et al.  A simplified dynamic model for existing buildings using CTF and thermal network models , 2008 .

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

[9]  Alberto Sangiovanni-Vincentelli.  Modeling and Optimal Control Algorithm Design for HVAC Systems in Energy Efficient Buildings , 2011 .

[10]  Ned Djilali,et al.  Transactive control of fast-acting demand response based on thermostatic loads in real-time retail electricity markets , 2018 .

[11]  Farrokh Janabi-Sharifi,et al.  Theory and applications of HVAC control systems – A review of model predictive control (MPC) , 2014 .

[12]  Federico Silvestro,et al.  Frequency control services by a building cooling system aggregate , 2016 .

[13]  Henrik Madsen,et al.  Identifying suitable models for the heat dynamics of buildings , 2011 .

[14]  N. Lu,et al.  A state-queueing model of thermostatically controlled appliances , 2004 .

[15]  V. Ismet Ugursal,et al.  Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector , 2008 .

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

[17]  Konstantinos G. Arvanitis,et al.  A nonlinear feedback technique for greenhouse environmental control , 2003 .

[18]  Yutao Wang,et al.  Quantifying the emergy flow of an urban complex and the ecological services of a satellite town: A case study of Zengcheng, China , 2016 .

[19]  Yang Shi,et al.  A Novel Optimal Operational Strategy for the CCHP System Based on Two Operating Modes , 2012, IEEE Transactions on Power Systems.

[20]  Haralambos Sarimveis,et al.  A combined model predictive control and time series forecasting framework for production-inventory systems , 2008 .

[21]  Hoay Beng Gooi,et al.  Cost Optimal Integration of Flexible Buildings in Congested Distribution Grids , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[22]  Gongsheng Huang,et al.  Model predictive control of VAV zone thermal systems concerning bi-linearity and gain nonlinearity , 2011 .

[23]  B. Liu,et al.  Progress towards sustainable intensification in China challenged by land-use change , 2018, Nature Sustainability.

[24]  Goran Andersson,et al.  Scheduling and Provision of Secondary Frequency Reserves by Aggregations of Commercial Buildings , 2016, IEEE Transactions on Sustainable Energy.

[25]  Jiang Xu,et al.  Study on Construction Quality Control of Urban Complex Project Based on BIM , 2017 .

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

[27]  Tao Jiang,et al.  Flexible operation of active distribution network using integrated smart buildings with heating, ventilation and air-conditioning systems , 2018, Applied Energy.

[28]  Shuzo Murakami,et al.  Combined simulation of airflow, radiation and moisture transport for heat release from a human body , 2000 .

[29]  Danny H.W. Li,et al.  Electricity use characteristics of purpose-built office buildings in subtropical climates , 2004 .

[30]  Arun Kumar,et al.  Reduced order modeling and parameter identification of a building energy system model through an optimization routine , 2016 .

[31]  Manuel A. Matos,et al.  Flexibility products and markets: Literature review , 2018 .

[32]  Alberto Hernandez Neto,et al.  Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .

[33]  Mesut Avci,et al.  Demand Response-Enabled Model Predictive HVAC Load Control in Buildings using Real-Time Electricity Pricing , 2013 .

[34]  Chuntian Cheng,et al.  Peak operation of hydropower system with parallel technique and progressive optimality algorithm , 2018 .

[35]  Rubiyah Yusof,et al.  A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system , 2016 .

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

[37]  Z. R. Radakovic,et al.  Application of temperature fuzzy controller in an indirect resistance furnace , 2002 .