Real time optimal control of district cooling system with thermal energy storage using neural networks

Abstract Thermal energy storage can be utilized as an effective component in energy systems to maximize cost savings when time-of-use (TOU) pricing or real-time pricing (RTP) is in place. This study proposes a novel approach that can effectively predict performance and determine control strategy of thermal energy storage (i.e., ice storage) in a district cooling system. The proposed approach utilizes Neural Network (NN) based model predictive control (MPC) strategy coupled with a genetic algorithm (GA) optimizer and examines the effectiveness of using a NN model for a district cooling system with ice storage. The NN offers a relatively fast performance estimation of a district cooling system with given external inputs. To simulate the proposed MPC controller, a physics-based model of the district cooling system is first developed and validated to act as a virtual plant for the controller to communicate system states in real times. Next, the NN modeling the plant is developed and trained during a cooling period so that the control strategy is tested under the RTP and TOU pricing. This model is optimized using the GA due to the on/off controls for the district cooling network. Finally, a thermal load prediction algorithm is integrated to test under perfect weather inputs and weather forecasts by considering 1-hour discretization in the MPC scheme. Results indicate that for the month of August, the optimal control scheme can effectively adapt to varying loads and varying prices to effectively reduce operating costs of the district cooling network by approximately 16% and 13% under the TOU pricing and the RTP, respectively.

[1]  Soteris A. Kalogirou,et al.  Artificial neural networks for the prediction of the energy consumption of a passive solar building , 2000 .

[2]  Ming-Wei Chang,et al.  Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 , 2004, IEEE Transactions on Power Systems.

[3]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[4]  Francesco Borrelli,et al.  Stochastic Model Predictive Control for Building HVAC Systems: Complexity and Conservatism , 2015, IEEE Transactions on Control Systems Technology.

[5]  Kody M. Powell,et al.  Heating, cooling, and electrical load forecasting for a large-scale district energy system , 2014 .

[6]  Karl-Erik Årzén,et al.  Modeling and optimization with Optimica and JModelica.org - Languages and tools for solving large-scale dynamic optimization problems , 2010, Comput. Chem. Eng..

[7]  Jiejin Cai,et al.  Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks , 2009 .

[8]  Moncef Krarti,et al.  Guidelines for improved performance of ice storage systems , 2003 .

[9]  Tin-Tai Chow,et al.  Performance evaluation of district cooling plant with ice storage , 2006 .

[10]  Fu Xiao,et al.  Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings , 2017 .

[11]  Jianmei Xiao,et al.  PSO-Based Model Predictive Control for Nonlinear Processes , 2005, ICNC.

[12]  A. Hindmarsh,et al.  CVODE, a stiff/nonstiff ODE solver in C , 1996 .

[13]  Juha Jokisalo,et al.  Cost-optimal thermal energy storage system for a residential building with heat pump heating and demand response control , 2016 .

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

[15]  Pedro J. Mago,et al.  Building hourly thermal load prediction using an indexed ARX model , 2012 .

[16]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[17]  Siaw Kiang Chou,et al.  Achieving better energy-efficient air conditioning - A review of technologies and strategies , 2013 .

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

[19]  J. Snyman,et al.  Penalty function solutions to optimal control problems with general constraints via a dynamic optimisation method , 1992 .

[20]  Fiorella Lauro,et al.  Model predictive control for building active demand response systems , 2015 .

[21]  Lieve Helsen,et al.  Reduction of heat pump induced peak electricity use and required generation capacity through thermal energy storage and demand response , 2017 .

[22]  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 .

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

[24]  Pedro J. Mago,et al.  Field validation study of a time and temperature indexed autoregressive with exogenous (ARX) model for building thermal load prediction , 2017 .

[25]  Xiwang Li,et al.  Building energy consumption on-line forecasting using physics based system identification , 2014 .

[26]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms in Engineering Applications , 1997, Springer Berlin Heidelberg.

[27]  Jianjun Hu,et al.  Model predictive control strategies for buildings with mixed-mode cooling , 2014 .

[28]  Zhu Neng,et al.  An improved office building cooling load prediction model based on multivariable linear regression , 2015 .

[29]  Yan Luo,et al.  Model predictive control based on particle swarm optimization of greenhouse climate for saving energy consumption , 2010, 2010 World Automation Congress.

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

[31]  Kody M. Powell,et al.  Optimal chiller loading in a district cooling system with thermal energy storage , 2013 .

[32]  James E. Braun,et al.  DEVELOPMENT AND APPLICATION OF AN INVERSE BUILDING MODEL FOR DEMAND RESPONSE IN SMALL COMMERCIAL BUILDINGS , 2016 .

[33]  Francesco Borrelli,et al.  Implementation of model predictive control for an HVAC system in a mid-size commercial building , 2014 .

[34]  Tiberiu Catalina,et al.  Multiple regression model for fast prediction of the heating energy demand , 2013 .

[35]  James E. Braun,et al.  An Inverse Gray-Box Model for Transient Building Load Prediction , 2002 .

[36]  Evangelos Rikos,et al.  A Model Predictive Control Approach to Microgrid Operation Optimization , 2014, IEEE Transactions on Control Systems Technology.

[37]  Sih-Li Chen,et al.  Optimization of an ice-storage air conditioning system using dynamic programming method , 2005 .

[38]  Victor M. Zavala,et al.  Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization , 2009, Comput. Chem. Eng..

[39]  Yat Huang Yau,et al.  A review on cool thermal storage technologies and operating strategies , 2012 .

[40]  Wen-Shing Lee,et al.  Optimization for ice-storage air-conditioning system using particle swarm algorithm , 2009 .

[41]  S. M. Hasnain.  Review on sustainable thermal energy storage technologies, Part II: cool thermal storage , 1998 .

[42]  Jacob H. Stang,et al.  Load prediction method for heat and electricity demand in buildings for the purpose of planning for mixed energy distribution systems , 2008 .

[43]  J. Aghaei,et al.  Demand response in smart electricity grids equipped with renewable energy sources: A review , 2013 .

[44]  José Domingo Álvarez,et al.  Optimizing building comfort temperature regulation via model predictive control , 2013 .

[45]  Kelum A. A. Gamage,et al.  Demand side management in smart grid: A review and proposals for future direction , 2014 .