Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids

With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for integrated control of building loads, batteries and other distributed energy resources. The available software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice, and are often incapable of operating independently of the grid during blackouts. In this paper, we present the “Solar+ Optimizer” (SPO), a control platform that provides demand flexibility, resiliency and reduced utility bills, built using open-source software. SPO employs Model Predictive Control (MPC) to produce real time optimal control strategies for the building loads and the distributed energy resources on site. SPO is designed to be vendor-agnostic, protocol-independent and resilient to loss of wide-area network connectivity. The software was evaluated in a real convenience store in northern California with on-site solar generation, battery storage and control of HVAC and commercial refrigeration loads. Preliminary tests showed price responsiveness of the building and cost savings of more than 10% in energy costs alone.

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

[2]  Geza Joos,et al.  The Need for Standardization: The Benefits to the Core Functions of the Microgrid Control System , 2017, IEEE Power and Energy Magazine.

[3]  Lazar Berbakov,et al.  Smart Energy Manager for Energy Efficient Buildings , 2019, IEEE EUROCON 2019 -18th International Conference on Smart Technologies.

[4]  Josh Wall,et al.  Trial results from a model predictive control and optimisation system for commercial building HVAC , 2014 .

[5]  THE COSTS AND BENEFITS OF REAL-TIME PRICING , 2017 .

[6]  Shay Bahramirad,et al.  Building Resilient Integrated Grids: One neighborhood at a time. , 2015, IEEE Electrification Magazine.

[7]  Hossein Lotfi,et al.  State of the Art in Research on Microgrids: A Review , 2015, IEEE Access.

[8]  Tianshu Wei,et al.  Deep reinforcement learning for building HVAC control , 2017, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC).

[9]  Mariagrazia Dotoli,et al.  IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings , 2020, Sensors.

[10]  Jereme Haack,et al.  VOLTTRON: An Agent Execution Platform for the Electric Power System , 2012 .

[11]  Zicheng Cai,et al.  Gnu-RL: A Precocial Reinforcement Learning Solution for Building HVAC Control Using a Differentiable MPC Policy , 2019, BuildSys@SenSys.

[12]  Ionel Vechiu,et al.  CVaR-based energy management scheme for optimal resilience and operational cost in commercial building microgrids , 2018, International Journal of Electrical Power & Energy Systems.

[13]  Gregor P. Henze,et al.  A model predictive control optimization environment for real-time commercial building application , 2013 .

[14]  Michael Wetter,et al.  Co-simulation of building energy and control systems with the Building Controls Virtual Test Bed , 2011 .

[15]  Gregor P. Henze,et al.  Evaluation of optimal control for active and passive building thermal storage , 2004 .

[16]  Enrico Macii,et al.  Building Energy Modelling and Monitoring by Integration of IoT Devices and Building Information Models , 2017, 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC).

[17]  Ming Jin,et al.  Advanced Building Control via Deep Reinforcement Learning , 2019, Energy Procedia.

[18]  Leo Liberti,et al.  Optimal HVAC Control as Demand Response with On-site Energy Storage and Generation System , 2015 .

[19]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[20]  Johannes F. Broenink,et al.  Modelica: An International Effort to Design the Next Generation Modelling Language , 1997 .

[21]  Edris Pouresmaeil,et al.  A two stage hierarchical control approach for the optimal energy management in commercial building microgrids based on local wind power and PEVs , 2018, Sustainable Cities and Society.

[22]  Robert Sabourin,et al.  Simplified model-based optimal control of VAV air-conditioning system , 2005 .

[23]  Bing Dong,et al.  Smart Building Energy Management Platform through VOLTTRON , 2017 .

[24]  Lingfeng Wang,et al.  Intelligent multi-agent control for integrated building and micro-grid systems , 2011, ISGT 2011.

[25]  Nancy Axford.  Wisdom of Age , 2001 .

[26]  Athanasios V. Vasilakos,et al.  Enhancing smart grid with microgrids: Challenges and opportunities , 2017 .

[27]  Shengwei Wang,et al.  Model-based optimal control of VAV air-conditioning system using genetic algorithm , 2000 .

[28]  James E. Braun,et al.  Development, implementation and performance of a model predictive controller for packaged air conditioners in small and medium-sized commercial building applications , 2018, Energy and Buildings.

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

[30]  Thomas Stuart,et al.  Grid Integration of Building Systems and 1 MW Photovoltaic Array using VOLTTRON , 2017, 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC).

[31]  Siliang Lu,et al.  A DEEP REINFORCEMENT LEARNING APPROACH TO USINGWHOLE BUILDING ENERGYMODEL FOR HVAC OPTIMAL CONTROL , 2018 .

[32]  Moritz Diehl,et al.  CasADi -- A symbolic package for automatic differentiation and optimal control , 2012 .

[33]  Sergio Bruno,et al.  A Demand Response Implementation in Tertiary Buildings Through Model Predictive Control , 2019, IEEE Transactions on Industry Applications.

[34]  Sila Kiliccote,et al.  Advanced Controls and Communications for Demand Response and Energy Efficiency in Commercial Buildings , 2006 .

[35]  Joe Huang,et al.  DEVELOPMENT OF TYPICAL YEAR WEATHER DATA FOR CHINESE LOCATIONS , 2002 .

[36]  M. Pipattanasomporn,et al.  BEMOSS: An agent platform to facilitate grid-interactive building operation with IoT devices , 2015, 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[37]  Sina Ober-Blöbaum,et al.  Improving optimal control of grid-connected lithium-ion batteries through more accurate battery and degradation modelling , 2017, ArXiv.

[38]  Jesús Lizana,et al.  Advances in thermal energy storage materials and their applications towards zero energy buildings: A critical review , 2017 .

[39]  Robert Sabourin,et al.  Optimization of HVAC Control System Strategy Using Two-Objective Genetic Algorithm , 2005 .

[40]  Balaji Rajagopalan,et al.  Model-predictive control of mixed-mode buildings with rule extraction , 2011 .

[41]  S. Katipamula,et al.  Coordination and Control of Flexible Building Loads for Renewable Integration Demonstrations using VOLTTRON TM October , 2017 .

[42]  J. Michalsky,et al.  Modeling daylight availability and irradiance components from direct and global irradiance , 1990 .

[43]  Jiachun Guo,et al.  Wisdom about age [aging electricity infrastructure] , 2006, IEEE Power and Energy Magazine.

[44]  Gregor P. Henze.  Model predictive control for buildings: a quantum leap? , 2013 .

[45]  Ralph Evins,et al.  Using simple predictive models to improve control of complex building systems , 2017, BuildSys@SenSys.

[46]  Hajir Pourbabak,et al.  The application of distributed control algorithms using VOLTTRON-based software platform , 2017, 2017 8th International Renewable Energy Congress (IREC).

[47]  Johan Åkesson,et al.  JModelica---an Open Source Platform for Optimization of Modelica Models , 2009 .

[48]  David E. Culler,et al.  WAVE: A Decentralized Authorization Framework with Transitive Delegation , 2019, USENIX Security Symposium.

[49]  Mani Srivastava,et al.  Brick: Towards a Unified Metadata Schema For Buildings , 2016, BuildSys@SenSys.

[50]  Duncan S. Callaway,et al.  Experimental Demonstration of Frequency Regulation by Commercial Buildings—Part I: Modeling and Hierarchical Control Design , 2016, IEEE Transactions on Smart Grid.

[51]  Malabika Basu,et al.  Microgrid: Architecture, policy and future trends , 2016 .

[52]  Fariborz Haghighat,et al.  Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .

[53]  David H. Blum,et al.  MPCPy: An Open-Source Software Platform for Model Predictive Control in Buildings , 2019 .

[54]  Stefano Squartini,et al.  rEMpy: a comprehensive software framework for residential energy management , 2018, Energy and Buildings.

[55]  Simeng Liu,et al.  Experimental Analysis of Model-Based Predictive Optimal Control for Active and Passive Building Thermal Storage Inventory , 2005 .

[56]  Yacine Rezgui,et al.  Upscaling energy control from building to districts: Current limitations and future perspectives , 2017 .

[57]  J. F. Bonnans,et al.  Continuous optimal control approaches to microgrid energy management , 2018 .

[58]  Nuria Forcada,et al.  Implementation of predictive control in a commercial building energy management system using neural networks , 2017 .

[59]  Sadrul Ula,et al.  A comparison between two MPC algorithms for demand charge reduction in a real-world microgrid system , 2016, 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC).

[60]  Biao Huang,et al.  A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems , 2017 .

[61]  Yacine Rezgui,et al.  A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control , 2018 .

[62]  Roshan L. Kini,et al.  Transactive Mitigation Of Variability In The Output Of 1 MW Photovoltaic Array Using VolttronTM , 2018, 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC).

[63]  Ki Uhn Ahn,et al.  Application of deep Q-networks for model-free optimal control balancing between different HVAC systems , 2020, Science and Technology for the Built Environment.

[64]  Khee Poh Lam,et al.  Practical implementation and evaluation of deep reinforcement learning control for a radiant heating system , 2018, BuildSys@SenSys.

[65]  Emrah Biyik,et al.  A predictive control strategy for optimal management of peak load, thermal comfort, energy storage and renewables in multi-zone buildings , 2019, Journal of Building Engineering.

[66]  Duncan S. Callaway,et al.  Experimental Demonstration of Frequency Regulation by Commercial Buildings—Part II: Results and Performance Evaluation , 2018, IEEE Transactions on Smart Grid.

[67]  Y. Parag,et al.  Microgrids: A review of technologies, key drivers, and outstanding issues , 2018, Renewable and Sustainable Energy Reviews.

[68]  Mario Vasak,et al.  Price-Optimal Energy Flow Control of a Building Microgrid Connected to a Smart Grid , 2018, 2018 26th Mediterranean Conference on Control and Automation (MED).

[69]  Elisa Guelpa,et al.  IoT Software Infrastructure for Energy Management and Simulation in Smart Cities , 2017, IEEE Transactions on Industrial Informatics.

[70]  James E. Braun,et al.  Development and experimental demonstration of a plug-and-play multiple RTU coordination control algorithm for small/medium commercial buildings , 2015 .

[71]  Ricardo P. Aguilera,et al.  Minimization of building energy cost by optimally managing PV and battery energy storage systems , 2017, 2017 20th International Conference on Electrical Machines and Systems (ICEMS).