A compositional modeling framework for the optimal energy management of a district network

This paper proposes a compositional modeling framework for the optimal energy management of a district network. The focus is on cooling of buildings, which can possibly share resources to the purpose of reducing maintenance costs and using devices at their maximal efficiency. Components of the network are described in terms of energy fluxes and combined via energy balance equations. Disturbances are accounted for as well through their contribution in terms of energy. Different district configurations can be built, and the dimension and complexity of the resulting model will depend on the number and type of components and on the adopted disturbance description. Control inputs are available to efficiently operate and coordinate the district components, thus enabling energy management strategies to minimize the electrical energy costs or track some consumption profile agreed with the main grid operator.

[1]  Alberto Bemporad,et al.  Control of systems integrating logic, dynamics, and constraints , 1999, Autom..

[2]  Long Bao Le,et al.  Optimal Bidding Strategy for Microgrids Considering Renewable Energy and Building Thermal Dynamics , 2014, IEEE Transactions on Smart Grid.

[3]  Constantinos A. Balaras,et al.  The role of thermal mass on the cooling load of buildings. An overview of computational methods , 1996 .

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

[5]  James E. Braun,et al.  REDUCED-ORDER BUILDING MODELING FOR APPLICATION TO MODEL-BASED PREDICTIVE CONTROL , 2012 .

[6]  Maria Prandini,et al.  Optimally shaping the stationary distribution of a constrained discrete time stochastic linear system via disturbance compensation , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[7]  Luigi Piroddi,et al.  An approximate dynamic programming approach to the energy management of a building cooling system , 2013, 2013 European Control Conference (ECC).

[8]  Thierry S. Nouidui,et al.  Equation-based languages- A new paradigm for building energy modeling, simulation and optimization , 2016 .

[9]  Rita Streblow,et al.  ADAPTIVE THERMAL BUILDING MODELS AND METHODS FOR SCALABLE SIMULATIONS OF MULTIPLE BUILDINGS USING MODELICA , 2016 .

[10]  Francesco Borrelli,et al.  Predictive Control for Energy Efficient Buildings with Thermal Storage: Modeling, Stimulation, and Experiments , 2012, IEEE Control Systems.

[11]  Thierry S. Nouidui,et al.  Modelica Buildings library , 2014 .

[12]  Yan Lu,et al.  Optimal scheduling of chiller plant with thermal energy storage using mixed integer linear programming , 2013, 2013 American Control Conference.

[13]  Giancarlo Ferrari-Trecate,et al.  Modeling and control of co-generation power plants: a hybrid system approach , 2002, IEEE Transactions on Control Systems Technology.

[14]  G. Papaefthymiou,et al.  MCMC for Wind Power Simulation , 2008, IEEE Transactions on Energy Conversion.

[15]  Drury B. Crawley,et al.  EnergyPlus: Energy simulation program , 2000 .

[16]  José Luis Guzmán,et al.  Efficient building energy management using distributed model predictive control , 2014 .

[17]  R. Buizza,et al.  Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models , 2009, IEEE Transactions on Energy Conversion.

[18]  Michael Wetter,et al.  Comparisons Of Building System Modeling Approaches For Control System Design , 2013, Building Simulation Conference Proceedings.

[19]  Yuan-Kang Wu,et al.  A literature review of wind forecasting technology in the world , 2007, 2007 IEEE Lausanne Power Tech.

[20]  M. M. Gouda,et al.  Building thermal model reduction using nonlinear constrained optimization , 2002 .

[21]  Ian Beausoleil-Morrison,et al.  On adaptive occupant-learning window blind and lighting controls , 2014 .

[22]  Ian Beausoleil-Morrison,et al.  Shortest-prediction-horizon model-based predictive control for individual offices , 2014 .

[23]  John Lygeros,et al.  Stochastic Hybrid Systems: A Powerful Framework for Complex, Large Scale Applications , 2010, Eur. J. Control.

[24]  Kody M. Powell,et al.  Dynamic optimization of a campus cooling system with thermal storage , 2013, 2013 European Control Conference (ECC).

[25]  Luigi Piroddi,et al.  Approximate dynamic programming-based control of a building cooling system with thermal storage , 2013, IEEE PES ISGT Europe 2013.

[26]  J. Jonkman,et al.  Definition of a 5-MW Reference Wind Turbine for Offshore System Development , 2009 .

[27]  Dimitrios V. Rovas,et al.  Intelligent BEMS design using detailed thermal simulation models and surrogate-based stochastic optimization , 2014 .

[28]  James E. Braun,et al.  Distributed Model Predictive Control for Building HVAC systems: A Case Study , 2014 .

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

[30]  Victor M. Zavala,et al.  Inference of building occupancy signals using moving horizon estimation and Fourier regularization , 2014 .

[31]  Aysin Ertüzün,et al.  Wind Speed Forecasting Based on Second Order Blind Identification and Autoregressive Model , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[32]  Alberto Leva,et al.  Object-oriented sub-zonal modelling for efficient energy-related building simulation , 2011 .

[33]  Francesco Borrelli,et al.  Model Predictive Control of thermal energy storage in building cooling systems , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[34]  James E. Braun,et al.  A Distributed Approach to Efficient Model Predictive Control of Building HVAC Systems , 2012 .

[35]  Nelson Fumo,et al.  Methodology to estimate building energy consumption using EnergyPlus Benchmark Models , 2010 .

[36]  Maria Prandini,et al.  Energy management of a building cooling system with thermal storage: a randomized solution with feedforward disturbance compensation , 2016, 2016 American Control Conference (ACC).

[37]  Maria Prandini,et al.  A compositional framework for energy management of a smart grid: A scalable stochastic hybrid model for cooling of a district network , 2016, 2016 12th IEEE International Conference on Control and Automation (ICCA).

[38]  Maria Prandini,et al.  Optimal energy management of a building cooling system with thermal storage: A convex formulation , 2015 .

[39]  Philip Haves,et al.  Model predictive control for the operation of building cooling systems , 2010, Proceedings of the 2010 American Control Conference.

[40]  Maria Prandini,et al.  Energy management for building district cooling: a distributed approach to resource sharing , 2016 .

[41]  Iakovos Michailidis,et al.  Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule , 2015 .

[42]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[43]  Maria Prandini,et al.  Distributed Constrained Optimization and Consensus in Uncertain Networks via Proximal Minimization , 2016, IEEE Transactions on Automatic Control.

[44]  Maria Prandini,et al.  A Smart Grid Energy Management Problem for Data-driven Design with Probabilistic Reachability Guarantees , 2017, ARCH@CPSWeek.

[45]  Luigi Piroddi,et al.  Energy Management of a Building Cooling System With Thermal Storage: An Approximate Dynamic Programming Solution , 2017, IEEE Transactions on Automation Science and Engineering.

[46]  Mor Harchol-Balter,et al.  Performance Modeling and Design of Computer Systems: Queueing Theory in Action , 2013 .

[47]  M. Kintner-Meyer,et al.  Optimal control of an HVAC system using cold storage and building thermal capacitance , 1995 .

[48]  Nursyarizal Mohd Nor,et al.  A review on optimized control systems for building energy and comfort management of smart sustainable buildings , 2014 .

[49]  Hongjie Wu,et al.  State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model , 2013 .

[50]  Victor M. Zavala,et al.  On-line economic optimization of energy systems using weather forecast information. , 2009 .

[51]  Maria Prandini,et al.  A chance-constrained approach to the quantized control of a heat ventilation and air conditioning system with prioritized constraints , 2016 .