Energy-efficient Control of a Smart Grid with Sustainable Homes based on Distributing Risk

The goal of this thesis is to develop a distributed control system for a smart grid with sustainable homes. A central challenge is how to enhance energy efficiency in the presence of uncertainty. A major source of uncertainty in a smart grid is intermittent energy production by renewable energy sources. In the face of global climate change, it is crucial to reduce dependence on fossil fuels and shift to renewable energy sources, such as wind and solar. However, a large-scale introduction of wind and solar generation to an electrical grid poses a significant risk of blackouts since the energy supplied by the renewables is unpredictable and intermittent. The uncertain behavior of renewable energy sources increases the risk of blackouts. Therefore, an important challenge is to develop an intelligent control mechanism for the electrical grid that is both reliable and efficient. Uncertain weather conditions and human behavior pose challenges for a smart home. For example, autonomous room temperature control of a residential building may occasionally make the room environment uncomfortable for residents. Autonomous controllers must be able to take residents' preferences as an input, and to control the indoor environment in an energy-efficient manner while limiting the risk of failure to meet the residents' requirements in the presence of uncertainties. In order to overcome these challenges, we propose a distributed robust control method for a smart grid that includes smart homes as its building components. The proposed method consists of three algorithms: 1) market-based contingent energy dispatcher for an electrical grid, 2) a risk-sensitive plan executive for temperature control of a residential building, and 3) a chance-constrained model-predictive controller with a probabilistic guarantee of constraint satisfaction, which can control continuously operating systems such as an electrical grid and a building. We build the three algorithms upon the chance-constrained programming framework: minimization of a given cost function with chance constraints, which bound the probability of failure to satisfy given state constraints. Although these technologies provide promising capabilities, they cannot contribute to sustainability unless they are accepted by the society. In this thesis we specify policy challenges for a smart grid and a smart home, and discuss policy options that gives economical and regulatory incentives for the society to introduce these technologies on a large scale. Thesis Supervisor: Brian C. Williams, Ph.D. Title: Professor of Aeronautics and Astronautics

[1]  Patrick Doherty,et al.  TALplanner: A temporal logic based forward chaining planner , 2001, Annals of Mathematics and Artificial Intelligence.

[2]  Brian Charles Williams,et al.  Robust Execution of Temporally Flexible Plans for Bipedal Walking Devices , 2006, ICAPS.

[3]  Andrey V. Savkin,et al.  Model predictive control based efficient operation of battery energy storage system for primary frequency control , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[4]  Fahiem Bacchus,et al.  Planning for temporally extended goals , 1996, Annals of Mathematics and Artificial Intelligence.

[5]  Michael Dixon,et al.  Responsive office environments , 1993, CACM.

[6]  Manfred Morari,et al.  A tractable approximation of chance constrained stochastic MPC based on affine disturbance feedback , 2008, 2008 47th IEEE Conference on Decision and Control.

[7]  D. Sontag.  1 Introduction to Dual Decomposition for Inference , 2010 .

[8]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[9]  Arnold Neumaier,et al.  Introduction to Numerical Analysis , 2001 .

[10]  Stochastic Model Predictive Controller for the Integration of Building Use and Temperature Regulation , 2011, AAAI.

[11]  A. Charnes,et al.  Chance-Constrained Programming , 1959 .

[12]  Lars James Christopher Blackmore,et al.  Robust Execution for Stochastic Hybrid Systems , 2008 .

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

[14]  C. Scherer,et al.  A game theoretic approach to nonlinear robust receding horizon control of constrained systems , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[15]  L. Blackmore,et al.  Convex Chance Constrained Predictive Control without Sampling , 2009 .

[16]  Thomas A. Henzinger,et al.  The benefits of relaxing punctuality , 1991, JACM.

[17]  Jan Tuinstra,et al.  Price Dynamics in Equilibrium Models, The Search for Equilibrium and the Emergence of Endogenous Fluctuations. Advances in Computational Economics. no 16. , 2000 .

[18]  Joseph A. Paradiso,et al.  Personalized HVAC control system , 2010, 2010 Internet of Things (IOT).

[19]  Chi-Keung Woo,et al.  What went wrong in California's electricity market? , 2001 .

[20]  Markus Gwerder,et al.  PREDICTIVE CONTROL FOR INTEGRATED ROOM AUTOMATION , 2005 .

[21]  Manfred Morari,et al.  Robust constrained model predictive control using linear matrix inequalities , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[22]  R.H. Lasseter,et al.  Microgrid: a conceptual solution , 2004, 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551).

[23]  Mario Ragwitz,et al.  A historical review of promotion strategies for electricity from renewable energy sources in EU countries , 2011 .

[24]  D. Mayne,et al.  Min-max feedback model predictive control for constrained linear systems , 1998, IEEE Trans. Autom. Control..

[25]  Behçet Açikmese,et al.  A nonlinear model predictive control algorithm with proven robustness and resolvability , 2006, 2006 American Control Conference.

[26]  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.

[27]  Dagfinn Gangsaas,et al.  Wind models for flight simulator certification of landing and approach guidance and control systems , 1974 .

[28]  P. Pandurang Nayak,et al.  Remote Agent: To Boldly Go Where No AI System Has Gone Before , 1998, Artif. Intell..

[29]  David S. Bayard,et al.  A robust model predictive control algorithm for incrementally conic uncertain/nonlinear systems , 2011 .

[30]  Luigi Chisci,et al.  Systems with persistent disturbances: predictive control with restricted constraints , 2001, Autom..

[31]  Manfred Morari,et al.  Reducing peak electricity demand in building climate control using real-time pricing and model predictive control , 2010, 49th IEEE Conference on Decision and Control (CDC).

[32]  Arkadi Nemirovski,et al.  Robust solutions of uncertain linear programs , 1999, Oper. Res. Lett..

[33]  J. Zico Kolter,et al.  A Large-Scale Study on Predicting and Contextualizing Building Energy Usage , 2011, AAAI.

[34]  Y. Gagnon,et al.  An analysis of feed-in tariff remuneration models: Implications for renewable energy investment , 2010 .

[35]  Paul Komor,et al.  Renewable Energy Policy , 2004 .

[36]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[37]  Pu Li,et al.  A probabilistically constrained model predictive controller , 2002, Autom..

[38]  R. Belmans,et al.  A literature survey of Optimal Power Flow problems in the electricity market context , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[39]  Rina Dechter,et al.  Temporal Constraint Networks , 1989, Artif. Intell..

[40]  M Morari,et al.  Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions , 2010, Proceedings of the 2010 American Control Conference.

[41]  A. Richards,et al.  Robust Receding Horizon Control using Generalized Constraint Tightening , 2007, 2007 American Control Conference.

[42]  David Toke,et al.  Renewable financial support systems and cost-effectiveness , 2007 .

[43]  Naum Zuselevich Shor,et al.  Minimization Methods for Non-Differentiable Functions , 1985, Springer Series in Computational Mathematics.

[44]  Fritz Wysotzki,et al.  Risk-Sensitive Reinforcement Learning Applied to Control under Constraints , 2005, J. Artif. Intell. Res..

[45]  Francisco D. Galiana,et al.  A survey of the optimal power flow literature , 1991 .

[46]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[47]  Goran Andersson,et al.  Dynamics and Control of Electric Power Systems , 2007 .

[48]  D.J. Leith,et al.  Gaussian process prior models for electrical load forecasting , 2004, 2004 International Conference on Probabilistic Methods Applied to Power Systems.

[49]  Rob Sherwood,et al.  Casper: Space Exploration through Continuous Planning , 2001, IEEE Intell. Syst..

[50]  Masahiro Ono,et al.  A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control , 2010, IEEE Transactions on Robotics.

[51]  Masahiro Ono,et al.  Market-based risk allocation for multi-agent systems , 2010, AAMAS.

[52]  T.C. Green,et al.  Fuel consumption minimization of a microgrid , 2005, IEEE Transactions on Industry Applications.

[53]  Masahiro Ono,et al.  Decentralized chance-constrained finite-horizon optimal control for multi-agent systems , 2010, 49th IEEE Conference on Decision and Control (CDC).

[54]  Goran Andersson,et al.  Framework for Multiple Time-Scale Cascaded MPC Application in Power Systems , 2011 .

[55]  H. L. Happ,et al.  OPTIMAL POWER DISPATCH -A COMPREHENSIVE SURVEY , 1977 .

[56]  Brian C. Williams,et al.  Kongming: a generative planner for hybrid systems with temporally extended goals , 2010 .

[57]  Masahiro Ono,et al.  Iterative Risk Allocation: A new approach to robust Model Predictive Control with a joint chance constraint , 2008, 2008 47th IEEE Conference on Decision and Control.

[58]  András Prékopa.  The use of discrete moment bounds in probabilisticconstrained stochastic programming models , 1999, Ann. Oper. Res..

[59]  Brian C. Williams,et al.  Coordinating Agile Systems through the Model-based Execution of Temporal Plans , 2005, AAAI.

[60]  K. Pandya,et al.  A SURVEY OF OPTIMAL POWER FLOW , 2008 .

[61]  Masahiro Ono,et al.  An Efficient Motion Planning Algorithm for Stochastic Dynamic Systems with Constraints on Probability of Failure , 2008, AAAI.

[62]  Christian Feisst,et al.  The Role of Electricity Infrastructure in Reducing Greenhouse Gas Emissions , 2008 .

[63]  Hui X. Li,et al.  A probabilistic approach to optimal robust path planning with obstacles , 2006, 2006 American Control Conference.

[64]  Joseph H. Eto,et al.  Cost of Power Interruptions to Electricity Consumers in the United States (U.S.) , 2006 .

[65]  Michael Nikolaou,et al.  Chance‐constrained model predictive control , 1999 .

[66]  Hui Li,et al.  Hybrid Planning with Temporally Extended Goals for Sustainable Ocean Observing , 2011, AAAI.

[67]  E. Kerrigan.  Robust Constraint Satisfaction: Invariant Sets and Predictive Control , 2000 .