Model Predictive Control of Distributed Air-Conditioning Loads to Compensate Fluctuations in Solar Power

Flexible loads such as residential air-conditioners (ACs) can be directly controlled to provide demand-side regulation and balance services in electricity grids. Large aggregations of ACs offer a resource akin to that of a distributed energy storage system, which may be used to compensate fluctuations in the power output of local renewable energy generation. This paper formulates distributed and centralized model predictive control (MPC) strategies to balance fluctuations in solar power generation by directly controlling the aggregate demand of clusters of distributed residential ACs. The proposed receding-horizon control strategies rely on a new second-order linear time-varying model for the aggregate demand response of a population of heterogeneous ACs to changes in thermostat setpoints under varying ambient temperature. The performance of the proposed MPC strategies is analyzed in a numerical simulation study implementing AC demand tracking of 1-min fluctuations in actual photovoltaic capacity based on persistence and sky imager short-term solar forecasts. The results show that distributed and centralized MPC strategies achieve comparable performance, with better performance of persistence forecasts in a shorter prediction horizon, and better performance with sky imager forecasts on a longer prediction horizon.

[1]  Scott J. Moura,et al.  Generation Following with Thermostatically Controlled Loads via Alternating Direction Method of Multipliers Sharing Algorithm , 2017 .

[2]  Scott Backhaus,et al.  Modeling and control of thermostatically controlled loads , 2011 .

[3]  Julio H. Braslavsky,et al.  Bayesian parameter estimation for direct load control of populations of air conditioners , 2014 .

[4]  John Lygeros,et al.  Modeling options for demand side participation of thermostatically controlled loads , 2013, 2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid.

[5]  Daniel Rowe,et al.  Short-term irradiance forecasting using skycams: Motivation and development , 2014 .

[6]  Ernesto Kofman,et al.  Load management: Model-based control of aggregate power for populations of thermostatically controlled loads , 2012 .

[7]  Javad Mohammadpour,et al.  Control of linear parameter varying systems with applications , 2012 .

[8]  John Ward,et al.  Real and reactive power control of distributed PV inverters for overvoltage prevention and increased renewable generation hosting capacity , 2015 .

[9]  Alberto Bemporad,et al.  Assessment of non-centralised model predictive control techniques for electrical power networks , 2012, Int. J. Control.

[10]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[11]  Graham C. Goodwin,et al.  Constrained Control and Estimation , 2005 .

[12]  Johanna L. Mathieu,et al.  State Estimation and Control of Electric Loads to Manage Real-Time Energy Imbalance , 2013 .

[13]  R. Malhamé,et al.  Electric load model synthesis by diffusion approximation of a high-order hybrid-state stochastic system , 1985 .

[14]  Tyrone L. Vincent,et al.  Aggregate Flexibility of Thermostatically Controlled Loads , 2015, IEEE Transactions on Power Systems.

[15]  Hosam K. Fathy,et al.  Modeling and Control of Aggregate Air Conditioning Loads for Robust Renewable Power Management , 2013, IEEE Transactions on Control Systems Technology.

[16]  Wei Zhang,et al.  Aggregated Modeling and Control of Air Conditioning Loads for Demand Response , 2013, IEEE Transactions on Power Systems.

[17]  Ian A. Hiskens,et al.  Achieving Controllability of Electric Loads , 2011, Proceedings of the IEEE.

[18]  Cristian Perfumo,et al.  Mapping the Effect of Ambient Temperature on the Power Demand of Populations of Air Conditioners , 2018, IEEE Transactions on Smart Grid.

[19]  Yang Shi,et al.  Model Predictive Control of Aggregated Heterogeneous Second-Order Thermostatically Controlled Loads for Ancillary Services , 2016, IEEE Transactions on Power Systems.

[20]  M. Raza,et al.  On recent advances in PV output power forecast , 2016 .

[21]  Rafael Wisniewski,et al.  Control for large scale demand response of thermostatic loads* , 2013, 2013 American Control Conference.

[22]  Duncan S. Callaway.  Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy , 2009 .

[23]  R. Malhamé,et al.  On the statistical properties of a cyclic diffusion process arising in the modeling of thermostat-controlled electric power system loads , 1988 .

[24]  Ian A. Hiskens,et al.  Model-Predictive Cascade Mitigation in Electric Power Systems With Storage and Renewables—Part I: Theory and Implementation , 2015, IEEE Transactions on Power Systems.

[25]  Hazlie Mokhlis,et al.  Mitigating methods of power fluctuation of photovoltaic (PV) sources – A review , 2016 .

[26]  R. Urraca,et al.  Review of photovoltaic power forecasting , 2016 .

[27]  Marcello Farina,et al.  Model predictive control of voltage profiles in MV networks with distributed generation , 2013, ArXiv.

[28]  Ana Busic,et al.  Ancillary Service to the Grid Using Intelligent Deferrable Loads , 2014, IEEE Transactions on Automatic Control.