Control of Electricity Loads in Future Electric Energy Systems

Traditionally, electricity power production has been adjusted to balance the time-varying electricity load. However, a transition to a system based on an increasing, fluctuating, and nondispatchable renewable power implies that the control of electricity loads becomes crucial. This article describes methods for control of electricity loads in future energy systems and methods for handling stochasticity of, for example, wind and solar power production. Hierarchies of aggregators and predictive controllers, for electricity loads in flexible demand-side response, are implemented to achieve a balance with the nondispatchable energy production. Two distinct approaches are described: direct control of the load consumption of individual DERs and indirect control by broadcasting an electricity price. The advantages and challenges of these two approaches are discussed, and examples of the suggested techniques are provided. Keywords: electricity loads; renewable energy; model predictive control; direct control; indirect control; aggregator; distributed energy resources; forecasting

[1]  H. Madsen,et al.  Forecasting Electricity Spot Prices Accounting for Wind Power Predictions , 2013, IEEE Transactions on Sustainable Energy.

[2]  Pierre Pinson,et al.  Non‐parametric probabilistic forecasts of wind power: required properties and evaluation , 2007 .

[3]  Henrik Madsen,et al.  Energy Comes Together in Denmark: The Key to a Future Fossil-Free Danish Power System , 2013, IEEE Power and Energy Magazine.

[4]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[5]  Niels Kjølstad Poulsen,et al.  Decentralized large-scale power balancing , 2013, IEEE PES ISGT Europe 2013.

[6]  Jakob Stoustrup,et al.  Congestion management in a smart grid via shadow prices , 2012 .

[7]  Niels Kjølstad Poulsen,et al.  Economic Model Predictive Control for building climate control in a Smart Grid , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[8]  James B. Rawlings,et al.  Finite horizon MPC for systems in innovation form , 2011, IEEE Conference on Decision and Control and European Control Conference.

[9]  John Bagterp Jørgensen,et al.  A riccati based homogeneous and self-dual interior-point method for linear economic model predictive control , 2013, 2013 IEEE International Conference on Control Applications (CCA).

[10]  Anders Rantzer,et al.  Dynamic dual decomposition for distributed control , 2009, 2009 American Control Conference.

[11]  Nicolai Fog Gade-Nielsen,et al.  Numerical Methods for Solution of the Extended Linear Quadratic Control Problem , 2012 .

[12]  Rıdvan Berber.  Methods of model based process control , 1995 .

[13]  Munther A. Dahleh,et al.  Volatility of Power Grids Under Real-Time Pricing , 2011, IEEE Transactions on Power Systems.

[14]  Niels Kjølstad Poulsen,et al.  Thermal storage power balancing with model predictive control , 2013, 2013 European Control Conference (ECC).

[15]  H. Madsen,et al.  From probabilistic forecasts to statistical scenarios of short-term wind power production , 2009 .

[16]  Henrik Madsen,et al.  Generalized predictive control for non-stationary systems , 1993, Autom..

[17]  Henrik Madsen,et al.  Identifying suitable models for the heat dynamics of buildings , 2011 .

[18]  Stephen P. Boyd,et al.  ROBUST LINEAR PROGRAMMING AND OPTIMAL CONTROL , 2002 .

[19]  Jean-Louis Lilien,et al.  Dynamic prediction of energy delivery capacity of power networks: Unlocking the value of real-time measurements , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[20]  Stephen J. Wright,et al.  Fast, large-scale model predictive control by partial enumeration , 2007, Autom..

[21]  Arkadi Nemirovski,et al.  Lectures on modern convex optimization - analysis, algorithms, and engineering applications , 2001, MPS-SIAM series on optimization.

[22]  H. Madsen,et al.  From wind ensembles to probabilistic information about future wind power production -- results from an actual application , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[23]  K. P. Wacks,et al.  Utility load management using home automation , 1991 .

[24]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[25]  James W. Taylor,et al.  Triple seasonal methods for short-term electricity demand forecasting , 2010, Eur. J. Oper. Res..

[26]  Henrik Madsen,et al.  Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts , 2006 .

[27]  Stephen P. Boyd,et al.  Applications of second-order cone programming , 1998 .

[28]  James W. Taylor.  An evaluation of methods for very short-term load forecasting using minute-by-minute British data , 2008 .

[29]  Riccardo Scattolini,et al.  Architectures for distributed and hierarchical Model Predictive Control - A review , 2009 .

[30]  Palle Andersen,et al.  Information modeling for direct control of distributed energy resources , 2013, 2013 American Control Conference.

[31]  Lazaros Gkatzikis,et al.  The Role of Aggregators in Smart Grid Demand Response Markets , 2013, IEEE Journal on Selected Areas in Communications.

[32]  Henrik Madsen,et al.  A review on the young history of the wind power short-term prediction , 2008 .

[33]  G. Dantzig,et al.  THE DECOMPOSITION ALGORITHM FOR LINEAR PROGRAMS , 1961 .

[34]  Stephen J. Wright,et al.  Application of Interior-Point Methods to Model Predictive Control , 1998 .

[35]  Henrik Madsen,et al.  A bilevel model for electricity retailers' participation in a demand response market environment , 2013 .

[36]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[37]  David W. Clarke,et al.  Generalized predictive control - Part I. The basic algorithm , 1987, Autom..

[38]  H. Madsen,et al.  Controlling Electricity Consumption by Forecasting its Response to Varying Prices , 2013, IEEE Transactions on Power Systems.

[39]  Dimitri P. Bertsekas,et al.  On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators , 1992, Math. Program..

[40]  P. Pinson,et al.  A Transmission-Cost-Based Model to Estimate the Amount of Market-Integrable Wind Resources , 2012, IEEE Transactions on Power Systems.

[41]  Stephen P. Boyd,et al.  Fast Model Predictive Control Using Online Optimization , 2010, IEEE Transactions on Control Systems Technology.

[42]  Henrik Madsen,et al.  Online short-term solar power forecasting , 2009 .