A Novel Scheduling Strategy for Controllable Loads With Power-Efficiency Characteristics

This paper proposes a novel scheduling strategy to dispatch controllable loads with nonlinear power-efficiency characteristics. Due to these characteristics, the controllable loads, such as desalination devices, suffer from efficiency loss when participating in day-ahead and real-time power regulations. The existing two-stage scheduling algorithms ignore the real-time efficiency loss at the day-ahead stage. In order to consider the real-time efficiency loss, a complex single-stage scheduling model with numerous optimization variables is required. This will greatly increase the computation burden to the optimization algorithm. To deal with this issue, a novel efficiency threshold-based control method is proposed in this paper. The proposed method only has three efficiency thresholds, thus the number of optimization variables is significantly reduced. Based on this method, a novel energy scheduling framework is designed for the controllable loads with power-efficiency characteristics. Numerical simulations indicate that the proposed approach can utilize the power regulation flexibility of controllable loads in both day-ahead and real-time stages to reduce the operation cost.

[1]  Walid Saad,et al.  Managing Price Uncertainty in Prosumer-Centric Energy Trading: A Prospect-Theoretic Stackelberg Game Approach , 2017, IEEE Transactions on Smart Grid.

[2]  Zhu Han,et al.  Autonomous Demand Response Using Stochastic Differential Games , 2015, IEEE Transactions on Smart Grid.

[3]  Rodrigo Palma-Behnke,et al.  A Microgrid Energy Management System Based on the Rolling Horizon Strategy , 2013, IEEE Transactions on Smart Grid.

[4]  Hamed Mohsenian-Rad,et al.  Optimal Demand Bidding for Time-Shiftable Loads , 2015, IEEE Transactions on Power Systems.

[5]  J. A. Carta,et al.  Wind-driven SWRO desalination prototype with and without batteries: A performance simulation using machine learning models , 2017, Desalination.

[6]  Q. Henry Wu,et al.  Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior , 2009, IEEE Transactions on Evolutionary Computation.

[7]  Tao Wang,et al.  A Novel TRUST-TECH Guided Branch-and-Bound Method for Nonlinear Integer Programming , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[8]  Gevork B. Gharehpetian,et al.  A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives , 2018 .

[9]  Hamed Mohsenian-Rad,et al.  Price-Maker Economic Bidding in Two-Settlement Pool-Based Markets: The Case of Time-Shiftable Loads , 2016, IEEE Transactions on Power Systems.

[10]  David Infield,et al.  A small-scale seawater reverse-osmosis system with excellent energy efficiency over a wide operating range , 2003 .

[11]  Francisco Javier García Latorre,et al.  Optimization of RO desalination systems powered by renewable energies. Part I: Wind energy , 2004 .

[12]  Qiang Yang,et al.  Fuzzy decision based energy dispatch in offshore industrial microgrid with desalination process and multi-type DGs , 2018 .

[13]  Panagiotis D. Christofides,et al.  Supervisory Predictive Control for Long-Term Scheduling of an Integrated Wind/Solar Energy Generation and Water Desalination System , 2012, IEEE Transactions on Control Systems Technology.

[14]  Ercan E. Kuruoglu,et al.  One-day ahead wind speed/power prediction based on polynomial autoregressive model , 2017 .

[15]  Alberto M. Pernia,et al.  Energy-Recovery Optimization of an Experimental CDI Desalination System , 2016, IEEE Transactions on Industrial Electronics.

[16]  Shahin Sirouspour,et al.  A Chance-Constraints-Based Control Strategy for Microgrids With Energy Storage and Integrated Electric Vehicles , 2018, IEEE Transactions on Smart Grid.

[17]  Meng Zhang,et al.  Energy Management for Renewable Microgrid in Reducing Diesel Generators Usage With Multiple Types of Battery , 2018, IEEE Transactions on Industrial Electronics.

[18]  Claudio A. Cañizares,et al.  Fuzzy Prediction Interval Models for Forecasting Renewable Resources and Loads in Microgrids , 2015, IEEE Transactions on Smart Grid.

[19]  Kankar Bhattacharya,et al.  Smart Distribution System Operations With Price-Responsive and Controllable Loads , 2015, IEEE Transactions on Smart Grid.

[20]  Ken Rainwater,et al.  Energy analysis and efficiency assessment of reverse osmosis desalination process , 2011 .

[21]  Danny H. K. Tsang,et al.  A Two-Stage Approach for Network Constrained Unit Commitment Problem With Demand Response , 2018, IEEE Transactions on Smart Grid.

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