Application of nonlinear model predictive control based on swarm optimization in power systems optimal operation with wind resources

Abstract With the increased share of power generation based on wind energy, the complexity of the unit commitment (UC) and economic dispatch (ED) problems increases due to the stochastic nature of wind power. Therefore, an accurate and fast optimization method is needed when the generation process involves large quantities of wind sources to effectively manage generation mix and load requirements. In this paper, model predictive control (MPC) is used to solve power system UC/ED problems with the presence of wind energies. Because the UC/ED problem is nonlinear, non-convex and mixed integer problem, the MPC must be used as nonlinear. To produce a nonlinear MPC (NMPC), MPC must be integrated with a fast optimization methodology. This paper presents a generic mathematical formula for a NMPC, integrated with swarm optimization technique to describe the nonlinear behavior in the mathematical formulation. This new formulation will be called swarm model predictive control (SMPC) optimization. The control model will be able to address the effect of the system disturbances and fluctuations using a controlled autoregressive integrated moving average (CARIMA). A general form of future predictions can be expressed as a function of input and output past data, and a future control sequence, and the degree of freedom in the SMPC problem. Also, the prediction part improves the swarm technique, because it identifies the size of search space in a better way. In this paper, UC schedule is designed using the swarm technique offline, while ED is solved using the proposed SMPC optimization method on real-time basis and fed into the automatic generation control system. There is no load deficit in real time ED results in less spinning reserve requirements compared with swarm optimization and dynamic program techniques used discretized load. The system under study is a standard IEEE 30 bus modelled in MATLAB environment, with all data from the city of Florida, USA.

[1]  Aftab Ahmad,et al.  UNIT COMMITMENT USING HYBRID APPROACHES , 2010 .

[2]  Marija D. Ilic,et al.  Model predictive economic/environmental dispatch of power systems with intermittent resources , 2009, 2009 IEEE Power & Energy Society General Meeting.

[3]  Hamid Dehghani,et al.  Reliability and Security Constrained Unit Commitment With Hybrid Optimization Method , 2014 .

[4]  Thillainathan Logenthiran,et al.  Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling , 2015 .

[5]  J. A. Rossiter,et al.  Model-Based Predictive Control : A Practical Approach , 2017 .

[6]  Ahmed M. Elaiw,et al.  Application of model predictive control to optimal dynamic dispatch of generation with emission limitations , 2012 .

[7]  Paul Schonfeld,et al.  A Hybrid Heuristic Technique for Optimizing Intermodal Logistics Timed Transfer Systems , 2012 .

[8]  Morteza Sarailoo,et al.  A novel model predictive control scheme based on bees algorithm in a class of nonlinear systems: Application to a three tank system , 2015, Neurocomputing.

[9]  Luigi Glielmo,et al.  Stochastic Model Predictive Control for economic/environmental operation management of microgrids , 2013, 2013 European Control Conference (ECC).

[10]  Sishaj P. Simon,et al.  Profit based unit commitment for GENCOs using parallel NACO in a distributed cluster , 2013, Swarm Evol. Comput..

[11]  Helon Vicente Hultmann Ayala,et al.  Capacitor placement of distribution systems using particle swarm optimization approaches , 2015 .

[12]  Patricia Melin,et al.  An Improved Particle Swarm Optimization Algorithm to Optimize Modular Neural Network Architectures , 2015, Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization.

[13]  David Schlipf,et al.  Nonlinear model predictive control of wind turbines using LIDAR , 2013 .

[14]  Rahul Bindlish,et al.  Nonlinear model predictive control of an industrial polymerization process , 2015, Comput. Chem. Eng..

[15]  P.A.-D.-V. Raj,et al.  Integrating genetic algorithms and tabu search for unit commitment problem , 2010 .

[16]  Dipti Srinivasan,et al.  An improved particle swarm optimisation algorithm applied to battery sizing for stand-alone hybrid power systems , 2016 .

[17]  M. Haeri,et al.  Robust model predictive control of nonlinear processes represented by Wiener or Hammerstein models , 2015 .

[18]  Serdar Özyön,et al.  Charged system search algorithm for emission constrained economic power dispatch problem , 2012 .

[19]  Sahbi Marrouchi,et al.  Unit Commitment Optimization Using Gradient-Genetic Algorithm and Fuzzy Logic Approaches , 2015, Complex System Modelling and Control Through Intelligent Soft Computations.