Cooperative energy management optimization based on distributed MPC in grid-connected microgrids community

Xing, Xiangwei; Xie, Liang-Liang; Meng, Hongmin

doi:10.1016/j.ijepes.2018.11.027

Cited by 66 publications

(39 citation statements)

References 36 publications

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“…As one of the most widely-applied control methods in industry, model predictive control (MPC) ranks second only after PID control algorithm [11]. Recently, MPC has been extended to solve the optimal load frequency control (LFC) problems of power systems [12][13][14][15][16] and operation optimization issues of microgrids [17][18][19][20]. Especially, it should be noted that there are some existing works regarding MPC-based frequency control of microgrids [12,[19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

et al. 2018

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Optimal frequency control of an islanded microgrid has been a challenging issue in the research field of microgrids. Recently, fractional-order calculus theory and some related control methods have attempted to handle this issue. In this paper, a novel fractional-order model predictive control (FOMPC) method is proposed to achieve the optimal frequency control of an islanded microgrid by introducing a fractional-order integral cost function into model predictive control (MPC) algorithm. Firstly, a discrete state-space model is derived for the optimal frequency control problem of an islanded microgrid. Afterward, a fractional-order integral cost function is designed to guide the FOMPC algorithm to obtain optimal control law by borrowing the Grünwald-Letnikov (GL) definition of fractional order calculus. Six simulation studies have been carried out to illustrate the superiority of FOMPC to conventional MPC under dynamical load disturbances, perturbed system parameters and random dynamical power fluctuation of wind turbines.

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Section: Introductionmentioning

confidence: 99%

Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

et al. 2018

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“…Equation (9) indicates the energy generated by the resources and load. 32 In this way for assessing the objective function, the accompanying Equation (10) is considered.…”

Section: Problem Formulationmentioning

confidence: 99%

A hybrid RBFNN–BBMO methodology for robust energy management in grid‐Connected microgrid

Kumari

2020

Int J Numerical Modelling

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This dissertation shows energy scheduling in grid-connected micro grid (MG) using an amazing hybrid method for optimal energy management. The hybrid method is the mixture of Radial Basis Neural Function Network (RBFNN) and Bumble Bees Mating Optimization (BBMO) and is therefore referred to as RBFNN-BBMO technique. The MG system consists of photovoltaic, wind turbine, battery storage and micro turbine systems. Here, batteries are used to maintain a constant and stable output power. The necessary load demand of the grid-connected MG scheme is continually monitored using the RBFNN method. BBMO optimizes the perfect combination of MG taking into account the expected load demand. In addition to decrease the impact of renewable energy prediction mistakes, a two strategy for MG energy management is adopted. The first approach is to program various renewable energy sources (RESs) during the operation of MG in order to minimize the electricity costs. The second approach is power flow balancing and forecasting errors, decreasing from the scheduled energy reference based on the summarized rule. Access of RESs, power supply and charging modules are the constraints. Two existing methods such as the Genetic Algorithm and the Cuttlefish Algorithm compare the supremacy of the proposed method.

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“…(17) After feedback correction, the output of the prediction model is revised from y m (k + 1) to y p (k + 1) [38]. In Formula (11), h is the error correction coefficient [39], whose value is adjusted according to the actual operation of the specific system.…”

Section: (2) Feedback Correctionmentioning

confidence: 99%

“…38]. In Formula (11), h is the error correction coefficient [39], whose value is adjusted according to the actual operation of the specific system. The core idea of predictive control is to decompose a long-term optimal control problem into several short-term optimization problems.…”

Section: ) Neural Network Rolling Optimizationmentioning

confidence: 99%

Research on Intelligent Predictive AGC of a Thermal Power Unit Based on Control Performance Standards

Peng

Zhao

2019

Energies

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In order to satisfy the growing demands of control performance and operation efficiency in the automatic generation control (AGC) system of a grid, a novel, intelligent predictive controller, combined with predictive control and neural network ideas, is proposed and applied to the AGC systems of thermal power units. This paper proposes a Bayesian neural network identification model for typical ultra-supercritical thermal power units, which was found to be accurate and can be used as a simulation model. Based on the model, this paper develops an intelligent predictive control for the AGC of thermal power units, which improves unit load operation and constitutes a novel, closed-loop AGC structure based on online control performance standard (CPS) evaluations. Intelligent predictive control is mainly improved because the neural network rolling optimization model replaces the traditional rolling optimization model in the rolling optimization module. The simulation results indicate that the intelligent predictive controller developed in the two-area interconnected power grid under CPS can, on the one hand, improve the load tracking performance of AGC thermal power units, and, on the other hand, the controller has strong robustness. Whether the system parameters change considerably or the AGC has different grid disturbances, the new type of the loop AGC system can still sufficiently meet the control requirements of the power grid.Energies 2019, 12, 4073 2 of 23 in [10], and the combination of various control methods [11] in the AGC system to improve development of the AGC system to some degree. Nanda et al. [12] proposed using the genetic algorithm (GA) to optimize control of the AGC system. However, this method has some shortcomings, as GA only carries out crossover and mutation operations, and it is likely to fall into local optimum or premature convergence. This has resulted in a dramatic reduction in the efficiency and searchability of the algorithm. Gozde and Taplamacioglu [13] developed a gain-scheduling proportional-integral (PI) controller for a dead zone, nonlinear AGC system. The authors used the craziness-based particle swarm optimization algorithm to minimize the standard error and solution time of different objective functions. Rabindra Kumar Sahu et al. [14] changed the controller design problem into a controller optimization problem for automatic generation control (AGC) of multi-area power systems with diverse energy sources. The proposed teaching learning based optimization (TLBO) algorithm was employed to optimize the parameters of the PID controller, and the superiority of the proposed TLBO controller has been demonstrated by simulation comparison experiments.Appropriate models serve for excellent control algorithms. As one of the intelligent algorithms, model predictive control (MPC) is advantageous because of its unique model prediction, rolling optimization, and feedback correction structure [15,16]. An MPC algorithm was presented to control coal convey systems in a coal-fired power plant [17]. Ne...

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Cooperative energy management optimization based on distributed MPC in grid-connected microgrids community

Cited by 66 publications

References 36 publications

Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

A hybrid RBFNN–BBMO methodology for robust energy management in grid‐Connected microgrid

Research on Intelligent Predictive AGC of a Thermal Power Unit Based on Control Performance Standards

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