Model predictive control based control strategy for battery energy storage system integrated power plant meeting deep load peak shaving demand

Zhu, Jianzhong; Cui, Xiaobo; Weidong, NI

doi:10.1016/j.est.2021.103811

Cited by 22 publications

(4 citation statements)

References 42 publications

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“…The support vector machine SVM is programmed on Matlab. The first 15000 of the above data are used as the training set, and the last 1000 are used as the test set [5] . The best c and g parameters are obtained through the grid optimization algorithm of the support vector machine, and the parameters of the load prediction model, NOx prediction model, exhaust gas temperature prediction model, and exhaust gas oxygen prediction model are obtained respectively shown in Tab.1.…”

Section: Simulation Analysismentioning

confidence: 99%

Optimal Design of Power Plant Boiler Based on Predictive Control

Dou

Cao

et al. 2023

J. Phys.: Conf. Ser.

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This paper uses a predictive control algorithm based on ant colony optimization (ACO) and support vector machines (SVM) to simulate the actual historical data of power plant boilers based on the problem that the traditional PID control of boiler combustion process is difficult to achieve the desired control effect, and concludes that the algorithm can effectively overcome the nonlinear, strong coupling, multi-object and large delay characteristics of power plant combustion process. However, its applicability needs to be improved.

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Section: Simulation Analysismentioning

confidence: 99%

Optimal Design of Power Plant Boiler Based on Predictive Control

Dou

Cao

et al. 2023

J. Phys.: Conf. Ser.

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“…The time constant of the filter has a direct impact on its performance; higher values reduce power fluctuation and result in a stable output power [19]. Nonetheless, it raises the BESS capacity [20,21], thereby increasing system costs [22]. Thus, the time constant must be adjusted appropriately, and numerous ways have previously been established [19].…”

Section: Introductionmentioning

confidence: 99%

“…A two-layered control technique to reduce wind power fluctuations and extend life of the battery by regulating internal power distribution among different battery components is developed [27]. In [20,21], a high pass filter (HPF) is utilized to capture power fluctuations and make it as a reference for the associated BESS where the HPF's cut-off frequency was determined by the SoC level. The study [28] used a double moving average filter to lower the BESS charge/discharge over time.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Model Predictive Control Strategy for Stable Solar-Wind Renewable Power Dispatch Coupled with Hydrogen Electrolyzer and Battery Energy Storage

Syed

Khalid

2023

International Journal of Energy Research

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Renewable energy sources such as photovoltaic (PV) and wind power are widely used; however, their intermittent nature impairs power supply quality by creating frequency distortions and irregularities in voltage. Battery energy storage systems (BESS) are utilized to flatten out and relieve fluctuation issues. To prevent the need for larger storage systems and to prolong their operational life through controlled charging and discharging, a method of control for BESS charging level regulation is necessary. This study presents a solar-wind power and battery state of charge (SoC) control technique using a hydrogen electrolyzer (HE) fuel cell unit. An Intelligent Model Predictive Controller (IMPC) has been developed that utilizes a neural network (NN) plant model for predictive minimization instead of using a mathematical plant model for dynamic management of the HE output, which allows for flattened PV-wind power supply with regulated BESS charging and discharging. The IMPC accepts as inputs the intermittent renewable power and different battery attributes and intelligently manages the HE production while staying within the imposed restrictions. The NN, as opposed to a mathematical plant model, captures the dynamics of the plant with exceptionally high accuracy. Moreover, the NN plant model performance increases as the data gathered from the actual system increases. The neural network model resolves concerns with the MPC model’s mathematical intricacy that occurs as the plant becomes more complicated. According to simulation results, the IMPC greatly reduces PV-wind power fluctuations, for solar power, the IMPC reduces the peak battery SoC by 26.7% and compared to FLC, the peak SoC is reduced by 11.2%. Similarly, for wind power, the peak SoC is reduced by 7.3% and is decreased 3.2% more than the FLC. To demonstrate the power smoothing effectiveness of the IMPC, the peak solar power ramp rate is reduced by 30.3% and the peak wind power ramp rate is reduced by 91.3%. The presented method encourages green hydrogen usage in the electrical sector for supplying firmed solar and wind power.

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“…For the sake of addressing the limited peak regulation capability of thermal power units and the volatile nature of wind power, substantial research efforts have been devoted to enhancing the flexibility of generation-side resources. In [9], scholars propose a deep peak regulation transformation for traditional thermal power units to enhance their regulating capacity. In [10], a plant-level operational domain model is established, and the dispatching interval is solved using the Particle Swarm Optimization (PSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on Two-Stage Regulation Method for Source–Load Flexibility Transformation in Power Systems

Hao,

Wang,

Liu

et al. 2023

Sustainability

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Under the premise of continuously increasing the grid-connected capacity of new energy, the fluctuation and anti-peak shaving characteristics of wind power have always constrained the development of green power systems. Considering the characteristics of power system flexibility resources, this paper introduces a two-stage regulation approach for power systems with enhanced source–load flexibility. In the day-ahead stage, an advanced peak regulation transformation is employed, leveraging the combined heat storage device of conventional thermal power units to enhance their peak regulation capability. Additionally, the Energy Intensive Load (EIL) is integrated into the regulation system. A two-level coordinated optimization model is developed, incorporating wind power integration and dispatching power allocation, with the aim of optimizing wind power integration and achieving the optimal allocation of dispatching power. In the intra-day stage, the connection of wind plants and energy storage devices is utilized to minimize the wind power fluctuations and improve the control ability over wind power variations. Compared with traditional methods, the wind power consumption in Scenario 1 and Scenario 2 increases by 2741.1 MW/h and 2478.5 MW/h respectively. Furthermore, the inclusion of an energy storage device in the intra-day stage significantly reduces the wind power fluctuations, maintaining a stable fluctuation rate within ±1%. Therefore, this method can effectively improve the level of wind power consumption and reduce the impact of real-time fluctuations on the power system.

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Model predictive control based control strategy for battery energy storage system integrated power plant meeting deep load peak shaving demand

Cited by 22 publications

References 42 publications

Optimal Design of Power Plant Boiler Based on Predictive Control

Optimal Design of Power Plant Boiler Based on Predictive Control

An Intelligent Model Predictive Control Strategy for Stable Solar-Wind Renewable Power Dispatch Coupled with Hydrogen Electrolyzer and Battery Energy Storage

Research on Two-Stage Regulation Method for Source–Load Flexibility Transformation in Power Systems

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