Hierarchical Distributed Model Predictive Control of Standalone Wind/Solar/Battery Power System

Kong, Xiaobing; Liu, Xiangjie; Ma, Lele; Lee, Kwang Y.

doi:10.1109/tsmc.2019.2897646

Cited by 129 publications

(58 citation statements)

References 31 publications

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“…Due to the merits of MPC in addressing the systematic processing of constrained multivariable, it is well developed to handle the issues of power systems and power plants [6]. Hierarchical and distributed MPC is more efficient for handling large and complex power system problems through future prediction of control actions.…”

Section: Nomenclature Abbreviationsmentioning

confidence: 99%

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Residential Community Load Management Based on Optimal Design of Standalone HRES With Model Predictive Control

et al. 2020

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Microgrids being an important entity in the distribution system, and to get their full advantages by incorporating maximum distributed generation, standalone hybrid renewable energy systems (HRESs), being environmentally-safe and economically-efficient, are considered as the promising solution to electrify remote areas where the grid power is not available. In this work, a techno-economic investigation with an optimal design of HRES is presented to fulfill the domestic electricity need for a residential area of the Sherani district in the Province of Baluchistan, Pakistan. Nine case studies based on PV/wind/diesel/battery are analyzed based on net present cost (NPC), cost of energy (COE), and emission to decide the feasible solution. HOMER tool is utilized to accomplish modeling and simulation for economic analysis and optimal sizing. Simulation results demonstrated that HRES with PV-wind-battery is the most viable option for the specified area, and the optimal sizing of components are also obtained with $ 28,620 NPC and $ 0.311/kWh COE which shows 81.65 % reduction in cost and 100 % preserving in toxic emission while fulfilling 100 % energy demand with 67.3 % of excess energy. Furthermore, MATLAB/Simulink modeling for the optimally designed system is built for technical analysis while its effectiveness is proved by keeping dc and ac buses voltage constant, safe operating range of battery state of charge (SOC) with active power balance between HRES components, as well as efficient ac voltage quality, regardless of generation disturbances and load fluctuations. The output signal has total harmonic distortion (THD) of 0.30 % as compared to 5.44 % with the conventional control scheme. The novelty lies in the sequential application of both HOMER and MATLAB simulations of the proposed HRES model and validation of the proposition for the studied area; by using and implementing model predictive control (MPC) of a reconfigurable inverter. WT

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Section: Nomenclature Abbreviationsmentioning

confidence: 99%

“…Currently, renewable energy share worldwide is only 11 % while it is expected to increase by 60 % in 2070. The global capacity of wind and solar photovoltaic (PV) is increased to 514.8 GW and 399.6 GW respectively [6]. Fig.…”

Section: Nomenclature Abbreviationsmentioning

confidence: 99%

Residential Community Load Management Based on Optimal Design of Standalone HRES With Model Predictive Control

et al. 2020

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“…Stochastic or uncertainty of intermittent energy resources is the key issue to handle for optimal operation of hybrid energy system management [19,20]. Currently, it mainly includes three approaches: (1) fuzzy programming;…”

Section: Introductionmentioning

confidence: 99%

MOEA/D-Based Probabilistic PBI Approach for Risk-Based Optimal Operation of Hybrid Energy System With Intermittent Power Uncertainty

Zhang

Yue

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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The stochastic nature of intermittent energy resources has brought significant challenges to the optimal operation of hybrid energy systems. This paper proposes a probabilistic multi-objective evolutionary algorithm based on decomposition (MOEA/D) method with two-step risk-based decision-making strategy to tackle this problem. A scenario based technique is first utilized to generate a stochastic model of the hybrid energy system. Those scenarios divide the feasible domain into several regions. Then, based on the MOEA/D framework, a probabilistic penalty-based boundary intersection (PBI) with gradient descent differential evolution (GDDE) algorithm is proposed to search the optimal scheme from these regions under different uncertainty budgets. To ensure reliable and low risk operation of the hybrid energy system, the Markov inequality is employed to deduce a proper interval of the uncertainty budget. Further, a fuzzy grid technique is proposed to choose the best scheme for real-world applications. Experimental results confirm that the probabilistic adjustable parameters can properly control the uncertainty budget and lower the risk probability. Further, it is also shown that the proposed MOEA/D-GDDE can significantly enhance the optimization efficiency.

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“…Besides this, the PID controller based on the Hammerstein type neural network is utilized to regulate the LFC of the multi-area power system [10]. Additionally, the fuzzy logic controller [31], distributed model predictive control [32], and hierarchical distributed model predictive control [33] are also implemented to investigate the frequency regulation in MG. Various optimization methods like a genetic algorithm (GA), jay algorithm, and particle swarm optimization (PSO) are used to optimize the conventional controllers PI/PID and fuzzy PI/PID controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

et al. 2020

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The number of electric vehicles and renewable energy resources integrated into the power system is increasing day by day. The objective behind the development of electric vehicles and renewable energy sources is to build a sustainable and green power system. The renewables either don't possess system inertia or have less system inertia, therefore, they don't effectively respond to the load variations. The battery storage system of electric vehicles is used as the first line of defense to counter the effect of load/frequency variations and make the system stable. As active power is inversely proportional to the system frequency, for this purpose electric vehicles are included in the microgrid environment. In this paper, an isolated microgrid having a reheat turbine system, wind turbine system, photovoltaic system, and electric vehicles is studied. The output of the renewables is not controlled to utilize its maximum output power. Therefore, adaptive droop control and fuzzy PI control mechanisms are implemented to cater to the frequency variations of the isolated microgrid; the former regulates the power of electric vehicles while maintaining the energy needs of each EV and the later controls the output power of reheat turbine system according to the frequency variation. Furthermore, the genetic algorithm optimization toolbox is utilized to optimize the parameters of the adaptive and fuzzy PI controllers. The proposed model is developed in MATLAB/Simulink which shows that these control techniques effectively sustained the system frequency of isolated microgrid in the desired limits. INDEX TERMS Adaptive droop control, electric vehicles, frequency regulation, fuzzy PI control, GA optimization technique, renewable energy sources, reheat turbine system.

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Hierarchical Distributed Model Predictive Control of Standalone Wind/Solar/Battery Power System

Cited by 129 publications

References 31 publications

Residential Community Load Management Based on Optimal Design of Standalone HRES With Model Predictive Control

Residential Community Load Management Based on Optimal Design of Standalone HRES With Model Predictive Control

MOEA/D-Based Probabilistic PBI Approach for Risk-Based Optimal Operation of Hybrid Energy System With Intermittent Power Uncertainty

Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

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