An Adaptive Hierarchical Control Method for Microgrid Considering Generation Cost

Ma, Lily; Zhang, Jiancheng

doi:10.1109/access.2020.3021027

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…The rate of change of frequency (RoCoF) and voltage (RoCoV) are also taken into consideration in the control system for ensuring stability as shown in Eqs. (24), and (25).…”

Section: Droop Control With Pll Control Techniquementioning

confidence: 99%

“…Moreover, the improved algorithm is used for solving placement and sizing of electrical energy storage system in MGs. The IGWO is employed to dynamically optimize the rated power of Distributed Generation (DG) of the tertiary controller 25 . Salp Swarm Inspired Algorithm (SSIA) is used to improve the dynamic response and power quality of an islanded microgrid 26 .…”

Section: Introductionmentioning

confidence: 99%

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Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

Mohamed,

Mahmoud,

Saied

et al. 2024

Sci Rep

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The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO’s superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness.

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“…The rate of change of frequency (RoCoF) and voltage (RoCoV) are also taken into consideration in the control system for ensuring stability as shown in Eqs. (24), and (25).…”

Section: Droop Control With Pll Control Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

Mohamed,

Mahmoud,

Saied

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In [92], to reduce the generation cost of the hierarchical control system, authors proposed tertiary control based on an improved grey wolf algorithm to regulate active and reactive power of DG output. In [93], the authors proposed adaptive distributed averaging integral control to achieve economic optimization in tertiary control, which provides better flexibility for changing the DG output.…”

Section: Traditional Tertiary Controlmentioning

confidence: 99%

Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods

Oshnoei

Blaabjerg

et al. 2023

Sustainability

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Microgrids create conditions for efficient use of integrated energy systems containing renewable energy sources. One of the major challenges in the control and operation of microgrids is managing the fluctuating renewable energy generation, as well as sudden load changes that can affect system frequency and voltage stability. To solve the above problems, hierarchical control techniques have received wide attention. At present, although some progress has been made in hierarchical control systems using classical control, machine learning-based approaches have shown promising features and performance in the control and operation management of microgrids. This paper reviews not only the application of classical control in hierarchical control systems in the last five years of references, but also the application of machine learning techniques. The survey also provides a comprehensive description of the use of different machine learning algorithms at different control levels, with a comparative analysis for their control methods, advantages and disadvantages, and implementation methods from multiple perspectives. The paper also presents the structure of primary and secondary control applications utilizing machine learning technology. In conclusion, it is highlighted that machine learning in microgrid hierarchical control can enhance control accuracy and address system optimization concerns. However, challenges, such as computational intensity, the need for stability analysis, and experimental validation, remain to be addressed.

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“…Khatibzadeh et al [16] studied the voltage stability in microgrids, applied a multi-agent control framework to control distributed flexible assets, and demonstrated that such a control ensures voltage and frequency stability in a microgird where end-consumers exchange energy. Ma and Zhang [17] applied an adaptive hierarchical control strategy considering grid stability as a constraint while minimizing the power generation costs in a microgrid with multiple prosumers. Martinez et al [18] proposed a learning-based management approach for a microgrid with competitive agents (i.e., each representing a distributed flexible asset), which improves voltage quality via reactive power-sharing between distributed generators.…”

Section: Related Workmentioning

confidence: 99%

Impacts of Local Energy Exchange in Low Voltage Communities: A German Case Study

Wege,

Gümrükcü,

Wortberg

et al. 2023

IEEE Access

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Local Energy Communities (LECs) provide a framework for community-oriented use of prosumer-owned renewable generation and storage units. For LECs to gain acceptance and eventually become widespread actors in future energy systems, their impacts on the grid and its participants must be investigated. However, the scientific landscape lacks sufficient quantitative analyses that specifically investigate the effects of LECs on the underlying grid infrastructure. This work conducts a case study to understand the system effects that may arise from the local cooperation of prosumers in future low voltage systems in Germany. To analyze these effects, performance metrics are defined, which help to compare the energy neutrality and grid reliability of low voltage (LV) grids with and without local energy exchanges. Furthermore, representative test grids are synthesized by considering the existing (2020) and predicted future (2030) distribution of energy assets, such as photovoltaics, battery storage, electrical heat pumps, and electric vehicles in Germany. For these grids, simulations considering different scenarios, including both the reference and community behavior of their participants, are conducted, and the defined performance metrics are applied. The simulations show that the LEC provides significant benefits in terms of energy neutrality and grid reliability. In this way, this study complements ongoing efforts in the design of LECs and incentives for customers, for example, in the form of favorable tariffs for locally generated energy.

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An Adaptive Hierarchical Control Method for Microgrid Considering Generation Cost

Cited by 8 publications

References 34 publications

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods

Impacts of Local Energy Exchange in Low Voltage Communities: A German Case Study

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