Hierarchical optimisation strategy in microgrid based on the consensus of multi‐agent system

Hao, Ran; Jiang, Ziqing; Ai, Qian; Yu, Zhen; Zhu, Yuchao

doi:10.1049/iet-gtd.2017.0393

Cited by 14 publications

(7 citation statements)

References 31 publications

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“…Computing time larger than GA GA [76][77][78][79][80][81][82][83][84] the evolutionary computation-based method provides global optimisation for the system regardless of historical data and system constraints. Iterative time-consuming process to achieve its optimal condition consensus algorithm [95][96][97][98][99][100][101][102][103][104][105][106][107][108][109][110][111][112] the precise computation method quickly converges all the agents to a common value with less data requirement. preferred at the primary level because of advantages over the other two control techniques mainly in terms of adaptive towards local variables, no communication requirement, significantly better voltage and current regulation along with easy implementation.…”

Section: Resultsmentioning

confidence: 99%

“…It is not mandatory to have a physical existence of an agent. It can be a collection of programs that interact and can take its own decision independently based on locally available information by applying guidelines fed into it [95,[107][108][109][110]. A time-invariant or dynamic information sharing scheme is frequently used by the cooperative control for the MAS.…”

Section: Consensus Algorithmmentioning

confidence: 99%

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Review of hierarchical control strategies for DC microgrid

Abhishek

Ranjan

Devassy

et al. 2020

IET Renewable Power Generation

128

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This work presents an extensive review of hierarchical control strategies that provide effective and robust control for a DC microgrid. DC microgrid is an efficient, scalable and reliable solution for electrification in remote areas and needs a reliable control scheme such as hierarchical control. The hierarchical control strategy is divided into three layers namely primary, secondary and tertiary based on their functionality. In this study, different methods of primary control for current and voltage regulation, secondary control for error-correction in voltage and current, power sharing in a microgrid and microgrid clusters and tertiary control for power and energy management with a primary focus on minimal power loss and operational cost in a DC microgrid system are reviewed in-depth. Along with this, the advantages and limitations of various control structures like centralised, decentralised, distributed are discussed in this study. After a comparative study of all control strategies, the optimum control schemes from the author's point of view are also presented.

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Section: Resultsmentioning

confidence: 99%

Section: Consensus Algorithmmentioning

confidence: 99%

Review of hierarchical control strategies for DC microgrid

Abhishek

Ranjan

Devassy

et al. 2020

IET Renewable Power Generation

128

View full text Add to dashboard Cite

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“…Then, the MAS theory is introduced in this study. MAS as a distributed autonomous system can improve the level of intelligence of the microgrid system [17]. Based on MAS theory, the microgrid system can adopt a decentralised coordinated control strategy to realise the hierarchical control [18].…”

Section: Hybrid Control Scheme Based On Mas Theory For Microgridmentioning

confidence: 99%

Event‐triggered hybrid control strategy based on hybrid automata and decision tree for microgrid

Dou

Teng

Zhang

et al. 2019

IET Generation, Transmission & Distribution

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“…Based on the chance constrained programming, a new probabilistic optimization framework is developed to optimize the operation cost of microgrid in [23]. A hierarchical optimization strategy is proposed to improve the automation level of DGs in [24]. Hybrid AC/ DC microgrid integrates the advantages of AC microgrid and DC microgrid and its power supply forms are more flexible and diverse, which can satisfy various load demands at the same time.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimal operation of hybrid AC/DC microgrid considering source-network-load coordination

Zheng

2019

J. Mod. Power Syst. Clean Energy

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Taking the consumption rate of renewable energy and the operation cost of hybrid AC/DC microgrid as the optimization objectives, the adjustment of load demand curves is carried out considering the demand side response (DSR) on the load side. The complementary utilization of renewable energy between AC area and DC area is achieved to meet the load demand on the source side. In the network side, the hybrid AC/DC microgrids purchase electricity from the power grid at the time-of-use (TOU) price and sell the surplus power of renewable energy to the power grid for profits. The improved memetic algorithm (IMA) is introduced and applied to solve the established mathematical model. The promotion effect of the proposed source-network-load coordination strategies on the optimal operation of hybrid AC/DC microgrid is verified.

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Hierarchical optimisation strategy in microgrid based on the consensus of multi‐agent system

Cited by 14 publications

References 31 publications

Review of hierarchical control strategies for DC microgrid

Review of hierarchical control strategies for DC microgrid

Event‐triggered hybrid control strategy based on hybrid automata and decision tree for microgrid

Multi-objective optimal operation of hybrid AC/DC microgrid considering source-network-load coordination

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