Distributed Robust Model Predictive Control-Based Energy Management Strategy for Islanded Multi-Microgrids Considering Uncertainty

Zhao, Zhuoli; Guo, Juntao; Luo, Xi; Lai, Chun Sing; Yang, Ping; Lai, Loi Lei; Guerrero, Josep M.; Shahidehpour, Mohammad

doi:10.1109/tsg.2022.3147370

Cited by 82 publications

(21 citation statements)

References 33 publications

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“…MMGs have the prospect in resource coordination in a medium range, satisfy more energy demand, and avoid the high cost of interconnection construction with utility grid. Zhao et al [12] introduced an energy management strategy of MMG based on distributed robust model predictive control to deal with the uncertainty of DER. The daily operating cost of each MG with the proposed strategy has been reduced by 5.3%, 8.9%, and 6.9% compared with the independent operation.…”

Section: Introductionmentioning

confidence: 99%

Energy Management in Microgrid and Multi-microgrid

Xing¹,

Jia²

2023

Preprint

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<p>Considered as basic structures of next-generation energy system, environment-friendly and flexible microgrid (MG) systems are potential solutions to deal with stochastic renewable energy sources (RESs). The essence of management is to maximize the benefits of available resources through coordination and optimization, which also applies to energy systems. Adaptable energy management approaches provide the possibility to construct effective and various energy interaction. The purpose of this paper is to present a systematic review of energy management in MG systems, and further to propose an energy management model. Therefore, this paper first comprehensively reviews recent research studies on MG, particularly in the interconnected mode (called as multi-microgrid, MMG). Based on the analysis of relative literatures, a STEER (stability, touch, efficiency, evenness, and resilience) strategy is proposed and formulated from the perspective of energy utilization for energy management. Then, critical technologies in energy management are reviewed and summarized from the aspects of control, communication, optimization, prediction, and evaluation. Furthermore, energy management system is illustrated from the perspectives of system function, management framework and operation logic, based on a four-layer hierarchical architecture. Eight main prospects on the future trend of energy management in MG and MMG are also presented.</p>

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Section: Introductionmentioning

confidence: 99%

Energy Management in Microgrid and Multi-microgrid

Xing¹,

Jia²

2023

Preprint

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“…Therefore, the energy management based on imperfect prediction information should provide necessary robust capacity to amend the supply-demand imbalance caused by prediction error. A distributed robust model predictive control -based energy management strategy is proposed in [5] for islanded multi-microgrids in order to reduce the adverse effects of uncertain renewable energy output. In our prior work [6], we proposed an adaptive distributed power scheduling algorithm for renewable microgrid cooperation.…”

Section: Introductionmentioning

confidence: 99%

Energy Management for Renewable Microgrid Cooperation: Theory and Algorithm

2023

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In the microgird (MG) system, cooperation of microgrids is a very essential issue. Hence, multi-microgrid (MMG) system has been proposed to allocate the energy and information exchange for each MG. However, the uncertainties of generations and loads are very difficult to deal with when considering the utilization of renewable energy (RE) resources. To tackle this issue, we study the optimal energy management for the cooperation of multiple MGs in this work. We propose an adaptive energy scheduling algorithm for MMG systems in order to reduce the long-term time-averaged operation cost under the constraints of renewable energy resource utilization. To deal with the unused renewable energy, virtual queues are constructed and Lyapunov framework are adopted to improvement of reliability, reduction of end-user power loss, and cultivation of renewable resource penetration. The simulation results show that the proposed algorithm is effective in coordinating multiple MGs within an MMG system so as to reduce the operation cost and improve the utilization of RE resources dramatically.INDEX TERMS Multi-Microgrid (MMG), Cooperation, energy management system, electric vehicle (EV) charging.

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“…Although this EMS optimised the energy cost for each microgrid and reduced the computational power needed, the robustness to prediction uncertainty and the effect of communication loss was not studied. The best optimisation and robustness results reported in the literature have been achieved using model predictive control (MPC) 4,6,8,9,20‐22 . This is because the prediction uncertainty inherent in renewable energy supplies can be included in the optimiser.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in 28‐30 the available generation (solar and wind) has been modelled using probability distributions, while in 24 the authors reported that probability density functions are used as a general trend to characterise the uncertainty of the prediction models. In 22 a robust MPC strategy for a multi‐microgrid system was presented. The strategy optimised the energy sold by each microgrid while ensuring their own power consumption in each local microgrid.…”

Section: Introductionmentioning

confidence: 99%

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A robust distributed energy management system for the coordinated operation of rural multi‐microgrids

Köbrich

Marín

Muñoz‐Carpintero

et al. 2022

Intl J of Energy Research

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Summary This paper presents a new robust distributed approach for the energy management of rural multi‐microgrids communities in Chile. These rural communities are often geographically isolated and have a poor connection to the main electricity grid. Operating them as individual microgrids allows the community to connect to the grid or behave as a stand‐alone system (islanded). Connecting and coordinating the operation of the microgrids can improve the system's robustness to energy and demand uncertainty, as well as reducing the dependency on the main grid. However, this coordination can be challenging because the communication between the different microgrids can sometimes deteriorate, moreover, both load and local generation profiles are challenging to predict. For these reasons, in this paper, a new robust distributed hierarchical Energy Management System (EMS) for coordination of multiple microgrids (multi‐microgrids) is introduced, which uses robust distributed Model Predictive Control (MPC). Distributed optimisation permits improving reliability in the presence of poor or lack of communications between microgrids. To consider the uncertainty associated with renewable energy generation and load consumption, fuzzy prediction interval models are included in the MPC design. The proposed method is compared with robust centralised MPC and deterministic distributed MPC to assess its performance. Simulation results are presented using data representative of a rural community in coastal Chile applied to the robust MPC design. The outcomes of this work demonstrate that the proposed robust distributed EMS can improve the power supply, apply peak shaving, and can successfully operate when there is a loss of communication between microgrids. The use of decreasing fuzzy intervals allows an accurate prediction of both load and renewable energy generation, making the system robust to uncertainty in the available resources. Moreover, results show that in absence of communication, the power supply obtained under different scenarios with the distributed EMS is better than the equivalent centralised EMS.

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Distributed Robust Model Predictive Control-Based Energy Management Strategy for Islanded Multi-Microgrids Considering Uncertainty

Cited by 82 publications

References 33 publications

Energy Management in Microgrid and Multi-microgrid

Energy Management in Microgrid and Multi-microgrid

Energy Management for Renewable Microgrid Cooperation: Theory and Algorithm

A robust distributed energy management system for the coordinated operation of rural multi‐microgrids

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