Interactive Model for Energy Management of Clustered Microgrids

Lu, Tianguang; Wang, Zhaoyu; Ai, Qian; Lee, Wei-Jen

doi:10.1109/tia.2017.2657628

Cited by 93 publications

(59 citation statements)

References 39 publications

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“…Furthermore, the hierarchical structure is also needed to take into account the power allocation between MGC and main grid [196]. It is worth mentioning that, the energy scheduling and power management schemes based on the hierarchical control have been discussed in [197][198][199][200][201]. A self-organized and decentralized energy management strategy of the MGC is devised in [198].…”

Section: Optimal Scheduling Methods For Mas-based Microgrid Clustersmentioning

confidence: 99%

See 1 more Smart Citation

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

366

182

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Abstract-The increasing integration of the distributed renewable energy sources highlights the requirement to design various control strategies for microgrids (MGs) and microgrid clusters (MGCs). The multi-agent system (MAS)-based distributed coordinated control strategies shows the benefits to balance the power and energy, stabilize voltage and frequency, achieve economic and coordinated operation among the MGs and MGCs. However, the complex and diverse combinations of distributed generations in multi-agent system increase the complexity of system control and operation. In order to design the optimized configuration and control strategy using MAS, the topology models and mathematic models such as the graph topology model, non-cooperative game model, the genetic algorithm and particle swarm optimization algorithm are summarized. The merits and drawbacks of these control methods are compared. Moreover, since the consensus is a vital problem in the complex dynamical systems, the distributed MAS-based consensus protocols are systematically reviewed. NOMENCLATURE

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Section: Optimal Scheduling Methods For Mas-based Microgrid Clustersmentioning

confidence: 99%

“…And a unified dispatching and hierarchical energy management scheme is designed and evaluated to effectively optimize and manage the MGC [198][199][200]. In [201], a two-level optimization model for the coordinated energy managements in MGC is established.…”

Section: Optimal Scheduling Methods For Mas-based Microgrid Clustersmentioning

confidence: 99%

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

366

182

View full text Add to dashboard Cite

show abstract

“…By doing this hybridization, it is very possible to improve the system reliability and resiliency for long-run operations due to the unique features that inherently exist in centralized/decentralized schemes [46].…”

Section: Hybrid P/emsmentioning

confidence: 99%

Optimal Design and Operation Management of Battery-Based Energy Storage Systems (BESS) in Microgrids

Anvari‐Moghaddam¹,

Dulout

Alonso

et al. 2018

Advancements in Energy Storage Technologies

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Energy storage systems (ESSs) can enhance the performance of energy networks in multiple ways; they can compensate the stochastic nature of renewable energies and support their large-scale integration into the grid environment. Energy storage options can also be used for economic operation of energy systems to cut down system's operating cost. By utilizing ESSs, it is very possible to store energy in off-peak hours with lower cost and energize the grid during peak load intervals avoiding high price spikes. Application of ESSs will also enable better utilization of distributed energy sources and provide higher controllability at supply/demand side which is helpful for load leveling or peak shaving purposes. Last but not least, ESSs can provide frequency regulation services in off-grid locations where there is a strong need to meet the power balance in different operating conditions. Each of the abovementioned applications of energy storage units requires certain performance measures and constraints, which has to be well considered in design phase and embedded in control and management strategies. This chapter mainly focuses on these aspects and provides a general framework for optimal design and operation management of battery-based ESSs in energy networks.

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“…GAs have been successfully applied to a range of different fields, the most important of which are: bioinformatics, control engineering, neural networks, and medicine [16]. GAs also found applications in power systems to find optimal distributed generation (DG) placement [20], network reconfiguration to minimize losses [21], and network reconfiguration in case of failures [22]. The proposed optimization procedure is shown in the flowchart in Figure 1.…”

Section: Optimization Procedures and Outputsmentioning

confidence: 99%

Aging Cost Optimization for Planning and Management of Energy Storage Systems

et al. 2017

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In recent years, many studies have proposed the use of energy storage systems (ESSs) for the mitigation of renewable energy source (RES) intermittent power output. However, the correct estimation of the ESS degradation costs is still an open issue, due to the difficult estimation of their aging in the presence of intermittent power inputs. This is particularly true for battery ESSs (BESSs), which have been proven to exhibit complex aging functions. Unfortunately, this collides with considering aging costs when performing ESS planning and management procedures, which are crucial for the exploitation of this technology. In order to overcome this issue, this paper presents the genetic algorithm-based multi-period optimal power flow (GA-MPOPF) procedure, which aims to economically optimize the management of ESSs by taking into account their degradation costs.The proposed methodology has been tested in two different applications: the planning of the correct positioning of a Li-ion BESS in the PG& E 69 bus network in the presence of high RES penetration, and the definition of its management strategy. Simulation results show that GA-MPOPF is able to optimize the ESS usage for time scales of up to one month, even for complex operative costs functions, showing at the same time excellent convergence properties.

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Interactive Model for Energy Management of Clustered Microgrids

Cited by 93 publications

References 39 publications

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Optimal Design and Operation Management of Battery-Based Energy Storage Systems (BESS) in Microgrids

Aging Cost Optimization for Planning and Management of Energy Storage Systems

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