Decentralized saddle-point dynamics solution for optimal power flow of distribution systems with multi-microgrids

Tang, Chong; Liu, Mingbo; Dai, Yue; Wang, Zhijun; Xie, Ming

doi:10.1016/j.apenergy.2019.113361

Cited by 20 publications

(9 citation statements)

References 35 publications

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“…Also, it takes an VOLUME 7, 2019 intolerably long time for MILP problem to be solved. The authors of [19] propose decentralized saddle point dynamics and quadratic programming to solve the optimal power problem. The methods suggested achieve low active power loss and high RES utilization.…”

Section: A Multi-microgrid Systemmentioning

confidence: 99%

“…To ensure real-time capability and application, this study uses both IEEE 30-bus and 118-bus distribution systems to evaluate the performance and efficiency of the proposed system. The distribution systems used in this study are considered because they are widely used by the research community for MMG [14] and [19]. The internal topology of each MG as shown in Fig.…”

Section: System Modelmentioning

confidence: 99%

“…In this section, the objective function of the proposed scheme is implemented on two test cases based on IEEE 118-bus [14] and IEEE 30-bus [19] distribution systems, respectively. However, we do not modify these distributed systems.…”

Section: F Training Of Deep Convolution Neural Networkmentioning

confidence: 99%

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Towards Real-Time Energy Management of Multi-Microgrid Using a Deep Convolution Neural Network and Cooperative Game Approach

et al. 2020

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Multi-microgrid (MMG) system is a new method that concurrently incorporates different types of distributed energy resources, energy storage systems and demand responses to provide reliable and independent electricity for the community. However, MMG system faces the problems of management, real-time economic operations and controls. Therefore, this study proposes an energy management system (EMS) that turns an infinite number of MMGs into a coherence and efficient system, where each MMG can achieve its goals and perspectives. The proposed EMS employs a cooperative game to achieve efficient coordination and operations of the MMG system and also ensures a fair energy cost allocation among members in the coalition. This study considers the energy cost allocation problem when the number of members in the coalition grows exponentially. The energy cost allocation problem is solved using a column generation algorithm. The proposed model includes energy storage systems, demand loads, realtime electricity prices and renewable energy. The estimate of the daily operating cost of the MMG using a proposed deep convolutional neural network (CNN) is analyzed in this study. An optimal scheduling policy to optimize the total daily operating cost of MMG is also proposed. Besides, other existing optimal scheduling policies, such as approximate dynamic programming (ADP), model prediction control (MPC), and greedy policy are considered for the comparison. To evaluate the effectiveness of the proposed model, the real-time electricity prices of the electric reliability council of Texas are used. Simulation results show that each MMG can achieve energy cost savings through a coalition of MMG. Moreover, the proposed optimal policy method achieves MG's daily operating cost reduction up to 87.86% as compared to 79.52% for the MPC method, 73.94% for the greedy policy method and 79.42% for ADP method.

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Section: A Multi-microgrid Systemmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

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Towards Real-Time Energy Management of Multi-Microgrid Using a Deep Convolution Neural Network and Cooperative Game Approach

et al. 2020

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“…where C DR ij (t) is the compensation income of the jth user in the ith VPP at time t; C ESS ij (t) is the peak shaving income of the jth ESS in the ith VPP at time t; C DG ij,sell (t) is the electricity sales revenue of the jth DG in the ith VPP at time t; C DG ij,pub (t) is a quadratic cost function, which represents the penalty cost of the jth DG in the ith VPP at time t; ΔP ZY ij (t) is the transferable load response capacity of the jth user in the ith VPP at time t; ΔP XJ ij (t) is the curtailable load response capacity of the jth user in the ith VPP at time t; P c ij (t) is the charging power of the jth ESS in the ith VPP at time t; P d ij (t) is the discharging power of the jth ESS in the ith VPP at time t; μ XJ ij (t) is the curtailment state of the jth users in the ith VPP at time t; μ ZY ij (t) is the transfer state of the jth users in the ith VPP at time t; μ c ij (t) is the charging state of the jth ESS in the ith VPP at time t; μ d ij (t) is the discharging state of the jth ESS in the ith VPP at time t; μ DG ij (t) is the operation state of the jth DG in the ith VPP at time t; ρ DR ij (t) is the unit capacity compensation price of the jth DR in the ith VPP at time t; ρ(t) is the electricity price at time t; a h and b h are the coefficients of quadratic cost function (Wang et al, 2019); and P pre ij (t) is the forecasting output of the jth DG in the ith VPP at time t.…”

Section: Objective Function Of Stagementioning

confidence: 99%

“…The rated output of PV is shown in Table 2. In this paper, the system electricity price at the curtailable time is used to compensate for the curtailable load (Luo and Song, 2015), and 80% of the system electricity price at the transferable time is used to compensate the transferable load (Liu et al, ) a h 0.1 and b h 0 (Wang et al, 2019).…”

Section: Case Study Case Introductionmentioning

confidence: 99%

Bi-Level Load Peak Shifting and Valley Filling Dispatch Model of Distribution Systems With Virtual Power Plants

et al. 2020

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Distributed energy resources (DERs) have been widely involved in the optimal dispatch of distribution systems which benefit from the characteristics of reliability, economy, flexibility, and environmental protection. And distribution systems are gradually transforming from passive networks to active distribution networks. However, it is difficult to manage DERs effectively because of their wide distribution, intermittency, and randomness. Virtual power plants (VPPs) can not only coordinate the contradiction between distribution systems and DERs but also consider the profits of DERs, which can realize the optimal dispatch of distribution systems effectively. In this paper, a bi-level dispatch model based on VPPs is proposed for load peak shaving and valley filling in distribution systems. The VPPs consist of distributed generations, energy storage devices, and demand response resources. The objective of the upper-level model is smoothing load curve, and the objective of the lower-level model is maximizing the profits of VPPs. Meanwhile, we consider the quadratic cost function to quantify the deviation between the actual output and the planned output of DGs. The effectiveness of the bi-level dispatch model in load shifting and valley filling is proved by various scenarios. In addition, the flexibility of the model in participating in distribution system dispatch is also verified.

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Operation Management of Microgrid Clusters

Moradi

Foroud

2021

Microgrids

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Decentralized saddle-point dynamics solution for optimal power flow of distribution systems with multi-microgrids

Cited by 20 publications

References 35 publications

Towards Real-Time Energy Management of Multi-Microgrid Using a Deep Convolution Neural Network and Cooperative Game Approach

Towards Real-Time Energy Management of Multi-Microgrid Using a Deep Convolution Neural Network and Cooperative Game Approach

Bi-Level Load Peak Shifting and Valley Filling Dispatch Model of Distribution Systems With Virtual Power Plants

Operation Management of Microgrid Clusters

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