Balancing collective and individual interests in transactive energy management of interconnected micro-grid clusters

Chen, Yang; Hu, Mengqi

doi:10.1016/j.energy.2016.05.052

Cited by 72 publications

(38 citation statements)

References 24 publications

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“…The optimal DER and energy storage management proposed in [26] can benefit power balance of grid. In recent research, a state-of-the-art technology named transactive energy was proposed for operational scheduling of resources with the aim of reducing energy cost and improving the stability of the distribution system through mechanisms of market or price control [27].…”

Section: Previous Workmentioning

confidence: 99%

A Multi-Market-Driven Approach to Energy Scheduling of Smart Microgrids in Distribution Networks

Yue

Anvari‐Moghaddam

et al. 2019

Sustainability

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In order to coordinate the economic desire of microgrid (MG) owners and the stability operation requirement of the distribution system operator (DSO), a multi-market participation framework is proposed to stimulate the energy transaction potential of MGs through distributed and centralized ways. Firstly, an MG equipped with storage can contribute to the stability improvement at special nodes of the distribution grid where the uncertain factors (such as intermittent renewable sources and electric vehicles) exist. The DSO is thus interested in encouraging specified MGs to provide voltage stability services by creating a distribution grid service market (DGSM), where the dynamic production-price auction is used to capture the competition of the distributed MGs. Moreover, an aggregator, serving as a broker and controller for MGs, is considered to participate in the day-ahead wholesale market. A Stackelberg game is modeled accordingly to solve the price and quantity package allocation between aggregator and MGs. Finally, the modified IEEE-33 bus distribution test system is used to demonstrate the applicability and effectiveness of the proposed multi-market mechanism. The results under this framework improve both MGs and utility.

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Section: Previous Workmentioning

confidence: 99%

A Multi-Market-Driven Approach to Energy Scheduling of Smart Microgrids in Distribution Networks

Yue

Anvari‐Moghaddam

et al. 2019

Sustainability

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“…12.00 pm). The interval between the gate closure and the beginning of power delivery is at least 12 hours (Chen and Hu, 2016). After the spot price has been settled and commitments have been made, participants are responsible for deviations due to unpredictable fluctuations in power production or consumption.…”

Section: Market Information and Assumptionsmentioning

confidence: 99%

“…There are several references addressing the optimal operation strategy for DERs in a transactive energy framework. These papers examine different bidding strategies, alternative electricity generation technologies and market structures and optimization approaches (Chen and Hu, 2016), (Wang et al, 2016), (Ghatikar et al, 2016), (Kardakos et al, 2016), (Bejestani et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Optimal scheduling of distributed energy resources as a virtual power plant in a transactive energy framework

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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A transactive energy system can provide an integral management scheme that facilitates power delivery with high efficiency and reliability. To close the gap between wholesale and retail markets, this paper presents a two-stage optimal scheduling model for distributed energy resources (DERs) in the form of a virtual power plant (VPP) participating in the day-ahead (DA) and real-time (RT) markets. In the first stage, the hourly scheduling strategy of the VPP is optimized, in order to maximize the total profit in the DA market. In the second stage, the outputs of the VPP are optimally adjusted, in order to minimize the imbalance cost in the RT market. The conditional-value-at-risk (CVaR) is used to assess the risk of profit variability due to the presence of uncertainties in renewable energy outputs, market prices and energy demands. The formulated two-stage models are solved by an enhanced particle swarm optimization algorithm (PSO) and the commercial solver AMPL/IPOPT 3.8.0. In the procedures of the enhanced PSO, two particles with the lowest and highest fitness values are used as the starting points, and then the interior point method will be employed to quickly locate local optima. The population size is set at 200, and the iteration number is set at 1000. Simulation results show that coordinated scheduling can effectively offset the renewable energy fluctuation and mitigate the impacts of uncertainties. With the two-level scheduling, the risk exposure can be mitigated, and the cost related to the risk aversion is also effectively reduced. The paper finds that coordinated two-level DERs scheduling is a flexible risk-hedging tool that can identify optimal operation, resulting in more affordable electricity prices for end users.

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“…Thus, the appropriate establishment of MAS model in MGC is the premise to coordinate control and analyze system stability. By using these methods, the friendly interaction between the MGC and grid, and completely absorption of new energy can be realized [168]. In order to analyze the merits and drawbacks for topology and mathematic models of the MAS comprehensively, various kinds of modeling methods for MGC are summarized in Table I.…”

Section: A Modeling Methods For Distributed Multi-agent Systemmentioning

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.

367

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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Balancing collective and individual interests in transactive energy management of interconnected micro-grid clusters

Cited by 72 publications

References 24 publications

A Multi-Market-Driven Approach to Energy Scheduling of Smart Microgrids in Distribution Networks

A Multi-Market-Driven Approach to Energy Scheduling of Smart Microgrids in Distribution Networks

Optimal scheduling of distributed energy resources as a virtual power plant in a transactive energy framework

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

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