Preventing Distribution Grid Congestion by Integrating Indirect Control in a Hierarchical Electric Vehicles’ Management System

Hu, Junjie; Si, Chengyong; Lind, Morten; Yu, Rongshan

doi:10.1109/tte.2016.2554469

Cited by 40 publications

(23 citation statements)

References 29 publications

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“…In this study, EVs schedules are based on aggregators' commitment with the Two-Part Tariff (TPT) (reserve and activation). This differs from previously proposed EVs market pricebased control methods Hu et al (2016b), Liu et al (2018c), since the aggregator knows the actual connected periods of EVs, and is able to control the fleet according to DSO requests. Figure 6 shows electric vehicles flexibility reserve capacity purchased by the DSO at each node, during the DA market.…”

Section: Network Kpis Evolutionmentioning

confidence: 90%

Design of Local Services Markets for Pricing DSO-TSO Procurement Coordination

Pastor

Nieto-Martin

Bunn

et al. 2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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Electric vehicles (EVs) as a distributed energy resource represent a key opportunity to achieve emission reductions worldwide, although their large uncoordinated deployment could congest distribution networks generating load profiles leading to system failures. Flexible management systems to coordinate strategies between agents at different levels require high volumes of decentralized information exchanged to improve network assets utilisation and operational costs reduction. Incumbent technologies such as Smart Contracts (SCs) from Distributed Ledger Technologies (DLT) would enable these coordination approaches with near real-time resolution. Therefore, this study aims to evaluate and propose flexible charging strategies for high penetration levels of electric vehicles and renewable energies on already congested distribution networks. It considers the use of Smart Contracts as enablers of new decentralised coordination architectures among network agents. A multi-agent coordination through a Two-Part Tariff (TPT) to provide flexibility services in congested distribution power networks is stylised. Then, a market setting with large penetration of EVs from an aggregated portfolio has been individually modelled for the UK Local Energy Market of Cornwall to relieve local network congestion. Results illustrate that EVs coordination strategies are able to reduce network costs and manage congestion, allowing up to 6.5% more renewable energy to be delivered into the grid.

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Section: Network Kpis Evolutionmentioning

confidence: 90%

Design of Local Services Markets for Pricing DSO-TSO Procurement Coordination

Pastor

Nieto-Martin

Bunn

et al. 2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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“…Flexibility of PEVs' charging demand can be leveraged to improve power system operation [20], [46]. This includes loss minimization [47], voltage profile control [48], frequency regulation [49], [50], transformer aging risk minimization [21], overloading prevention of power distribution transformers [51], precipitating in ancillary services [52], [53], and congestion management [54]. In [54] a comprehensive method is proposed for expansion planning of urban electrified transportation networks.…”

Section: B Related Workmentioning

confidence: 99%

“…This includes loss minimization [47], voltage profile control [48], frequency regulation [49], [50], transformer aging risk minimization [21], overloading prevention of power distribution transformers [51], precipitating in ancillary services [52], [53], and congestion management [54]. In [54] a comprehensive method is proposed for expansion planning of urban electrified transportation networks. This approach finds the optimal investment strategy for both power distribution network and transportation network in terms of the optimal sites and sizes of new roads, charging infrastructures, power distribution lines, and distributed generation units [54].…”

Section: B Related Workmentioning

confidence: 99%

Distributed Holistic Framework for Smart City Infrastructures: Tale of Interdependent Electrified Transportation Network and Power Grid

2019

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Plug-in Electric Vehicles (PEVs) play a pivotal role in transportation electrification. The flexible nature of PEVs' charging demand can be utilized for reducing charging cost as well as optimizing the operating cost of power and transportation networks. Utilizing charging flexibility of geographically spread PEVs requires design and implementation of efficient optimization algorithms. There is a synergy between electro mobility (e-Mobility) infrastructures (including charging stations) and PEVs. In this paper, we introduce a holistic framework to model interdependent nature of power systems and electrified transportation networks, enhance the operational performance of these systems as a network-of-networks, and explain the required information exchange via coupling agents (e.g., PEVs and charging stations). We develop a holistic framework that enables distributed coordination of interdependent networks through the IoT lens. To this end, we propose to use a fully distributed consensus+innovations approach. This iterative algorithm achieves a distributed solution of the decision making for each agent through local computations and limited communication with other neighboring agents that are influential in that specific decision. For instance, the optimal routing decision of a PEV involves a different set of agents as compared with the optimal charging strategy of the same PEV. The exogenous information from an external network/agent can affect internal operation of the other agents. For instance, having some information about traffic congestion at the transportation networks changes the decision of PEVs to charge their battery at another location. Our approach constitutes solving an iterative problem, which utilizes communication at the smart city layer, as a network of infrastructures, including power grid and electrified transportation network, that enables fully distributed coordination of agents. Fully distributed decision making enables scalability of the solution and plug-and-play capability, as well as increasing the information privacy of PEVs by only requiring limited local information exchange with neighboring agents. We investigate a detailed application of our framework for interdependent power systems and electrified transportation networks. To this end, we first identify the functionalities, constraints, objectives, and decision variables of each network. Then, we investigate the interdependent interactions among these networks and their corresponding agents. INDEX TERMS Consensus+innovations, distributed processing, smart city, interdependent infrastructures, electrified transportation networks, smart mobility, power systems, electric vehicles, cooperative charging, optimality conditions.

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“…The power of single EVs is then optimized according to the data from the GMS. In this paper, only the optimal dispatching of EV fleets is discussed, and the dispatching method of single EVs can refer to [20] and is not comprehensively discussed in this work.…”

Section: Objective Functionmentioning

confidence: 99%

Cooperative Optimization of Electric Vehicles and Renewable Energy Resources in a Regional Multi-Microgrid System

Chen

Duan

2019

Applied Sciences

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By integrating renewable energy sources (RESs) with electric vehicles (EVs) in microgrids, we are able to reduce carbon emissions as well as alleviate the dependence on fossil fuels. In order to improve the economy of an integrated system and fully exploit the potentiality of EVs’ mobile energy storage while achieving a reasonable configuration of RESs, a cooperative optimization method is proposed to cooperatively optimize the economic dispatching and capacity allocation of both RESs and EVs in the context of a regional multi-microgrid system. An across-time-and-space energy transmission (ATSET) of the EVs was considered, and the impact of ATSET of EVs on economic dispatching and capacity allocation of multi-microgrid system was analyzed. In order to overcome the difficulty of finding the global optimum of the non-smooth total cost function, an improved particle swarm optimization (IPSO) algorithm was used to solve the cooperative optimization problem. Case studies were performed, and the simulation results show that the proposed cooperative optimization method can significantly decrease the total cost of a multi-microgrid system.

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Preventing Distribution Grid Congestion by Integrating Indirect Control in a Hierarchical Electric Vehicles’ Management System

Cited by 40 publications

References 29 publications

Design of Local Services Markets for Pricing DSO-TSO Procurement Coordination

Design of Local Services Markets for Pricing DSO-TSO Procurement Coordination

Distributed Holistic Framework for Smart City Infrastructures: Tale of Interdependent Electrified Transportation Network and Power Grid

Cooperative Optimization of Electric Vehicles and Renewable Energy Resources in a Regional Multi-Microgrid System

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