Evaluating flexibility values for congestion management in distribution networks within Dutch pilots

Fonteijn, Rik; Amstel, Marieke van; Nguyen, Phuong H.; Morren, Johan; Bonnema, G. Maarten; Slootweg, J.G.

doi:10.1049/joe.2018.9314

Cited by 17 publications

(13 citation statements)

References 11 publications

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“…VCCs provide the customer with a variable maximum power they can withdraw from the grid according to a schedule (either fixed or dynamic) set by the DSO, while benefiting them with lower network tariffs. For example, users can have a reduced maximum capacity during peak load hours but an increased maximum capacity during off-peak hours [87], as…”

Section: Flexibility Procurement Frameworkmentioning

confidence: 99%

Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services

Venegas

Petit

Pérez

2021

Renewable and Sustainable Energy Reviews

101

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Section: Flexibility Procurement Frameworkmentioning

confidence: 99%

Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services

Venegas

Petit

Pérez

2021

Renewable and Sustainable Energy Reviews

101

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“…The main goal of this research-as opposed to most other research, whose subject is public charging facilities [4][5][6][7]-is to determine what a smart charging system has to offer either for companies with EV fleets, or companies considering the move to an EV fleet, and how a balanced charging system can be achieved. Supporting the main goal are secondary goals that are intended to give a clear picture of the situation, system solutions, and implementation effects.…”

Section: Research Goal and Questionsmentioning

confidence: 99%

“…In contrast to traditional charging methods, where EVs are always charged at the highest possible speed, smart charging uses various data streams to determine the optimal charging speeds for its intended goal. Whereas the goal of most present research in the field of smart charging is to balance the load on the national [4,5], regional, or local [6,7] electricity grids, this article focuses on utilizing a smart charging system to efficiently charge a company's EV fleet, thus making a step to the top row of Figure 1.…”

Section: Introductionmentioning

confidence: 99%

In-Company Smart Charging: Development of a Simulation Model to Facilitate a Smart EV Charging System

Voss¹,

Haveman²,

Bonnema

2021

Energies

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Current electric vehicle (EV) charging systems have limited smart functionality, and most research focuses on load-balancing the national or regional grid. In this article, we focus on supporting the early design of a smart charging system that can effectively and efficiently charge a company’s EV fleet, maximizing the use of self-generated Photo-Voltaic energy. The support takes place in the form of the Vehicle Charging Simulation (VeCS) model. System performance is determined by operational costs, CO2 emissions and employee satisfaction. Two impactful smart charging functions concern adaptive charging speeds and charging point management. Simulation algorithms for these functions are developed. The VeCS model is developed to simulate implementation of a smart charging system incorporating both charging infrastructure and local Photo-Voltaics input, using a company’s travel and energy data, prior to having the EVs in place. The model takes into account travel behaviour, energy input and energy consumption on a daily basis. The model shows the number of charged vehicles, whether incomplete charges occur, and energy flow during the day. The model also facilitates simulation of an entire year to determine overall cost and emission benefits. It also estimates charging costs and CO2 emissions that can be compared to the non-EV situation. With the VeCS model, the impact of various system design and implementation choices can be explored before EVs are used. Two system designs are proposed for the case company; a short-term version with current technology and a future version with various smart functionalities. Overall, the model can contribute to substantiated advice for a company regarding implementation of charging infrastructure.

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“…However, with the three-Cluster pricing, they will save £117/year, and for the real-time pricing, they will save £107/year. While time-based demand response strategies create a system by which the existing market pays for the flexibility, the smart control strategy requires the DSO to purchase flexibility at a certain price either via direct bilateral agreement or in a local flexibility market [69]. In this case, flexibility may be procured to avoid or delay network investments in distribution lines or transformers.…”

Section: Co2 Emissionsmentioning

confidence: 99%

Design, Valuation and Comparison of Demand Response Strategies for Congestion Management

et al. 2020

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Decarbonisation of heat and transport will cause congestion issues in distribution networks. To avoid expensive network investments, demand flexibility is necessary to move loads from peak to off-peak periods. We provide a method and metric for assessing and selecting the optimal demand response strategy for a given network congestion scenario and applied it to a case study network in Coleraine, Northern Ireland. We proposed a Price Approximation/Mean Grouping strategy to deal with the issue of congestions occurring at the lowest-price period in real-time pricing schemes. The Mean Grouping strategy increased the average lowest-price hours from 1.32 to 3.76. We show that a three-cluster tariff is effective in solving medium congestion issues in Northern Ireland and could save consumers an average of £117/year on their heating bill. However, for networks with low headroom suffering from serious congestion issues, a smart control strategy is needed.

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Evaluating flexibility values for congestion management in distribution networks within Dutch pilots

Cited by 17 publications

References 11 publications

Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services

Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services

In-Company Smart Charging: Development of a Simulation Model to Facilitate a Smart EV Charging System

Design, Valuation and Comparison of Demand Response Strategies for Congestion Management

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