Flexibility products and markets: Literature review

Villar, José; Bessa, Ricardo J.; Matos, Manuel

doi:10.1016/j.epsr.2017.09.005

Cited by 253 publications

(128 citation statements)

References 16 publications

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“…To address any imbalances, the operator will accept offers to increase or curtail demand and/or generation in near real-time. Electricity can also be traded ahead of time; in the day-ahead market, for example, generators and suppliers agree contracts for the delivery of energy typically during hour periods on the following day [1]. Vehicle-to-grid (V2G) is a technology that allows electric vehicles to contribute to such flexibility services by discharging or curtailing demand when required [2,3].…”

Section: Introductionmentioning

confidence: 99%

Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid

et al. 2020

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Vehicle-to-grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from a vehicle tracking service, we assessed the technique's ability to learn and predict when a fleet of 48 vehicles was parked close to charging stations and compared this with two moving average techniques. We found the ability of all three approaches to predict when individual vehicles could potentially connect to charging stations to be comparable, resulting in the same set of 30 vehicles identified as good candidates to participate in a vehicle-to-grid service. We concluded that this was due to the relatively small feature set and that machine learning techniques were likely to outperform averaging techniques for more complex feature sets. We also explored the ability of the approaches to predict total vehicle availability and found that automated machine learning achieved the best performance with an accuracy of 91.4%. Such technology would be of value to vehicle-to-grid aggregation services.

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Section: Introductionmentioning

confidence: 99%

Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid

et al. 2020

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“…For example, an index for flexibility of DR units has been introduced and various classifications and forms of flexibility have been presented in [15]. Then, different flexibility products and markets have been classified by also considering their location [16]. However, all different flexible resources and system levels were not thoroughly considered [16].…”

Section: Introductionmentioning

confidence: 99%

“…Then, different flexibility products and markets have been classified by also considering their location [16]. However, all different flexible resources and system levels were not thoroughly considered [16]. An in-depth review of flexible ramping products (FRPs), which are mainly intended to the transmission-level networks (TSO), was presented in [17].…”

Section: Introductionmentioning

confidence: 99%

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

et al. 2019

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The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading.

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“…Such coordination and information exchange between both parties regarding flexibility activations is essential to avoid needles flexibility requirements, which can lead to further grid problems [64]. As suggested in [30], this coordination can be done by communicating the flexibility offers to the DSO in advance, in order to discard those offers that can create local congestions in the distribution network, or through assigning the task of demand flexibility management to the DSO like a technical virtual power plant. The work carried out in this paper does not require a dedicated communication channel between the TSO and DSO, as the expected information exchange will be minimal.…”

Section: Demand Flexibility Buyersmentioning

confidence: 99%

“…The flexibility addressed in this study is the flexibility of demand and it can be affected by the type of customer and nature of flexible load involved [30]. While there are several attributes that can describe demand flexibility, the main criteria of interest to this paper are described as follows:…”

Section: Demand Flexibilitymentioning

confidence: 99%

A Decentralized Local Flexibility Market Considering the Uncertainty of Demand

2018

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Abstract:The role of the distribution system operator (DSO) is evolving with the increasing possibilities of demand management and flexibility. Rather than implementing conventional approaches to mitigate network congestions, such as upgrading existing assets, demand flexibility services have been gaining much attention lately as a solution to defer the need for network reinforcements. In this paper, a framework for a decentralized local market that enables flexibility services trading at the distribution level is introduced. This market operates on two timeframes, day-ahead and real-time and it allows the DSO to procure flexibility services which can help in its congestion management process. The contribution of this work lies in considering the uncertainty of demand during the day-ahead period. As a result, we introduce a probabilistic process that supports the DSO in assessing the true need of obtaining flexibility services based on the probability of congestion occurrence in the following day of operation. Besides being able to procure firm flexibility for high probable congestions, a new option is introduced, called the right-to-use option, which enables the DSO to reserve a specific amount of flexibility, to be called upon later if necessary, for congestions that have medium probabilities of taking place. In addition, a real-time market for flexibility trading is presented, which allows the DSO to procure flexibility services for unforeseen congestions with short notice. Also, the effect of the penetration level of flexibility on the DSO's total cost is discussed and assessed. Finally, a case study is carried out for a real distribution network feeder in Spain to illustrate the impact of the proposed flexibility framework on the DSO's congestion management process.

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Flexibility products and markets: Literature review

Cited by 253 publications

References 16 publications

Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid

Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

A Decentralized Local Flexibility Market Considering the Uncertainty of Demand

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