Identifying barriers to large-scale integration of variable renewable electricity into the electricity market: A literature review of market design

Jing, Hu; Harmsen, Robert; Crijns-Graus, Wina; Worrell, Ernst; Broek, Machteld van den

doi:10.1016/j.rser.2017.06.028

Cited by 186 publications

(143 citation statements)

References 41 publications

Supporting

Mentioning

143

Contrasting

Order By: Relevance

“…where ω k := [ω 1,k , ω 2,k , ω 3,k , ω 4,k ]. Suppose ω i,k and υ k satisfy zero-mean Gaussian distributions, namely ω i,k ∼ N 0, σ 2 x,i for i ∈ [1,4] and υ k ∈ N 0, σ 2 g .…”

Section: Model Formulationmentioning

confidence: 99%

“…Lithium-ion (Li-ion) batteries are the key component of a growing body of energy-related applications [1], such as microgrids [2,3], electricity retailing market [4], uninterruptible power supplies (UPSs) [5], and electric vehicles (EVs) [6]. However, battery degradation exists anytime once produced and can be excessively accelerated if inappropriately managed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

et al. 2019

View full text Add to dashboard Cite

This paper develops a new prediction method for the aging trajectory of lithium-ion batteries with significantly reduced experimental tests. This method is driven by data collected from two types of battery operation modes. The first type is accelerated aging tests that are performed under stress factors, such as overcharging, over-discharging and large current rates, and cover most of the battery lifespan. In the second operation mode, the same kinds of cells are aged at normal speeds to generate a partial aging profile. An accelerated aging model is developed based on the first type of data and is then migrated as a new model to describe the normalspeed aging behavior. Under the framework of Bayesian Monte Carlo algorithms, the new model is parameterized based on the second type of data and is used for prediction of the remaining battery aging trajectory. The proposed prediction method is validated on three types of commercial batteries and also compared with two benchmark algorithms. The sensitivity of results to the number of cycles is investigated for both modes. Illustrative results demonstrate that based on the normal-speed aging data collected in the first 30 cycles, the proposed method can predict the entire aging trajectories (up to 500 cycles) at a root-mean-square error of less than 2.5% for all considered scenarios. When only using the first five-cycle data for model training, such a prediction error is bounded by 5% for aging trajectories of all the tested batteries.

show abstract

Section: Model Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Namely, aggregators can operate on the spot markets, i.e., by energy arbitrage, and on the balancing markets [8], however, some market barriers are present. For example, in many European countries, it is impossible for renewable electricity generation to operate on balancing markets, while this generation is very suitable to use for downward regulation [46]. Another current market inefficiency is the risk that current renewables generation incentive schemes (especially feed-in tariffs) decrease the value of newly installed renewables generation over time.…”

Section: Market Layermentioning

confidence: 99%

An Exploration of the Three-Layer Model Including Stakeholders, Markets and Technologies for Assessments of Residential Smart Grids

et al. 2018

View full text Add to dashboard Cite

In this paper, a framework is presented for the evaluation of smart grid environment which is called the three-layer model. This three-layer model comprises three specific categories, or ‘layers’, namely, the stakeholder, market and technologies layers. Each layer is defined and explored herein, using an extensive literature study regarding their key elements, their descriptions and an overview of the findings from the literature. The assumption behind this study is that a solid understanding of each of the three layers and their interrelations will help in more effective assessment of residential smart grid pilots in order to better design products and services and deploy smart grid technologies in networks. Based on our review, we conclude that, in many studies, social factors associated with smart grid pilots, such as markets, social acceptance, and end-user and stakeholder demands, are most commonly defined as uncertainties and are therefore considered separately from the technical aspects of smart grids. As such, it is recommended that, in future assessments, the stakeholder and market layers should be combined with the technologies layer so as to enhance interaction between these three layers, and to be able to better evaluate residential smart energy systems in a multidisciplinary context.

show abstract

“…Large electricy market involving several countries and regions proposed by the US and EU appears in recent year [68]. A trend of retailed electricity market is inevitable in the future [69][70][71] . In retailed market, generations hold non-cooperative relationships with each other.…”

Section: Calculation Of Ne Within a Retailed Mgmentioning

confidence: 99%

A Review on Multi-Agent Technology in Micro-Grid Control

Ding

Yin

2018

Electron Sci Technol Appl

View full text Add to dashboard Cite

Micro-Grid (MG) integrates renewable generation, storage devices and controllable generations, it provides efficent utilization of clean energy while keeping stable external characteristics. Capability of continuous power supply, high scalability and flexible operation modes can satifiy the current demand of joint operation of renewable generation and Macro-Grid, and will provide a solid foundation for smart grid technology in the future. Thus, MG is an excellent integration of renewable energy utilization with a bright future, Multi-Agent System (MAS) is a new hierarchical control platform and can completely cover all the devices within a MG, its flexible control modes meet the needs of various operations of MG, and the capability of distributed computing supports intelligent functions of MG in the future. Therefore, developing premium functions for MAS in MG control will promote the development of both MG and Smart Grid technologies. This paper reviews the current applications of MAS technology for MG both in basic and advanced control demands. For basic demands concerning safe operations for MG, functions of MAS are available, but a further improvement of performance is essential for future researches to increase penetration of MAS in MG control; For advanced demands, MAS should increase calculation speed to meet the complex need of MG. In the last part, the future focuses are also depicted.

show abstract

Identifying barriers to large-scale integration of variable renewable electricity into the electricity market: A literature review of market design

Cited by 186 publications

References 41 publications

Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

An Exploration of the Three-Layer Model Including Stakeholders, Markets and Technologies for Assessments of Residential Smart Grids

A Review on Multi-Agent Technology in Micro-Grid Control

Contact Info

Product

Resources

About