Evaluating and planning flexibility in sustainable power systems

Jian, Ma; Silva, Vera; Belhomme, R.; Kirschen, Daniel S.; Ochoa, Luis F.

doi:10.1109/pesmg.2013.6672221

Cited by 119 publications

(143 citation statements)

References 24 publications

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“…While having a broader scope than production cost models, CEMs typically have more limited operational detail (Sullivan, Eurek, and Margolis 2014). Existing CEMs often implicitly or explicitly include some representations of flexibilityboth the need for, and the potential supply of, flexibility (e.g., Ma et al 2013;Johnston et al 2013;Short et al 2011;EPRI 2014)-however, the computational complexity needed to simulate investment decisions necessitates simplifications that make it difficult to fully capture operational flexibility needs and contributions. Thus, we improve these prior methods and the present report describes our approach.…”

Section: Introductionmentioning

confidence: 99%

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Capturing the Impact of Storage and Other Flexible Technologies on Electric System Planning

Hale

Stoll

Mai

2016

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Executive SummaryPower systems of the future are likely to require additional flexibility due to the operating characteristics of many clean energy technologies, particularly those relying on renewable energy sources. This subject has been well studied from an operational perspective, but it has been more difficult to incorporate into capacity expansion models (CEMs) that study investment decisions on the decadal scale. There are two primary reasons for this. First, the necessary input data, including cost and resource projections, for flexibility options like demand response and storage are not widely available and are highly uncertain. Second, it is computationally difficult to simultaneously represent both investment and operational decisions in detail, with the latter being necessary to adequately value system flexibility in realistic systems.The primary purpose of this report is to present new capabilities that were developed for a particular CEM, NREL's Resource Planning Model (RPM), to better reflect the impact of variable wind and solar generation on system operations and resource adequacy, and, complementarily, to model energy-constrained flexibility resources. The additional variable generation modeling capabilities enable a more-accurate representation of the need for additional flexibility in systems with high penetrations of variable generation and, as a result, better reflect the value of technologies that can provide flexibility. We demonstrate the use of the new capabilities by examining the role of two broad technological sources of flexibility in power systems-utility-scale storage and interruptible load-and we model how they might contribute to a range of grid services for operations and planning. A wide range of different energy storage technologies is modeled, including multiple types of battery technologies. We also model interruptible load, a type of demand response that can be used to reduce demand, usually during peak hours, within pre-determined capacity and duration limits.The new capabilities use load duration curves from hourly data spanning a full year, thus enabling RPM to capture the impact of variable generation and the potential value of storage and interruptible load during infrequent, but important periods and hours within the year. The techniques enable RPM to accurately calculate the need for grid services and simultaneously consider, in its investment decision, the potential for storage and interruptible load to provide value to the system in multiple distinct ways: firm capacity for resource adequacy, operating reserve provision, energy arbitrage, and reductions in renewable curtailment. The ability of storage and interruptible load to lower system costs by providing these services, as well as the capital and implementation costs and other characteristics of the flexible technologies, are factored into RPM's economic investment decisions. These new techniques adapted for RPM can also be implemented in other CEMs.Using these capabilities, we analyze deployment of storage and...

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Section: Introductionmentioning

confidence: 99%

“…They also examine the economic attractiveness of the mitigation options themselves. Others have developed new metrics to evaluate the operational flexibility of different portfolios (e.g., Lannoye, Flynn, and O'Malley 2012;Ma et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Capturing the Impact of Storage and Other Flexible Technologies on Electric System Planning

Hale

Stoll

Mai

2016

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“…In [7], the insufficient ramping resource expectation (IRRE) metric has been developed to assess power system flexibility from a probabilistic viewpoint. A flexibility index that reflects the individual generating unit and the whole system was proposed for incorporation into the unit construction and commitment model [12]. The flexibility envelope that describes the flexibility potential dynamics of a power system and individual resources was introduced in [13].…”

Section: Introductionmentioning

confidence: 99%

Operation Flexibility Evaluation and Its Application to Optimal Planning of Bundled Wind-Thermal-Storage Generation System

Yang

Zhang

et al. 2018

Electronics

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The growing penetration of wind power in a power system brings great challenges to system operation flexibility. For generation planning in presence of high wind power penetration, it is essential to take the operation flexibility of the system into account. Firstly, this paper developed the system operation flexibility metrics through considering the flexibility contribution of thermal generating units (TGUs) by operational state transition. Secondly, a planning model for the bundled wind-thermal-storage generation system (BWTSGS) that considers the operation flexibility constraints is proposed. The planning model is used to determine the power and energy rating of an energy storage system (ESS) as well as the type and number of TGUs. A daily scheduling simulation model of a BWTSGS is proposed to calculate the operation cost for the planning model and consider the sequential operation characteristics of the BWTSGS. Further, in order to accelerate the computation, a wind power sequential clustering technique based on the discrete Fourier transform (DFT) method is developed for improving the computational efficiency. Case studies have been conducted on a 1000-MW wind farm to demonstrate the validity and effectiveness of the proposed model.

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“…Their limited controllability and predictability results in an increased need for power system flexibility, while flexible conventional power plants currently experience decreasing profitability as a result of low electricity prices and a limited number of operating hours [1]. Flexibility is the ability to provide up-and downward power adjustments to deal with short-term imbalances between generation and consumption of electric energy [2,3]. This flexibility can be provided by flexible generation and consumption, and electricity storage, but can also be activated in neighboring regions through interconnection capacity and the further integration of adjacent markets (Fig.…”

Section: Introductionmentioning

confidence: 99%

Auction-based allocation of shared electricity storage resources through physical storage rights

Brijs

Huppmann

Siddiqui

et al. 2016

Journal of Energy Storage

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This article proposes a new electricity storage business model based on multiple simultaneously considered revenue streams, which can be attributed to different market activities and players. These players thus share electricity storage resources and compete to obtain the right to use them in a dynamic allocation mechanism. It is based on the design of a new periodically organized auction to allocate shared storage resources through physical storage rights between different market players and accompanying applications. Through such a flexibility platform owners of flexible resources can commercialize their flexible capacity over different applications, while market players looking for additional flexibility can obtain this through a pay-per-use principle and thus not having to make long-term investment commitments. As such, they can quickly adapt their portfolio according to the market situation. Alternatively, through such an allocation mechanism players can effectively share storage resources. Players may be incentivized to participate as they can share the investment cost, mitigate risk, exploit economies of scale, overcome regulatory barriers, and merge time-varying and player-dependent flexibility needs. The mechanism allocates the limited storage resources to the most valuable application for each market-clearing, based on the competing players' willingness-to-pay. An illustrative case study is provided in which three players share storage resources that are allocated through a daily auction with hourly market-clearings.

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Evaluating and planning flexibility in sustainable power systems

Cited by 119 publications

References 24 publications

Capturing the Impact of Storage and Other Flexible Technologies on Electric System Planning

Capturing the Impact of Storage and Other Flexible Technologies on Electric System Planning

Operation Flexibility Evaluation and Its Application to Optimal Planning of Bundled Wind-Thermal-Storage Generation System

Auction-based allocation of shared electricity storage resources through physical storage rights

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