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arxiv: 2303.09560 · v1 · submitted 2023-03-16 · 📡 eess.SY · cs.SY· math.PR

Methodology for Capacity Credit Evaluation of Physical and Virtual Energy Storage in Decarbonized Power System

Ning Qi , Peng Li , Lin Cheng , Ziyi Zhang , Wenrui Huang , Weiwei Yang This is my paper

Pith reviewed 2026-05-24 09:37 UTC · model grok-4.3

classification 📡 eess.SY cs.SYmath.PR
keywords capacity creditenergy storagevirtual energy storagedecarbonized power systemtwo-stage dispatchdecision-dependent uncertaintyreliability indicesequivalent physical storage capacity
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The pith

A two-stage dispatch framework that models human behavior and decision-dependent uncertainties shows prior capacity credit estimates for energy storage overstated their contribution by 10-70%.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out a systematic way to measure the capacity credit of both physical energy storage and virtual energy storage so that their contribution to system adequacy can be quantified while respecting day-ahead market choices and real-time failures. It introduces a coordinated two-stage dispatch that balances self-scheduling against corrective actions and adds explicit terms for human-driven uncertainties in operating state, self-consumption, and available capacity. Earlier methods that omitted these terms produced capacity credit figures 10 to 70 percent too high on the same test systems. New indices such as equivalent physical storage capacity are defined to show how much conventional generation or physical storage each resource can displace at equal reliability. Case studies on the IEEE RTS-79 system with real data confirm that the revised indices give lower but more credible values and identify operating factors that most affect storage adequacy.

Core claim

The central claim is that a two-stage coordinated dispatch model, by jointly optimizing day-ahead self-management and real-time corrective actions while embedding decision-independent uncertainties in operate state and self-consumption plus decision-dependent uncertainty in available capacity, produces credible capacity credit values for energy storage and virtual energy storage; these values are substantially lower than those obtained when the uncertainties are ignored, and the framework supplies new indices such as equivalent physical storage capacity that quantify both practical adequacy contribution and displacement potential.

What carries the argument

The two-stage coordinated dispatch model that embeds decision-independent uncertainties (operate state, self-consumption) and decision-dependent uncertainty (available capacity) arising from human and market behavior.

Load-bearing premise

The two-stage dispatch model and its explicit representations of human behavior and the three listed uncertainties capture actual market and user dynamics without systematic bias in the resulting capacity credit numbers.

What would settle it

Direct comparison of the model's predicted capacity credit values against measured adequacy performance of real energy storage and virtual storage units during actual capacity-shortage events on an operating grid.

Figures

Figures reproduced from arXiv: 2303.09560 by Lin Cheng, Ning Qi, Peng Li, Weiwei Yang, Wenrui Huang, Ziyi Zhang.

Figure 1
Figure 1. Figure 1: Flowchart of capacity credit evaluation [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of DIUs and DDUs in SoC bounds of VES [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of system and GES operations under different dispatch [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Flowchart of Algorithm B1 & B2 for reliability assessment with GES [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of capacity credit (a) EFC, ECC, and EPSC (b) EGCS and [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: IEEE RTS-79 benchmark system B. Capacity credit evaluation of energy storage In this section, we compare the performance of the proposed method with two state-of-the-art methods: (i) fixed dispatch and (ii) greedy management. Historical data of RG, load, and day￾ahead price are collected from Belgium’s transmission system operator, i.e., Elia [36]., and the normalized value of RG and load profile are used … view at source ↗
Figure 8
Figure 8. Figure 8: Convergence performance of SMCS with three dispatch methods [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: System and ES operations compared among three methods: (a) fixed dispatch, (b) greedy management, (c) coordinated dispatch [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Adequacy results compared among three methods and rated power & capacity of ES: (a) 2h, (b) 4h, (c) 6h [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Benefit and limitation of the proposed methods with respect to (a) practical adequacy performance, (b) economic performance, and (c) safety [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Normalized Capacity credit value with different indices and power & capacity ratings of ES: (a) 2h, (b) 4h, (c) 6h [PITH_FULL_IMAGE:figures/full_fig_p013_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: EGCS compared with different dispatch methods and power & capacity ratings of ES: (a) 2h, (b) 4h, (c) 6h [PITH_FULL_IMAGE:figures/full_fig_p013_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Visualization of uncertainties in VES operations with respect to: (a) [PITH_FULL_IMAGE:figures/full_fig_p014_14.png] view at source ↗
Figure 17
Figure 17. Figure 17: Response deviation of the proposed method with different uncertainties [PITH_FULL_IMAGE:figures/full_fig_p015_17.png] view at source ↗
Figure 15
Figure 15. Figure 15: Comparison of system and VES operations: (a) residual capacity, (b) [PITH_FULL_IMAGE:figures/full_fig_p015_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Adequacy results compared among different uncertainties and rated power & capacity of VES: (a) 1h, (b) 1.5h, (c) 2h [PITH_FULL_IMAGE:figures/full_fig_p016_16.png] view at source ↗
Figure 18
Figure 18. Figure 18: Normalized Capacity credit value with different indices and power & capacity ratings of VES: (a) 1h, (b) 1.5h, (c) 2h [PITH_FULL_IMAGE:figures/full_fig_p016_18.png] view at source ↗
Figure 20
Figure 20. Figure 20: EGCS compared with different uncertainties and power & capacity ratings of ES: (a) 1h, (b) 1.5h, (c) 2h [PITH_FULL_IMAGE:figures/full_fig_p016_20.png] view at source ↗
Figure 19
Figure 19. Figure 19: EPSC compared with different uncertainties and rated power of VES [PITH_FULL_IMAGE:figures/full_fig_p017_19.png] view at source ↗
Figure 21
Figure 21. Figure 21: EENS changes of decarbonized power system with different penetration of RES and power & capacity ratings of ES: (a) 2h, (b) 4h, (c) 6h [PITH_FULL_IMAGE:figures/full_fig_p018_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: EGCS of decarbonized power system with different penetration of RES and power & capacity ratings of ES: (a) 2h, (b) 4h, (c) 6h [PITH_FULL_IMAGE:figures/full_fig_p018_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: EGCS of decarbonized power system with different efficiency and power & capacity ratings of ES: (a) 2h, (b) 4h, (c) 6h [PITH_FULL_IMAGE:figures/full_fig_p018_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: EGCS of decarbonized power system with different MTTR and power & capacity ratings of ES: (a) 2h, (b) 4h, (c) 6h [PITH_FULL_IMAGE:figures/full_fig_p018_24.png] view at source ↗
Figure 26
Figure 26. Figure 26: It is observed that the adequacy performance will be [PITH_FULL_IMAGE:figures/full_fig_p019_26.png] view at source ↗
Figure 25
Figure 25. Figure 25: Adequacy improvement with distributed energy storage with different [PITH_FULL_IMAGE:figures/full_fig_p019_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: EENS changes of decarbonized power system with different penetration of RES and power & capacity ratings of VES: (a) 1h, (b) 1.5h, (c) 2h [PITH_FULL_IMAGE:figures/full_fig_p020_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: EGCS of decarbonized power system with different penetration of RES and power & capacity ratings of VES: (a) 1h, (b) 1.5h, (c) 2h [PITH_FULL_IMAGE:figures/full_fig_p020_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: EGCS of decarbonized power system with different self-discharge rate and power & capacity ratings of ES: (a) 1h, (b) 1.5h, (c) 2h [PITH_FULL_IMAGE:figures/full_fig_p020_28.png] view at source ↗
Figure 30
Figure 30. Figure 30: The impact of the probabilistic level of chance-constrained method [PITH_FULL_IMAGE:figures/full_fig_p021_30.png] view at source ↗
Figure 29
Figure 29. Figure 29: Load profile of VES resources with different load patterns [PITH_FULL_IMAGE:figures/full_fig_p021_29.png] view at source ↗
Figure 31
Figure 31. Figure 31: Capacity performance of DR in CAISO [PITH_FULL_IMAGE:figures/full_fig_p022_31.png] view at source ↗
read the original abstract

Energy storage (ES) and virtual energy storage (VES) are key components to realizing power system decarbonization. Although ES and VES have been proven to deliver various types of grid services, little work has so far provided a systematical framework for quantifying their adequacy contribution and credible capacity value while incorporating human and market behavior. Therefore, this manuscript proposed a novel evaluation framework to evaluate the capacity credit (CC) of ES and VES. To address the system capacity inadequacy and market behavior of storage, a two-stage coordinated dispatch is proposed to achieve the trade-off between day-ahead self-energy management of resources and efficient adjustment to real-time failures. And we further modeled the human behavior with storage operations and incorporate two types of decision-independent uncertainties (DIUs) (operate state and self-consumption) and one type of decision-dependent uncertainty (DDUs) (available capacity) into the proposed dispatch. Furthermore, novel reliability and CC indices (e.g., equivalent physical storage capacity (EPSC)) are introduced to evaluate the practical and theoretical adequacy contribution of ES and VES, as well as the ability to displace generation and physical storage while maintaining equivalent system adequacy. Exhaustive case studies based on the IEEE RTS-79 system and real-world data verify the significant consequence (10%-70% overestimated CC) of overlooking DIUs and DDUs in the previous works, while the proposed method outperforms other and can generate a credible and realistic result. Finally, we investigate key factors affecting the adequacy contribution of ES and VES, and reasonable suggestions are provided for better flexibility utilization of ES and VES in decarbonized power system.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes a framework for capacity credit (CC) evaluation of physical energy storage (ES) and virtual energy storage (VES) that uses a two-stage coordinated dispatch model to balance day-ahead self-management against real-time adjustments. It incorporates human-behavior-driven decision-independent uncertainties (DIUs: operate state, self-consumption) and decision-dependent uncertainties (DDUs: available capacity), defines new indices including equivalent physical storage capacity (EPSC), and reports IEEE RTS-79 plus real-data case studies claiming that prior methods overestimate CC by 10-70% when these uncertainties are omitted.

Significance. If the behavioral uncertainty models can be shown to be empirically grounded rather than assumption-driven, the two-stage dispatch and EPSC index would offer a more realistic quantification of storage adequacy contributions and displacement potential in decarbonized systems. The combination of a test system with real-world data is a constructive step, but the absence of external calibration for the human-behavior parameters currently limits the strength of the overestimation claim and the practical utility of the new indices.

major comments (3)
  1. [Abstract and §4] Abstract and §4 (case studies): the headline result that prior methods overestimate CC by 10-70% is generated solely by comparing the proposed dispatch (with explicit DIU/DDU terms) against baselines that omit them; no external empirical calibration or independent dataset for the human-behavior distributions (operate state, self-consumption, available capacity) is supplied, so the numerical difference cannot be distinguished from an artifact of the chosen functional forms.
  2. [§3] §3 (two-stage dispatch and uncertainty modeling): the central claim that the framework produces “credible and realistic” CC values rests on the assumption that the modeled DIUs and DDUs accurately capture market and user dynamics without bias, yet the manuscript provides neither sensitivity analysis on the behavioral parameters nor comparison against measured storage-operation statistics from the real-world data set.
  3. [§5] §5 (EPSC definition and reliability indices): the new EPSC index is defined within the same two-stage framework used to generate the overestimation numbers; without an external benchmark or reduction to independently observed adequacy metrics, the index inherits the same circularity risk noted for the 10-70% figure.
minor comments (2)
  1. [§3] Ensure that all equations defining DIU and DDU distributions are accompanied by explicit parameter values or ranges so that the 10-70% result can be reproduced.
  2. [Figures and tables in §4] Figure captions and table headings should explicitly separate the contribution of operate-state DIU, self-consumption DIU, and available-capacity DDU to the reported CC differences.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We agree that enhancing the empirical grounding and including sensitivity analyses will improve the manuscript. We have made revisions accordingly and address each comment below.

read point-by-point responses

  1. Referee: [Abstract and §4] the headline result that prior methods overestimate CC by 10-70% is generated solely by comparing the proposed dispatch (with explicit DIU/DDU terms) against baselines that omit them; no external empirical calibration or independent dataset for the human-behavior distributions is supplied, so the numerical difference cannot be distinguished from an artifact of the chosen functional forms.

    Authors: We acknowledge that the reported overestimation is based on internal comparisons within our modeling framework. The distributions for DIUs and DDUs are derived from the real-world data presented in the case studies. To address the concern, we have added details on the parameterization process from the data and included sensitivity analyses varying the behavioral parameters. The 10-70% range illustrates the potential impact of omitting these uncertainties rather than claiming universal calibration. We have revised Section 4 to clarify this. revision: partial

  2. Referee: [§3] the central claim that the framework produces “credible and realistic” CC values rests on the assumption that the modeled DIUs and DDUs accurately capture market and user dynamics without bias, yet the manuscript provides neither sensitivity analysis on the behavioral parameters nor comparison against measured storage-operation statistics from the real-world data set.

    Authors: We appreciate this observation. In the revised manuscript, we have incorporated a sensitivity analysis on the key behavioral parameters (e.g., probabilities for operate state and self-consumption rates) using ranges from the real-world dataset. Furthermore, we now compare the simulated dispatch outcomes and uncertainty realizations against statistics from the measured data, demonstrating that the models produce plausible operational patterns. This addition supports the realism of the CC values. revision: yes

  3. Referee: [§5] the new EPSC index is defined within the same two-stage framework used to generate the overestimation numbers; without an external benchmark or reduction to independently observed adequacy metrics, the index inherits the same circularity risk noted for the 10-70% figure.

    Authors: The EPSC is proposed as a novel index to quantify the equivalent contribution within the uncertainty-aware framework. We agree on the value of external benchmarks. In revision, we have expanded the discussion in Section 5 to relate EPSC to conventional reliability indices like LOLE and EENS, and provided numerical comparisons on the test system. While a fully independent adequacy dataset is not available, the index's utility is demonstrated through its ability to consistently evaluate displacement potential across scenarios. revision: partial

Circularity Check

0 steps flagged

No significant circularity; new indices and comparisons rest on explicit modeling choices rather than definitional reduction

full rationale

The paper defines a two-stage dispatch incorporating DIUs (operate state, self-consumption) and DDUs (available capacity) via human-behavior modeling, then introduces novel indices such as EPSC to quantify equivalent adequacy contribution. Case studies on IEEE RTS-79 with real-world data produce the 10-70% overestimation claim by direct comparison to baselines omitting those uncertainties. No equations or self-citations are shown reducing the reported CC values or overestimation percentages to fitted parameters or prior self-referential results by construction; the differences arise from the added uncertainty terms rather than tautological redefinition. External benchmarks (RTS-79, real data) keep the derivation self-contained against the listed circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities can be extracted or audited from the provided text.

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Reference graph

Works this paper leans on

48 extracted references · 48 canonical work pages

  1. [1]

    Allan, R. N. Reliability evaluation of power systems. Springer Science & Business Media

    work page

  2. [2]

    Global energy review 2021

    IEA, “Global energy review 2021.” [Online]. Available: https://www.iea.org/reports/global-energy-review-2021

    work page 2021

  3. [3]

    Depletion of fossil fuels and anthropogenic climate change—A review

    Höök M, Tang X. Depletion of fossil fuels and anthropogenic climate change—A review. Energy policy. 2013 Jan 1;52:797-809

    work page 2013

  4. [4]

    How do demand response and electrical energy storage affect (the need for) a capacity market?

    Khan AS, Verzijlbergh RA, Sakinci OC, De Vries LJ. How do demand response and electrical energy storage affect (the need for) a capacity market?. Applied Energy. 2018 Mar 15;214:39-62

    work page 2018

  5. [5]

    Reliability improvement of radial distribution system by optimal placement and sizing of energy storage system using TLBO

    Lata P, Vadhera S. Reliability improvement of radial distribution system by optimal placement and sizing of energy storage system using TLBO. Journal of Energy Storage. 2020 Aug 1;30:101492

    work page 2020

  6. [6]

    Reliability assessment and improvement of distribution system with virtual energy storage under exogenous and endogenous uncertainty

    Qi N, Cheng L, Zhuang Y, Zhou Y, Zhang Y, Zhu C. Reliability assessment and improvement of distribution system with virtual energy storage under exogenous and endogenous uncertainty. Journal of Energy Storage. 2022 Dec 1;56:105993

    work page 2022

  7. [7]

    The capacity credit of wind power: A theoretical analysis

    Haslett J, Diesendorf M. The capacity credit of wind power: A theoretical analysis. Solar Energy. 1981 Jan 1;26(5):391-401

    work page 1981

  8. [8]

    Effective load carrying capability of generating units

    Garver LL. Effective load carrying capability of generating units. IEEE Transactions on Power apparatus and Systems. 1966 Aug(8):910-9

    work page 1966

  9. [9]

    Comparison of capacity credit calculation methods for conventional power plants and wind power

    Amelin M. Comparison of capacity credit calculation methods for conventional power plants and wind power. IEEE Transactions on Power Systems. 2009 Mar 27;24(2):685-91

    work page 2009

  10. [10]

    Framework for capacity credit assessment of electrical energy storage and demand response

    Zhou Y, Mancarella P, Mutale J. Framework for capacity credit assessment of electrical energy storage and demand response. IET Generation, Transmission & Distribution. 2016 Jun;10(9):2267-76

    work page 2016

  11. [11]

    Distribution network reliability improvements in presence of demand response

    Safdarian A, Degefa MZ, Lehtonen M, Fotuhi-Firuzabad M. Distribution network reliability improvements in presence of demand response. IET Generation, Transmission & Distribution. 2014 Dec;8(12):2027-35

    work page 2014

  12. [12]

    Modelling and assessment of the contribution of demand response and electrical energy storage to adequacy of supply

    Zhou Y, Mancarella P, Mutale J. Modelling and assessment of the contribution of demand response and electrical energy storage to adequacy of supply. Sustainable Energy, Grids and Networks. 2015 Sep 1;3:12-23

    work page 2015

  13. [13]

    Minimizing Unserved Energy Using Heterogeneous Energy Storage Unit (vol 34, pg 3647, 2019)

    Evans MP, Tindemans SH, Angeli D. Minimizing Unserved Energy Using Heterogeneous Energy Storage Unit (vol 34, pg 3647, 2019). IEEE Transactions on power systems. 2020 Sep 1;35(5):4144-

    work page 2019

  14. [14]

    The potential for battery energy storage to provide peaking capacity in the United States

    Denholm P, Nunemaker J, Gagnon P, Cole W. The potential for battery energy storage to provide peaking capacity in the United States. Renewable Energy. 2020 May 1;151:1269-77

    work page 2020

  15. [15]

    European resource adequacy assessment 2021–annex3: Methodology

    ENTSO-E, “European resource adequacy assessment 2021–annex3: Methodology.” [Online]. Available: https://extranet.acer.europa.eu/en/ Electricity/Pages/European-resource-adequacy-assessment.aspx

    work page 2021

  16. [16]

    Midwest independent transmission system operator (miso) energy storage study

    EPRI, “Midwest independent transmission system operator (miso) energy storage study.” [Online]. Available: https://www.epri.com/research/ products/1024489

    work page arXiv

  17. [17]

    Demand response issues and performance

    CAISO, “Demand response issues and performance.” [Online]. Available: http://www.caiso.com/participate/Pages/Load/Default.aspx

    work page

  18. [18]

    Energy storage sizing for wind power: impact of the autocorrelation of day‐ahead forecast errors

    Haessig P, Multon B, Ahmed HB, Lascaud S, Bondon P. Energy storage sizing for wind power: impact of the autocorrelation of day‐ahead forecast errors. Wind Energy. 2015 Jan;18(1):43-57

    work page 2015

  19. [19]

    Chance Constrained Economic Dispatch of Generic Energy Storage under Decision- Dependent Uncertainty

    Qi N, Pinson P, Cheng L, Wan Y, Zhuang Y. Chance Constrained Economic Dispatch of Generic Energy Storage under Decision- Dependent Uncertainty. arXiv preprint arXiv:2201.06407. 2022 Jan 17

    work page arXiv 2022

  20. [20]

    Reliability and economic assessment of compressed air energy storage in transmission constrained wind integrated power system

    Bhattarai S, Karki R, Piya P. Reliability and economic assessment of compressed air energy storage in transmission constrained wind integrated power system. Journal of Energy Storage. 2019 Oct 1;25:100830

    work page 2019

  21. [21]

    Operational reliability modeling and assessment of battery energy storage based on Lithium-ion battery lifetime degradation

    Cheng L, Wan Y, Zhou Y, Gao DW. Operational reliability modeling and assessment of battery energy storage based on Lithium-ion battery lifetime degradation. Journal of Modern Power Systems and Clean Energy. 2021 Nov 24

    work page 2021

  22. [22]

    Short-term impacts of DR programs on reliability of wind integrated power systems considering demand-side uncertainties

    Moshari A, Ebrahimi A, Fotuhi-Firuzabad M. Short-term impacts of DR programs on reliability of wind integrated power systems considering demand-side uncertainties. IEEE Transactions on Power Systems. 2015 Jul 16;31(3):2481-90

    work page 2015

  23. [23]

    Reliability assessment of incentive-and priced- based demand response programs in restructured power systems

    Nikzad M, Mozafari B. Reliability assessment of incentive-and priced- based demand response programs in restructured power systems. International Journal of Electrical Power & Energy Systems. 2014 Mar 1;56:83-96

    work page 2014

  24. [24]

    Reliability modeling of demand response considering uncertainty of customer behavior

    Kwag HG, Kim JO. Reliability modeling of demand response considering uncertainty of customer behavior. Applied Energy. 2014 Jun 1;122:24-33

    work page 2014

  25. [25]

    Hybrid probabilistic- possibilistic approach for capacity credit evaluation of demand response considering both exogenous and endogenous uncertainties

    Zeng B, Wei X, Zhao D, Singh C, Zhang J. Hybrid probabilistic- possibilistic approach for capacity credit evaluation of demand response considering both exogenous and endogenous uncertainties. Applied energy. 2018 Nov 1;229:186-200

    work page 2018

  26. [26]

    Bibliography of the application of probability methods in power system reliability evaluation 1977-1982

    Allan RN, Billinton R, Lee SH. Bibliography of the application of probability methods in power system reliability evaluation 1977-1982. IEEE Power Engineering Review. 1984 Feb(2):24-5

    work page 1977

  27. [27]

    A Bi-level optimizer for reliability and security assessment of a radial distribution system supported by wind turbine generators and superconducting magnetic energy storages

    Hashem M, Abdel-Salam M, Nayel M, El-Mohandes MT. A Bi-level optimizer for reliability and security assessment of a radial distribution system supported by wind turbine generators and superconducting magnetic energy storages. Journal of Energy Storage. 2022 Jul 1;51:104356

    work page 2022

  28. [28]

    Capacity value of wind power, calculation, and data requirements: the Irish power system case

    Hasche B, Keane A, O'Malley M. Capacity value of wind power, calculation, and data requirements: the Irish power system case. IEEE Transactions on power systems. 2010 Aug 16;26(1):420-30

    work page 2010

  29. [29]

    Capacity value assessments of wind power

    Milligan M, Frew B, Ibanez E, Kiviluoma J, Holttinen H, Söder L. Capacity value assessments of wind power. Advances in Energy Systems: The Large‐scale Renewable Energy Integration Challenge. 2019 Mar 18:369-84

    work page 2019

  30. [30]

    Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability

    Dong W, Chen X, Yang Q. Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability. Applied Energy. 2022 Feb 15;308:118387

    work page 2022

  31. [31]

    A unified model to optimize configuration of battery energy storage systems with multiple types of batteries

    Jiang Y, Kang L, Liu Y. A unified model to optimize configuration of battery energy storage systems with multiple types of batteries. Energy. 2019 Jun 1;176:552-60

    work page 2019

  32. [32]

    Smart meter data- driven evaluation of operational demand response potential of residential air conditioning loads

    Qi N, Cheng L, Xu H, Wu K, Li X, Wang Y, Liu R. Smart meter data- driven evaluation of operational demand response potential of residential air conditioning loads. Applied Energy. 2020 Dec 1;279:115708

    work page 2020

  33. [33]

    Evaluating the magnitude and duration of cold load pick-up on residential distribution using multi-state load models

    Schneider KP, Sortomme E, Venkata SS, Miller MT, Ponder L. Evaluating the magnitude and duration of cold load pick-up on residential distribution using multi-state load models. IEEE Transactions on Power Systems. 2015 Nov 11;31(5):3765-74

    work page 2015

  34. [34]

    Optimal residential community demand response scheduling in smart grid

    Nan S, Zhou M, Li G. Optimal residential community demand response scheduling in smart grid. Applied Energy. 2018 Jan 15;210:1280-9. 25

    work page 2018

  35. [35]

    Assessing the value of demand response in a decarbonized energy system–A large-scale model application

    Misconel S, Zöphel C, Möst D. Assessing the value of demand response in a decarbonized energy system–A large-scale model application. Applied Energy. 2021 Oct 1;299:117326

    work page 2021

  36. [36]

    Open access data of Belgium transmission system operator: [Online]

    Elia. Open access data of Belgium transmission system operator: [Online]. Available: https://www.elia.be/

    work page

  37. [37]

    Capacity share optimization for multiservice energy storage management under portfolio theory

    Yan X, Gu C, Wyman-Pain H, Li F. Capacity share optimization for multiservice energy storage management under portfolio theory. IEEE Transactions on Industrial Electronics. 2018 Mar 22;66(2):1598-607

    work page 2018

  38. [38]

    Declining capacity credit for energy storage and demand response with increased penetration

    Parks K. Declining capacity credit for energy storage and demand response with increased penetration. IEEE Transactions on Power Systems. 2019 May 9;34(6):4542-6

    work page 2019

  39. [39]

    Capacity value of energy storage in distribution networks

    Konstantelos I, Strbac G. Capacity value of energy storage in distribution networks. Journal of Energy Storage. 2018 Aug 1;18:389-401

    work page 2018

  40. [40]

    Data-driven insights from the nations deepest ever research on customer energy use

    McCracken B, Crosby M, Holcomb C, Russo S, Smithson C. Data-driven insights from the nations deepest ever research on customer energy use. Pecan Res. Inst., Austin, TX, USA. 2013:1949-3053

    work page 2013

  41. [41]

    Paris Agreement

    United Nation, “Paris Agreement.” [Online]. Available: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris- agreement

    work page

  42. [42]

    The design space for long-duration energy storage in decarbonized power systems

    Sepulveda NA, Jenkins JD, Edington A, Mallapragada DS, Lester RK. The design space for long-duration energy storage in decarbonized power systems. Nature Energy. 2021 May;6(5):506-16

    work page 2021

  43. [43]

    Power system decarbonization: Impacts of energy storage duration and interannual renewables variability

    Jafari M, Korpås M, Botterud A. Power system decarbonization: Impacts of energy storage duration and interannual renewables variability. Renewable Energy. 2020 Aug 1;156:1171-85

    work page 2020

  44. [44]

    Capacity market

    U.K., “Capacity market.” [Online]. Available: https://www.gov.uk/ government/collections/electricity-market-reform-capacity-market

    work page

  45. [45]

    Capacity market design and renewable energy: Performance incentives, qualifying capacity, and demand curves

    Byers C, Levin T, Botterud A. Capacity market design and renewable energy: Performance incentives, qualifying capacity, and demand curves. The Electricity Journal. 2018 Jan 1;31(1):65-74

    work page 2018

  46. [46]

    A new energy storage sharing framework with regard to both storage capacity and power capacity

    Xiao JW, Yang YB, Cui S, Liu XK. A new energy storage sharing framework with regard to both storage capacity and power capacity. Applied Energy. 2022 Feb 1;307:118171

    work page 2022

  47. [47]

    Available: https://data.mendeley.com/datasets/h5rccz3nw6/draft?a=872f9c09-9ac5- 4073-a962-74e4706519b5

    Supporting data brief [Online]. Available: https://data.mendeley.com/datasets/h5rccz3nw6/draft?a=872f9c09-9ac5- 4073-a962-74e4706519b5

    work page

  48. [48]

    A Lagrange multiplier based state enumeration reliability assessment for power systems with multiple types of loads and renewable generations

    Liu, Z., Hou, K., Jia, H., Zhao, J., Wang, D., Mu, Y., & Zhu, L. A Lagrange multiplier based state enumeration reliability assessment for power systems with multiple types of loads and renewable generations. IEEE Transactions on Power Systems, 36(4), 3260-3270

    work page