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Enregistrement W7009484408

Dynamic Modelling and Performance Analysis of Energy Storage Systems for Frequency Regulation in Bulk Power Systems

2021· dissertation· en· W7009484408 sur OpenAlexaboutno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2021
Typedissertation
Langueen
DomaineEngineering
ThématiqueMicrogrid Control and Optimization
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFrequency regulationEnergy storageElectric power systemRenewable energyFlywheel energy storageAutomatic frequency controlTransmission systemFrequency responseFlywheel
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Renewable Energy Sources (RESs) provide a feasible alternative to supply electrical loads without the unfavorable environmental impacts of fossil fuels. However, despite the significant environmental benefits of RESs, several operational challenges associated with their high levels of penetration in power systems need to be addressed. Extensive research has shown that Energy Storage Systems (ESSs) facilitate increased penetration levels of RESs by providing flexibility to the system, especially considering the technical maturity and decreasing cost of these technologies; hence, penetration of ESS, such as batteries and flywheels is likely to grow significantly in the coming years. Indeed, services that have been traditionally procured from synchronous generators such as Frequency Regulation (FR) are already being provided by ESSs. However, appropriate frequency control must be considered to take advantage of the fast response capability of ESS facilities, while coordinating their response with the bulk conventional generators currently used for FR. Some characteristics of the bulk power grids, regulation signals, and the State of Charge (SoC) management of the ESSs need be considered for the design of proper FR controls.
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\nIn this thesis, a FR model is proposed of a large interconnected power system including ESSs such as Battery Energy Storage Systems (BESSs) and Flywheel Energy Storage Systems (FESSs), considering all relevant stages in the frequency control process. The model, which considers Communication Delays (CDs) in the transmission of signals in the FR control loop, is developed from the viewpoint of an Independent System Operator (ISO), using the Ontario Power System (OPS) as case study. To this effect, empirically-based and generic SoC models for FESS and BESS considering the charging and discharging process characteristics are proposed. The system, ESSs, and SoC components are modelled in detail from a FR perspective and validated using real system and ESSs data, and a practical transient stability model of the North American Eastern Interconnection (NAEI) in Dynamic Security Assessment Tools (DSATools™) platform. The proposed model is validated with and considers all main stages of the FR control process, including CDs and the SoC management model of the ESS facilities, ensuring a realistic closed-loop response. Simulation studies show that the proposed model accurately represents the FR process of a large interconnected power system including ESSs, and can be used for accurate FR studies. The impact of CDs and SoC management of ESS facilities on the Area Control Error (ACE), and the computational efficiency of the proposed FR model are studied and discussed.
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\nA novel H2 filter design is proposed to optimally split the FR signal between conventional and fast regulating ESS assets, considering typical CDs. The design approach includes filtering the FR signal by producing a slowly-varying component or Traditional Regulation Signal (RegA) to be provided to the slow regulating resources (i.e., Traditional Generators (TGs)), while the remaining fast component or Dynamic Regulation Signal (RegD) is provided to the fast response ESS facilities (FESS and BESS) to take advantage of their fast response characteristics. The design of the H2 filter is formulated as an optimal control design problem, and the proposed filter is integrated into the previously validated FR model with ESSs to form an Integrated Model, which includes a Proposed Set-Point (PSP) calculation and an anti-windup strategy. The PSP allows FR capacity from ESSs to be comparable to TGs FR capacity while keeping the system stable, which is not the case in the current FR process for the OPS. The proposed anti-windup strategy is added to avoid saturation when both TGs and ESSs reach their limits, or TGs reach their limits while the ESS facilities are not able to follow the PSP signals because of their SoC limits. Thus, the proposed filter sends RegA and RegD signals considering the SoC of fast response resources and capacity limits of ESSs and TGs, and depend on the conditions of the system, working in a coordinated manner. The FR performance with the H2 filter signals, RegA and RegD, is also compared with the existing FR process in the OPS, focusing on studying the impact of CDs and limited regulation capacity, and the effect of the PSP calculation and anti-windup strategy. The results show that the H2 filter design and signal splitting strategy improves the FR process performance significantly, in terms of reducing the ACE, and thus reduce the need for regulation capacity. 
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\nFinally, a detailed methodology is developed to obtain Marginal Rate of Technical Substitution (MRTS) curves for the Independent Electricity System Operator (IESO). The IESO’s MRTS curves consider different ESSs and discharging times (i.e., 15 min for FESS, and 15 min, 1 h, 2 h, and 4h for BESS), scenarios (i.e., peak hours, non-peak hours, morning ramp hours, and evening ramp hours), and seasons. The criteria agreed upon with the IESO for the generation of heat maps and MRTS is also presented. Furthermore, the procedure to select the representative typical days per season to be used in the generation of the MRTS curves is explained in detail, and an example of how to interpret one of the MRTS curves is explained. Heat maps and MRTS curves are proposed as analysis tools to allow ISOs to select the desired performance metric, and the combination of RegA and RegD resources that would allow to achieve it while still reducing the total regulation capacity. Although this methodology is applied to the IESO, it could be applied to other ISOs with appropriate modifications.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,030
Score d'incertitude au seuil0,996

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,157
Écart entre enseignants0,153 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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