Quantification of operating reserves with high penetration of wind power considering extreme values

Obando, Johan; González, Gabriel Villarrubia; Moreno, Ricardo

doi:10.11591/ijece.v10i2.pp1693-1700

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…The first have been analyzed, through the optimal energy flow for the dispatch of the generation resources [15]- [17]. As a result, the formulation of the multi-period DC optimal power flow (DCOPF) has been enhanced in order to incorporate renewable energy generation's variability, considering factors such as electricity demand and wind availability uncertainty [18]- [21].…”

Section: Int J Elec and Comp Engmentioning

confidence: 99%

Power system operation considering detailed modelling of the natural gas supply network

Moreno

Larrahondo

Florez

2021

IJECE

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The energy transition from fossil-fuel generators to renewable energies represents a paramount challenge. This is mainly due to the uncertainty and unpredictability associated with renewable resources. A greater flexibility is requested for power system operation to fulfill demand requirements considering security and economic restrictions. In particular, the use of gas-fired generators has increased to enhance system flexibility in response to the integration of renewable energy sources. This paper provides a comprehensive formulation for modeling a natural gas supply network to provide gas for thermal generators, considering the use of wind power sources for the operation of the electrical system over a 24-hour period. The results indicate the requirements of gas with different wind power level of integration. The model is evaluated on a network of 20 NG nodes and on a 24-bus IEEE RTS system with various operative settings during a 24-hour period.

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Section: Int J Elec and Comp Engmentioning

confidence: 99%

Power system operation considering detailed modelling of the natural gas supply network

Moreno

Larrahondo

Florez

2021

IJECE

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“…Thus, multiple economic models related to the electricity sector are being developed recently that consider elements such as: consumersupplier relationship, the capacity of the consumer to change his/ her role (producer-prosumer), the form of consumer participation (active or passive), the intensity of collaboration between peers (with contracts or not), the inclusion of new production assets such as renewable energy (Obando et al, 2020), energy storage systems (Cantillo and Moreno, 2021), and demand response (Moreno et al, 2019), and the ownership and channels transfer used (blockchain among others) (Acquier et al, 2019;Athanassiou and Kotsi, 2018;Ertz et al, 2019;Ferraro and Conway, 2020;Garcia-Garcia et al, 2020;Hamari et al, 2016;Menor-Campos et al, 2019). All these elements were considered through different models made with different computing tools (Moreno-Chuquen and Cantillo-Luna, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Framework From Peer-to-Peer Electricity Trading Based on Communities Transactions

Moreno¹,

Hoyos²,

Cantillo³

2021

IJEEP

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Recently, several authors and reports inform about peer-to-peer transactions of goods and services. Usually, the electricity markets work trading energy at large scale in wholesale electricity markets. Given the integration of distributed energy resources such as PV generators, storage at small scale, demand response and electric vehicles then there are new options to trade energy at small scale by focusing in the producer-consumer relationship. This derives in alternatives based on peer-to-peer (P2P) electricity markets to trade energy employing tools as the blockchain and deployment of distributed energy resources (DERs). This paper presents a framework to trade energy at small scale with a flexible hybrid model of P2P based on transactions between communities and peers, for each period of time, each peer can change its role, the prosumer and producer peers can offer their generated energy. The consumer peers can adjust their consumption behavior based on price and quantity. Thus, the role of the community manager comes into play to associations between peers as an intermediary of the community and the grid. Therefore, a model of transactions with P2P offers was developed for different structures and sales prices. Finally, the framework to trade energy in a hybrid P2P model is evaluated in a demand curve considering a 24-h period for several cases.

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“…Obando et al. [12] proposed to use extreme values through Monte‐Carlo simulations to quantify operating reserves. Note that, besides the widely known random factors of unit faults and generation/demand fluctuation, specific factors may also influence the operating reserve capacity of a power plant considerably [13].…”

Section: Introductionmentioning

confidence: 99%

“…A dynamic sizing method proposed in [11] determines the required reserve capacity on a daily basis, using system imbalance risk estimation based on historical observations of system conditions by means of machine learning algorithms. Obando et al [12] proposed to use extreme values through Monte-Carlo simulations to quantify operating reserves. Note that, besides the widely known random factors of unit faults and generation/demand fluctuation, specific factors may also influence the operating reserve capacity of a power plant considerably [13].…”

Section: Introductionmentioning

confidence: 99%

Tracing spinning reserve inadequacy risk via hybrid importance sampling with an optimised partially collapsed Gibbs sampler

Wang

2021

IET Renewable Power Gen

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From the simulation perspective, spinning reserve risk evaluation of power system is commonly a rare-event assessing issue, for which importance sampling is an appealing solution technique. This paper proposes a hybrid importance sampling method for tracing spinning reserve inadequacy risk of power system integrating with renewables. The proposed method is oriented towards a generic high-dimensional integral calculus, which allows for continuous and discrete random variables mixed together for casting kinds of risk indices. To improve the robustness in high-dimensional applications, a partially collapsed Gibbs sampler is devised to help in exploring adaptive training samples which feature typical rare outage events. Interestingly, the originators engendering extreme spinning reserve inadequacy events can be traced using the sampling information of the proposed method. The performance of the proposed method is tested in an updated RTS-79 system vis-à-vis several existing methods. Spinning reserve inadequacy risk tracing is explained by uncovering a scenario frequency distribution concerning the short-term expected energy not supplied. INTRODUCTIONNowadays, the power systems have evolved to be an indispensable facility of great complexity to support civil life and social development. The increasing complexity primarily intended to be adapted for customer needs and electric markets and newly adopted techniques etc., commonly endows the contemporary power systems with basic defensive abilities against regional credible contingencies. However, we have already witnessed grave blackout consequences of complex power systems initiated by unpredictable rare contingencies, for example equipment decay or human-produced or natural hazards [1]. One recognised reason for spawning the observed rare initiators successful evolving into blackout essentially comes down to the lack of awareness of flexible operating reserve backing up very rare supply deficit.Operating reserve planning accounting for probabilistic factors can be traced back to 1963 [2], and since that period, the literature has documented several operating reserve planning schemes to economically hedging against outage risk, especially for renewable energy systems in recent passing years [3][4][5][6][7][8].This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Quantification of operating reserves with high penetration of wind power considering extreme values

Cited by 6 publications

References 23 publications

Power system operation considering detailed modelling of the natural gas supply network

Power system operation considering detailed modelling of the natural gas supply network

A Framework From Peer-to-Peer Electricity Trading Based on Communities Transactions

Tracing spinning reserve inadequacy risk via hybrid importance sampling with an optimised partially collapsed Gibbs sampler

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