Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power

Morales, Juan M.; Conejo, Antonio J.; Pérez-Ruiz, Juan

doi:10.1109/tpwrs.2009.2016598

Cited by 486 publications

(82 citation statements)

References 18 publications

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“…The implication is that when there is wind, the generation costs for the system decrease, but the security costs increase, as other studies have also concluded (e.g. Morales, Conejo, & Pérez-Ruiz, 2009). …”

Section: Costs Of Managing Wind Operational Challengesmentioning

confidence: 76%

Power system reliability impacts of wind generation and operational reserve requirements

Gil

2015

ing.inv.

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Due to its variability, wind generation integration presents a significant challenge to power system operators in order to maintain adequate reliability levels while ensuring least cost operation. This paper explores the trade-off between the benefits associated to a higher wind penetration and the additional operational reserve requirements that they impose. Such exploration is valued in terms of its effect on power system reliability, measured as an amount of unserved energy. The paper also focuses on how changing the Value of Lost Load (VoLL) can be used to attain different reliability targets, and how wind power penetration and the diversity of the wind energy resource will impact quality of supply (in terms of instances of unserved energy). The evaluation of different penetrations of wind power generation, different wind speed profiles, wind resource diversity, and different operational reserve requirements, is conducted on the Chilean Northern Interconnected System (SING) using statistical modeling of wind speed time series and computer simulation through a 24-hour ahead unit commitment algorithm and a Monte Carlo simulation scheme. Results for the SING suggest that while wind generation can significantly reduce generation costs, it can also imply higher security costs to reach acceptable reliability levels.Keywords: Wind power, uncertainty modeling, variable generation, reserve requirements, value of lost load. RESUMENDebido a su variabilidad, la integración de generación eólica presenta desafíos significativos para los operadores de los sistemas eléctricos a la hora de mantener niveles de confiabilidad adecuados asegurando, de forma simultánea, una operación a mínimo costo. Este artículo explora los beneficios asociados a una mayor penetración de generación eólica y los requerimientos adicionales de reservas operacionales que esta impone. Dicha exploración se realiza en términos de los efectos de la energía eólica en la confiabilidad del sistema, medida como cantidad de energía no servida. El artículo, además, se enfoca en cómo es que al cambiar el costo de falla se puede motivar el alcance de diferentes metas de confiabilidad, y cómo la penetración y diversidad del recurso eólico impactan la calidad de servicio (en términos de instancias de energía no servida). La evaluación de diferentes niveles de penetración, diferentes perfiles de velocidad de viento, diversidad del recurso eólico y distintos requerimientos de reservas operacionales, se lleva a cabo en el Sistema Interconectado del Norte Grande (SING) chileno usando modelos estadísticos de velocidad de viento y simulación computacional mediante un algoritmo de pre-despacho y un esquema de Monte Carlo. Los resultados para el SING sugieren que mientras la generación eólica puede reducir significativamente los costos de generación del sistema, también implica mayores costos de seguridad para alcanzar niveles de confiabilidad adecuados.

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Section: Costs Of Managing Wind Operational Challengesmentioning

confidence: 76%

Power system reliability impacts of wind generation and operational reserve requirements

Gil

2015

ing.inv.

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“…Elsewhere, the literature often determines the expected cost in real-time operation directly from the solution of the SUC model (cost of each scenario at the second stage multiplied by the respective probability of occurrence), e.g., [46], or by implementing additional Monte Carlo simulations for a number of net load realizations, each one solved one-shot for the whole scheduling horizon based on the commitments produced by the UC process, e.g., [41]. In order to replicate more accurately the RTD applications in real world and reveal some implications relative to the nature of the RTD regime used, the deterministic RTD, herein, is simulated as a rolling dispatch procedure (i.e., solved sequentially for each real-time interval), with none or limited [57] look-ahead capability, or with the use of the variability reserve [32][33][34][35] determined by the UC application.…”

Section: Real-time Dispatch (Rtd)mentioning

confidence: 99%

“…Advances in optimization software and computer hardware are required for more practical execution times to be attained in SUC_15 policy. Nevertheless, decreasing the scheduling horizon in the order of 4-6 h (or less, in the case of RTUC), implementing a less detailed modeling for the generating unit operating states, or using other techniques [46] like grouping generating units by type, considering must-run units, or implementing "warm-start" strategies, shall reduce execution times to a great extent.…”

Section: Computational Requirementsmentioning

confidence: 99%

Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration

2017

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Abstract:The uncertain and variable nature of renewable energy sources in modern power systems raises significant challenges in achieving the dual objective of reliable and economically efficient system operation. To address these challenges, advanced scheduling strategies have evolved during the past years, including the co-optimization of energy and reserves under deterministic or stochastic Unit Commitment (UC) modeling frameworks. This paper presents different deterministic and stochastic day-ahead UC formulations, with focus on the determination, allocation and deployment of reserves. An explicit distinction is proposed between the uncertainty and the variability reserve, capturing the twofold nature of renewable generation. The concept of multi-timing scheduling is proposed and applied in all UC policies, which allows for the optimal procurement of such reserves based on intra-hourly (real-time) intervals, when concurrently optimizing energy and commitments over hourly intervals. The day-ahead scheduling results are tested against different real-time dispatch regimes, with none or limited look-ahead capability, or with the use of the variability reserve, utilizing a modified version of the Greek power system. The results demonstrate the enhanced reliability achieved by applying the multi-timing scheduling concept and explicitly considering the variability reserve, and certain features regarding the allocation and deployment of reserves are discussed.

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“…The existing optimization approaches regarding UC with large wind power integration can be mainly classified into four types: deterministic UC (DUC) [6][7][8], robust UC [9][10][11], interval UC [12][13][14], and stochastic UC (SUC) [15][16][17][18][19][20][21]. For UC approaches, computational efficiency and the economy of solutions are two crucial objectives, which are quite difficult to achieve concurrently using the existing approaches.…”

Section: Introductionmentioning

confidence: 99%

Network-Constrained Unit Commitment Based on Reserve Models Fully Considering the Stochastic Characteristics of Wind Power

et al. 2018

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Abstract:The existing optimization approaches regarding network-constrained unit commitment with large wind power integration face great difficulties in reconciling the two crucial but contradictory objectives: computational efficiency and the economy of the solutions. This paper proposes a new network-constrained unit commitment approach, which aims to better achieve these two objectives, by introducing newly proposed reserve models and simplified network constraints. This approach constructs the reserve models based on a sufficiently large number of stochastic wind power scenarios to fully and accurately capture the stochastic characteristics of wind power. These reserve models are directly incorporated into the traditional unit commitment formulation to simultaneously optimize the on/off decision variables and system reserve levels, therefore, this approach can comprehensively evaluate the costs and benefits of the scheduled reserves and thus produce very economical schedule. Meanwhile, these reserve models bring in very little computational burden because they simply consist of a small number of continuous variables and linear constraints. Besides, this approach can evaluate the impact of network congestion on the schedule by just introducing a small number of network constraints that are closely related to network congestion, i.e., the simplified network constraints, and thus concurrently ensures its high computational efficiency. Numerical results show that the proposed approach can produce more economical schedule than stochastic approach and deterministic approach but has similar computational efficiency as the deterministic approach.

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Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power

Cited by 486 publications

References 18 publications

Power system reliability impacts of wind generation and operational reserve requirements

Power system reliability impacts of wind generation and operational reserve requirements

Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration

Network-Constrained Unit Commitment Based on Reserve Models Fully Considering the Stochastic Characteristics of Wind Power

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