Stochastic optimal dispatch of integrating concentrating solar power plants with wind farms

Zhao, Shanshan; Fang, Yuchen; Wei, Ziyu

doi:10.1016/j.ijepes.2019.01.043

Cited by 41 publications

(15 citation statements)

References 25 publications

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“…It does not increase the uncertainty of the system. Instead, it exploits the energy storage characteristics of TES to make solar energy a dispatchable resource [21]. As a dispatchable renewable energy resource, a CSP plant has a similar ramping capability to that of a gas-fired generator which can reach a maximum 20% of the installed capacity per minute [15].…”

Section: B Frequency Regulation Characteristics Of Cspmentioning

confidence: 99%

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Coordinated Operation of Concentrating Solar Power Plant and Wind Farm for Frequency Regulation

Fang

Zhao

Wang

et al. 2021

Journal of Modern Power Systems and Clean Energy

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As a dispatchable renewable energy technology, the fast ramping capability of concentrating solar power (CSP) can be exploited to provide regulation services. However, frequent adjustments in real-time power output of CSP, which stems out of strategies offered by ill-designed market, may affect the durability and the profitability of the CSP plant, especially when it provides fast regulation services in a real-time operation. We propose the coordinated operation of a CSP plant and wind farm by exploiting their complementarity in accuracy and durability for providing frequency regulation. The coordinated operation can respond to regulation signals effectively and achieve a better performance than conventional thermal generators. We further propose an optimal bidding strategy for both energy and frequency regulations for the coordinated operation of CSP plant and wind farm in day-ahead market (DAM). The validity of the coordinated operation model and the proposed bidding strategy is verified by a case study including a base case and sensitivity analyses on several impacting factors in electricity markets.

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Section: B Frequency Regulation Characteristics Of Cspmentioning

confidence: 99%

“…2) Constraints 1) Operational constraints of CSP plant [21], [26]: the CSP plant consists of SF, TES, and PB. The energy is transferred between the different modules by heat-transfer fluid (HTF).…”

Section: ) Objective Functionmentioning

confidence: 99%

Coordinated Operation of Concentrating Solar Power Plant and Wind Farm for Frequency Regulation

Fang

Zhao

Wang

et al. 2021

Journal of Modern Power Systems and Clean Energy

Self Cite

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“…In reference [12], a scheduling model for the coordinated operation of hydro and wind power is established by utilizing the complementary characteristics of hydro and wind energy, and a stochastic programming model is adopted to cope with the uncertainty of wind power output. In addition, reference [13], in considering the influence of solar radiation intensity and wind power uncertainty on the whole scheduling process, establishes a stochastic optimal scheduling model for centralized wind-solar power stations. Moreover, a day-ahead stochastic scheduling model of thermal-hydro-wind-photovoltaic power system is presented in reference [14], which adopts Monte Carlo simulation to generate the output scenarios of wind power and photovoltaic power to describe their uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Stage Optimization Model for Two-Side Daily Reserve Capacity of a Power System Considering Demand Response and Wind Power Consumption

Dong¹,

Anyuan²,

Liu³

et al. 2019

Sustainability

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Today, wind power is becoming an important energy source for the future development of electric energy due to its clean and environmentally friendly characteristics. However, due to the uncertainty of incoming wind, the utilization efficiency of wind energy is extremely low, which means the problem of wind curtailment becomes more and more serious. To solve the issue of wind power large-scale consumption, a two-stage stochastic optimization model is established in this paper. Different from other research frameworks, a novel two-side reserve capacity mechanism, which simultaneously takes into account supply side and demand side, is designed to ensure the stable consumption of wind power in the real-time market stage. Specifically, the reserve capacity of thermal power units is considered on the supply side, and the demand response is introduced as the reserve capacity on the demand side. At the same time, the compensation mechanism of reserve capacity is introduced to encourage generation companies (GENCOs) to actively participate in the power balance process of the real-time market. In terms of solution method, compared with the traditional k-means clustering method, this paper uses the K-means classification based on numerical weather prediction (K-means-NWP) scenario clustering method to better describe the fluctuation of wind power output. Finally, an example simulation is conducted to analyze the influence of reserve capacity compensation mechanism and system parameters on wind power consumption results. The results demonstrate that with the introduction of reserve capacity compensation mechanism, the wind curtailment quantity of the power system has a significant reduction. Besides, the income of GENCOs is gradually increasing, which motivates their enthusiasm to provide reserve capacity. Furthermore, the reserve capacity mechanism designed in this paper promotes the consumption of wind power and the sustainable development of renewable energy.

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“…Pousinho et al [16] investigated the day-ahead market and the reserve market for a wind/CSP hybrid, and compared the results with a genetic algorithm; they found out the MILP to be more efficient. Wind/CSP hybridization, along with electrical heaters, was investigated by Yang et al [17] and Zhao et al [18] through the use of MILP algorithm; the latter introduced chance-constrained algorithm for taking under consideration the wind and solar uncertainty. Hamilton et al [19] investigated a CSP-TES-PV-batteries system, with dispatch optimization with a MILP algorithm.…”

Section: Introductionmentioning

confidence: 99%

Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications

2021

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Using solar power for industrial process heat is an increasing trend to fight against climate change thanks to renewable heat. Process heat demand and solar flux can both present intermittency issues in industrial systems, therefore solar systems with storage introduce a degree of freedom on which optimization, on a mathematical basis, can be performed. As the efficiency of solar thermal receivers varies as a function of temperature and solar flux, it seems natural to consider an optimization on the operating temperature of the solar field. In this paper, a Mixed Integer Linear Programming (MILP) algorithm is developed to optimize the operating temperature in a system consisting of a concentrated solar thermal field with storage, hybridized with a boiler. The MILP algorithm optimizes the control trajectory on a time horizon of 48 h in order to minimize boiler use. Objective function corresponds to the boiler use, for completion of the heat from the solar field, whereas the linear constraints are a simplified representation of the system. The solar field mass flow rate is the optimization variable which is directly linked to the outlet temperature of the solar field. The control trajectory consists of the solar field mass flow rate and outlet temperature, along with the auxiliary mass flow rate going directly to the boiler. The control trajectory is then injected in a 0D model of the plant which performs more detailed calculations. For the purpose of the study, a Linear Fresnel system is investigated, with generic heat demand curves and constant temperature demand. The value of the developed algorithm is compared with two other control approaches: one operating at the nominal solar field output temperature, and the other one operating at the actual demand mass flow rate. Finally, a case study and a sensitivity analysis are presented. The MILP’s control shows to be more performant, up to a relative increase of the annual solar fraction of 4% at 350 °C process temperature. Novelty of this work resides in the MILP optimization of temperature levels presenting high non-linearities, applied to a solar thermal system with storage for process heat applications.

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Stochastic optimal dispatch of integrating concentrating solar power plants with wind farms

Cited by 41 publications

References 25 publications

Coordinated Operation of Concentrating Solar Power Plant and Wind Farm for Frequency Regulation

Coordinated Operation of Concentrating Solar Power Plant and Wind Farm for Frequency Regulation

Two-Stage Optimization Model for Two-Side Daily Reserve Capacity of a Power System Considering Demand Response and Wind Power Consumption

Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications

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