Model-predictive control for dispatch planning of concentrating solar power plants under real-time spot electricity prices

Mohammadzadeh, Navid; Truong‐Ba, Huy; Cholette, Michael E.; Steinberg, Ted; Manzolini, Giampaolo

doi:10.1016/j.solener.2022.09.020

Cited by 8 publications

(2 citation statements)

References 73 publications

(65 reference statements)

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“…where -reference DNI for the normalized value for operating hours; ℎ -sunshine duration, h/ year; -relative load corresponding to the average arrival of solar radiation [18] =…”

Section: The Mathematical Modelmentioning

confidence: 99%

An Analytical Approach to Power Optimization of Concentrating Solar Power Plants with Thermal Storage

Cheilytko,

Alexopoulos,

Pozhuyev

et al. 2024

Preprint

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This paper deals with the problem of determining the optimal capacity of Concentrated Solar Power (CSP) plants, especially in the context of hybrid solar power plants. This work presents an innovative analytical approach to optimize the capacity of concentrated solar plants. The proposed method is based on the use of additional non-dimensional parameters, in particular the design factor and the solar multiple factor. The paper presents a mathematical optimization model that focuses on the capacity of concentrated solar power plants where thermal storage plays a key role in the energy source. The analytical approach provides a more complete understanding of the design process for hybrid power plants. In addition, the use of additional factors and the combination of the proposed method with existing numerical methods allows for a more refined optimization, which allows for a more accurate selection of the capacity for specific geographical conditions. Importantly, the proposed method significantly increases the speed of computation compared to traditional numerical methods. Finally, the authors present the results of the analysis of the proposed system of equations for calculating the levelized cost of electricity (LCOE) for hybrid solar power plants. The nonlinearity of the LCOE on the main calculation parameters is shown.

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“…where -reference DNI for the normalized value for operating hours; ℎ -sunshine duration, h/ year; -relative load corresponding to the average arrival of solar radiation [18] =…”

Section: The Mathematical Modelmentioning

confidence: 99%

An Analytical Approach to Power Optimization of Concentrating Solar Power Plants with Thermal Storage

Cheilytko,

Alexopoulos,

Pozhuyev

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The advantage of this method is that it can handle high-noise data and is highly adaptable to changes in the data (Yang et al, 2022). However, it ignores the influence of other factors, such as policy adjustments and weather changes, and therefore has limited forecasting accuracy (Mohammadzadeh et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

Lin,

Chen,

Zhao

et al. 2024

Front. Energy Res.

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With the continuous promotion of the unified electricity spot market in the southern region, the formation mechanism of spot market price and its forecast will become one of the core elements for the healthy development of the market. Effective spot market price prediction, on one hand, can respond to the spot power market supply and demand relationship; on the other hand, market players can develop reasonable trading strategies based on the results of the power market price prediction. The methods adopted in this paper include: Analyzing the principle and mechanism of spot market price formation. Identifying relevant factors for electricity price prediction in the spot market. Utilizing a clustering model and Spearman’s correlation to classify diverse information on electricity prices and extracting data that aligns with the demand for electricity price prediction. Leveraging complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to disassemble the electricity price curve, forming a multilevel electricity price sequence. Using an XGT model to match information across different levels of the electricity price sequence. Employing the ocean trapping algorithm-optimized Bidirectional Long Short-Term Memory (MPA-CNN-BiLSTM) to forecast spot market electricity prices. Through a comparative analysis of different models, this study validates the effectiveness of the proposed MPA-CNN-BiLSTM model. The model provides valuable insights for market players, aiding in the formulation of reasonable strategies based on the market's supply and demand dynamics. The findings underscore the importance of accurate spot market price prediction in navigating the complexities of the electricity market. This research contributes to the discourse on intelligent forecasting models in electricity markets, supporting the sustainable development of the unified spot market in the southern region.

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CVaR-based planning of park-level integrated energy system considering extreme scenarios of energy prices

Lin,

Zhou,

Tan

et al. 2024

International Journal of Electrical Power & Energy Systems

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Model-predictive control for dispatch planning of concentrating solar power plants under real-time spot electricity prices

Cited by 8 publications

References 73 publications

An Analytical Approach to Power Optimization of Concentrating Solar Power Plants with Thermal Storage

An Analytical Approach to Power Optimization of Concentrating Solar Power Plants with Thermal Storage

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

CVaR-based planning of park-level integrated energy system considering extreme scenarios of energy prices

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