Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems

Forecasting of large-scale renewable energy clusters composed of wind power generation, photovoltaic and concentrating solar power (CSP) generation encounters complex uncertainties due to spatial scale dispersion and time scale random fluctuation. In response to this, a short-term forecasting method is proposed to improve the hybrid forecasting accuracy of multiple generation types in the same region. It is formed through training the long short-term memory (LSTM) network using spatial panel data. Historical power data and meteorological data for CSP plant, wind farm and photovoltaic (PV) plant are included in the dataset. Based on the data set, the correlation between these three types of power generation is proved by Pearson coefficient, and the feasibility of improving the forecasting ability through the hybrid renewable energy clusters is analyzed. Moreover, cases study indicates that the uncertainty of renewable energy cluster power tends to weaken due to partial controllability of CSP generation. Compared with the traditional prediction method, the hybrid prediction method has better prediction accuracy in the real case of renewable energy cluster in Northwest China.

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Thermal Modeling of a Multi-Cavity Array Receiver Performance for Concentrating Solar Power Generation

Volume 1: Advances in Solar Buildings and Conservation; Climate Control and the Environment; Alternate Fuels and Infrastructure; ARPA-E; Combined Energy Cycles, CHP, CCHP, and Smart Grids; Concentrating Solar Power; Economic, Environmental, and Policy Aspects of Alternate Energy; Geothermal Energy, Harvesting, Ocean Energy and Other Emerging Technologies; Hydrogen Energy Technologies; Low/Zero Emission Power Plants and Carbon Sequestration; Micro and Nano Technology Applications and Materials ◽

10.1115/es2015-49172 ◽

2015 ◽

Cited By ~ 1

Author(s):

Austin Fleming ◽

Zhiwen Ma ◽

Tim Wendelin ◽

Heng Ban ◽

Charlie Folsom

Keyword(s):

Power Generation ◽

Renewable Energy ◽

High Temperature ◽

Solar Power ◽

Freezing Point ◽

Low Cost ◽

Thermal Modeling ◽

Solid Particles ◽

Concentrating Solar Power ◽

Receiver Performance

Concentrating solar power (CSP) plants can provide dispatchable power with the thermal energy storage (TES) capability for greater renewable-energy grid penetration. To increase the market competitiveness, CSP technology needs to increase the solar-to-electric efficiency and reduce costs in the areas of solar collection from the heliostat field to the receiver, energy conversion systems, and TES. The current state-of-the-art molten-salt systems have limitations regarding both the potential for cost reduction and improvements in performance. Even with significant improvements in operating performance, these systems face major challenges to satisfy the performance targets, which include high-temperature stability (>650°C), low freezing point (<0°C), and material compatibility with high-temperature metals (>650°C) at a reduced cost. The fluidized-bed CSP (FB-CSP) plant being developed by the National Renewable Energy Laboratory (NREL) has the potential to overcome the above issues with substantially lower cost. The particle receiver is a critical component to enable the FB-CSP system. This paper introduces the development of an innovative receiver design using the blackbody design mechanism by collecting solar heat with absorber tubes that transfer the radiant heat to flowing particles. The particle and receiver materials can withstand temperatures of >1000°C because the receiver can use low-cost materials, such as ceramics and stainless steel, and the solid particles can be any low-cost, stable materials such as sand or ash for particle containment and TES. The heated particles can be stored in containers for TES or supply heat for power generation. This study investigated the performance of convection, reflection, and infrared (IR) re-radiation losses on the absorber solar receiving side. We developed a flux model to predict the reflection losses from the absorber tubes based on the NREL SolTrace program, and conducted thermal modeling by using the Fluent Software. This paper presents the thermal modeling and results on the receiver performance. The receiver configuration may have broad applications for different heattransfer fluids (HTFs), including gas, liquid, or the solid particle-based system in our receiver development.

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Optimal dispatch of wind power-photovoltaic-concentrating solar power combined power generation system based on improved PSO

Journal of Physics Conference Series ◽

10.1088/1742-6596/1983/1/012060 ◽

2021 ◽

Vol 1983 (1) ◽

pp. 012060

Author(s):

Yan Guo ◽

Zhicheng Ma ◽

Xiaoying Zhang ◽

Qiang Zhou ◽

Wei Chen

Keyword(s):

Power Generation ◽

Wind Power ◽

Solar Power ◽

Generation System ◽

Concentrating Solar Power ◽

Optimal Dispatch ◽

Power Generation System ◽

Improved Pso

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Day-Ahead Scheduling for Renewable Energy Generation Systems considering Concentrating Solar Power Plants

Mathematical Problems in Engineering ◽

10.1155/2021/9488222 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Xiaojuan Lu ◽

Leilei Cheng

Keyword(s):

Power Generation ◽

Renewable Energy ◽

Power Plants ◽

Solar Power ◽

Thermal Power ◽

Energy Resource ◽

Energy Generation ◽

Concentrating Solar Power ◽

Solar Power Plants ◽

Renewable Energy Generation

With the advent of the new types of electrical systems that attach more importance to the renewability of the energy resource, issues arising out of the randomness and volatility of the renewable energy resource, such as the safety, reliability, and economic operation of the underlying power generation system, are expected to be challenging. Generally speaking, the power generation company can do a reasonable dispatch of each unit according to weather forecast and load demand information. Focusing on concentrating solar power (CSP) plants (wind power, photovoltaic, battery energy storage, and thermal power plants), this paper proposes a day-ahead scheduling model for renewable energy generation systems. The model also considers demand response and related generator set constraints. The problem is described as a mixed-integer nonlinear programming (MINLP) problem, which can be solved by the CPLEX solver to obtain an optimal solution. At the same time, the paper compares and analyzes the impact of concentrating solar power plants on other renewable energy generation and thermal power operation systems. The results show that the renewable energy generation system can lower power generation costs, reduce load fluctuation, and enhance the energy storage rate.

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