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Hydrothermal coordination in power systems with large-scale integration of renewable energy sources

机译:大规模集成可再生能源的电力系统中的水热协调

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Purpose - The purpose of this paper is to examine the feasibility of an ideal power network that combines many different renewable energy technologies such as wind power, concentrated solar power (CSP) and hydroelectric power. This paper emphasizes in finding the benefits arising from hydrothermal coordination compared to the non-regulated integration of the hydroelectric units, as well as the benefits from the integration of wind power and CSP. Design/methodology/approach - Artificial Neural Networks were used to estimate wind power output. As for the CSP system, a three-tier architecture which includes a solar field, a transmission-storage system and a production unit was used. Each one of those separate sections is analyzed and the process is modeled. As for the hydroelectric plant, the knowledge of the water's flow rated has helped estimating the power output, taking into account the technical restrictions and losses during transmission. Also, the economic dispatch problem was solved by using artificial intelligence methods. Findings - Hydrothermal coordination leads to greater thermal participation reduction and cost reduction than a non-regulated integration of the hydrothermal unit. The latter is independent from the degree of integration of the other renewable sources (wind power, CSP). Originality/value - Hydrothermal coordination in a power system which includes thermal units and CSP for cost and emissions reduction.
机译:目的-本文的目的是研究结合许多不同可再生能源技术(例如风能,聚光太阳能(CSP)和水力发电)的理想电力网络的可行性。本文着重于寻找与水力发电机组的非规范集成相比,由水热协调产生的收益,以及从风电和CSP集成中获得的收益。设计/方法/方法-人工神经网络用于估算风能输出。对于CSP系统,使用了一个三层体系结构,包括一个太阳能场,一个传输存储系统和一个生产单元。分析这些单独的部分中的每一个,并对过程进行建模。对于水力发电厂,考虑到技术限制和输电过程中的损失,了解水的额定流量有助于估算功率输出。而且,通过使用人工智能方法解决了经济调度问题。研究发现-与非规范的热液机组集成相比,热液协调导致更大的热参与减少和成本降低。后者与其他可再生资源(风能,CSP)的整合程度无关。独创性/价值-电力系统中的水热协调,包括热力单元和CSP,以降低成本和减少排放。

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