Enhancing peak shaving capability by optimizing reheat-steam temperature control of a double-reheat boiler

Wang, Chaoyang; Qiao, Yongqiang; Liu, Ming; Zhao, Yongliang; Yan, Junjie

doi:10.1016/j.apenergy.2019.114341

Cited by 45 publications

(6 citation statements)

References 28 publications

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“…A coal power-plant model from [50] underwent different simulation runs and was presented in [81] where the focus was shifted towards the reheated steam temperature control under cycling operation. The ramping-up and -down rate was the main simulation parameter within the range 1%/min-6%/min.…”

Section: Control and Fuelmentioning

confidence: 99%

Review of Process Modeling of Solid-Fuel Thermal Power Plants for Flexible and Off-Design Operation

et al. 2020

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Since the widespread deployment of non-dispatchable, intermittent, and highly variable power production from renewable energy sources (RES), the demand for flexible power production has been steadily growing. As new-built dispatchable power plants have not been very quickly adapted to the emerging flexible operation, this task has been addressed by existing plants as well. Existing solid-fuel thermal power plants have undergone an extensive study to increase their flexible operation. Thermodynamic process-modeling tools have been extensively used for plant modeling. Steady- and transient-state simulations have been performed under various operating regimes, supplying valuable results for efficient power-plant operation. Flexibility aspects regarding low-load operation and steady operational conditions are mostly investigated with steady-state simulations. Flexibility aspects related to variation over time such as ramping rates are investigated with transient simulations. The off-design operation is mainly attributed to the existing fleet of power plants, struggling to balance between their former operational schemes as base and/or medium-load plants. However, off-design operation is also considered for new plants in the design phase and is included as a simulation aspect. Process modeling turns out to be a proven tool for calculating plant flexibility and predicting extreme operating conditions, defining further steps for a new operational scheme, drafting accident mitigation control procedures or, furthermore, provisioning more complex and cross-field future tasks. A review of the off-design aspect as a simulation approach is undertaken and presented in this work. Finally, challenges and future perspectives for this aspect of solid-fuel thermal power plants are discussed.

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Section: Control and Fuelmentioning

confidence: 99%

Review of Process Modeling of Solid-Fuel Thermal Power Plants for Flexible and Off-Design Operation

et al. 2020

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“…Carbon peaking and carbon neutrality goals can be realized by continuously increasing the installed capacity of renewable energy power in China, but thermal power units need to participate in long-term flexible regulation to curb the impact of renewable energy power on the stable operation of grids (Meysam et al, 2017;Kang et al, 2020;Shahbaz et al, 2020;Zhang et al, 2021;Tan et al, 2022). Conditions change frequently during the flexible operation of thermal power units (Zeng et al, 2019;Wang et al, 2020a), resulting in large changes in the NO x concentration at the entrance of selective catalytic reduction (SCR) denitrification systems. This scenario hinders the effective control of denitrification systems of thermal power units.…”

Section: Introductionmentioning

confidence: 99%

NOx concentration prediction in coal-fired power plant based on CNN-LSTM algorithm

et al. 2023

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Measuring the nitrogen oxides concentration accurately at the inlet of the selective catalytic reduction denitrification system plays an important role in controlling the nitrogen oxides concentration for coal-fired power plants, and a coupling relationship exists between nitrogen oxides concentration and multiple operational variables. Here, a modeling method based on feature fusion and long short-term memory network is proposed to mine the spatial and temporal coupling relationship between input variables for improving the prediction accuracy. First, the collected data were converted to image-like sequences. Then, the high-dimensional features of image-like sequences were fused by a convolutional neural network, and the spatial coupling features among the variables were mined. Finally, the constructed fusion features were input into the long short-term memory network to further explore the time coupling characteristics among the variables and complete the prediction of nitrogen oxides concentration at the inlet of the selective catalytic reduction denitrification system. The simulation results show that the prediction error of nitrogen oxides concentration at the inlet of selective catalytic reduction denitrification system based on CNN-LSTM model is 15.15% lower than that of traditional LSTM model.

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“…Therefore, there are strong non‐linear dynamics and more coupling variables over a wide operating range. Furthermore, the extra reheat steam circulation system enhances the inertia and delay during transients [12]. Thus, the regulation of steam temperatures of DR USC units is more difficult than that of single‐reheat units [13].…”

Section: Introductionmentioning

confidence: 99%

Modelling and regulation of steam temperatures of a 1000‐MW double‐reheat boiler with long short‐term memory

Zhu,

Tan,

et al. 2023

IET Control Theory & Appl

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With the increase in the proportion of new energy power generation in grids, there is an urgent need for the flexible operation of coal‐fired power plants. However, as the most efficient commercial coal‐fired power generation technology, the flexibility of double‐reheat ultra‐supercritical units is limited due to the steam temperature fluctuation in transient processes. To inhibit the steam temperature fluctuations, a data‐driven method, called long short‐term memory (LSTM) was introduced to model the dynamics of the steam temperatures of a 1000‐MW double‐reheat coal‐fired boiler. Moreover, an advanced controller was proposed by combining the LSTM‐based dynamic model and the model predictive control architecture. The results demonstrate that the LSTM model can be used to capture the time delay, multivariable coupling and non‐linear dynamics of the steam temperatures and the proposed controller exhibits excellent performance in steam temperature regulation. The superheated steam temperature fluctuation decreases from 15.4 to 9.8°C, the primary reheat temperature fluctuation decreases from 34.6 to 8.3°C, and the secondary reheat temperature fluctuation decreases from 41.4 to 8.8°C. The proposed method has good potential to stabilize steam temperatures during transient processes of wide‐range load cycling, and finally improves the flexibility of the double‐reheat ultra‐supercritical unit.

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Enhancing peak shaving capability by optimizing reheat-steam temperature control of a double-reheat boiler

Cited by 45 publications

References 28 publications

Review of Process Modeling of Solid-Fuel Thermal Power Plants for Flexible and Off-Design Operation

Review of Process Modeling of Solid-Fuel Thermal Power Plants for Flexible and Off-Design Operation

NOx concentration prediction in coal-fired power plant based on CNN-LSTM algorithm

Modelling and regulation of steam temperatures of a 1000‐MW double‐reheat boiler with long short‐term memory

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