This page has only limited features, please log in for full access.

Unclaimed
L. Sun
School of Energy and Environment Engineering, Southeast University, Nanjing 210096, China

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Original paper
Published: 02 May 2021 in Clean Technologies and Environmental Policy
Reads 0
Downloads 0

Facing the growing pressure of climate change and environmental protection, integrated energy systems (IESs), which comprise different energy sources, have become promising candidates for future energy systems. However, the capacity configuration of each source remains challenging due to the various couplings, randomness of renewables and numerical optimization difficulty. In this paper, a hierarchical optimization framework is proposed to determine the component capacities of trigeneration IESs, i.e., systems involving combined cooling, heating and power (CCHP) generation. The potential variation in the demand and renewable resource availability are considered stochastic factors and captured as scenarios generated according to a probability function. In the first level, with the component capacities and scenarios defined, a mixed-integer linear programming (MILP) problem is formulated to minimize the total system cost. Then, in the second level, the Monte Carlo method is applied to calculate the expectation by feeding different scenarios into the MILP and sampling the minimal costs. Finally, as the second level returns the expected value of the system cost considering the given component capacities, a genetic algorithm is adopted in the third level to search the optimal component capacities. Compared to the conventional deterministic optimization method, the proposed stochastic optimization method reduces the annual operational cost while allowing a wider operational range. In addition, it is revealed that the inclusion of heat storage and grid connections yields notable benefits in terms of IES cost reduction.

ACS Style

Yisong Zhang; Jingjing Jiang; Xian Zhang; Li Sun. A hierarchical genetic algorithm and mixed-integer linear programming-based stochastic optimization of the configuration of integrated trigeneration energy systems. Clean Technologies and Environmental Policy 2021, 1 -15.

AMA Style

Yisong Zhang, Jingjing Jiang, Xian Zhang, Li Sun. A hierarchical genetic algorithm and mixed-integer linear programming-based stochastic optimization of the configuration of integrated trigeneration energy systems. Clean Technologies and Environmental Policy. 2021; ():1-15.

Chicago/Turabian Style

Yisong Zhang; Jingjing Jiang; Xian Zhang; Li Sun. 2021. "A hierarchical genetic algorithm and mixed-integer linear programming-based stochastic optimization of the configuration of integrated trigeneration energy systems." Clean Technologies and Environmental Policy , no. : 1-15.

Journal article
Published: 15 April 2021 in Process Safety and Environmental Protection
Reads 0
Downloads 0

Control of wet limestone flue gas desulfurisation (WFGD) system is critical for pollution reduction of the coal-fired power plant. To fulfill the environmental requirements with the least cost, economic model predictive control (EMPC) is utilized in this paper to tackle the difficulties of WFGD, such as nonlinearity, couplings and trade-off between efficiency and safety. First, a comprehensive first-principle model is developed to describe the overall nonlinear characteristics of WFGD, with special considerations on Gas-to-liquid contact zone and slurry pool module. Second, four objectives are formulated to evaluate the economic criterion from different perspectives, including cost, safety, control efforts and performance. Utopia strategy, combined with NSGA-II, is used to solve the EMPC problem and obtain the Pareto front of the multi-objective optimization. Simulation results show that the proposed strategy is able to efficiently ensure the emission requirement and reduce the economic cost simultaneously, regardless of load disturbance, adjustment of emission lower limit and fluctuation of limestone market price.

ACS Style

Ping Liu; Lukuan Yang; Li Sun. Multi-objective economic model predictive control of wet limestone flue gas desulfurisation system. Process Safety and Environmental Protection 2021, 150, 269 -280.

AMA Style

Ping Liu, Lukuan Yang, Li Sun. Multi-objective economic model predictive control of wet limestone flue gas desulfurisation system. Process Safety and Environmental Protection. 2021; 150 ():269-280.

Chicago/Turabian Style

Ping Liu; Lukuan Yang; Li Sun. 2021. "Multi-objective economic model predictive control of wet limestone flue gas desulfurisation system." Process Safety and Environmental Protection 150, no. : 269-280.

Journal article
Published: 25 March 2021 in Sustainability
Reads 0
Downloads 0

Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.

ACS Style

Ying Wang; Bo Feng; Qing-Song Hua; Li Sun. Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method. Sustainability 2021, 13, 3665 .

AMA Style

Ying Wang, Bo Feng, Qing-Song Hua, Li Sun. Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method. Sustainability. 2021; 13 (7):3665.

Chicago/Turabian Style

Ying Wang; Bo Feng; Qing-Song Hua; Li Sun. 2021. "Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method." Sustainability 13, no. 7: 3665.

Journal article
Published: 14 January 2021 in IEEE Transactions on Industrial Electronics
Reads 0
Downloads 0

Active disturbance rejection controller (ADRC) has achieved soaring success in motion controls featured by rapid dynamics. However, it turns obstreperous to implement it in the power plant process with considerable time-delay, largely because of the tuning difficulty. To this end, this paper proposes a quantitative tuning rule for the time-delayed ADRC (TD-ADRC) structure based on the typical first order plus time delay (FOPTD) model. By compensating the FOPDT process as an integrator plus time delay in low frequencies, the gain parameter of TD-ADRC can be related to a scaled time constant which shapes the closed-loop tracking performance. Bandwidth parameter of extended state observer is scaled as a dimensionless parameter. A sufficient stability condition of TD-ADRC is theoretically derived in terms of the scaled parameter pair, the range of which falls within the practical interest. Relative delay margin is revealed as a critical robustness metric among others, a default pair of scaled parameter setting is recommended as well as an explicit retuning guideline according to the user's preference for performance or robustness. Simulation and laboratory water tank experiment validate the tuning efficacy and a coal mill temperature control test depicts a promising prospective of the proposed method in process control practice.

ACS Style

Li Sun; Wenchao Xue; Donghai Li; Hongxia Zhu; Zhi-Gang Su. Quantitative Tuning of Active Disturbance Rejection Controller for FOPDT Model with Application to Power Plant Control. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.

AMA Style

Li Sun, Wenchao Xue, Donghai Li, Hongxia Zhu, Zhi-Gang Su. Quantitative Tuning of Active Disturbance Rejection Controller for FOPDT Model with Application to Power Plant Control. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.

Chicago/Turabian Style

Li Sun; Wenchao Xue; Donghai Li; Hongxia Zhu; Zhi-Gang Su. 2021. "Quantitative Tuning of Active Disturbance Rejection Controller for FOPDT Model with Application to Power Plant Control." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.

Research article
Published: 25 October 2020 in International Journal of Energy Research
Reads 0
Downloads 0

A scheduling model is a prerequisite for an operation strategy of integrated energy system (IES). Existing scheduling models of IES, however, are typically based on heat‐transfer variables either completely or partially, which oversimplify detailed thermal characteristics. To this end, a novel scheduling model is proposed where all thermal processes are modeled by temperature and flowrate of working fluids. This improvement renders the capability to the scheduling model to incorporate different thermal processes. Furthermore, the nonlinear product terms of temperature and flowrate in the proposed model are linearized by the binary expansion method. Based on the linearized scheduling model, a stochastic model predictive control (SMPC) operation strategy is exploited to optimize the economic performance by energy forecast, scenario reduction, rolling optimization, and feedback correction. Afterwards, four operation modes considering different temperature changes of the devices, networks, and the environment are performed and compared. The results found that thermal characteristics will affect device operation results and the degree of influence varies. The network temperature changes have the broadest influence, followed by the device and the ambient temperature changes. Moreover, system operation costs are also affected by detailed thermal characteristics. The total cost, the gas cost, and the electricity cost under Mode 2 are almost the same to those of Mode 1. However, the first two costs are reduced by 3.4% and 5.3% under Mode 3, and are reduced by 2.7% and 4% under Mode 4, despite that the electricity cost increases by 0.2% under Mode 3 and remains almost the same under Mode 4. These indicate that reliability and economy of an IES are affected by thermal characteristics, and it is thus the necessity to consider detailed thermal characteristics in an operation. Moreover, the results demonstrate the capability of the generalized temperature‐flowrate based scheduling model and the effectiveness of the SMPC operation strategy.

ACS Style

Shangshang Wei; Yiguo Li; Li Sun; Junli Zhang; Jiong Shen; Zuyi Li. Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics. International Journal of Energy Research 2020, 45, 4081 -4097.

AMA Style

Shangshang Wei, Yiguo Li, Li Sun, Junli Zhang, Jiong Shen, Zuyi Li. Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics. International Journal of Energy Research. 2020; 45 (3):4081-4097.

Chicago/Turabian Style

Shangshang Wei; Yiguo Li; Li Sun; Junli Zhang; Jiong Shen; Zuyi Li. 2020. "Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics." International Journal of Energy Research 45, no. 3: 4081-4097.

Cover image
Published: 09 October 2020 in International Journal of Energy Research
Reads 0
Downloads 0

ContinuedThe cover image is based on the Research Article Neural network‐based learning and estimation of battery state‐of‐charge: A comparison study between direct and indirect methodology by Wen Sun et al., https://doi.org/10.1002/er.5654.

ACS Style

Wen Sun; Yicheng Qiu; Li Sun; Qingsong Hua. Cover Image. International Journal of Energy Research 2020, 44, 1 .

AMA Style

Wen Sun, Yicheng Qiu, Li Sun, Qingsong Hua. Cover Image. International Journal of Energy Research. 2020; 44 (13):1.

Chicago/Turabian Style

Wen Sun; Yicheng Qiu; Li Sun; Qingsong Hua. 2020. "Cover Image." International Journal of Energy Research 44, no. 13: 1.

Journal article
Published: 06 October 2020 in Sustainability
Reads 0
Downloads 0

Superheated steam temperature (SST) is one of the most critical parameters for the process safety, overall efficiency and pollution reduction of coal-fired power plants. However, SST control is challenging due to various disturbances and model uncertainties, especially in the face of the growing penetration of intermittent renewable energy into the power grid. To this end, a cascaded Disturbance Observer-PI (DOB-PI) control strategy is proposed to enhance control performance. The observer design and parameter tuning are carried out through mechanism analysis on the proposed structure. Furthermore, a robust loop shaping method is introduced as a hard constraint to balance the control performance and robustness. The controller parameters are optimized based on the multi-objective artificial bee colony optimization (MOABC) algorithm. Simulation results show that the proposed cascaded DOB-PI control strategy can significantly improve the disturbance rejection performance of both the inner- and outer-loops of the SST control system. This paper indicates promising prospects for the proposed method in future applications.

ACS Style

Yong-Sheng Hao; Zhuo Chen; Li Sun; Junyu Liang; Hongxia Zhu. Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer. Sustainability 2020, 12, 8235 .

AMA Style

Yong-Sheng Hao, Zhuo Chen, Li Sun, Junyu Liang, Hongxia Zhu. Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer. Sustainability. 2020; 12 (19):8235.

Chicago/Turabian Style

Yong-Sheng Hao; Zhuo Chen; Li Sun; Junyu Liang; Hongxia Zhu. 2020. "Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer." Sustainability 12, no. 19: 8235.

Journal article
Published: 20 August 2020 in ISA Transactions
Reads 0
Downloads 0

This paper addresses a general sampling method of the unscented Kalman filter (UKF) for nonlinear state estimation. The sampling method for standard UKF is analyzed, and we propose a theorem to address the conditions that UKF provides a third order accuracy in terms of Taylor series expansion for expectation estimation by changing the number and placements of the sampling points. This theorem can be used to develop new UKF. Based on this theorem, we propose a method to design the placements of the sampling points, including the directions and lengths by optimization strategies. Simulation studies demonstrate that the proposed UKF is effective and can significantly improve the filter performance.

ACS Style

Qie Liu; Yingming Tian; Yi Chai; Min Liu; Li Sun. Design of unscented Kalman filter based on the adjustments of the number and placements of the sampling points. ISA Transactions 2020, 108, 188 -195.

AMA Style

Qie Liu, Yingming Tian, Yi Chai, Min Liu, Li Sun. Design of unscented Kalman filter based on the adjustments of the number and placements of the sampling points. ISA Transactions. 2020; 108 ():188-195.

Chicago/Turabian Style

Qie Liu; Yingming Tian; Yi Chai; Min Liu; Li Sun. 2020. "Design of unscented Kalman filter based on the adjustments of the number and placements of the sampling points." ISA Transactions 108, no. : 188-195.

Research article
Published: 16 July 2020 in International Journal of Energy Research
Reads 0
Downloads 0

Faced with the ever‐increasing urban environmental pollution, the electric vehicles (EVs) have received increasing attention in the automotive industry. Lithium‐ion batteries, serving as electrochemical power storage, have been extensively used in EVs because of the lightweight, no local pollution and high power density. The increasing awareness on the safe operation and reliability of the battery requires an efficient battery management system (BMS), among the parameters monitored by which, state‐of‐charge (SOC) is critical in preventing overcharge, deep discharge, and irreversible damage. This article investigates the neural network (NN)‐based modeling, learning, and estimation of SOC by comparing two different methodologies, that is, direct structure with SOC as network output and indirect structure with voltage as output. Firstly, the nonlinear autoregressive exogenous neural network (NARX‐NN) is introduced, in which SOC is directly deemed as an NN output for learning and estimation. Secondly, a radial basis function (RBF)‐based NN with unscented Kalman filter (RBFNN‐UKF) is proposed, in which the terminal voltage is used as output. Instead, SOC is deemed as an internal state which would be estimated indirectly based on the feedback error of voltage. Experimental results demonstrate that both estimators can achieve accurate SOC estimation for regular cases, in spite of the inaccurate initial conditions. However, the direct NN structure is revealed as not capable of dealing with the cases with sensor bias, which, however, can be well accommodated in the indirect structure by extending the sensor bias as an augmented state. Benefiting from the uncertainty augmentation and feedback compensation, the indirect RBFNN‐UKF shows superiority over the direct estimation in the practical experiments, depicting a promising prospect in the future onboard EV‐BMS application.

ACS Style

Wen Sun; Yicheng Qiu; Li Sun; Qingsong Hua. Neural network‐based learning and estimation of battery state‐of‐charge : A comparison study between direct and indirect methodology. International Journal of Energy Research 2020, 44, 1 .

AMA Style

Wen Sun, Yicheng Qiu, Li Sun, Qingsong Hua. Neural network‐based learning and estimation of battery state‐of‐charge : A comparison study between direct and indirect methodology. International Journal of Energy Research. 2020; 44 (13):1.

Chicago/Turabian Style

Wen Sun; Yicheng Qiu; Li Sun; Qingsong Hua. 2020. "Neural network‐based learning and estimation of battery state‐of‐charge : A comparison study between direct and indirect methodology." International Journal of Energy Research 44, no. 13: 1.

Journal article
Published: 02 June 2020 in Control Engineering Practice
Reads 0
Downloads 0

Bed temperature control, being of great significance for the safety, durability and NOx reduction of circulating fluidized bed (CFB) boiler, is nowadays becoming increasingly challenging due to the participation of CFB boiler in load regulation and the existence of various uncertainties such as the feed fuel quality variation and other unknown disturbances. To this end, this paper develops an engineering-friendly multi-model predictive sliding mode control (MM-PSMC) strategy for bed temperature regulation to attain a better adaption to wide load variation. A second-order plus time-delay (SOPDT) model based predictive sliding mode controller (PSMC) is designed to deal with the time-delay, model parameter uncertainty and external disturbances, and taken as local controller in MM-PSMC. An improved hysteresis switching logic is introduced to select a controller that is most suitable for the current operating condition and avoid system chattering caused by fast and unnecessary switching of controllers among local models. Simulation results on a 220t/h CFB boiler demonstrate the merits of the proposed MM-PSMC strategy over a wide operating range, depicting a promising prospect in industrial applications.

ACS Style

Hongxia Zhu; Jiong Shen; Kwang Y. Lee; Li Sun. Multi-model based predictive sliding mode control for bed temperature regulation in circulating fluidized bed boiler. Control Engineering Practice 2020, 101, 104484 .

AMA Style

Hongxia Zhu, Jiong Shen, Kwang Y. Lee, Li Sun. Multi-model based predictive sliding mode control for bed temperature regulation in circulating fluidized bed boiler. Control Engineering Practice. 2020; 101 ():104484.

Chicago/Turabian Style

Hongxia Zhu; Jiong Shen; Kwang Y. Lee; Li Sun. 2020. "Multi-model based predictive sliding mode control for bed temperature regulation in circulating fluidized bed boiler." Control Engineering Practice 101, no. : 104484.

Journal article
Published: 30 May 2020 in International Journal of Hydrogen Energy
Reads 0
Downloads 0

Humidity of proton exchange membrane fuel cell, a critical underlying variable that affects stack efficiency and durability, is quite challenging to be well controlled due to the measuring difficulty and multiple couplings with other variables. For precise humidity description and control, this paper utilizes the internal water content as the feedback signal, whose dynamic model is developed by taking into account various auxiliary couplings. Based on the mechanistic model, the dynamic responses of the internal water content are exhibited and analyzed by manipulating the external current, air compressor voltage and humidifier electrical power, respectively. It is shown that the internal water content is able to better describe the real humidity environment, in contrast with the conventional method using cathode inlet relative humidity. Furthermore, a combined feed-forward and feedback control structure is proposed to maintain the internal humidity as close to the desired as possible. Simulation results show that the proposed control method is able to maintain the fuel cell operating within the safe and efficient region, and the humidification power consumption can be reduced because of the precise and less conservative description on the internal humidity.

ACS Style

Hao Fu; Jiong Shen; Li Sun; Kwang Y. Lee. Fuel cell humidity modeling and control using cathode internal water content. International Journal of Hydrogen Energy 2020, 46, 9905 -9917.

AMA Style

Hao Fu, Jiong Shen, Li Sun, Kwang Y. Lee. Fuel cell humidity modeling and control using cathode internal water content. International Journal of Hydrogen Energy. 2020; 46 (15):9905-9917.

Chicago/Turabian Style

Hao Fu; Jiong Shen; Li Sun; Kwang Y. Lee. 2020. "Fuel cell humidity modeling and control using cathode internal water content." International Journal of Hydrogen Energy 46, no. 15: 9905-9917.

Journal article
Published: 21 May 2020 in Journal of Energy Storage
Reads 0
Downloads 0

With the booming development of renewable energy systems, energy storage technology is undoubtedly becoming an underlying role and serving as the enabling technology for the long-term reliability of the intermittent renewable energy systems containing photovoltaic and wind power generation. Electrical storage via Li-ion battery and hydrogen storage via electrolyser and fuel cell are two promising candidates providing a fast response in load leveling. However, the researches on the costs and benefits of each method are very limited, especially from a life-cycle perspective. To this end, this paper firstly builds an economic model containing the investment and operational cost of each component. For comprehensiveness, both electricity production and heat recovery are considered in the economic model. Subsequently, a life-cycle capital optimization problem is formulated to determine the optimal configuration of a hybrid renewable energy system, with constraints on balancing the given load profile. In order to make the resulting global mixed-integer linear programming (MILP) problem tractable, a pair of binary integer variables are introduced to describe the power flow direction from or into the energy storage systems. Finally, the optimized results of both energy storage methods are quantitatively compared to analyze the costs and advantages. Furthermore, the potential of the hydrogen energy storage is discussed based on the prediction of cost reduction in the future hydrogen industry. The analysis in this paper provides instructive guidance for the economic configuration of hybrid energy systems in the present and in the future.

ACS Style

Yisong Zhang; Q.S. Hua; Li Sun; Qie Liu. Life Cycle Optimization of Renewable Energy Systems Configuration with Hybrid Battery/Hydrogen Storage: A Comparative Study. Journal of Energy Storage 2020, 30, 101470 .

AMA Style

Yisong Zhang, Q.S. Hua, Li Sun, Qie Liu. Life Cycle Optimization of Renewable Energy Systems Configuration with Hybrid Battery/Hydrogen Storage: A Comparative Study. Journal of Energy Storage. 2020; 30 ():101470.

Chicago/Turabian Style

Yisong Zhang; Q.S. Hua; Li Sun; Qie Liu. 2020. "Life Cycle Optimization of Renewable Energy Systems Configuration with Hybrid Battery/Hydrogen Storage: A Comparative Study." Journal of Energy Storage 30, no. : 101470.

Journal article
Published: 20 May 2020 in Sustainability
Reads 0
Downloads 0

Boiler forced draft systems play a critical role in maintaining power plant safety and efficiency. However, their control is notoriously intractable in terms of modelling difficulty, multiple disturbances and severe noise. To this end, this paper develops a data-driven paradigm by combining some popular data analytics methods in both modelling and control. First, singular value decomposition (SVD) is utilized for data classification, which further cooperates with back propagation (BP) neural network to de-noise the measurements. Second, prediction error method (PEM) is used to analyze the historical data and identify the dynamic model, whose responses agree well with the actual plant data. Third, by estimating the lumped disturbances via the real-time data, active disturbance rejection control (ADRC) is employed to control the forced draft system, whose stability is analyzed in the frequency domain. Simulation results demonstrate the efficiency and superiority of the proposed method over proportional-integral-differential (PID) controller and model predictive controller, depicting a promising prospect in the future industry practice.

ACS Style

Qianchao Wang; Hongcan Xu; Lei Pan; Li Sun. Active Disturbance Rejection Control of Boiler Forced Draft System: A Data-Driven Practice. Sustainability 2020, 12, 4171 .

AMA Style

Qianchao Wang, Hongcan Xu, Lei Pan, Li Sun. Active Disturbance Rejection Control of Boiler Forced Draft System: A Data-Driven Practice. Sustainability. 2020; 12 (10):4171.

Chicago/Turabian Style

Qianchao Wang; Hongcan Xu; Lei Pan; Li Sun. 2020. "Active Disturbance Rejection Control of Boiler Forced Draft System: A Data-Driven Practice." Sustainability 12, no. 10: 4171.

Preprint content
Published: 18 May 2020
Reads 0
Downloads 0
ACS Style

Li Sun; Wen Sun; Fengqi You. Core Temperature Modelling and Monitoring of Lithium-ion Battery in the Presence of Sensor Bias. 2020, 1 .

AMA Style

Li Sun, Wen Sun, Fengqi You. Core Temperature Modelling and Monitoring of Lithium-ion Battery in the Presence of Sensor Bias. . 2020; ():1.

Chicago/Turabian Style

Li Sun; Wen Sun; Fengqi You. 2020. "Core Temperature Modelling and Monitoring of Lithium-ion Battery in the Presence of Sensor Bias." , no. : 1.

Journal article
Published: 04 February 2020 in Journal of Energy Storage
Reads 0
Downloads 0

Faced with the ever-increasing pressures from climate change and environmental pollution, stand-alone photovoltaic (PV) power generation is promising in providing electricity to the air conditioners. However, the solar energy is usually susceptible to the weather changes, making the power supply unreliable. To mitigate the effects caused by the solar intermittency, additional energy storage buffer is necessary. In this paper, stand-alone PV chilling systems with water tank thermal energy storage (TES) and battery electric energy storage (EES) strategies are quantitatively compared by evaluating the thermodynamic efficiency, respectively. A chiller model is firstly built, based on which the initial steady states are derived. Secondly, the EES and TES system models are built based on the compressor speed control strategy and refrigeration cycle model. The resulting chilled water temperature fluctuation curves in TES and EES PV chillers are subsequently obtained. Quantitative comparative results in this paper are threefold. i) The average chilled water temperature of the TES strategy is 11.08% lower than that in the EES strategy; ii) The average cooling energy amount stored in the TES strategy is 43.6% larger than that in the EES strategy, indicating that the chilled water tank has a better energy storage potential in the given PV chiller system; iii) The water volume of TES is optimized to derive the maximum cooling energy storage rate which is 76.92% larger than that in the EES system.

ACS Style

Yuting Wang; Q.S. Hua; Li Sun; Qie Liu. Thermodynamic efficiency comparison between thermal and electric storage for photovoltaic-driven chilling system. Journal of Energy Storage 2020, 28, 101253 .

AMA Style

Yuting Wang, Q.S. Hua, Li Sun, Qie Liu. Thermodynamic efficiency comparison between thermal and electric storage for photovoltaic-driven chilling system. Journal of Energy Storage. 2020; 28 ():101253.

Chicago/Turabian Style

Yuting Wang; Q.S. Hua; Li Sun; Qie Liu. 2020. "Thermodynamic efficiency comparison between thermal and electric storage for photovoltaic-driven chilling system." Journal of Energy Storage 28, no. : 101253.

Journal article
Published: 01 January 2020 in IEEE Transactions on Industrial Electronics
Reads 0
Downloads 0

The filter design for nonlinear uncertain systems is quite challenging since efficient estimation is required against stochastic noises, nonlinear uncertain dynamics as well as their concurrent effects. To this end, this paper develops a novel filter algorithm by augmenting the disturbance as well as unknown nonlinear dynamics as an extended state and constructing consistent Kalman-Bucy algorithm. The proposed extended state based Kalman-Bucy filter is shown to be of bounded estimation error, and the estimation accuracy can be online evaluated. More importantly, the estimation of asymptotic minimum variance is realized in condition that the changing rate of uncertainty approaches to zero. Therefore, the proposed extended state filter enables effective mitigation of disturbance and unknown nonlinear dynamics in real time by feedback control. The proposed algorithm is experimentally verified via a temperature control application in proton exchange membrane fuel cell, in which the thermocouple noise and the electrochemical uncertainty are seriously presented. The temperature variation of the extended state based Kalman-Bucy filter based control is greatly reduced, in comparison with the conventional control. The results in this paper depict a promising prospect of the proposed method for industrial control applications to handle both noises and nonlinear uncertain dynamics.

ACS Style

Wenchao Xue; Xiaocheng Zhang; Li Sun; Haitao Fang. Extended State Filter Based Disturbance and Uncertainty Mitigation for Nonlinear Uncertain Systems With Application to Fuel Cell Temperature Control. IEEE Transactions on Industrial Electronics 2020, 67, 10682 -10692.

AMA Style

Wenchao Xue, Xiaocheng Zhang, Li Sun, Haitao Fang. Extended State Filter Based Disturbance and Uncertainty Mitigation for Nonlinear Uncertain Systems With Application to Fuel Cell Temperature Control. IEEE Transactions on Industrial Electronics. 2020; 67 (12):10682-10692.

Chicago/Turabian Style

Wenchao Xue; Xiaocheng Zhang; Li Sun; Haitao Fang. 2020. "Extended State Filter Based Disturbance and Uncertainty Mitigation for Nonlinear Uncertain Systems With Application to Fuel Cell Temperature Control." IEEE Transactions on Industrial Electronics 67, no. 12: 10682-10692.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
Reads 0
Downloads 0

A controller-weighted multi-model predictive control (MMPC) strategy based on local model network (LMN) is proposed to address the nonlinearity and wide operating range of the boiler-turbine (B-T) system with constraints. The LMN model of the nonlinear plant is identified off-line based on data-driven modeling method. Since each local model is valid only in local regime, different local constraints are considered in designing local predictive controllers corresponding to different local models. The local controllers are run in parallel and each controller is assigned with a weight by the implicit scheduling unit. The weighted sum of the outputs of local controllers is taken as a global control signal and applied to the plant. The efficacy of the proposed MMPC is validated by simulations on a boiler-turbine system.

ACS Style

Hongxia Zhu; Gang Zhao; Li Sun; Kwang Y. Lee. Local Model Network Based Multi-Model Predictive Control for a Boiler - Turbine System. IFAC-PapersOnLine 2020, 53, 12530 -12535.

AMA Style

Hongxia Zhu, Gang Zhao, Li Sun, Kwang Y. Lee. Local Model Network Based Multi-Model Predictive Control for a Boiler - Turbine System. IFAC-PapersOnLine. 2020; 53 (2):12530-12535.

Chicago/Turabian Style

Hongxia Zhu; Gang Zhao; Li Sun; Kwang Y. Lee. 2020. "Local Model Network Based Multi-Model Predictive Control for a Boiler - Turbine System." IFAC-PapersOnLine 53, no. 2: 12530-12535.

Journal article
Published: 30 December 2019 in Applied Energy
Reads 0
Downloads 0

Open-cathode proton exchange membrane fuel cell (PEMFC) is promising in small-scale power generation due to its compact channel that integrates air supply and coolant flow. The operational temperature is significant for the safety and efficiency, but the temperature control is challenging due to the various uncertainties resulted from model inaccuracy and unexpected disturbances. To this end, the uncertainties and disturbances are lumped as a unified item, which is then augmented as an extended state to the original system. The extended uncertain state is estimated via the real-time input-output data and then compensated by active disturbance rejection control (ADRC). A series of linear models are identified via step response tests, showing the strong nonlinearity. Besides, PI and ADRC controllers are respectively designed based on the nominal linear model. The performance guarantee of ADRC is theoretically proved under uncertainty. Extensive simulations of the proposed models demonstrate the uncertainty compensation ability of ADRC. Finally, practical tests on a 300 W PEMFC experimental bench show that the proposed ADRC method has the obvious advantage over the conventional PI controller in both tracking and regulation performances.

ACS Style

Li Sun; Yuhui Jin; Fengqi You. Active disturbance rejection temperature control of open-cathode proton exchange membrane fuel cell. Applied Energy 2019, 261, 114381 .

AMA Style

Li Sun, Yuhui Jin, Fengqi You. Active disturbance rejection temperature control of open-cathode proton exchange membrane fuel cell. Applied Energy. 2019; 261 ():114381.

Chicago/Turabian Style

Li Sun; Yuhui Jin; Fengqi You. 2019. "Active disturbance rejection temperature control of open-cathode proton exchange membrane fuel cell." Applied Energy 261, no. : 114381.

Journal article
Published: 23 December 2019 in Applied Mathematical Modelling
Reads 0
Downloads 0

Desulfurization systems in coal-fired power stations often suffer the problem of high operating costs caused by a rule-of-thumb control strategy, which implies great potential for optimization of the operation. Due to the complex desulfurization mechanism, frequently fluctuating unit load, and severe disturbance, it is challenging to determine the optimal operating parameters based on the traditional mechanistic models, and the operating parameters are closely related to the operational efficiency of the flue gas desulfurization system. In this paper, an operation strategy optimization method for the desulfurization process is proposed based on a data mining framework, which is able to determine online the optimal operating parameter settings from a large amount of historical data. First, Principal Component Analysis (PCA) is used to reduce data redundancy by mapping the data into a new vector space. Based on the new vector space, an enhanced fuzzy C-means clustering (Enhanced-FCM) is developed to cluster the historical data into groups sharing similar characteristics. Taking sulfur dioxide emission concentration as a constraint condition, the system is optimized with economic benefits and desulfurization efficiency as the objective function. When performing optimization, the group that current operating conditions belong to is determined first, then the operating parameters of the best performance are searched within the group and provided as the optimization results. The method is validated and tested based on the data from a wet flue gas desulfurization (WFGD) system of a 1000 MWe supercritical coal-fired power plant in China. The results indicate that the proposed operation strategy can appropriately obtain operating parameter settings at different conditions, and effectively reduce the desulfurization cost under the constraint of meeting emission requirements.

ACS Style

Shan Liu; Li Sun; Senlin Zhu; Jie Li; Xi Chen; Wenqi Zhong. Operation strategy optimization of desulfurization system based on data mining. Applied Mathematical Modelling 2019, 81, 144 -158.

AMA Style

Shan Liu, Li Sun, Senlin Zhu, Jie Li, Xi Chen, Wenqi Zhong. Operation strategy optimization of desulfurization system based on data mining. Applied Mathematical Modelling. 2019; 81 ():144-158.

Chicago/Turabian Style

Shan Liu; Li Sun; Senlin Zhu; Jie Li; Xi Chen; Wenqi Zhong. 2019. "Operation strategy optimization of desulfurization system based on data mining." Applied Mathematical Modelling 81, no. : 144-158.

Journal article
Published: 11 December 2019 in Sustainability
Reads 0
Downloads 0

Optimal phasor measurement units (PMU) placement was developed to determine the number and locations of PMUs on the premise of full observability of the whole network. In order to enhance reliability under contingencies, redundancy should also be considered beside the number of PMUs in optimal phasor measurement units placement problem. Thus, in this paper, a multi-objective model was established to consider the two conflicting components simultaneously, solved by ε-constraint method and the fuzzy satisfying approach. The redundancy here was formulated as average possibility of observability including random component outages, and full possibility formula was applied to calculate the average possibility of observability in the case of single line outage. Finally, the model was employed to the IEEE-57 bus system, and the results verified that the developed model could provide a placement scheme with higher reliability.

ACS Style

Yu Huang; Shuqin Li; Xinyue Liu; Yan Zhang; Li Sun; Kai Yang. A Probabilistic Multi-Objective Model for Phasor Measurement Units Placement in the Presence of Line Outage. Sustainability 2019, 11, 7097 .

AMA Style

Yu Huang, Shuqin Li, Xinyue Liu, Yan Zhang, Li Sun, Kai Yang. A Probabilistic Multi-Objective Model for Phasor Measurement Units Placement in the Presence of Line Outage. Sustainability. 2019; 11 (24):7097.

Chicago/Turabian Style

Yu Huang; Shuqin Li; Xinyue Liu; Yan Zhang; Li Sun; Kai Yang. 2019. "A Probabilistic Multi-Objective Model for Phasor Measurement Units Placement in the Presence of Line Outage." Sustainability 11, no. 24: 7097.