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Kwang Y. Lee
School of Engineering and Computer Science, Baylor University, Waco, TX 76706, USA

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Journal article
Published: 21 July 2021 in Energies
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The growth in renewable energy integration over the past few years, primarily fueled by the drop in capital cost, has revealed the requirement for more sustainable methods of integration. This paper presents a collocated hybrid plant consisting of solar photovoltaic (PV) and Ternary pumped-storage hydro (TPSH) and designs controls that integrate the PV plant such that the behavior and the controllability of the hybrid plant are similar to those of a conventional plant within operational constraints. The PV array control and hybrid plant control implement a neural–network-based framework to coordinate the response, de-loading, and curtailment of multiple arrays with the response of the TPSH. With the help of the designed controls, a symbiotic relationship is developed between the two energy resources, where the PV compensates for the TPSH nonlinearities and provides required speed of response, while the TPSH firms the PV system and allows the PV to be integrated using its existing infrastructure. Simulations demonstrate that the designed controls enable the PV system to track references, while the TPSH’s firming and shifting transforms the PV system into a base load plant for most of the day and extends its hours of operation.

ACS Style

Soumyadeep Nag; Kwang Lee. Neural Network-Based Control for Hybrid PV and Ternary Pumped-Storage Hydro Plants. Energies 2021, 14, 4397 .

AMA Style

Soumyadeep Nag, Kwang Lee. Neural Network-Based Control for Hybrid PV and Ternary Pumped-Storage Hydro Plants. Energies. 2021; 14 (15):4397.

Chicago/Turabian Style

Soumyadeep Nag; Kwang Lee. 2021. "Neural Network-Based Control for Hybrid PV and Ternary Pumped-Storage Hydro Plants." Energies 14, no. 15: 4397.

Journal article
Published: 23 November 2020 in Energies
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In the parking lots of public commercial areas, such as shopping malls, hospitals, and scenic spots, the parking spaces with electric vehicle (EV) charging facilities are often occupied by ordinary cars. How to regulate the parking order in the parking lot is a key issue in the operation and management of the parking facilities. In this paper, a method of assessing parking fees for vehicles parked at the charging facilities is proposed based on an economic penalty strategy, including fixed-penalty and dynamic-penalty strategies. First, a traffic flow model of the parking lot in public area is established. Then, a price and consumption model of parking fees and parking lot utilization is established, along with different penalty strategies. Finally, taking the parking lot of a shopping mall as an example, the penalty strategies are optimized through particle swarm optimization (PSO) algorithm. The simulation results show that the method proposed can help to improve the utilization of EV charging facilities in parking lots and guide the orderly parking and charging of EVs at the same time.

ACS Style

Ruifeng Shi; Jie Zhang; Hao Su; Zihang Liang; Kwang Y. Lee. An Economic Penalty Scheme for Optimal Parking Lot Utilization with EV Charging Requirements. Energies 2020, 13, 6155 .

AMA Style

Ruifeng Shi, Jie Zhang, Hao Su, Zihang Liang, Kwang Y. Lee. An Economic Penalty Scheme for Optimal Parking Lot Utilization with EV Charging Requirements. Energies. 2020; 13 (22):6155.

Chicago/Turabian Style

Ruifeng Shi; Jie Zhang; Hao Su; Zihang Liang; Kwang Y. Lee. 2020. "An Economic Penalty Scheme for Optimal Parking Lot Utilization with EV Charging Requirements." Energies 13, no. 22: 6155.

Article
Published: 25 October 2020 in International Journal of Control, Automation and Systems
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This paper presents a hierarchical peer-to-peer-energy transaction model (P2P-ETM) considering the renewable energy preference. The model divides the characteristics of energy into normal and green energy. Green energy was assumed to be more expensive than normal energy, and prosumers and consumers considered in this model assumed that they have a preference for this green energy. Prosumer and consumer can trade energy with each other and buy normal and green energy from the main-grid. Prosumer can also sell surplus energy to the main-grid. To solve the energy transaction problem, we present a hierarchical approach considering the energy transaction and prosumers’ goal. The purpose of each prosumer such as energy purchase cost minimization or energy sales maximization for energy transaction is considered in the first step as the self-scheduling of prosumers. After prosumers’ self-scheduling we derive the energy transaction price and energy trading capacity through social welfare maximization. The proposed methodology is tested and validated on a virtual network. Through a case study using mixed integer linear programming (MILP), this model has demonstrated a decrease in the total operation cost in accordance with energy trading.

ACS Style

Dae-Hyun Park; Yong-Gi Park; Jae-Hyung Roh; Kwang Y. Lee; Jong-Bae Park. A Hierarchical Peer-to-Peer Energy Transaction Model Considering Prosumer’s Green Energy Preference. International Journal of Control, Automation and Systems 2020, 19, 311 -317.

AMA Style

Dae-Hyun Park, Yong-Gi Park, Jae-Hyung Roh, Kwang Y. Lee, Jong-Bae Park. A Hierarchical Peer-to-Peer Energy Transaction Model Considering Prosumer’s Green Energy Preference. International Journal of Control, Automation and Systems. 2020; 19 (1):311-317.

Chicago/Turabian Style

Dae-Hyun Park; Yong-Gi Park; Jae-Hyung Roh; Kwang Y. Lee; Jong-Bae Park. 2020. "A Hierarchical Peer-to-Peer Energy Transaction Model Considering Prosumer’s Green Energy Preference." International Journal of Control, Automation and Systems 19, no. 1: 311-317.

Journal article
Published: 11 August 2020 in Energies
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With increasing renewable penetration and projected increase in natural disasters, the reliability and resiliency of a power system become crucial issues. As network inertia drops with increasing penetration of renewables, operators search for flexible resources that can help cope with a disruptive event or manage renewable intermittency. Energy storage is a solution, but the type of storage solution needs to be profitable to exist in the current and upcoming power markets. Advanced pumped-storage hydropower (PSH) is one solution that can help cope with such requirements, which will in turn help to increase the renewable penetration in the system. This paper qualitatively compares the revenue earning potential of PSH configurations, including, adjustable-speed PSH (AS-PSH) and ternary PSH (T-PSH) in comparison to conventional PSH (C-PSH) from the arbitrage and regulation markets, with and without the presence of wind penetration. In addition, a framework for quantitative analysis of any energy storage system has been proposed. A 24-bus RTS system is studied with summer and winter variations in load and wind power. Through revenue and operational mode analysis, this paper reveals that T-PSH has the highest revenue earning potential, which is mainly due to its ability to operate with a hydraulic short circuit.

ACS Style

Soumyadeep Nag; Kwang Y. Lee. Network and Reserve Constrained Economic Analysis of Conventional, Adjustable-Speed and Ternary Pumped-Storage Hydropower. Energies 2020, 13, 4140 .

AMA Style

Soumyadeep Nag, Kwang Y. Lee. Network and Reserve Constrained Economic Analysis of Conventional, Adjustable-Speed and Ternary Pumped-Storage Hydropower. Energies. 2020; 13 (16):4140.

Chicago/Turabian Style

Soumyadeep Nag; Kwang Y. Lee. 2020. "Network and Reserve Constrained Economic Analysis of Conventional, Adjustable-Speed and Ternary Pumped-Storage Hydropower." Energies 13, no. 16: 4140.

Journal article
Published: 02 June 2020 in Control Engineering Practice
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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: 11 March 2020 in Energies
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This paper proposes a maximum power point tracking (MPPT) and voltage regulation method based on model predictive control (MPC) for the two-stage grid-tied photovoltaic (PV) system, which can achieve MPPT and output voltage regulation of a PV system simultaneously. The MPPT algorithm based on MPC is implemented in a DC-DC boost converter. The reference voltage at maximum power point is obtained by dual step Incremental Conductance (I&C) algorithm under the rapidly varying illumination intensity, and the MPPT controller only needs to minimize one cost function of PV current, without pulse width modulation (PWM) module. To inject the generated PV power into the grid with high quality, this paper designs voltage regulation controller based on MPC to maintain the output voltage of the PV system at the desired value. The MPC controller outputs the optimal duty signal with the input and state constraints in the inner loop, and the PI controller in the outer loop is designed to improve the dynamic performance. The proposed method based on MPC was demonstrated using the SimPower systems tool in MATLAB/Simulink. Analysis and simulation results for the PV system show possible improvements on the closed-loop performance such as fast response and low overshoot.

ACS Style

Miaomiao Ma; Xiangjie Liu; Kwang Y. Lee. Maximum Power Point Tracking and Voltage Regulation of Two-Stage Grid-Tied PV System Based on Model Predictive Control. Energies 2020, 13, 1304 .

AMA Style

Miaomiao Ma, Xiangjie Liu, Kwang Y. Lee. Maximum Power Point Tracking and Voltage Regulation of Two-Stage Grid-Tied PV System Based on Model Predictive Control. Energies. 2020; 13 (6):1304.

Chicago/Turabian Style

Miaomiao Ma; Xiangjie Liu; Kwang Y. Lee. 2020. "Maximum Power Point Tracking and Voltage Regulation of Two-Stage Grid-Tied PV System Based on Model Predictive Control." Energies 13, no. 6: 1304.

Journal article
Published: 08 February 2020 in Renewable Energy
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The electric vehicle to grid (V2G) interaction technology can improve the utilization of renewable energy and stabilize its grid connection. At the same time, renewable energy can be used for a microgrid nearby, or incorporated into a large grid, to effectively address the volatility of renewable energy sources. Motivated by the increasing number of electric vehicles (EVs) and the randomness of renewable energy output, this paper proposes an effective strategy to improve the security and economy of the microgrid system. The uncertainty of wind power and EV’s state of charge (SOC) is modeled as uncertainty prediction sets. And considering the worst-case scenario, this proposed strategy can increase the absorption ratio of renewable energy while orderly guiding the charging and discharging of EVs in peak-load reduction and valley filling and thus, lower operating costs under various practical constraints. To solve the problem of over-conservatism of the robust optimization, this paper introduces a dispatch interval coefficient to adjust the degree of conservatism, while improving the economy of microgrids system. The robustness and feasibility of the proposed dispatch strategy are demonstrated by numerical case studies.

ACS Style

Ruifeng Shi; Shaopeng Li; Penghui Zhang; Kwang Y. Lee. Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization. Renewable Energy 2020, 153, 1067 -1080.

AMA Style

Ruifeng Shi, Shaopeng Li, Penghui Zhang, Kwang Y. Lee. Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization. Renewable Energy. 2020; 153 ():1067-1080.

Chicago/Turabian Style

Ruifeng Shi; Shaopeng Li; Penghui Zhang; Kwang Y. Lee. 2020. "Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization." Renewable Energy 153, no. : 1067-1080.

Journal article
Published: 02 January 2020 in Energies
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This paper proposes a supplementary control for tighter control of the air–fuel ratio (AFR), which directly affects the environmental emissions of thermal power plants. Dynamic matrix control (DMC) is applied to the supplementary control of the existing combustion control loops and the conventional double cross limiting algorithm for combustion safety is formulated as constraints in the proposed DMC. The proposed supplementary control is simulated for a 600-MW drum-type power plant and 1000 MW ultra-supercritical once-through boiler power plant. The results show the tight control of the AFR in both types of thermal power plants to reduce environmental emissions.

ACS Style

Taehyun Lee; Eungsu Han; Un-Chul Moon; Kwang Y. Lee. Supplementary Control of Air–Fuel Ratio Using Dynamic Matrix Control for Thermal Power Plant Emission. Energies 2020, 13, 226 .

AMA Style

Taehyun Lee, Eungsu Han, Un-Chul Moon, Kwang Y. Lee. Supplementary Control of Air–Fuel Ratio Using Dynamic Matrix Control for Thermal Power Plant Emission. Energies. 2020; 13 (1):226.

Chicago/Turabian Style

Taehyun Lee; Eungsu Han; Un-Chul Moon; Kwang Y. Lee. 2020. "Supplementary Control of Air–Fuel Ratio Using Dynamic Matrix Control for Thermal Power Plant Emission." Energies 13, no. 1: 226.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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In this paper, a visual-detection (VD)-based fruit fly optimization algorithm (FOA) is proposed for solving a robust analysis problem of integrated energy systems (IES) with energy storage based on the information gap decision theory (IGDT). In the searching phase, the VD-based decision delay and visual feature detection are incorporated within the FOA. VD-FOA changes the search radius of fruit fly according to the variance of smell concentration, which can solve the problem that fruit fly optimization algorithm is easy to fall into local optimization, where standard test functions are also adopted to test the proposed algorithm. The proposed VD-FOA is superior to the basic FOA and is applied to solve the IGDT-based robust analysis problem of IES with energy storage. The simulation results show the applicability and effectiveness of the proposed algorithm.

ACS Style

Weizhen Hou; Jiayu Li; Jing Xu; Kwang Y. Lee; Yu Huang. Visual-Detection based Fruit Fly Optimization Algorithm for Robust Analysis of Integrated Energy Systems. IFAC-PapersOnLine 2020, 53, 13562 -13567.

AMA Style

Weizhen Hou, Jiayu Li, Jing Xu, Kwang Y. Lee, Yu Huang. Visual-Detection based Fruit Fly Optimization Algorithm for Robust Analysis of Integrated Energy Systems. IFAC-PapersOnLine. 2020; 53 (2):13562-13567.

Chicago/Turabian Style

Weizhen Hou; Jiayu Li; Jing Xu; Kwang Y. Lee; Yu Huang. 2020. "Visual-Detection based Fruit Fly Optimization Algorithm for Robust Analysis of Integrated Energy Systems." IFAC-PapersOnLine 53, no. 2: 13562-13567.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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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: 01 January 2020 in IFAC-PapersOnLine
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This paper investigates, how Ternary Pumped Storage Hydropower (T-PSH) can help enhance power system resiliency by contributing primary frequency regulation in both pumping and generating modes. As renewable penetration increases, power system inertia decreases. Simultaneously, the frequency of storms and earthquakes have increased. As such power system resiliency is a key issue in low inertia power systems. To cater to this issue, the authors investigate the ability of T-PSH to provide primary frequency support in pumping and generating mode. The governor dynamics of the IEEE 9-bus system and T-PSH have been modeled and integrated. When the system is subjected to a step increase or decrease in load, results display that not only can the T-PSH provide pump mode regulation using the hydraulic short-circuit, but it can also transit smoothly between pumping and generating mode within a few seconds using the clutch. By changing its mode, the T-PSH unit can provide a regulation capability equal to twice that of the unit rating.

ACS Style

Soumyadeep Nag; Kwang Y. Lee. Power System Resiliency Enhancement with Ternary Pumped – Storage Hydropower. IFAC-PapersOnLine 2020, 53, 12714 -12718.

AMA Style

Soumyadeep Nag, Kwang Y. Lee. Power System Resiliency Enhancement with Ternary Pumped – Storage Hydropower. IFAC-PapersOnLine. 2020; 53 (2):12714-12718.

Chicago/Turabian Style

Soumyadeep Nag; Kwang Y. Lee. 2020. "Power System Resiliency Enhancement with Ternary Pumped – Storage Hydropower." IFAC-PapersOnLine 53, no. 2: 12714-12718.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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Aiming at the influence of the uncertainty of renewable energy generation on the power distribution of smart grid, a distributed optimal scheduling strategy for smart grid energy storage units based on consensus algorithm was proposed. This method does not rely on the central controller, but through the local communication between the energy storage units, according to their own information and acquired neighbor information to adjust the deviation between the actual and planed power in real time. In addition, in order to verify that the algorithm can optimize the network loss, MATPOWER is used to calculate the network loss before and after optimization. The system simulation results show that the proposed distributed scheduling strategy can ensure that all storage units converge to the same optimal value, and make the power grid run more economically.

ACS Style

Hui Zhang; Linjun Shi; Guanghui Hua; Kwang Y. Lee. An Optimal Active Power Scheduling Strategy with Renewable Energy Based on Distributed Consensus Algorithms. IFAC-PapersOnLine 2020, 53, 13489 -13494.

AMA Style

Hui Zhang, Linjun Shi, Guanghui Hua, Kwang Y. Lee. An Optimal Active Power Scheduling Strategy with Renewable Energy Based on Distributed Consensus Algorithms. IFAC-PapersOnLine. 2020; 53 (2):13489-13494.

Chicago/Turabian Style

Hui Zhang; Linjun Shi; Guanghui Hua; Kwang Y. Lee. 2020. "An Optimal Active Power Scheduling Strategy with Renewable Energy Based on Distributed Consensus Algorithms." IFAC-PapersOnLine 53, no. 2: 13489-13494.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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The integrated energy system (IES) plays an important role in the development of clean energy through the complementary advantages of multi energy and the absorption capacity of renewable energy. However, because of the multi energy coupling and the intermittence of renewable energy, the risk of system dynamic instability increases. In order to solve the above issues, this paper considers the influence of the dynamic characteristics of the system from the level of planning and design stage. Based on the solar intensity and load demand curve of typical winter days and considering the daily economic operation of typical days and the dynamic characteristics of the system, an IES capacity configuration optimization model is established, which takes into account the system investment cost and the dynamic characteristics of the system. Then the genetic algorithm with penalty function is used to optimize the solution. Finally, according to the typical winter day data of a certain area in Nanjing, P.R.China, the rationality and validity of the model are verified, and the scientific configuration of an IES capacity considering dynamic performance is realized, which provides ideas and support for the planning and design of an IES later.

ACS Style

Yuxuan Li; Junli Zhang; Xiao Wu; Jiong Shen; Kwang Y. Lee. Capacity Configuration of Integrated Energy System Considering Equipment Inertia. IFAC-PapersOnLine 2020, 53, 13088 -13093.

AMA Style

Yuxuan Li, Junli Zhang, Xiao Wu, Jiong Shen, Kwang Y. Lee. Capacity Configuration of Integrated Energy System Considering Equipment Inertia. IFAC-PapersOnLine. 2020; 53 (2):13088-13093.

Chicago/Turabian Style

Yuxuan Li; Junli Zhang; Xiao Wu; Jiong Shen; Kwang Y. Lee. 2020. "Capacity Configuration of Integrated Energy System Considering Equipment Inertia." IFAC-PapersOnLine 53, no. 2: 13088-13093.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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Superheated steam temperature is one of the most important process variables for controlling the steam quality of thermal power units. In order to improve the accuracy of superheated steam temperature and the stability of valves for desuperheating water, this paper proposed a novel control strategy called united long short-term memory (LSTM) and model predictive control (MPC), which is weighted by particle swarm optimization. First, a deeply learnt inverse model is made to express the potential nonlinear dynamic characteristics of data and to predict the future valve opening in short-term. Secondly, model predictive control is used to control the secondary superheated steam temperature. Thirdly, the two predicted valve opening are weighted by particle swarm optimization. The combined deep learning inverse model control and MPC can make up the deficiencies of each other, i.e., over fitting of deep learning inverse model and linearity of MPC. The simulation experiments proved the advantage of LSTM-MPC in comparison with traditional PID and single MPC control.

ACS Style

Qianchao Wang; Lei Pan; Kwang Y Lee. Improving Superheated Steam Temperature Control Using United Long Short Term Memory and MPC. IFAC-PapersOnLine 2020, 53, 13345 -13350.

AMA Style

Qianchao Wang, Lei Pan, Kwang Y Lee. Improving Superheated Steam Temperature Control Using United Long Short Term Memory and MPC. IFAC-PapersOnLine. 2020; 53 (2):13345-13350.

Chicago/Turabian Style

Qianchao Wang; Lei Pan; Kwang Y Lee. 2020. "Improving Superheated Steam Temperature Control Using United Long Short Term Memory and MPC." IFAC-PapersOnLine 53, no. 2: 13345-13350.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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Most stand-alone integrated energy systems (IES) with renewable energy can only meet the demand of electrical load, not both electrical load and thermal load. Those studies on combined heat and power cogeneration systems mainly focus on the optimal scheduling of each source, ignoring the difference in dynamic response between electrical and thermal processes. In fact, the different response speeds of electrical and thermal objects will bring in challenges to control. In order to specify and solve the issue, this paper proposes a detailed mechanism model of a standard IES. A model predictive control (MPC) controller is designed and tuned based on the state-space form of the system. The control simulation results imply the feasibility of the MPC controller in coordinating both electricity and heat.

ACS Style

Yuhui Jin; Junli Zhang; Xiao Wu; Jiong Shen; Kwang Y. Lee. Coordinated Control for Combined Heat and Power Load of an Integrated Energy System. IFAC-PapersOnLine 2020, 53, 13184 -13189.

AMA Style

Yuhui Jin, Junli Zhang, Xiao Wu, Jiong Shen, Kwang Y. Lee. Coordinated Control for Combined Heat and Power Load of an Integrated Energy System. IFAC-PapersOnLine. 2020; 53 (2):13184-13189.

Chicago/Turabian Style

Yuhui Jin; Junli Zhang; Xiao Wu; Jiong Shen; Kwang Y. Lee. 2020. "Coordinated Control for Combined Heat and Power Load of an Integrated Energy System." IFAC-PapersOnLine 53, no. 2: 13184-13189.

Journal article
Published: 23 October 2019 in Energies
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The ultra-supercritical (USC) coal-fired boiler-turbine unit has been widely used in modern power plants due to its high efficiency and low emissions. Since it is a typical multivariable system with large inertia, severe nonlinearity, and strong coupling, building an accurate model of the system using traditional identification methods are almost impossible. In this paper, a deep neural network framework using stacked auto-encoders (SAEs) is presented as an effective way to model the USC unit. In the training process of SAE, maximum correntropy is chosen as the loss function, since it can effectively alleviate the influence of the outliers existing in USC unit data. The SAE model is trained and validated using the real-time measurement data generated in the USC unit, and then compared with the traditional multilayer perceptron network. The results show that SAE has superiority both in forecasting the dynamic behavior as well as eliminating the influence of outliers. Therefore, it can be applicable for the simulation analysis of a 1000 MW USC unit.

ACS Style

Hao Zhang; Xiangjie Liu; Xiaobing Kong; Kwang Y. Lee; Liu; Kong; Lee. Stacked Auto-Encoder Modeling of an Ultra-Supercritical Boiler-Turbine System. Energies 2019, 12, 4035 .

AMA Style

Hao Zhang, Xiangjie Liu, Xiaobing Kong, Kwang Y. Lee, Liu, Kong, Lee. Stacked Auto-Encoder Modeling of an Ultra-Supercritical Boiler-Turbine System. Energies. 2019; 12 (21):4035.

Chicago/Turabian Style

Hao Zhang; Xiangjie Liu; Xiaobing Kong; Kwang Y. Lee; Liu; Kong; Lee. 2019. "Stacked Auto-Encoder Modeling of an Ultra-Supercritical Boiler-Turbine System." Energies 12, no. 21: 4035.

Journal article
Published: 18 September 2019 in Sustainability
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This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.

ACS Style

Hongxia Zhu; Gang Zhao; Li Sun; Kwang Y. Lee. Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm. Sustainability 2019, 11, 5102 .

AMA Style

Hongxia Zhu, Gang Zhao, Li Sun, Kwang Y. Lee. Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm. Sustainability. 2019; 11 (18):5102.

Chicago/Turabian Style

Hongxia Zhu; Gang Zhao; Li Sun; Kwang Y. Lee. 2019. "Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm." Sustainability 11, no. 18: 5102.

Journal article
Published: 12 September 2019 in Energies
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With decreasing costs of renewable energy harvesting devices, penetration of solar panels and wind turbines have increased manifold. Under such high levels of penetration, coping with increased intermittency and unpredictability and maintaining power system resiliency under reduced inertia conditions has become a critical issue. Pumped storage hydro (PSH) is the most matured and economic form of storage that can serve the purpose of capacity for over 4 to 8 h. However, to increase network inertia and add required flexibility to low inertia power systems, significant paradigm shifting modifications have been engineered to result in the development of Ternary PSH (TPSH). In this paper a test system to consider governor interaction is constructed. The dynamic models of conventional PSH (CPSH) and TPSH are constructed and integrated to the test system to examine the effect of CPSH and TPSH in the hydraulic short circuit (TPSH-HSC). The ability and the effect of mode change (from pump to turbine) without the loss synchronism of TPSH has also been examined. Results display the superior capability and effect of TPSH with its HSC capability to contribute to frequency regulation during pumping mode and the effect of rapid mode change, as compared to its primitive alternative, CPSH.

ACS Style

Soumyadeep Nag; Kwang Y. Lee; D. Suchitra. A Comparison of the Dynamic Performance of Conventional and Ternary Pumped Storage Hydro. Energies 2019, 12, 3513 .

AMA Style

Soumyadeep Nag, Kwang Y. Lee, D. Suchitra. A Comparison of the Dynamic Performance of Conventional and Ternary Pumped Storage Hydro. Energies. 2019; 12 (18):3513.

Chicago/Turabian Style

Soumyadeep Nag; Kwang Y. Lee; D. Suchitra. 2019. "A Comparison of the Dynamic Performance of Conventional and Ternary Pumped Storage Hydro." Energies 12, no. 18: 3513.

Journal article
Published: 10 September 2019 in IFAC-PapersOnLine
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This paper contributes to the theme of intelligent integration of energy storage and the control of prosumer resources. The paper illustrates the use of a zero-order Sugeno fuzzy model to perform bidirectional real power control from an electric vehicle (EV), i.e., vehicle to grid (V2G) and grid to vehicle (G2V). The paper proposes an initial design of the fuzzy logic controller (FLC) following which the FLC is optimized using genetic algorithm (GA) for better performance under varying charging speed requirements of the user and better efficiency. A perturb and observe (P&O) algorithm based reactive power control is also proposed for voltage support through these EV chargers. The designed FLC can not only respond to grid conditions based on the time of use (TOU) but also to users charging speed requirements. Also, the initial design of an FLC may not suit a particular user’s requirement but can be optimized to meet the requirements.

ACS Style

Soumyadeep Nag; Kwang. Y. Lee. Optimized Fuzzy Logic Controller for Responsive Charging of Electric Vehicles. IFAC-PapersOnLine 2019, 52, 147 -152.

AMA Style

Soumyadeep Nag, Kwang. Y. Lee. Optimized Fuzzy Logic Controller for Responsive Charging of Electric Vehicles. IFAC-PapersOnLine. 2019; 52 (4):147-152.

Chicago/Turabian Style

Soumyadeep Nag; Kwang. Y. Lee. 2019. "Optimized Fuzzy Logic Controller for Responsive Charging of Electric Vehicles." IFAC-PapersOnLine 52, no. 4: 147-152.

Journal article
Published: 10 September 2019 in IFAC-PapersOnLine
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This paper performs optimization-based controller design to avoid control interaction among multiple converter-interfaced generators (CIGs). To assess the control interaction between CIGs, an impedance-based stability analysis. Unexpected instability resulting from the CIG disconnection is investigated in a test case where three CIGs are connected to a microgrid. Parameter sensitivity is performed to determine which control parameter has more influence on the stability, and particle swarm optimization is utilized to adjust the control parameters to enhance the stability with computational efficiency. Simulation results demonstrated that the proposed tuning procedure helps ensure the stability of the test case even if a CIG is disconnected from the grid.

ACS Style

Youngho Cho; Ryangkyu Kim; Mingyu Song; Kwang Y. Lee; Kyeon Hur. Optimal Controller Design for Stabilizing a Power System with Multiple Converter-interfaced Generators. IFAC-PapersOnLine 2019, 52, 75 -80.

AMA Style

Youngho Cho, Ryangkyu Kim, Mingyu Song, Kwang Y. Lee, Kyeon Hur. Optimal Controller Design for Stabilizing a Power System with Multiple Converter-interfaced Generators. IFAC-PapersOnLine. 2019; 52 (4):75-80.

Chicago/Turabian Style

Youngho Cho; Ryangkyu Kim; Mingyu Song; Kwang Y. Lee; Kyeon Hur. 2019. "Optimal Controller Design for Stabilizing a Power System with Multiple Converter-interfaced Generators." IFAC-PapersOnLine 52, no. 4: 75-80.