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Qiuwei Wu
Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan 250061, Shandong, P.R.China

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Journal article
Published: 27 July 2021 in Energy
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The day-ahead operational schedule of the integrated electricity and heat system may be suboptimal due to prediction errors of renewables and loads. This paper proposes a real-time optimal operation scheme for the integrated electricity and heat system considering reserve provision from large scale heat pumps, which utilizes model predictive control and different operating reserves to gradually balance forecast errors of renewables and loads in the co-optimization of reserve deployment and heat regulation. The real-time operation is divided into two stages including real-time pre-scheduling and real-time balancing. A two-stage model predictive control approach is proposed to deploy following reserve and regulating reserve for real-time pre-scheduling and real-time balancing, respectively. The following reserve in the real-time pre-scheduling is used to deal with the day-ahead forecast errors, while the regulating reserve in the real-time balancing is to handle real-time forecast errors. In addition, a detailed reserve provision model of large-scale heat pumps is built. The case studies are conducted on a 6-bus integrated electricity and heat system. The simulation results show that the proposed two-stage approach uses following and regulating reserves from large-scale heat pumps to further reduce operational cost, wind power curtailment, and load shedding. The MPC approach can obtain a feasible solution closer to the ideal solution.

ACS Style

Menglin Zhang; Qiuwei Wu; Jinyu Wen; Xizhen Xue; Zhongwei Lin; Fang Fang. Real-time optimal operation of integrated electricity and heat system considering reserve provision of large-scale heat pumps. Energy 2021, 121606 .

AMA Style

Menglin Zhang, Qiuwei Wu, Jinyu Wen, Xizhen Xue, Zhongwei Lin, Fang Fang. Real-time optimal operation of integrated electricity and heat system considering reserve provision of large-scale heat pumps. Energy. 2021; ():121606.

Chicago/Turabian Style

Menglin Zhang; Qiuwei Wu; Jinyu Wen; Xizhen Xue; Zhongwei Lin; Fang Fang. 2021. "Real-time optimal operation of integrated electricity and heat system considering reserve provision of large-scale heat pumps." Energy , no. : 121606.

Journal article
Published: 14 July 2021 in International Journal of Electrical Power & Energy Systems
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With the large-scale deployment of distributed energy resources (DERs) in distribution networks, network congestion could occur due to the non-coordinated operation of DERs. The dynamic tariff (DT) method as a decentralized day-ahead congestion management method has been widely studied. In the DT method, it is assumed that the distribution system operator (DSO) and aggregators use the same energy requirement parameters, which is impractical due to the DSO’s forecast error. The discrepancy between the DSO’s forecast parameters and aggregator’s accurate parameters leads to a certain level of uncertainty that needs to be handled when employing the DT method. Therefore, a robust DT method is proposed for day-ahead congestion management while dealing with the uncertainty in the DT framework. A three-level robust DT model is formulated to obtain a robust DT solution, based on which the network constraints are respected even in the worst-case scenario. Moreover, due to the nonconvexity of the three-level robust DT model, the robust DT model is reformulated as a two-level optimization model, and a heuristic solution method is developed to obtain the robust DT solution with an iterative procedure. The Roy Billinton Test System (RBTS) was used to conduct case studies to validate the effectiveness of the proposed robust DT method for day-ahead congestion management in distribution networks. The case study results demonstrate that the deterministic DT method may be ineffective due to the DSO’s forecast errors whereas the proposed robust DT method can resolve congestion efficiently under the uncertain condition.

ACS Style

Feifan Shen; Qiuwei Wu. Robust dynamic tariff method for day-ahead congestion management of distribution networks. International Journal of Electrical Power & Energy Systems 2021, 134, 107366 .

AMA Style

Feifan Shen, Qiuwei Wu. Robust dynamic tariff method for day-ahead congestion management of distribution networks. International Journal of Electrical Power & Energy Systems. 2021; 134 ():107366.

Chicago/Turabian Style

Feifan Shen; Qiuwei Wu. 2021. "Robust dynamic tariff method for day-ahead congestion management of distribution networks." International Journal of Electrical Power & Energy Systems 134, no. : 107366.

Journal article
Published: 08 July 2021 in IEEE Transactions on Power Systems
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This paper proposes a distributed coordinated voltage control scheme for distribution networks with distributed generation (DG) and on-load tap changer (OLTC). In this scheme, static synchronous compensators (STATCOMs), DG units and OLTC are coordinated to regulate voltages of all buses to be close to the nominal value in the distribution network, mitigate voltage fluctuations, and minimize the number of operations of OLTC while considering different temporal characteristics of voltage regulation devices. The optimization problem of coordinating DG units and STATCOMs is decomposed by the gradient projection (GP) method. The local controller optimizes the reactive power outputs of DGs and STATCOMs according to local voltage and reactive power measurements, and still achieves the optimal coordination of DG units and STATCOMS in a decentralized manner without a central controller or communication between local controllers. The OLTC control scheme is designed to correct the long-term voltage deviations based on model predictive control (MPC) while minimizing the number of operations. The local controllers send the calculated reactive power references of DG and STATCOMs to the OLTC controller, which achieves distributed coordinated voltage control and mitigates the computation burden.

ACS Style

Wenshu Jiao; Jian Chen; Qiuwei Wu; Sheng Huang; Canbing Li; Bin Zhou. Distributed Coordinated Voltage Control for Distribution Networks with DG and OLTC based on MPC and Gradient Projection. IEEE Transactions on Power Systems 2021, PP, 1 -1.

AMA Style

Wenshu Jiao, Jian Chen, Qiuwei Wu, Sheng Huang, Canbing Li, Bin Zhou. Distributed Coordinated Voltage Control for Distribution Networks with DG and OLTC based on MPC and Gradient Projection. IEEE Transactions on Power Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Wenshu Jiao; Jian Chen; Qiuwei Wu; Sheng Huang; Canbing Li; Bin Zhou. 2021. "Distributed Coordinated Voltage Control for Distribution Networks with DG and OLTC based on MPC and Gradient Projection." IEEE Transactions on Power Systems PP, no. 99: 1-1.

Journal article
Published: 28 June 2021 in IEEE Transactions on Power Systems
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This paper studies the communication time-delay issues in islanded microgrids (MGs) with the distributed secondary control architecture. Firstly, a time-delayed MG small-signal model is developed. Then, a new weight-average-prediction (WAP) controller is proposed to compensate the delayed system state. By introducing a time-delayed differential term in the proposed control law, the traditional time-delayed small-signal model is transformed into a neutral time-delayed mathematic model. Based on the developed model, the stability analysis is conducted considering both fixed time delay and time-varying delay. For the fixed time delay, a novel graphic analytical method is proposed to evaluate the time delay margin, which eliminates the conservatism compared with existing time-domain methods. For the time-varying delay, stability condition is established by a Lyapunov-Krasovskii function and linear matrix inequalities. In addition, some non-linear WAP control methods are discussed to guide the parameter tuning with a higher resolution. Lastly, the proposed method and analytical result are verified in the OPAL-RT real-time test platform. The results demonstrate the effectiveness and high performance of the proposed controller.

ACS Style

Weitao Yao; Yu Wang; Yan Xu; Chao Deng; Qiuwei Wu. Distributed Weight-Average-Prediction Control and Stability Analysis for an Islanded Microgrid with Communication Time Delay. IEEE Transactions on Power Systems 2021, PP, 1 -1.

AMA Style

Weitao Yao, Yu Wang, Yan Xu, Chao Deng, Qiuwei Wu. Distributed Weight-Average-Prediction Control and Stability Analysis for an Islanded Microgrid with Communication Time Delay. IEEE Transactions on Power Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Weitao Yao; Yu Wang; Yan Xu; Chao Deng; Qiuwei Wu. 2021. "Distributed Weight-Average-Prediction Control and Stability Analysis for an Islanded Microgrid with Communication Time Delay." IEEE Transactions on Power Systems PP, no. 99: 1-1.

Journal article
Published: 17 June 2021 in International Journal of Electrical Power & Energy Systems
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Increasing penetration of volatile renewable sources in the electric power system increases the need of new sources of balancing capacity. As a power-to-x technology, heat pumps (HPs) couple the electricity and heating systems, while offering a potential source of regulating capacity. This paper presents new findings on primary frequency support from large-scale HPs through local droop and synthetic inertia control methods with the objective of improving the system frequency control performance following a disturbance. The complexity and operation of large-scale HPs are different from the common model representation of HPs as thermostatically controllable load, which makes the existing studies and HP frequency support methods inaccurate. This work applies a state space model representation of the large-scale HPs, which enables a performance evaluation of existing local frequency control methods. Simulation results show the improved grid frequency control performance following a disturbance supported by large-scale HPs. Simultaneously, the heat and power response of large-scale HPs with frequency control support is compared to that of small-scale HPs.

ACS Style

Theis Bo Harild Rasmussen; Qiuwei Wu; Menglin Zhang. Primary frequency support from local control of large-scale heat pumps. International Journal of Electrical Power & Energy Systems 2021, 133, 107270 .

AMA Style

Theis Bo Harild Rasmussen, Qiuwei Wu, Menglin Zhang. Primary frequency support from local control of large-scale heat pumps. International Journal of Electrical Power & Energy Systems. 2021; 133 ():107270.

Chicago/Turabian Style

Theis Bo Harild Rasmussen; Qiuwei Wu; Menglin Zhang. 2021. "Primary frequency support from local control of large-scale heat pumps." International Journal of Electrical Power & Energy Systems 133, no. : 107270.

Journal article
Published: 14 June 2021 in Energy
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District heating systems can provide considerable flexibility for electric power systems through combined heat and power units and heat pumps. This paper proposes a chance-constrained two-stage energy and multi-type reserves scheduling scheme for integrated electricity and heating systems to handle both wind power forecast errors and the outage of the largest generator. The combined heat and power units and heat pumps not only provide following reserves for offsetting wind power forecast errors under normal conditions, but also provide primary frequency response reserves for arresting system frequency decline after the outage of the largest generator. The primary frequency response reserves from combined heat and power units and heat pumps are optimized satisfying system steady-state frequency requirement while considering the reserve costs. To manage the risk level of load shedding caused by wind power forecast errors, chance constraints are adopted to achieve the trade-off between the sufficiency of following reserves and system economic efficiency. The nonlinear and nonconvex scheduling model is reformulated as a mixed-integer linear program via linearization and convex approximation based on conditional value-at-risk. The effectiveness of the proposed scheduling scheme in improving the system frequency regulation, system economic efficiency, and wind power integration is verified through the case studies on a 6-bus and 6-node integrated electricity and heating system and a regional large-scale test system.

ACS Style

Jin Tan; Qiuwei Wu; Menglin Zhang; Wei Wei; Feng Liu; Bo Pan. Chance-Constrained Energy and Multi-type Reserves Scheduling Exploiting Flexibility from Combined Power and Heat Units and Heat Pumps. Energy 2021, 233, 121176 .

AMA Style

Jin Tan, Qiuwei Wu, Menglin Zhang, Wei Wei, Feng Liu, Bo Pan. Chance-Constrained Energy and Multi-type Reserves Scheduling Exploiting Flexibility from Combined Power and Heat Units and Heat Pumps. Energy. 2021; 233 ():121176.

Chicago/Turabian Style

Jin Tan; Qiuwei Wu; Menglin Zhang; Wei Wei; Feng Liu; Bo Pan. 2021. "Chance-Constrained Energy and Multi-type Reserves Scheduling Exploiting Flexibility from Combined Power and Heat Units and Heat Pumps." Energy 233, no. : 121176.

Journal article
Published: 01 June 2021 in Global Energy Interconnection
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With the development of carbon electricity, achieving a low-carbon economy has become a prevailing and inevitable trend. Improving low-carbon expansion generation planning is critical for carbon emission mitigation and a low- carbon economy. In this paper, a two-layer low-carbon expansion generation planning approach considering the uncertainty of renewable energy at multiple time scales is proposed. First, renewable energy sequences considering the uncertainty in multiple time scales are generated based on the Copula function and the probability distribution of renewable energy. Second, a two-layer generation planning model considering carbon trading and carbon capture technology is established. Specifically, the upper layer model optimizes the investment decision considering the uncertainty at a monthly scale, and the lower layer one optimizes the scheduling considering the peak shaving at an hourly scale and the flexibility at a 15-minute scale. Finally, the results of different influence factors on low-carbon generation expansion planning are compared in a provincial power grid, which demonstrate the effectiveness of the proposed model.

ACS Style

Yuanze Mi; Chunyang Liu; Jinye Yang; Hengxu Zhang; Qiuwei Wu. Low-carbon generation expansion planning considering uncertainty of renewable energy at multi-time scales. Global Energy Interconnection 2021, 4, 261 -272.

AMA Style

Yuanze Mi, Chunyang Liu, Jinye Yang, Hengxu Zhang, Qiuwei Wu. Low-carbon generation expansion planning considering uncertainty of renewable energy at multi-time scales. Global Energy Interconnection. 2021; 4 (3):261-272.

Chicago/Turabian Style

Yuanze Mi; Chunyang Liu; Jinye Yang; Hengxu Zhang; Qiuwei Wu. 2021. "Low-carbon generation expansion planning considering uncertainty of renewable energy at multi-time scales." Global Energy Interconnection 4, no. 3: 261-272.

Journal article
Published: 30 April 2021 in International Journal of Electrical Power & Energy Systems
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In hydropower generation, the combination of permanent magnet generators and power electronic converters allows for variable speed operation and brings many advantages to the plant. Efficiency optimization is a valuable goal for the generator design. However, traditional optimization methods only optimize the rated efficiency without considering the overall efficiency improvement in the annual cycle. To overcome this drawback, this paper proposes a new design optimization method for permanent magnet synchronous generators in hydropower plants, which helps to improve the combined operating efficiency during the dry, normal, and rainy hydrological periods. First, the pre-operating conditions of the generator are obtained by studying the upstream periodic hydrological data and the optimal operating trajectory of the turbine. Secondly, an analytical model of generator efficiency under different hydrological conditions is developed based on an accurate subdomain model and weighted as the objective function. Furthermore, an improved genetic algorithm is proposed to solve the objective function to obtain the generator structural parameters. Finally, the improvement of the annual cycle efficiency of the generator is verified by simulation and experimental tests under different load conditions.

ACS Style

Wenjuan Zhang; Litao Dai; Zhiman Xiang; Qiuwei Wu; Sheng Huang; Jian Gao. Optimal design of hydro permanent magnet synchronous generators for improving annual cycle efficiency. International Journal of Electrical Power & Energy Systems 2021, 131, 107096 .

AMA Style

Wenjuan Zhang, Litao Dai, Zhiman Xiang, Qiuwei Wu, Sheng Huang, Jian Gao. Optimal design of hydro permanent magnet synchronous generators for improving annual cycle efficiency. International Journal of Electrical Power & Energy Systems. 2021; 131 ():107096.

Chicago/Turabian Style

Wenjuan Zhang; Litao Dai; Zhiman Xiang; Qiuwei Wu; Sheng Huang; Jian Gao. 2021. "Optimal design of hydro permanent magnet synchronous generators for improving annual cycle efficiency." International Journal of Electrical Power & Energy Systems 131, no. : 107096.

Review
Published: 28 April 2021 in Renewable and Sustainable Energy Reviews
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The integration of different energy systems is receiving increasing attention as a promising solution to accommodate the rising share of renewable energy sources (RES). The synergies among different energy carriers can be exploited to introduce flexibility to the system and compensate for the uncertain and fluctuating production from RES. In particular, the power and natural gas networks are becoming increasingly coupled due to the growth in electricity produced from gas-fired power plants, and the emergence of power-to-gas technologies. This paper aims at analyzing the existing literature about the short-term optimal operation of integrated electrical-gas systems (IEGSs) and identifying the benefits of coordinated optimization compared to independent scheduling of the two sectors. A comprehensive overview of the mathematical modeling of the two systems and the linking components is presented. The results show that lower operating costs and higher utilization of renewables are achieved by adopting fully integrated optimization strategies. Moreover, linepack modeling is crucial to fully capture the intrinsic storage capability of the gas network. Finally, modeling the uncertainties from RES production is imperative, since these are reflected in the gas network. Whilst significant research about IEGS modeling has been undertaken, there are still several challenges in solving the co-optimization problem. In this review, possible solution approaches are discussed, identifying linearization and convex relaxation techniques as powerful tools to approximate the nonconvex and nonlinear gas flow equation. Furthermore, decomposition techniques and decentralized optimization schemes can be used to more efficiently solve the problem, and, concurrently, tackle regulatory and privacy issues.

ACS Style

Enrica Raheli; Qiuwei Wu; Menglin Zhang; Changyun Wen. Optimal coordinated operation of integrated natural gas and electric power systems: A review of modeling and solution methods. Renewable and Sustainable Energy Reviews 2021, 145, 111134 .

AMA Style

Enrica Raheli, Qiuwei Wu, Menglin Zhang, Changyun Wen. Optimal coordinated operation of integrated natural gas and electric power systems: A review of modeling and solution methods. Renewable and Sustainable Energy Reviews. 2021; 145 ():111134.

Chicago/Turabian Style

Enrica Raheli; Qiuwei Wu; Menglin Zhang; Changyun Wen. 2021. "Optimal coordinated operation of integrated natural gas and electric power systems: A review of modeling and solution methods." Renewable and Sustainable Energy Reviews 145, no. : 111134.

Journal article
Published: 22 April 2021 in IEEE Transactions on Power Delivery
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With the continuously increasing penetration of networked microgrids (MGs) on the local utility grid (UG), MGs face the challenge to avoid increasing system fault currents during low-voltage ride-through (LVRT). To solve this challenge, an active fault current limitation (AFCL) method is proposed with three parts: 1) a novel phase angle adjustment (PAA) strategy is conducted to relieve the impact of MGs output fault current on system fault current; 2) the current injection (CI) strategy for LVRT is formulated to fit the function of PAA; 3) a novel converter current generation (CCG) strategy is developed to achieve a better voltage support ability by considering network impedance characteristics. The proposed AFCL method is applied to the back-to-back converter, as a connection interface between MGs and UG. Extensive tests and pertinent results have verified the improvements of proposed AFCL method with better LVRT performance, while the networked MGs output fault current does not increase the amplitude of system fault current.

ACS Style

Xubin Liu; Xinyu Chen; Mohammad Shahidehpour; Canbing Li; Qiuwei Wu; Yuhang Wu; Jinyu Wen. Active Fault Current Limitation for Low-Voltage Ride-Through of Networked Microgrids. IEEE Transactions on Power Delivery 2021, PP, 1 -1.

AMA Style

Xubin Liu, Xinyu Chen, Mohammad Shahidehpour, Canbing Li, Qiuwei Wu, Yuhang Wu, Jinyu Wen. Active Fault Current Limitation for Low-Voltage Ride-Through of Networked Microgrids. IEEE Transactions on Power Delivery. 2021; PP (99):1-1.

Chicago/Turabian Style

Xubin Liu; Xinyu Chen; Mohammad Shahidehpour; Canbing Li; Qiuwei Wu; Yuhang Wu; Jinyu Wen. 2021. "Active Fault Current Limitation for Low-Voltage Ride-Through of Networked Microgrids." IEEE Transactions on Power Delivery PP, no. 99: 1-1.

Journal article
Published: 31 March 2021 in International Journal of Electrical Power & Energy Systems
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Integration of the electric power system, natural gas system, and district heating system can reduce the operational cost and improve the utilization of renewable energy sources. The day-ahead schedule for the optimal operation of the integrated energy system may not be economically optimal in real-time due to the prediction errors of multiple uncertainty sources. To balance the real-time prediction errors economically, this paper proposes a model predictive control (MPC) based real-time scheduling strategy to optimize the real-time operation of the integrated energy system, which makes real-time operational decisions based on the measured state of the system and future information of uncertainties. In the MPC based real-time scheduling, the penalty for the deviation between the day-ahead and real-time schedules is considered to minimize the regulation cost. In addition, multiple uncertainty sources are taken into account. An online learning method is utilized in MPC to predict the future information of these uncertainties. Besides, the power-to-x technology and thermal energy and gas storage devices are considered to improve the capability of the system to balance these uncertainties. The simulation results show that the MPC based real-time scheduling outperforms the traditional real-time scheduling on economic efficiency and wind power utilization.

ACS Style

Ana Turk; Qiuwei Wu; Menglin Zhang. Model predictive control based real-time scheduling for balancing multiple uncertainties in integrated energy system with power-to-x. International Journal of Electrical Power & Energy Systems 2021, 130, 107015 .

AMA Style

Ana Turk, Qiuwei Wu, Menglin Zhang. Model predictive control based real-time scheduling for balancing multiple uncertainties in integrated energy system with power-to-x. International Journal of Electrical Power & Energy Systems. 2021; 130 ():107015.

Chicago/Turabian Style

Ana Turk; Qiuwei Wu; Menglin Zhang. 2021. "Model predictive control based real-time scheduling for balancing multiple uncertainties in integrated energy system with power-to-x." International Journal of Electrical Power & Energy Systems 130, no. : 107015.

Journal article
Published: 15 March 2021 in International Journal of Electrical Power & Energy Systems
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Short-term wind power scenarios significantly affect the economic efficiency of the stochastic power system scheduling. In order to better capture the nonlinear spatio-temporal correlations of wind power, this paper proposes a scenario generation method integrated with non-separable spatio-temporal covariance function and fluctuation-based clustering for short-term wind power output. By taking advantage of well-calibrated marginal distribution modeled by Gaussian mixture model, the non-separable covariance function is incorporated in the scenario generation method to capture the complex interactions between spatial and temporal components of wind power. To estimate the covariance matrix more precisely, the historical data is grouped into K clusters with different fluctuations using the K-means clustering algorithm. Two indices are proposed to evaluate the scenarios in capturing the spatial and temporal correlations from the perspective of system operators. The proposed method is applied to a modified IEEE-118 system with four wind farms. Simulation results verify the superiority of the proposed method in capturing spatial and temporal correlations, and validate the economic benefits for the power system operation.

ACS Style

Jin Tan; Qiuwei Wu; Menglin Zhang; Wei Wei; Nikos Hatziargyriou; Feng Liu; Theodoros Konstantinou. Wind power scenario generation with non-separable spatio-temporal covariance function and fluctuation-based clustering. International Journal of Electrical Power & Energy Systems 2021, 130, 106955 .

AMA Style

Jin Tan, Qiuwei Wu, Menglin Zhang, Wei Wei, Nikos Hatziargyriou, Feng Liu, Theodoros Konstantinou. Wind power scenario generation with non-separable spatio-temporal covariance function and fluctuation-based clustering. International Journal of Electrical Power & Energy Systems. 2021; 130 ():106955.

Chicago/Turabian Style

Jin Tan; Qiuwei Wu; Menglin Zhang; Wei Wei; Nikos Hatziargyriou; Feng Liu; Theodoros Konstantinou. 2021. "Wind power scenario generation with non-separable spatio-temporal covariance function and fluctuation-based clustering." International Journal of Electrical Power & Energy Systems 130, no. : 106955.

Journal article
Published: 11 March 2021 in IEEE Transactions on Industrial Informatics
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An adaptive droop-based hierarchical optimal voltage control (DHOVC) scheme is proposed for voltage-source converter high-voltage-direct-current (VSC-HVDC) connected offshore wind farms (WFs). The wind turbines (WTs) and WF side VSC (WFVSC) are coordinated to minimize the voltage deviations of buses inside the WF from the nominal voltage and mitigate reactive power (Var) fluctuations of WTs. The model predictive control (MPC) is used to improve the performance of the DHOVC scheme during a certain predictive horizon. A hierarchical solution method based on the alternating direction method of multipliers (ADMM) is developed to reduce the calculation burden of the central controller while improving the information privacy protection. During the predictive horizon, the WTs and WFVSC are coordinated to achieve the near global optimal performance without global information. A WF with 32 X 5MW WTs was used in the matlab/simulink to test the proposed DHOVC scheme.

ACS Style

Sheng Huang; Qiuwei Wu; Wu Liao; Gongping Wu; Xueping Li; Juan Wei. Adaptive Droop-Based Hierarchical Optimal Voltage Control Scheme for VSC-HVdc Connected Offshore Wind Farm. IEEE Transactions on Industrial Informatics 2021, 17, 8165 -8176.

AMA Style

Sheng Huang, Qiuwei Wu, Wu Liao, Gongping Wu, Xueping Li, Juan Wei. Adaptive Droop-Based Hierarchical Optimal Voltage Control Scheme for VSC-HVdc Connected Offshore Wind Farm. IEEE Transactions on Industrial Informatics. 2021; 17 (12):8165-8176.

Chicago/Turabian Style

Sheng Huang; Qiuwei Wu; Wu Liao; Gongping Wu; Xueping Li; Juan Wei. 2021. "Adaptive Droop-Based Hierarchical Optimal Voltage Control Scheme for VSC-HVdc Connected Offshore Wind Farm." IEEE Transactions on Industrial Informatics 17, no. 12: 8165-8176.

Journal article
Published: 08 March 2021 in IEEE Transactions on Sustainable Energy
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This paper proposes a bi-level optimal integration scheme for buildings space heating loads in the integrated community energy system (ICES). The optimal integration scheme consists of an efficient energy management method and a heating pricing method for the ICES with buildings. At the upper level, the ICES operator optimizes the schedules of energy generation and supply, and the heating prices to buildings to maximize its profit. At the lower level, consumers in buildings optimize the water flow rates in the radiators to minimize their heating costs. The thermal dynamics of the building with controllable indoor radiators is modeled using the Resistor-Capacitor thermal network model. Moreover, the model predictive control (MPC) is integrated with the bi-level optimization to achieve economic and reliable scheduling of the ICES and buildings under uncertainties. The bi-level MPC optimization is reformulated as an MPC based mixed-integer linear program using the Karush-Kuhn-Tucker optimality conditions and several linearization techniques. Numerical studies show that the bi-level MPC method can obtain a balanced scheduling scheme between the energy costs of consumers in buildings and the ICES operator's profits. The MPC method can ensure higher profits of the ICES operator and simultaneously, lower energy costs of consumers in buildings.

ACS Style

Xiaolong Jin; Qiuwei Wu; Hongjie Jia; Nikos D. Hatziargyriou. Optimal Integration of Building Heating Loads in Integrated Heating/Electricity Community Energy Systems: A Bi-Level MPC Approach. IEEE Transactions on Sustainable Energy 2021, 12, 1741 -1754.

AMA Style

Xiaolong Jin, Qiuwei Wu, Hongjie Jia, Nikos D. Hatziargyriou. Optimal Integration of Building Heating Loads in Integrated Heating/Electricity Community Energy Systems: A Bi-Level MPC Approach. IEEE Transactions on Sustainable Energy. 2021; 12 (3):1741-1754.

Chicago/Turabian Style

Xiaolong Jin; Qiuwei Wu; Hongjie Jia; Nikos D. Hatziargyriou. 2021. "Optimal Integration of Building Heating Loads in Integrated Heating/Electricity Community Energy Systems: A Bi-Level MPC Approach." IEEE Transactions on Sustainable Energy 12, no. 3: 1741-1754.

Journal article
Published: 02 March 2021 in IEEE Transactions on Power Systems
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This paper proposes a new decentralized data-driven load res-toration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independently operated systems, and ensures the feasibility of the decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TDS and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance.

ACS Style

Jin Zhao; Qiuwei Wu; Nikos D. Hatziargyriou; Fangxing Fran Li; Fei Teng. Decentralized Data-Driven Load Restoration in Coupled Transmission and Distribution System With Wind Power. IEEE Transactions on Power Systems 2021, 36, 4435 -4444.

AMA Style

Jin Zhao, Qiuwei Wu, Nikos D. Hatziargyriou, Fangxing Fran Li, Fei Teng. Decentralized Data-Driven Load Restoration in Coupled Transmission and Distribution System With Wind Power. IEEE Transactions on Power Systems. 2021; 36 (5):4435-4444.

Chicago/Turabian Style

Jin Zhao; Qiuwei Wu; Nikos D. Hatziargyriou; Fangxing Fran Li; Fei Teng. 2021. "Decentralized Data-Driven Load Restoration in Coupled Transmission and Distribution System With Wind Power." IEEE Transactions on Power Systems 36, no. 5: 4435-4444.

Journal article
Published: 23 February 2021 in Energy
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The power system operators are facing significant challenges in the operation due to the increasing penetration level of renewable energy sources (RESs). The flexibility from the district heating system (DHS) is attracting considerable interest to deal with RES uncertainty. This paper formulates a distributionally robust chance-constrained (DRCC) optimization model of the integrated electricity and heating system (IEHS) dispatch to hedge the uncertainty of RES and exploit the flexibility of the DHS. In particular, the uncertainty from the electrical power system (EPS) is propagated to the DHS so that both systems can respond to the uncertainty of RES. The uncertainty of the wind power is modeled by an ambiguity set, which defines a family of probability distributions with the same first and second-moment property. Real-time regulation actions of both the EPS and DHS to respond to the wind power forecast errors are modeled through the data-driven affine control policies. To achieve computational tractability, the proposed DRCC model is reformulated as a second-order cone program (SOCP). The simulation results tested on the integrated six-bus and seven-node system demonstrate that the proposed DRCC model outperforms the chance-constraints dispatch based on Gaussian distribution for the secure operation of the IEHS.

ACS Style

Mikhail Skalyga; Qiuwei Wu; Menglin Zhang. Uncertainty-fully-aware coordinated dispatch of integrated electricity and heat system. Energy 2021, 224, 120182 .

AMA Style

Mikhail Skalyga, Qiuwei Wu, Menglin Zhang. Uncertainty-fully-aware coordinated dispatch of integrated electricity and heat system. Energy. 2021; 224 ():120182.

Chicago/Turabian Style

Mikhail Skalyga; Qiuwei Wu; Menglin Zhang. 2021. "Uncertainty-fully-aware coordinated dispatch of integrated electricity and heat system." Energy 224, no. : 120182.

Journal article
Published: 11 February 2021 in IEEE Transactions on Power Systems
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With the proliferation of distributed generators and energy storage systems, traditional passive consumers in power systems have been gradually evolving into the so-called ‘`prosumers", i.e., proactive consumers, which can both produce and consume power. To encourage energy exchange among prosumers, energy sharing is increasingly adopted, which is usually formulated as a generalized Nash game (GNG). In this paper, a distributed approach is proposed to seek the Generalized Nash equilibrium (GNE) of the energy sharing game. To this end, we first prove the strong monotonicity of the game. Then, the GNG is converted into an equivalent optimization problem. An algorithm based on Nesterov’s methods is thereby devised to solve the equivalent problem and consequently find the GNE in a distributed manner. The convergence of the proposed algorithm is proved rigorously based on the nonexpansive operator theory. The performance of the algorithm is validated by experiments with three prosumers, and the scalability is tested by simulations using 1888 prosumers.

ACS Style

Zhaojian Wang; Feng Liu; Zhiyuan Ma; Yue Chen; Mengshuo Jia; Wei Wei; Qiuwei Wu. Distributed Generalized Nash Equilibrium Seeking for Energy Sharing Games in Prosumers. IEEE Transactions on Power Systems 2021, 36, 3973 -3986.

AMA Style

Zhaojian Wang, Feng Liu, Zhiyuan Ma, Yue Chen, Mengshuo Jia, Wei Wei, Qiuwei Wu. Distributed Generalized Nash Equilibrium Seeking for Energy Sharing Games in Prosumers. IEEE Transactions on Power Systems. 2021; 36 (5):3973-3986.

Chicago/Turabian Style

Zhaojian Wang; Feng Liu; Zhiyuan Ma; Yue Chen; Mengshuo Jia; Wei Wei; Qiuwei Wu. 2021. "Distributed Generalized Nash Equilibrium Seeking for Energy Sharing Games in Prosumers." IEEE Transactions on Power Systems 36, no. 5: 3973-3986.

Journal article
Published: 06 February 2021 in International Journal of Electrical Power & Energy Systems
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In this paper, a distributed optimal active and reactive power control (DARPC) strategy based on the alternating direction method of multipliers (ADMM) is proposed for wind farms (WFs). The WFs operate in a distributed manner to minimize the network power loss, voltage deviations of buses from the rated voltage, and active power output deviations of WTs from their proportional distribution (PD)-based power reference. An optimization problem is formulated as a quadratic programming (QP) problem by using the linearized DistFlow model. The ADMM-based solution is used to decompose the centralized optimization problem to several subproblems which are solved in individual local controllers with exchanged information from their practical neighbor controllers. Compared with existing distributed/hierarchical optimal control, the impacts of the active power injection from the WTs are taken into consideration and optimized while meeting the dispatch command from the transmission system operator (TSO). The ADMM-based distributed solution eliminates the requirement of a central unit. Compared with conventional centralized optimal control, the scalability of the WFs is improved. A WF consisting of 20 WTs is simulated in MATLAB/Simulink to test the control effectiveness of the proposed DARPC strategy.

ACS Style

Wu Liao; Peiyao Li; Qiuwei Wu; Sheng Huang; Gongping Wu; Fei Rong. Distributed optimal active and reactive power control for wind farms based on ADMM. International Journal of Electrical Power & Energy Systems 2021, 129, 106799 .

AMA Style

Wu Liao, Peiyao Li, Qiuwei Wu, Sheng Huang, Gongping Wu, Fei Rong. Distributed optimal active and reactive power control for wind farms based on ADMM. International Journal of Electrical Power & Energy Systems. 2021; 129 ():106799.

Chicago/Turabian Style

Wu Liao; Peiyao Li; Qiuwei Wu; Sheng Huang; Gongping Wu; Fei Rong. 2021. "Distributed optimal active and reactive power control for wind farms based on ADMM." International Journal of Electrical Power & Energy Systems 129, no. : 106799.

Journal article
Published: 30 January 2021 in International Journal of Electrical Power & Energy Systems
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This paper proposed a multi-time scale robust energy management method for active distribution networks with the multiple terminal soft open point where the active distribution network and microgrids belonged to different ownerships. In the day-ahead Stackelberg game model, the time-of-use price and active power exchange plan between the active distribution network and microgrids were optimized under power flow constraints to balance economic benefits of all participants. Then a reactive power re-optimization model was established to minimize system voltage deviation under the tolerant cost constraint by utilizing residual capacity of voltage source converters. In the intraday model, considering the fluctuations of actual power exchange of microgrids, V2-P and V2-Q droop control mode was implemented in the soft open point, and a slope robust optimization model was established to improve system robust security within the uncertainty set. Case studies show that the proposed method can efficiently utilize the flexible power flow regulation capability of the multiple terminal soft open point, and improve both economic benefits and reliability of the system.

ACS Style

Fengzhou Sun; Miao Yu; Qiuwei Wu; Wei Wei. A multi-time scale energy management method for active distribution networks with multiple terminal soft open point. International Journal of Electrical Power & Energy Systems 2021, 128, 106767 .

AMA Style

Fengzhou Sun, Miao Yu, Qiuwei Wu, Wei Wei. A multi-time scale energy management method for active distribution networks with multiple terminal soft open point. International Journal of Electrical Power & Energy Systems. 2021; 128 ():106767.

Chicago/Turabian Style

Fengzhou Sun; Miao Yu; Qiuwei Wu; Wei Wei. 2021. "A multi-time scale energy management method for active distribution networks with multiple terminal soft open point." International Journal of Electrical Power & Energy Systems 128, no. : 106767.

Journal article
Published: 25 January 2021 in IEEE Transactions on Sustainable Energy
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The wind turbine (WT) terminal overvoltage during grid voltage swell events may result in tripping the WT and consequently threaten the secure operation of large-scale wind farms (WFs). In this paper, an optimal coordination of droop control and adaptive model predictive control (MPC) scheme is proposed to enhance the high-voltage ride-through (HVRT) and post-event recovery of large-scale WFs. During the HVRT, the reactive power reference is generated in each WT controller by following an optimal droop coefficient to realize a fast voltage reduction at the WT terminal. The droop coefficients are pre-calculated by taking the WF collection system topology and voltage swell magnitude into consideration. At the post-event recovery stage, an adaptive MPC-based voltage recovery control scheme is proposed to improve post-event voltage dynamic restoration performance. The droop coefficients of the WT controllers are optimized based on the voltage sensitivity coefficients and voltage swell magnitude. With the proposed control scheme, all the WT terminal voltage can be maintained within their feasible range and the response time of post-event voltage recovery is significantly shortened. The proposed control scheme is validated and tested under various operating scenarios.

ACS Style

Juan Wei; Yijia Cao; Qiuwei Wu; Canbing Li; Sheng Huang; Bin Zhou; Da Xu. Coordinated Droop Control and Adaptive Model Predictive Control for Enhancing HVRT and Post-Event Recovery of Large-Scale Wind Farm. IEEE Transactions on Sustainable Energy 2021, 12, 1549 -1560.

AMA Style

Juan Wei, Yijia Cao, Qiuwei Wu, Canbing Li, Sheng Huang, Bin Zhou, Da Xu. Coordinated Droop Control and Adaptive Model Predictive Control for Enhancing HVRT and Post-Event Recovery of Large-Scale Wind Farm. IEEE Transactions on Sustainable Energy. 2021; 12 (3):1549-1560.

Chicago/Turabian Style

Juan Wei; Yijia Cao; Qiuwei Wu; Canbing Li; Sheng Huang; Bin Zhou; Da Xu. 2021. "Coordinated Droop Control and Adaptive Model Predictive Control for Enhancing HVRT and Post-Event Recovery of Large-Scale Wind Farm." IEEE Transactions on Sustainable Energy 12, no. 3: 1549-1560.