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Xiangyu Kong
Tianjin University

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Chapter
Published: 09 June 2021 in Numerical Methods for Energy Applications
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The interconnected microgrid system (IMS) is a promising solution for the problem of growing penetration of renewable-based microgrids into the power system. To optimally coordinate the operation of microgrids owned by different owners while considering uncertainties in market environment, a bi-level distributed optimized operation method for IMS with uncertainties is proposed in this chapter. A hierarchical and distributed operational communication architecture of IMS is first established. A bi-level distributed optimization model was built for IMS, where at the upper level, the IMS operates purchase-sale mode or demand response mode with the distribution network operator and optimizes the trading power with microgrids to maximize revenue. At the lower level, the chance constraint programming is used to describe and deal with the uncertainty of renewable energy and loads and optimize the output and energy storage of distributed energy with the goal of minimum cost. The analytical target cascading and augmented Lagrange method are combined to decouple and reconstruct the bi-level model for distributed solution and establish a fair price mechanism. The optimal solutions of the problem are obtained through parallel iteration, in which the price signal plays a coordinated role in the distributed iterative optimization process. Abundant case studies verify the advantages of the model and the performance of the proposed method.

ACS Style

Xiangyu Kong; Dehong Liu; Wenqi Lu; Chengshan Wang; Yu Shen; Wei Hu; Mehdi Rahmani-Andebili. Hierarchical and Distributed Dispatching of Microgrids Considering Uncertainty. Numerical Methods for Energy Applications 2021, 85 -120.

AMA Style

Xiangyu Kong, Dehong Liu, Wenqi Lu, Chengshan Wang, Yu Shen, Wei Hu, Mehdi Rahmani-Andebili. Hierarchical and Distributed Dispatching of Microgrids Considering Uncertainty. Numerical Methods for Energy Applications. 2021; ():85-120.

Chicago/Turabian Style

Xiangyu Kong; Dehong Liu; Wenqi Lu; Chengshan Wang; Yu Shen; Wei Hu; Mehdi Rahmani-Andebili. 2021. "Hierarchical and Distributed Dispatching of Microgrids Considering Uncertainty." Numerical Methods for Energy Applications , no. : 85-120.

Chapter
Published: 09 June 2021 in Numerical Methods for Energy Applications
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The penetration of distributed energy resources (DER) is growing worldwide, and microgrid (MG) is an approprate way to realize intergration of these DERs. The new reform of power system promotes the market-oriented operation of microgrids. This chapter takes the park microgrid with multi-stakeholder as the object, and to promote the interaction between the main grid and DERs in MG, a two-level optimization model of microgrid bidding transaction based on multi-agent system is established. In the lower-level optimization, considering the deviation penalty of power generation and the previous round bidding results, the optimal bidding strategy model is established to maximize the benefit of bidding unit agent. In the upper-level model, bidding strategies of DERs as constraints, a multiple objective mixed-integer linear programming model was built to optimize the overall objectives of clearing price and imbalanced deviation, searching for the optimal clearing price and the generation plan of DERs. Due to the complexity of the two-layer optimization model, a novel artificial immune system (AIS) was established and integrated into the multi-agent system to help DERs participate in the optimal bidding operation of MG. The antigen is transformed by the environmental information, the price of the main grid, other DERs’ bidding strategies, and the predicted deviation coefficient while considering the uncertainties of DER facilities. The proposed optimized operation mode is compared with the traditional operation mode in the case study, verifying that the proposed method can realize the optimal operation of the MG and the coordinated interaction with the main grid, increasing the benefit of stakeholders. The AIS algorithm is also compared with traditional algorithms, proving the superiority in optimizing.

ACS Style

Xiangyu Kong; Dehong Liu; Fangyuan Sun; Chengshan Wang; Xianxu Huo; Shupeng Li. Operation Strategy of Park Microgrid with Multi‐stakeholder Based on Artificial Immune System. Numerical Methods for Energy Applications 2021, 121 -150.

AMA Style

Xiangyu Kong, Dehong Liu, Fangyuan Sun, Chengshan Wang, Xianxu Huo, Shupeng Li. Operation Strategy of Park Microgrid with Multi‐stakeholder Based on Artificial Immune System. Numerical Methods for Energy Applications. 2021; ():121-150.

Chicago/Turabian Style

Xiangyu Kong; Dehong Liu; Fangyuan Sun; Chengshan Wang; Xianxu Huo; Shupeng Li. 2021. "Operation Strategy of Park Microgrid with Multi‐stakeholder Based on Artificial Immune System." Numerical Methods for Energy Applications , no. : 121-150.

Journal article
Published: 05 May 2021 in IEEE Transactions on Smart Grid
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This paper presents an online smart meter measurement error estimation algorithm. Extended Kalman filter (EKF) and limit memory recursive least square (LMRLS) methods are used for remote calibration of a large amount of user-side smart meters. Then, a modified joint estimation model is obtained by selecting the estimation step that conforms to the actual working condition and filtering the abnormal estimation value according to the line loss rate characteristics. Finally, based on the experimental data obtained by the program-controlled load simulation system, the precision of metering error estimation is verified. The results show that the method improves the precision of error estimation by analyzing the coupling between line loss rates and metering error estimation. By using the limited memory RLS algorithm, the influence of old measured data on error parameter estimation is reduced so that new data can be added to correct error parameter estimation to enhance the precision of the real-time smart meter error estimation.

ACS Style

Xiangyu Kong; Xiaopeng Zhang; Ning Lu; Yuying Ma; Ye Li. Online Smart Meter Measurement Error Estimation Based on EKF and LMRLS Method. IEEE Transactions on Smart Grid 2021, 12, 4269 -4279.

AMA Style

Xiangyu Kong, Xiaopeng Zhang, Ning Lu, Yuying Ma, Ye Li. Online Smart Meter Measurement Error Estimation Based on EKF and LMRLS Method. IEEE Transactions on Smart Grid. 2021; 12 (5):4269-4279.

Chicago/Turabian Style

Xiangyu Kong; Xiaopeng Zhang; Ning Lu; Yuying Ma; Ye Li. 2021. "Online Smart Meter Measurement Error Estimation Based on EKF and LMRLS Method." IEEE Transactions on Smart Grid 12, no. 5: 4269-4279.

Journal article
Published: 01 October 2020 in International Journal of Electrical Power & Energy Systems
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The theft of electricity affects power supply quality and safety of grid operation, and non-technical losses (NTL) have become the major reason of unfair power supply and economic losses for power companies. For more effective electricity theft inspection, an electricity theft detection method based on similarity measure and decision tree combined K-Nearest Neighbor and support vector machine (DT-KSVM) is proposed in the paper. Firstly, the condensed feature set is devised based on feature selection strategy, typical power consumption characteristic curves of users are obtained based on kernel fuzzy C-means algorithm (KFCM). Next, to solve the problem of lack of stealing data and realize the reasonable use of advanced metering infrastructure (AMI). One dimensional Wasserstein generative adversarial networks (1D-WGAN) is used to generate more simulated stealing data. Then the numerical and morphological features in the similarity measurement process are comprehensively considered to conduct preliminary detection of NTL. And DT-KSVM is used to perform secondary detection and identify suspicious customers. At last, simulation experiments verify the effectiveness of the proposed method.

ACS Style

Xiangyu Kong; Xin Zhao; Chao Liu; Qiushuo Li; Delong Dong; Ye Li. Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM. International Journal of Electrical Power & Energy Systems 2020, 125, 106544 .

AMA Style

Xiangyu Kong, Xin Zhao, Chao Liu, Qiushuo Li, Delong Dong, Ye Li. Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM. International Journal of Electrical Power & Energy Systems. 2020; 125 ():106544.

Chicago/Turabian Style

Xiangyu Kong; Xin Zhao; Chao Liu; Qiushuo Li; Delong Dong; Ye Li. 2020. "Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM." International Journal of Electrical Power & Energy Systems 125, no. : 106544.

Journal article
Published: 18 September 2020 in Sustainable Cities and Society
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The safety and reliability of urban power supply are critical to the sustainability of cities and society. Based on the analysis of big data, a power supply reliability evaluation method for urban distribution networks considering uncertain factors is proposed in this paper. The method has good adaptability and can support the analysis of safety improvement measures. By investigating historical data on distribution network topology and parameters, the main influencing factors affecting power supply reliability and the uncertainties of these factors are screened out. An improved Elman neural network (IENN) is used, and the main process of reliability evaluation is obtained for the complex urban distribution network. It can effectively simplify the calculation and includes multiple uncertain factors to improve evaluation accuracy. Case studies with actual urban distribution network data are used to verify the feasibility and effectiveness of the proposed method. Finally, some useful conclusions are given, including the problems of urban distribution network power supply, and the improvement measures for power supply to support the development of sustainable cities and society.

ACS Style

Xiangyu Kong; Chao Liu; Yu Shen; Wei Hu; Tianqiao Ma. Power supply reliability evaluation based on big data analysis for distribution networks considering uncertain factors. Sustainable Cities and Society 2020, 63, 102483 .

AMA Style

Xiangyu Kong, Chao Liu, Yu Shen, Wei Hu, Tianqiao Ma. Power supply reliability evaluation based on big data analysis for distribution networks considering uncertain factors. Sustainable Cities and Society. 2020; 63 ():102483.

Chicago/Turabian Style

Xiangyu Kong; Chao Liu; Yu Shen; Wei Hu; Tianqiao Ma. 2020. "Power supply reliability evaluation based on big data analysis for distribution networks considering uncertain factors." Sustainable Cities and Society 63, no. : 102483.

Journal article
Published: 17 September 2020 in Applied Energy
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In recent years, with the rapid development of the energy Internet and the deepening of the complementary coupling of various energy sources, the concept of multi-energy virtual power plant comes into being. At the same time, insufficient research on optimal scheduling of multi-energy virtual power plants under multiple uncertainties. Here we propose a robust stochastic optimal dispatching method to solve the scheduling problem under multiple uncertainties. For the source side uncertainties, the uncertain set of cardinalities with a robust adjustable coefficient is adopted to describe the output of wind turbines and photovoltaics. For the load side uncertainties, the Wasserstein generative adversarial network with gradient penalty is used to generate electric, thermal, cooling, and natural gas load scenarios, and the K-medoids clustering is used to get typical scenes. A two-stage robust stochastic optimal model of the min-max-min structure was established. Based on the dual transformation theory and the column constraint generation algorithm, the original model was solved alternately. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. The proposed method can get the scheduling scheme with the lowest operating cost in the worst scenario and is conducive to reducing the overall scheduling cost of the system.

ACS Style

Xiangyu Kong; Jie Xiao; Dehong Liu; Jianzhong Wu; Chengshan Wang; Yu Shen. Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties. Applied Energy 2020, 279, 115707 .

AMA Style

Xiangyu Kong, Jie Xiao, Dehong Liu, Jianzhong Wu, Chengshan Wang, Yu Shen. Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties. Applied Energy. 2020; 279 ():115707.

Chicago/Turabian Style

Xiangyu Kong; Jie Xiao; Dehong Liu; Jianzhong Wu; Chengshan Wang; Yu Shen. 2020. "Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties." Applied Energy 279, no. : 115707.

Journal article
Published: 15 August 2020 in Energy
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To guarantee the heat demand during winter, most combined heat and power (CHP) units in the integrated energy system (IES) usually work under following heat load (FTL) mode, and the renewable energy accommodation is limited. With the development of Power Internet of Things (PIoT), the information exchange in IES become more frequent. Through flexible interaction between different networks in IES, the accommodation capacity of renewable energy can increase significantly. Therefore, this paper focus on the optimization of IES under the background of PIoT. Firstly, based on the influence of PIoT on IES, a novel integrated demand response (DR) way and the model of the critical components in IES are established. Secondly, a Bi-level economic dispatching method for regional IES is developed, considering the cyber-physical infrastructure of PIoT and IES. The upper level of the dispatching method is used to optimize the overall IES operation; the lower level is to optimize the output of demand-side facilities and integrated DR. Thirdly, with adaptive particle swarm optimization (APSO) algorithm, the solution method for the Bi-level dispatch is established. Finally, the feasibility and effectiveness of the proposed method are verified in a standard IES and a real system in northern China.

ACS Style

Xiangyu Kong; Fangyuan Sun; Xianxu Huo; Xue Li; Yu Shen. Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things. Energy 2020, 210, 118590 .

AMA Style

Xiangyu Kong, Fangyuan Sun, Xianxu Huo, Xue Li, Yu Shen. Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things. Energy. 2020; 210 ():118590.

Chicago/Turabian Style

Xiangyu Kong; Fangyuan Sun; Xianxu Huo; Xue Li; Yu Shen. 2020. "Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things." Energy 210, no. : 118590.

Journal article
Published: 23 June 2020 in Applied Energy
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The interconnected microgrid system (IMS) is a promising solution for the problem of growing penetration of renewable-based microgrids into the power system. To optimally coordinate the operation of microgrids owned by different owners while considering uncertainties in market environment, a bi-level distributed optimized operation method for IMS with uncertainties is proposed in this paper. A hierarchical and distributed operational communication architecture of IMS is first established. A bi-level distributed optimization model was built for IMS, where at the upper level, the IMS operates purchase-sale mode or demand response mode with the distribution network operator and optimizes the trading power with microgrids to maximize revenue. At the lower level, the chance constraint programming is used to describe and deal with the uncertainty of renewable energy and loads and optimize the output and energy storage of distributed energy with the goal of minimum cost. The analytical target cascading and augmented Lagrange method are combined to decouple and reconstruct the bi-level model for distributed solution and establishing a fair price mechanism. The optimal solutions of the problem are obtained through parallel iteration, in which the price signal plays a coordinated role in the distributed iterative optimization process. Abundant case studies verify the advantages of the model and the performance of the proposed method.

ACS Style

Xiangyu Kong; Dehong Liu; Chengshan Wang; Fangyuan Sun; Shupeng Li. Optimal operation strategy for interconnected microgrids in market environment considering uncertainty. Applied Energy 2020, 275, 115336 .

AMA Style

Xiangyu Kong, Dehong Liu, Chengshan Wang, Fangyuan Sun, Shupeng Li. Optimal operation strategy for interconnected microgrids in market environment considering uncertainty. Applied Energy. 2020; 275 ():115336.

Chicago/Turabian Style

Xiangyu Kong; Dehong Liu; Chengshan Wang; Fangyuan Sun; Shupeng Li. 2020. "Optimal operation strategy for interconnected microgrids in market environment considering uncertainty." Applied Energy 275, no. : 115336.

Journal article
Published: 18 June 2020 in IEEE Access
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Thermostatically controlled loads (TCLs) have become a major tool for the demand response (DR) program when air conditioners cause peak loads in a day during the winter or summer. To solve the problem of a direct load control with TCL usually affecting user comfort and hardly considering responsiveness, a power retailer air-conditioning load aggregation operation control and demand response method was proposed in this research. From the perspective of a power retailer, a compensation mechanism for TCL was constructed, which was composed of a basic incentive program and an additional incentive program. The basic incentive program aimed to encourage users with a low response degree to increase the response capacity in order to participate in DR. An auxiliary service market control strategy based on a new compensation mechanism of the electricity retailer was detailed, which fully considered the enthusiasm of the user in mobilizing the response and reducing the load reduction fluctuation when using the state-queuing (SQ) model. Case studies were provided to verify the effectiveness of the proposed method. Compared with other compensation schemes, the simulation results showed that the compensation mechanism provided in this research was more reasonable, and it could smooth the load and reduce fluctuations. The compensation distribution among the user groups could effectively control the uniform distribution in the user groups in the temperature range, and it could mobilize users at different temperatures to participate in DR.

ACS Style

Xiangyu Kong; Bowei Sun; Jian Zhang; Shupeng Li; Qun Yang. Power Retailer Air-Conditioning Load Aggregation Operation Control Method and Demand Response. IEEE Access 2020, 8, 112041 -112056.

AMA Style

Xiangyu Kong, Bowei Sun, Jian Zhang, Shupeng Li, Qun Yang. Power Retailer Air-Conditioning Load Aggregation Operation Control Method and Demand Response. IEEE Access. 2020; 8 ():112041-112056.

Chicago/Turabian Style

Xiangyu Kong; Bowei Sun; Jian Zhang; Shupeng Li; Qun Yang. 2020. "Power Retailer Air-Conditioning Load Aggregation Operation Control Method and Demand Response." IEEE Access 8, no. : 112041-112056.

Journal article
Published: 03 June 2020 in Applied Energy
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Incentive-based demand response is playing an increasingly important role in ensuring the safe operation of the power grid and reducing system costs, and advances in information and communications technology have made it possible to implement it online. However, in regions where incentive-based demand response has never been implemented, the response behavior of customers is unknown, in this case, how to quickly and accurately set the incentive price is a challenge for service providers. This paper proposes a pricing method that combines long short-term memory networks and reinforcement learning to solve the pricing problem of service providers when the customers’ response behavior is unknown. Taking the total profit of all response time slots in one day as the optimization goal, long and short-term memory networks are used to learn the relationship between customers’ response behavior and incentive price, and reinforcement learning is used to explore and determine the optimal price. The results show that the combination of these two methods can perform virtual exploration of the optimal price, which solves the disadvantage that reinforcement learning can only rely on delayed rewards to perform exploration in the real scene, thereby speeding up the process of setting the optimal price. In addition, because the influence of the incentive prices combination of different time slots on the profit of the service provider is considered, the negative effect of myopia optimization is avoided.

ACS Style

Xiangyu Kong; Deqian Kong; Jingtao Yao; Linquan Bai; Jie Xiao. Online pricing of demand response based on long short-term memory and reinforcement learning. Applied Energy 2020, 271, 114945 .

AMA Style

Xiangyu Kong, Deqian Kong, Jingtao Yao, Linquan Bai, Jie Xiao. Online pricing of demand response based on long short-term memory and reinforcement learning. Applied Energy. 2020; 271 ():114945.

Chicago/Turabian Style

Xiangyu Kong; Deqian Kong; Jingtao Yao; Linquan Bai; Jie Xiao. 2020. "Online pricing of demand response based on long short-term memory and reinforcement learning." Applied Energy 271, no. : 114945.

Journal article
Published: 01 June 2020 in Energies
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With the development of smart devices and information technology, it is possible for users to optimize their usage of electrical equipment through the home energy management system (HEMS). To solve the problems of daily optimal scheduling and emergency demand response (DR) in an uncertain environment, this paper provides an opportunity constraint programming model for the random variables contained in the constraint conditions. Considering the probability distribution of the random variables, a home energy management method for DR based on chance-constrained programming is proposed. Different confidence levels are set to reflect the influence mechanism of random variables on constraint conditions. An improved particle swarm optimization algorithm is used to solve the problem. Finally, the demand response characteristics in daily and emergency situations are analyzed by simulation examples, and the effectiveness of the method is verified.

ACS Style

Xiangyu Kong; Siqiong Zhang; Bowei Sun; Qun Yang; Shupeng Li; Shijian Zhu. Research on Home Energy Management Method for Demand Response Based on Chance-Constrained Programming. Energies 2020, 13, 2790 .

AMA Style

Xiangyu Kong, Siqiong Zhang, Bowei Sun, Qun Yang, Shupeng Li, Shijian Zhu. Research on Home Energy Management Method for Demand Response Based on Chance-Constrained Programming. Energies. 2020; 13 (11):2790.

Chicago/Turabian Style

Xiangyu Kong; Siqiong Zhang; Bowei Sun; Qun Yang; Shupeng Li; Shijian Zhu. 2020. "Research on Home Energy Management Method for Demand Response Based on Chance-Constrained Programming." Energies 13, no. 11: 2790.

Journal article
Published: 30 May 2020 in Applied Sciences
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Various countries in the world are vigorously developing energy-saving industries and attaching importance to the improvement of household energy efficiency, but it is difficult to evaluate user power consumption characteristics due to insufficient information and large data granularity. It is, however, possible to evaluate the energy efficiency of household users via non-intrusive load monitoring (NILM). This paper explores the energy efficiency assessment of residential users and proposes a household energy efficiency assessment method based on NILM data. An energy efficiency assessment index of residents is provided by analyzing factors that affect residents’ energy efficiency. This index is clear, operable, and easy to obtain and quantify. Based on NILM information, clustering, and comprehensive evaluation, as well as combining the entropy weight method with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), a user’s energy efficiency can be evaluated and analyzed. Some case studies are provided to verify the validity of the proposed method based on non-intrusive information, to analyze the characteristics and deficiencies of the user’s energy consumption, and to give corresponding energy recommendations.

ACS Style

Xiangyu Kong; Shijian Zhu; Xianxu Huo; Shupeng Li; Ye Li; Siqiong Zhang. A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data. Applied Sciences 2020, 10, 3820 .

AMA Style

Xiangyu Kong, Shijian Zhu, Xianxu Huo, Shupeng Li, Ye Li, Siqiong Zhang. A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data. Applied Sciences. 2020; 10 (11):3820.

Chicago/Turabian Style

Xiangyu Kong; Shijian Zhu; Xianxu Huo; Shupeng Li; Ye Li; Siqiong Zhang. 2020. "A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data." Applied Sciences 10, no. 11: 3820.

Journal article
Published: 08 May 2020 in Energy
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Remote estimation of smart meter errors based on measurement data analysis from the user smart meter comes into focus because field calibration has high maintenance cost and difficult to analyze in time. In this paper, a remote estimation method of smart meter errors based on Dimension Reduction Estimation Model (DREM) and Damped Recursion Least Squares (DRLS) is proposed, in which DREM deals with the insolvability of actual model, and DRLS algorithm improves the stability and accuracy of estimation. To verify the effectiveness and practicality the method proposed is applied in both laboratory and actual distribution feeder unit. The results show that the proposed method did not need to calculate the network loss independently in advance, and it can estimate the smart meter errors and network loss rate in real-time. Finally, the important parameters involved in the model and algorithm are discussed, and the advice of parameter selection is proposed.

ACS Style

Xiangyu Kong; Xiaopeng Zhang; Gang Li; Delong Dong; Ye Li. An estimation method of smart meter errors based on DREM and DRLS. Energy 2020, 204, 117774 .

AMA Style

Xiangyu Kong, Xiaopeng Zhang, Gang Li, Delong Dong, Ye Li. An estimation method of smart meter errors based on DREM and DRLS. Energy. 2020; 204 ():117774.

Chicago/Turabian Style

Xiangyu Kong; Xiaopeng Zhang; Gang Li; Delong Dong; Ye Li. 2020. "An estimation method of smart meter errors based on DREM and DRLS." Energy 204, no. : 117774.

Journal article
Published: 02 February 2020 in Applied Sciences
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With the development of smart grid and low-carbon electricity, a high proportion of renewable energy is connected to the grid. In addition, the peak-valley difference of system load increases, which makes the traditional grid scheduling method no longer suitable. Therefore, this paper proposes a two-stage low-carbon economic scheduling model considering the characteristics of wind, light, thermal power units, and demand response at different time scales. This model not only concerns the deep peak state of thermal power units under the condition of large-scale renewable energy, but also sets the uncertain models of PDR (Price-based Demand Response) virtual units and IDR (Incentive Demand Response) virtual units. Taking the system operation cost and carbon treatment cost as the target, the improved bat algorithm and 2PM (Two-point Estimation Method) are used to solve the problem. The introduction of climbing costs and low load operating costs can more truly reflect the increased cost of thermal power units. Meanwhile, the source-load interaction can weigh renewable energy limited costs and the increased costs of balancing volatility. The proposed method can be applied to optimal dispatch and safe operation analysis of the power grid with a high proportion of renewable energy. Compared with traditional methods, the total scheduling cost of the system can be reduced, and the rights and obligations of contributors to system operation can be guaranteed to the greatest extent.

ACS Style

Xiangyu Kong; Shuping Quan; Fangyuan Sun; Zhengguang Chen; Xingguo Wang; Zexin Zhou. Two-Stage Optimal Scheduling of Large-Scale Renewable Energy System Considering the Uncertainty of Generation and Load. Applied Sciences 2020, 10, 971 .

AMA Style

Xiangyu Kong, Shuping Quan, Fangyuan Sun, Zhengguang Chen, Xingguo Wang, Zexin Zhou. Two-Stage Optimal Scheduling of Large-Scale Renewable Energy System Considering the Uncertainty of Generation and Load. Applied Sciences. 2020; 10 (3):971.

Chicago/Turabian Style

Xiangyu Kong; Shuping Quan; Fangyuan Sun; Zhengguang Chen; Xingguo Wang; Zexin Zhou. 2020. "Two-Stage Optimal Scheduling of Large-Scale Renewable Energy System Considering the Uncertainty of Generation and Load." Applied Sciences 10, no. 3: 971.

Journal article
Published: 30 December 2019 in Applied Energy
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Accurate short-term load forecasting (STLF) is an important basis for daily dispatching of the power grid, but the non-stationary characteristics of the load series add to the challenge of this task. Many researchers have been working to improve the accuracy and speed of forecasting models, but stability is equally important. This paper develops a forecasting method based on error correction using dynamic mode decomposition (DMD) for STLF, including data selection, error forecasting, and error correction. In the data selection stage, three types of data are selected as input data of the model, including previous day data, same day data in previous week and similar day data obtained by grey relational analysis (GRA). In the error forecasting stage, the data driving characteristics of the DMD algorithm is used to capture the potential spatiotemporal dynamics of error series, thereby realizing the error forecasting. In the error correction stage, on the basis of combining the forecasting results of load and error, an extreme value constraint method (EVCM) is developed to further correct the load demand series. Based on the load data of different regions, this paper selects different performance indicators, such as MAPE, MAE, RMSE, Variance and direction accuracy (DA), to prove that the proposed method has the advantages of accuracy and stability.

ACS Style

Xiangyu Kong; Chuang Li; Chengshan Wang; Yusen Zhang; Jian Zhang. Short-term electrical load forecasting based on error correction using dynamic mode decomposition. Applied Energy 2019, 261, 114368 .

AMA Style

Xiangyu Kong, Chuang Li, Chengshan Wang, Yusen Zhang, Jian Zhang. Short-term electrical load forecasting based on error correction using dynamic mode decomposition. Applied Energy. 2019; 261 ():114368.

Chicago/Turabian Style

Xiangyu Kong; Chuang Li; Chengshan Wang; Yusen Zhang; Jian Zhang. 2019. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition." Applied Energy 261, no. : 114368.

Journal article
Published: 11 November 2019 in Energies
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With the continuous development of smart distribution networks, their observable problems have become more serious. Research on the optimal placement of the distribution phasor measurement unit (D-PMU) is an important way to improve the measurability, observability and controllability of a smart distribution network. In this paper, the optimal D-PMU placement methods and implementation technology were studied to determine the optimal D-PMU placement scheme. Considering the bus vulnerability index and the different operating states of the system, the more practical one-time optimal placement methods to ensure complete system observability was proposed. On this basis, the system's measurement redundancy and unobservable depth were considered to realize the multistage optimal D-PMU placement. The corresponding mathematical model and solution flow were given. Then the implementation technology of the methods was studied and the optimal D-PMU placement assistant decision-making software for smart distribution network was developed. Thereby, the structure and requirements of different distribution networks can be satisfied. The application analysis, functional architecture and the overall design process were given. Finally, the methods and software were analyzed by using the IEEE 33 bus system and an actual project, the Guangzhou Nansha Yuan'an Substation. The verification results showed that the method and software mentioned in this paper can provide convenient and quick operation for optimal D-PMU placement, improve the efficiency of smart distribution network planning work, and promote the theoretical application level of smart distribution network planning results.

ACS Style

Xiangyu Kong; Xiaoxiao Yuan; Yuting Wang; Yong Xu; Li Yu; Kong; Yuan; Wang; Xu; Yu. Research on Optimal D-PMU Placement Technology to Improve the Observability of Smart Distribution Networks. Energies 2019, 12, 4297 .

AMA Style

Xiangyu Kong, Xiaoxiao Yuan, Yuting Wang, Yong Xu, Li Yu, Kong, Yuan, Wang, Xu, Yu. Research on Optimal D-PMU Placement Technology to Improve the Observability of Smart Distribution Networks. Energies. 2019; 12 (22):4297.

Chicago/Turabian Style

Xiangyu Kong; Xiaoxiao Yuan; Yuting Wang; Yong Xu; Li Yu; Kong; Yuan; Wang; Xu; Yu. 2019. "Research on Optimal D-PMU Placement Technology to Improve the Observability of Smart Distribution Networks." Energies 12, no. 22: 4297.

Journal article
Published: 17 October 2019 in Applied Energy
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With the expansion in scale and complexity of distribution networks, distributed state estimation (DSE), a real-time database for other on-line applications, is becoming popular for large-scale active distribution networks (ADN). Measurements from phasor measurement units (PMUs) with the same time stamp can assist DSE to obtain faster and more accurate estimation; however, the configuration of PMUs and communication links should be updated to support data collection and transmission. This paper proposes an optimal PMUs and communication links placement method for DSE in distribution networks. A network partitioning method is presented with the aim of balancing calculation times among subareas. Then, a binary integer linear programming model that simultaneously considers the optimal placement of PMUs, phasor data concentrators (PDCs) and communication links is proposed. The economy of the configuration scheme is guaranteed on the premise that the network is fully observable. Finally, case studies on the IEEE 33-node, PG&E 69-node and IEEE 123-node systems verify the feasibility of the proposed method.

ACS Style

Zhida Zhao; Hao Yu; Peng Li; Xiangyu Kong; Jianzhong Wu; Chengshan Wang. Optimal placement of PMUs and communication links for distributed state estimation in distribution networks. Applied Energy 2019, 256, 113963 .

AMA Style

Zhida Zhao, Hao Yu, Peng Li, Xiangyu Kong, Jianzhong Wu, Chengshan Wang. Optimal placement of PMUs and communication links for distributed state estimation in distribution networks. Applied Energy. 2019; 256 ():113963.

Chicago/Turabian Style

Zhida Zhao; Hao Yu; Peng Li; Xiangyu Kong; Jianzhong Wu; Chengshan Wang. 2019. "Optimal placement of PMUs and communication links for distributed state estimation in distribution networks." Applied Energy 256, no. : 113963.

Journal article
Published: 02 October 2019 in IEEE Transactions on Power Systems
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Demand-side management (DSM) increases the complexity of the forecasting environment, which makes traditional forecasting methods difficult to meet the firm's need for predictive accuracy. Since deep learning can comprehensively consider various factors to improve the prediction results, this paper improves the deep belief network from three aspects of input data, model and performance, and uses it to solve the short-term load forecasting problem in DSM. In the data optimization stage, the Hankel matrix is constructed to increase the input weight of DSM data, and the gray relational analysis is used to select strongly correlated data from other data. In the model optimization stage, the Gauss-Bernoulli restricted Boltzmann machine is used as the first restricted Boltzmann machine of the deep network to transform the continuity features of the input data into binomial distribution features. In the performance optimization stage, a pre-training method combining error constraint and unsupervised learning is proposed to provide good initial parameters, and the global fine-tuning of network parameters is realized based on genetic algorithm. Based on the actual data of Tianjin Power Grid in China, the experimental results show that the proposed method is superior to other methods.

ACS Style

Xiangyu Kong; Chuang Li; Feng Zheng; Chengshan Wang. Improved Deep Belief Network for Short-Term Load Forecasting Considering Demand-Side Management. IEEE Transactions on Power Systems 2019, 35, 1531 -1538.

AMA Style

Xiangyu Kong, Chuang Li, Feng Zheng, Chengshan Wang. Improved Deep Belief Network for Short-Term Load Forecasting Considering Demand-Side Management. IEEE Transactions on Power Systems. 2019; 35 (2):1531-1538.

Chicago/Turabian Style

Xiangyu Kong; Chuang Li; Feng Zheng; Chengshan Wang. 2019. "Improved Deep Belief Network for Short-Term Load Forecasting Considering Demand-Side Management." IEEE Transactions on Power Systems 35, no. 2: 1531-1538.

Journal article
Published: 18 September 2019 in Energy
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The utilization of distributed energy resources (DERs) is growing worldwide, and the commercial prospects of microgrids (MGs) are clear. To optimally coordinate the power outputs of DERs owned by different owners while considering uncertainties in the commercial MG, a multi-agent optimal bidding strategy based on the artificial immune system (AIS) is proposed. The method takes the multi-agent system (MAS) control structure of the MG, distributing the profits of different owners through the market mechanism to conduct the optimization. A novel AIS is established and integrated into the MAS to help DERs participate in the optimal bidding operation of MG. The antigen is transformed by the environmental information, the price of the main grid, other DERs’ bidding strategies, and the predicted deviation coefficient while accounting for the uncertainties of DER facilities, which is solved by AIS to find the optimal bidding strategy. A mixed-integer programming model is solved by the bidding manager agent to get bidding results, which are fed back to the DERs to help them form the next round of strategies until the result reaches the equilibrium. Results show that the proposed method is efficient in coordinating the power generation with uncertainty and maximizing the interests of each investor.

ACS Style

Xiangyu Kong; Dehong Liu; Jie Xiao; Chengshan Wang. A multi-agent optimal bidding strategy in microgrids based on artificial immune system. Energy 2019, 189, 116154 .

AMA Style

Xiangyu Kong, Dehong Liu, Jie Xiao, Chengshan Wang. A multi-agent optimal bidding strategy in microgrids based on artificial immune system. Energy. 2019; 189 ():116154.

Chicago/Turabian Style

Xiangyu Kong; Dehong Liu; Jie Xiao; Chengshan Wang. 2019. "A multi-agent optimal bidding strategy in microgrids based on artificial immune system." Energy 189, no. : 116154.

Review article
Published: 12 August 2019 in International Journal of Electrical Power & Energy Systems
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Aiming at the power supply capability evaluation under the background of controllable source-network-load in power system, a multi-objective power supply capability evaluation method for active distribution network considering the active control cost is proposed. Firstly, uncertain factors such as renewable distributed generations’ output and demand response are modeled. Then, the maximization of regional power supply capacity and the minimization of active control costs are taken as the optimization objective from the perspective of both the planning and operation. Considering the constraints of distributed generations output ability, the network topology, load controllable levels, and so on, a multi-objective optimization uncertainty model for the active distribution network is constructed. In addition, the crossover operator and the selection strategy of NSGA-II are improved based on the non-uniform arithmetic crossover and phase-out strategy, which is used to solve the proposed optimization model. The Pareto optimal solution set obtained by the multi-objective optimization algorithm has a large scale and contains a wealth of information, and a method based on entropy-TOPSIS is also provided to select one eclectic solution set by the operator. Finally, the effectiveness of the proposed evaluation method and the performance of the improved algorithm are verified by the improved IEEE 33-bus distribution system and one of China’s actual power grid.

ACS Style

Xiangyu Kong; Chengsi Yong; Chengshan Wang; Peng Li; Li Yu; Ying Chen. Multi-objective power supply capacity evaluation method for active distribution network in power market environment. International Journal of Electrical Power & Energy Systems 2019, 115, 105467 .

AMA Style

Xiangyu Kong, Chengsi Yong, Chengshan Wang, Peng Li, Li Yu, Ying Chen. Multi-objective power supply capacity evaluation method for active distribution network in power market environment. International Journal of Electrical Power & Energy Systems. 2019; 115 ():105467.

Chicago/Turabian Style

Xiangyu Kong; Chengsi Yong; Chengshan Wang; Peng Li; Li Yu; Ying Chen. 2019. "Multi-objective power supply capacity evaluation method for active distribution network in power market environment." International Journal of Electrical Power & Energy Systems 115, no. : 105467.