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Table of Content

    28 June 2024, Volume 58 Issue 6 Previous Issue    Next Issue
    New Type Power System and the Integrated Energy
    Review of High Voltage Ride-Through Control Method of Large-Scale Wind Farm
    WEI Juan, LI Canbing, HUANG Sheng, CHEN Sijie, GE Rui, SHEN Feifan, WEI Lai
    2024, 58 (6):  783-797.  doi: 10.16183/j.cnki.jsjtu.2022.416
    Abstract ( 2012 )   HTML ( 15 )   PDF (1884KB) ( 466 )   Save

    As the major demand for the development and utilization of new energy, the large-scale development of wind power is a key support in achieving the strategic goal of “cabron peaking and carbon neutrality” for China. The problem of safe and stable operation of wind farms caused by external grid faults has become one of the key bottlenecks restricting the large-scale, clustered, and intelligent development of wind power. This paper mainly focuses on the voltage surge condition of the power grid. First, it analyzes the transient characteristics of high voltage ride-through (HVRT) of the doubly-fed induction generator-wind turbine, permanent magnet synchronous generator-wind turbine, and wind farms. Then, it summarizes the corresponding HVRT and post-fault voltage recovery coordinated optimal control strategies based on the different control areas, and it classifies and compares the working principles and advantages and disadvantages of various control strategies. Afterwards, it recapitulates the principle, advantages and disadvantages, and effects of the existing HVRT control method for large-scale wind farms, and analyzes the differences between the single wind turbine and the large-scale wind farms from the perspective of control structure. Finally, it discusses the development trend and potential research hotspots of wind farm voltage intelligent safety control in the future, aiming to provide reference for improving the large-scale application of wind power and the safe operation of power grids in China.

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    Dynamic Equivalence Modeling of Short-Circuit Faults in Wind Farms Considering Wake Effects
    YU Hao, LI Canbing, YE Zhiliang, PENG Sui, REN Wanxin, CHEN Sijie, TANG Binwei, CHEN Dawei
    2024, 58 (6):  798-805.  doi: 10.16183/j.cnki.jsjtu.2022.476
    Abstract ( 1099 )   HTML ( 8 )   PDF (2071KB) ( 1338 )   Save

    Fast and accurate analysis of the short-circuit characteristics of large wind farms has important engineering application value, and the short-circuit characteristics of wind farms under the influence of the wake effect vary greatly. Therefore, it is necessary to establish a wind farm short-circuit fault time equivalence model. A wind farm short-circuit fault dynamic equivalence method considering the effect of wake effect is proposed. First, the wake effect factor is defined to reflect the degree of the unit affected by the wake effect. Then, the wake effect factor is used as the grouping basis to reduce the variability of operating state of the units within the group under the influence of the wake effect. A positive- negative- zero-sequence network equivalence method is analyzed to improve the effectiveness of the equivalence model in asymmetric short-circuit faults. An equivalence method suitable for zero-sequence network is proposed and a platform is built for verification. The simulation results show that the dynamic short-circuit fault equivalence model proposed can accurately reflect the active and reactive short-circuit output characteristics of wind farms under the influence of the wake effect.

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    Short-Term Interval Forecasting of Photovoltaic Power Based on CEEMDAN-GSA-LSTM and SVR
    LI Fen, SUN Ling, WANG Yawei, QU Aifang, MEI Nian, ZHAO Jinbin
    2024, 58 (6):  806-818.  doi: 10.16183/j.cnki.jsjtu.2022.511
    Abstract ( 1559 )   HTML ( 9 )   PDF (2987KB) ( 266 )   Save

    Aimed at the intermittency and fluctuation of photovoltaic output power, a short-term interval prediction model of photovoltaic power is proposed. First, the model uses the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) to decompose the historical photovoltaic output data into different components and define them as time-series components and random components according to their correlation with time-series features such as declination and time angles. Then, the long short-term memory (LSTM) neural network and the support vector regression (SVR) model optimized by the gravitational search algorithm (GSA) are used to predict the time series components and the random components respectively, and the prediction results of the time series components and the random components are superimposed to obtain the point prediction result. After the error is subjected to Johnson transformation and normal distribution modeling, the photovoltaic power interval prediction result is obtained. Finally, the effectiveness of the method is verified by an example. The comparison of the proposed model with other existing prediction models under different weather conditions suggests that the proposed model has a higher accuracy and a better robustness, which can provide precise confidence intervals based on point prediction values.

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    TPE-Based Boosting Short-Term Load Forecasting Method
    LUO Min, YANG Jinfeng, YU Hui, LAI Yuchen, GUO Yangyun, ZHOU Shangli, XIANG Rui, TONG Xing, CHEN Xiao
    2024, 58 (6):  819-825.  doi: 10.16183/j.cnki.jsjtu.2022.483
    Abstract ( 1341 )   HTML ( 14 )   PDF (3058KB) ( 310 )   Save

    Short-term load forecasting is generally applied in power system real-time dispatching and day-ahead generation planning, which is of great significance for power system economic dispatching and safe operation of the system. Many researches on short-term load forecasting using smart models have been conducted at home and abroad. However, how to obtain the optimal structure and parameters accurately and quickly poses a challenge to short-term load forecasting, because the prediction performance of smart forecasting methods is more easily affected by the structure and parameters of the method, and the personality difference of the prediction object itself makes it difficult for the parameters to be reused. Aiming at this problem, a tree-structured Parzen estimator (TPE)-based boosting short-term load forecasting method is proposed. The results show that the proposed method can achieve rapid optimization of structure and parameters, which is verified in the application in short-term load forecasting of a southern province in China to improve the prediction accuracy.

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    Two-Stage Robust Planning for Transmission Network Considering Adaptive Decision of Carbon Trading Volume
    JIANG Biao, LIU Jia, ZENG Pingliang, TANG Zao, LI Yalou
    2024, 58 (6):  826-835.  doi: 10.16183/j.cnki.jsjtu.2022.473
    Abstract ( 1267 )   HTML ( 5 )   PDF (1824KB) ( 172 )   Save

    Low carbon is the future development trend of new power system, and the simulation of horizontal annual carbon trading volume at the planning level plays an important role in realizing the low-carbon economic operation of future power grid. Based on this understanding, a robust planning method for transmission network considering adaptive decision-making of carbon trading volume is proposed. First, a measurement method of carbon quota trading cost based on the baseline method is constructed, and a two-stage robust programming model considering the uncertainty of wind power and load is established considering the constraint of carbon trading volume. Then, the relax-and-enforce decoupling method is used to decouple the sub-problem into security feasibility detection sub-problem and low carbon feasibility detection sub-problem according to time, and the two-stage robust programming model is solved by using the column and constraint generation algorithm. Based on the cut information feedback from the security constraint and the carbon quota trading volume constraint in each uncertain scenario, the mapping relationship between the feasible solution space of the model and the setting of the threshold parameter of carbon quota trading volume is analyzed, and the upper and lower limits of carbon trading volume are adaptively decided. Finally, the improved IEEE-RTS 24-node system is simulated to analyze the impacts of the low-carbon planning model, carbon quota trading volume, and carbon price on the planning results. The results show that the proposed model realizes the coordinated optimization of the planning scheme in terms of economy and low carbon.

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    Droop-Free Distributed Energy Storage Control Based on Diffusion Algorithm
    MI Yang, ZHANG Haojie, QIAN Yiming, XING Haijun, GONG Jinxia, SUN Gaiping
    2024, 58 (6):  836-845.  doi: 10.16183/j.cnki.jsjtu.2022.487
    Abstract ( 1686 )   HTML ( 4 )   PDF (4743KB) ( 174 )   Save

    A droop-free distributed energy storage control strategy based on the diffusion algorithm is proposed to address the inability of droop control to simultaneously achieve bus voltage stability and accurate power distribution among energy storage systems when the line impedance is mismatched and bus voltage is inconsistent. First, the diffusion algorithm is applied to the distributed estimation of direct current (DC) microgrid to obtain the global average, and the difference between the voltage rating and the average voltage is used as a compensation term to restore the bus voltage deviation. Then, in order to achieve the accurate distribution of power between energy storage systems with different rated capacities and different states of charge (SOC), a standard power of energy storage is designed and the difference between the standard power and the average standard power is used as a compensation term for the equilibrium of SOC between energy storage systems. Finally, a model based on RT-LAB is built to verify the effectiveness of the designed control strategy in four different operating modes. The experimental results show that the proposed control strategy can achieve the bus voltage recovery and the accurate distribution of energy storage power in the isolated DC microgrid.

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    Non-Intrusive Load Disaggregation Using Sequence-to-Point Integrating External Attention Mechanism
    LI Lijuan, LIU Hai, LIU Hongliang, ZHANG Qingsong, CHEN Yongdong
    2024, 58 (6):  846-854.  doi: 10.16183/j.cnki.jsjtu.2022.534
    Abstract ( 1262 )   HTML ( 6 )   PDF (2987KB) ( 289 )   Save

    Non-intrusive load disaggregation (NILD) can deeply explore the value of customer power consumption data, providing an important reference for decision analysis such as power equipment fault monitoring and demand response. Aimed at the conflict between the training time and the accuracy of non-intrusive load disaggregation, a non-intrusive load disaggregation algorithm using sequence-to-point integrating external attention (EA) mechanism is proposed. First, the original data is pre-processed by data purification, normalization, and some other operations, and the train data is built with a same length window. The equipment feature is extracted through the encoder layer. Then, the feature weights of important parts are enhanced by introducing an external attention mechanism. Finally, the results are yielded through the decoder layer. Simulation calculation of the proposed model and the current mainstream model is performed using the publicly available datasets, REDD and UK-DALE, while the indicators of signal aggregate error, mean absolute error, normalized disaggregation error, model disaggregation curves, feature map, and user energe consumption are compared and analyzed. The proposed model overcomes the shortcomings of attention scattering in the convolutional layer, enhances the ability to extract and utilize effective information, and has a more accurate decomposition accuracy without increasing the training time cost.

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    A Data-Driven Method Embedded with Topological Information for Voltage-Power Sensitivity Estimation in Distribution Network
    LIU Shu, ZHOU Min, GAO Yuanhai, XU Xiaoyuan, YAN Zheng
    2024, 58 (6):  855-862.  doi: 10.16183/j.cnki.jsjtu.2022.485
    Abstract ( 1351 )   HTML ( 7 )   PDF (2674KB) ( 183 )   Save

    The multicollinearity of measurement data leads to the low accuracy of the data-driven methods for estimating voltage-power sensitivity in distribution networks. In this paper, a data-driven method embedded with topological information is proposed to address the problem. First, the voltage-power sensitivity matrix is decomposed into principal and secondary components, where the principal component is closely related to the distribution network topology and the secondary component is the error between the principal component and the actual value. Then, the principal and secondary components are estimated sequentially in two stages, and their data-driven estimation models based on quadratic programming are established, respectively. The key of the model in the first stage is the constraint based on the distribution network topology information, and the key of the model in the second stage is the constraint that the ratio of the secondary component to the principal component is tiny. Finally, the accuracy and efficiency of the proposed method is validated in the IEEE 33-bus system with a set of measurement data, and comparisons are made with ordinary least square regression, ridge regression, and LASSO regression. The simulation results show that the accuracy of the proposed method is significantly improved by orders of magnitude.

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    Capacity Allocation Strategy of Energy Storage in Low-Carbon Park Considering Equivalent Energy Storage Characteristics of Thermal System
    CHEN Hui, HE Gengsheng, LIU Yuliang, ZENG Hongmei, ZHANG Shixu, LI Yaowang
    2024, 58 (6):  863-871.  doi: 10.16183/j.cnki.jsjtu.2022.507
    Abstract ( 1704 )   HTML ( 4 )   PDF (2493KB) ( 344 )   Save

    Under the low-carbon development goal, energy storage allocation is the key measure to ensure the safe and economic operation of low-carbon parks, and to reduce carbon emissions. To solve the problems of inaccurate carbon emission calculation and insufficient utilization of equivalent energy storage resources in low-carbon parks, this paper proposes a dynamic emission factor calculation method based on the carbon emission flow theory, which realizes the accurate measurement of indirect carbon emissions from park electricity consumption. Then, taking into account the available equivalent energy storage resources in the park, it proposes an energy storage capacity optimization allocation model considering the equivalent energy storage characteristics of thermal system, and uses the big M method to equivalently transform the nonlinear constraints in the model. Finally, it conducts simulation analysis based on a case system to verify the correctness and effectiveness of the proposed model.

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    A Cooperative Game Allocation Strategy for Wind-Solar-Pumped Storage-Hydrogen Multi-Stakeholder Energy System
    DUAN Jia’nan, XIE Jun, XING Shanxi
    2024, 58 (6):  872-880.  doi: 10.16183/j.cnki.jsjtu.2022.531
    Abstract ( 1797 )   HTML ( 4 )   PDF (1400KB) ( 255 )   Save

    To meet the construction demand of clean energy demonstration bases, a gain allocation strategy for the joint optimization operation of wind-solar-pumped storage-hydrogen multi-stakeholder energy system based on the cooperative game theory is proposed. In order to take into consideration the security of system operation, evaluation indicators for the complementarity of on-grid output are constructed. The stakeholders of wind, solar, pumped storage, and power-to-hydrogen cooperate through the internal electricity transaction to construct a joint scheduling model with the optimization goal of maximizing the operation benefits. Then, the minimum cost remaining saving (MCRS) method in the cooperative game theory is applied to allocate the synergistic benefits based on the scheduling results. The simulation results of a 12-stakeholder wind-solar-pumped storage-hydrogen clean energy demonstration base show that each stakeholder can derive positive gains through joint operation, and the reservoir capacity of pumped storage station, on-grid price and operation security demand will affect the cooperative synergistic benefits of the system.

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    Multi-Time Scale Probabilistic Production Simulation of Wind-Solar Hydrogen Integrated Energy System Considering Hydrogen Storage
    FAN Hong, XING Mengqing, WANG Lankun, TIAN Shuxin
    2024, 58 (6):  881-892.  doi: 10.16183/j.cnki.jsjtu.2022.379
    Abstract ( 1555 )   HTML ( 4 )   PDF (3966KB) ( 242 )   Save

    The uncertainty of wind power output and the difficulty of power storage restrict the development of new energy. As a high-quality secondary energy, hydrogen energy is green and pollution-free and has a high energy density. In order to cope with the volatility and randomness of new energy output, this paper proposes a multi-time scale probabilistic production simulation method for wind-solar hydrogen integrated energy system considering hydrogen storage. First, thermal energy recovery is considered in the hydrogen storage system model, and the wind-solar hydrogen integrated energy system model including electrothermal hydrogen multiple energy storage is constructed. Then, multi-time scale probabilistic production simulation is conducted for the wind-solar hydrogen integrated energy system, and the system maintenance arrangement and hydrogen storage seasonal distribution scheme are obtained through medium and long-term production simulation. The simulation results are taken as the boundary for short-term production simulation to achieve the cooperation of electrothermal hydrogen multiple energy storage and to smooth the random fluctuation of wind and solar power. Finally, an IEEE-RTS79 node example is given to verify that the proposed method can improve the reliability, flexibility and low-carbon feature of system operation.

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    Cost Modeling and Analysis of Integrated Energy Microgrid Group Participation in Frequency Regulation Auxiliary Service Market Based on CPSS
    LI Zhenkun, YU Qiuyang, WEI Yanjun, TIAN Shuxin
    2024, 58 (6):  893-903.  doi: 10.16183/j.cnki.jsjtu.2022.460
    Abstract ( 1077 )   HTML ( 3 )   PDF (2323KB) ( 302 )   Save

    With the implementation of the carbon peaking and carbon neutrality strategy, the proportion of new energy in the power system continues to increase, while the frequency regulation ability of the system gradually decreases. To address this problem, this paper studies the participation of distributed integrated energy microgrid group in frequency regulation auxiliary service. First, based on the concept of microgrid individuals participation in frequency regulation market after aggregation, the mode of integrated energy microgrid participating in frequency regulation auxiliary service market is described. Then, a cyber-physical-social system (CPSS) of integrated energy microgrid group is established. Based on this model, the frequency regulation cost of microgrid group is calculated. The cost model considers multiple frequency regulation modes, and classifies the social attributes of microgrid by using the grey weighted clustering method to simulate and analyze the willingness and probability value of microgrid individuals to participate in the frequency regulation market. Afterwards, based on Monte Carlo simulation, the cost calculation method for microgrid aggregators to participate in the frequency regulation auxiliary service is proposed, which provides the basis for aggregators to participate in the frequency regulation market quotation. Finally, an aggregator with five integrated energy microgrids is simulated and analyzed, and the curve of its frequency regulation cost is obtained. The simulation results show that the impact of the social attributes of the individual microgrid on the frequency regulation cost of the aggregator can reach 6.921%, so the uncertainty risk caused by the social attributes of the microgrid should be fully considered in the bidding process.

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    Energy Management Strategy of Integrated Electricity-Heat Energy System Based on Federated Reinforcement Learning
    WANG Jinfeng, WANG Qi, REN Zhengmou, SUN Xiaochen, SUN Yi, ZHAO Yiyi
    2024, 58 (6):  904-915.  doi: 10.16183/j.cnki.jsjtu.2022.418
    Abstract ( 1671 )   HTML ( 6 )   PDF (4615KB) ( 435 )   Save

    The energy management of the electricity-heating integrated energy system (IES) is related to the economic benefits and multi-energy complementary capabilities of a park, but it faces the challenges of the randomness of renewable energy and the uncertainty of load. First, in this paper, a mathematical model of the energy management problem for the electricity-heating IES is conducted, and each energy supply subsystem is empowered as an agent. Based on the deep deterministic policy gradient (DDPG) algorithm, a system energy management model is established that comprehensively considers the real-time energy load of the subsystem, the time-of-use pricing, and the output of each equipment. Then, the federated learning technology is used to interact with the gradient parameters of the energy management model of the three subsystems during the training process to synergistically optimize the training effect of the model, which can protect the data privacy of each subsystem while breaking the data barriers. Finally, an example analysis verifies that the proposed federated-DDPG energy management model can effectively improve the economic benefits of the park-level IES.

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    Dynamic Double-Layer Energy Management Strategy for Park Power Grid Considering Vehicle-to-Grid
    QIU Gefei, FENG Zehua, SHEN Fu, HE Chao, HE Honghui, LIU Kaiming
    2024, 58 (6):  916-925.  doi: 10.16183/j.cnki.jsjtu.2022.524
    Abstract ( 1216 )   HTML ( 7 )   PDF (2317KB) ( 85 )   Save

    This paper proposes a two-layer optimal control strategy for the park power grid, aiming at addressing the energy management challenges arising from the fluctuations in the output of clean energy sources and the random changes in the number of electric vehicles (EV) at the charging station. The upper layer establishes a dynamic optimization model of landscape storage based on the model predictive control technology, which comprehensively incorporates both the battery energy storage system of the park and the battery energy storage system of EVs, enabling them to participate in balancing the energy demand of the power grid, while the lower layer considers meeting the charging demand of EV owners and the vehicle-to-grid management demand of upper layer for energy dispatching simultaneously, and formulates an orderly charging and discharging strategy for EVs in the parking lot of the power grid. The simulation results show that the proposed strategy effectively can integrate scattered EV storage in the park into a unified virtual energy storage system, expand the energy storage capacity of the grid, and increase the consumption of renewable distributed generation supply, ultimately improving economic benefits for the park grid.

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    Low-Carbon Planning for Buildings Considering Ladder Carbon Reward and Punishment and Integrated Demand Response
    SHANG Mengqi, GAO Hongjun, HE Shuaijia, LIU Junyong
    2024, 58 (6):  926-940.  doi: 10.16183/j.cnki.jsjtu.2022.527
    Abstract ( 1480 )   HTML ( 7 )   PDF (3083KB) ( 216 )   Save

    With the rapid development of commercial complexes in recent years, energy consumption and carbon emissions of buildings are growing continuously. In this context, the low-carbon planning of commercial complexes is studied including shopping, restaurants, offices, and accommodation. In addition, a low-carbon planning model for building considering ladder carbon reward and punishment with the introduction of the time-sharing carbon measurement model of superior network power purchase and an integrated demand response (IDR) considering the differentiated predicted mean vote (PMV) of each functional area of the building is established. First, the time-sharing carbon measurement model of superior network power purchase is introduced to evaluate the equivalent carbon emissions of the building. Then, a ladder carbon reward and punishment model is built to measure the carbon emissions of the building. Based on the characteristics of the commercial complex planned in this paper, the IDR considering load time shifting and energy use reduction, end-use energy equipment substitution, and energy use type conversion at the superior energy end is constructed. Afterwards, a low-carbon planning model for building is established to determine the equipment type and capacity considering the PMV for each functional area of the building. Especially, the objective function is to optimize the total annual planning cost of the building by taking into account carbon reward and punishment costs of the building. A distributionally robust optimization model based on Kullback-Leibler divergence is proposed to cope with the output volatility of the connected distributed photovoltaic and photovoltaic curtain wall. Finally, the effects of the ladder carbon reward and punishment mechanism of the time-sharing carbon measurement model of superior network power purchase, the IDR of the differentiated PMV of each functional area of the building, and the volatility of the connected photovoltaic output on the building planning are analyzed to verify the effectiveness of the planning model proposed for energy saving and emission reduction in the building.

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    Power Smoothing Strategy for Industrial Park Tie-Line Considering Production Safety
    XU Jian, YU Qingfang, LIAO Siyang, KE Deping, SUN Yuanzhang
    2024, 58 (6):  941-953.  doi: 10.16183/j.cnki.jsjtu.2022.516
    Abstract ( 174 )   HTML ( 5 )   PDF (3970KB) ( 206 )   Save

    The high proportion of renewable energy integration to industrial parks has become the main way for energy-intensive industrial loads to achieve low-carbon transition. The sharp and frequent power fluctuation of tie-line is the key problem of low-carbon transition for industrial parks. Therefore, an industrial park tie-line power smoothing strategy considering production safety is proposed. First, the control model of the production equipment for electrolytic aluminum and arc furnace is analyzed, the power closed-loop feedback control model of industrial load is established based on power fluctuation feedback. Then, considering the core influencing factors of industrial production, the state of temperature with energy-intensive equipment is established, a control cost model for demand response is formed, and the control objective to each industrial load is assigned based on the optimization objective for the minimum penalty cost. Finally, the MATLAB online calculation and RTDS simulation real-time interactive example is established based on Wenshan power grid in Yunnan Province. The simulation results show that the smelting temperature is updated adaptively, the influence of power regulation on production is reduced effectively, and the economic cost of tie-line is reduced by 36.4%.

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    Load Frequency Control of Islanding Micro-Grid with High-Proportional Renewable Energy Based on Desired Dynamics Equation
    WU Zhenlong, LIU Yanhong, XUE Yali, LI Donghai, CHEN Yangquan
    2024, 58 (6):  954-964.  doi: 10.16183/j.cnki.jsjtu.2022.459
    Abstract ( 185 )   HTML ( 10 )   PDF (4809KB) ( 189 )   Save

    A proportional-integral-derivative (PID) control strategy based on desired dynamics equation is proposed to solve the problem of load frequency control in micro-grid integrated with a large proportion of renewable energy. Based on the analysis of the load frequency control model and control difficulties of the micro-grid, a PID control strategy based on desired dynamics equation is designed. By analyzing the influence of controller parameters on the control performance using the single variable method, a simple and practical parameter procedure is summarized and the proposed controller strategy is applied to the load frequency control of the micro-grid. The simulation comparison with various controllers under different conditions show that the proposed control strategy can obtain the best control performance with good robustness and have a significant value for engineering application.

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