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Localization of faults in a large power system is one of the most important and difficult tasks of power systems monitoring. A fault, typically a shorted line, can be seen almost instantaneously by all measurement devices throughout the system, but determining its location in a geographically vast and topologically complex system is difficult. The task becomes even more difficult if measurements devices are placed only at some network nodes. We show that regression graph neural networks we construct, combined with a suitable statistical methodology, can solve this task very well. A chief advance of our methods is that we construct networks that produce localization without having being trained on data that contain fault localization information. We show that a synergy of statistics and deep learning can produce results that none of these approaches applied separately can achieve. [ABSTRACT FROM AUTHOR]

Considering the characteristics of the power system, where "the source moves with the load", the load side is primarily responsible for the carbon emissions of the regional power grid. Consequently, users' electricity consumption behavior has a significant impact on system carbon emissions. Therefore, this paper proposes a multi-objective bi-level optimization strategy for source-load coordination, considering dual demand responses for both electricity and carbon. The upper layer establishes a multi-objective low-carbon economic dispatch model for power grid operators, aiming to minimize the system's total operating cost, the total direct carbon emissions of the power grid, and the disparity in regional carbon emissions. In the lower layer, a low-carbon economic dispatch model for load aggregators is established to minimize the total cost for load aggregators. To obtain the dynamic carbon emission factor signal, a complex power flow tracking method that considers the power supply path is proposed, and a carbon flow tracking model is established. NSGA-II is used to obtain the Pareto optimal frontier set for the upper model, and the 'optimal' scheme is determined based on the fuzzy satisfaction decision. The example analysis demonstrates that the interactive carbon reduction effect under the guidance of dual signals is the most effective. This approach fully exploits the carbon reduction potential of the flexible load, enhancing both the economic efficiency and low-carbon operation of the system. [ABSTRACT FROM AUTHOR]

Introduction: In order to dispatch frequency regulation resources in regional power grids efficiently and promote the development of spot markets, China Southern Power Grid (CSG) established the unified frequency regulation control area. However, the existing regional control performance standards (CPS) for evaluating the performance of frequency control in bulk power systems is no longer suitable for the unified frequency control mode. Method: This paper proposed an innovative frequency control performance standard, named tertiary control performance standards (TCPS) and used Gaussian mixture model (GMM) and folded normal distribution (FND) to describe the distributions of frequency deviations and power deviations of tie line. Result and Discussion: Based on these probability models, the parameters of the proposed TCPS can be determined optimized. Finally, case studies were carried out with the practical data from CSG and indicated that the parameters of proposed TCPS index could be calculated and improved the control performance for the real time power balance of the regional power grid with spot markets. [ABSTRACT FROM AUTHOR]

This paper provides an optimization strategy for flexible operation at the system level to guide the real-time flexible ramping of the power grid. Firstly, the concept of regional power grid flexibility is clarified, and the ramping factor is proposed as a flexibility metric. On this basis, taking the output priority of each node as the objective function and considering constraints such as line flow, power balance, and system flexibility, a mathematical model for regional power grid transmitting flexible power externally is proposed. Compared with the traditional models, this model focuses more on flexibility rather than economy. A case analysis based on the improved IEEE 30-Bus System verifies the feasibility and effectiveness of the proposed strategy and its advantages over the traditional models. [ABSTRACT FROM AUTHOR]

The voltage quality decline caused by the high proportion of new energy connected to power grids is a key factor limiting the capacity of new energy consumption. Therefore， an optimal reactive voltage control method based on the maximum capacity of new energy consumption is proposed. The new energy generation forecast data is used to assess the maximum generation capacity， establish the reactive voltage optimization model and solve the voltage control target at each node for forwarding decision control. According to the topological properties and operational characteristics of the regional power grids， dynamic zoning， strategy prediction， alarm blocking， and other concerning functions are designed to improve the safety and stability of the control method. The simulation analysis results of an example show that the method features ideal reactive voltage support and can promote new energy consumption in the regional power grids.

Electricity consumption is a crucial source of carbon emissions. Many studies have focused on the emissions relevant to electricity generation, yet it is also important to evaluate the carbon intensity of final electricity use for the emission assessment of various industries. Considering fossil fuel consumption, regional exchange, and transmission loss, this study proposed a method to assess the emission intensities of electricity generation, supply, and use. Based on statistical data, the carbon intensities of North, Northeast, East, Central, Northwest, and South China power grids were evaluated during the period from 2005 to 2020, and the driving factors including energy structure, energy intensity, clean production, supply structure, and power loss effects were discussed using Logistic Mean Divided Index (LMDI) decomposition. An overall descending trend was observed for the carbon intensity, and the national average carbon intensities of electricity generation, supply, and use were 0.599, 0.599, and 0.622 kgCO2e/kWh, respectively. The carbon intensity of South China gird was the lowest at 0.425 kgCO2e/kWh in 2020. Moreover, the energy intensity effect and clean production effect were identified as the most influential factors of carbon intensity. The proposed method and results can be helpful to assess carbon emissions relevant to final electricity use. [ABSTRACT FROM AUTHOR]

This paper provides a method for simplified description of a regional power grid model aimed to deliver a grid reduction, and improve grid performance observability. The derived power grid model can be used to analyze the regional allocation of the decentralized energy generation and consumption. The expansion of wind and solar generation in the power system affects the residual load. The power balance between electricity consumption and generation was calculated and analyzed based on the temporal and spatial scales. The proposed grid clustering method is a useful approach for performance analysis in systems with a growing share of renewable generation.

Abstract In this study, the “cradle‐to‐gate” greenhouse gas (GHG) intensities of six types of power generation in China are analyzed using a life cycle assessment approach, including wind power, solar photovoltaic power, nuclear power, hydropower, biomass power, and thermal power. According to the mix of regional power grids in China and GHG intensities of various types of power generation, the GHG intensities of hybrid power on regional power grid scale are calculated. The results show that they are closely corresponding to the grid mix of each region. Besides, the value of northeast China is the highest and the largest variation between regions is about twice. Furthermore, the efforts made by the Chinese government promoting energy shift are expected to accelerate the decrease of the electricity system GHG intensity to 376.9 gCO2‐eq/kW h by 2035, about 51.0% lower than that in 2017. And the total GHG emissions are predicted to reach 4.5 × 109 tCO2‐eq in 2035, while in “below 2°C” scenario this value will decrease to 3.6 × 109 tCO2‐eq. This study compiles the life cycle inventory of China's electricity generation on spatial and temporal scale, and can provide suggestions on the development of regional and national electricity systems.

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The power generation of distributed power generation systems in regional power grids is mainly affected by external environmental factors, and there are volatility and intermittent power generation issues. This paper establishes a relatively simple multiple linear regression prediction model to predict grid-connected photovoltaic power generation systems. The article uses a hybrid modelling method to filter more important weather forecast input variables through a step-by-step linear regression method to realize the visual calculation of power data. The research results show that the multiple linear regression equation methods proposed in the article have pervasive application value in the actual application of solar photovoltaic power consumption data.

In the context of building a new power system, the high proportion of renewable energy access and the high coupling of information and physical systems lead to more complex grid networks and more diverse risk sources, and the importance of regional power grid security evaluation has also increased. From the perspective of power grid risk sources, firstly, a regional power grid security evaluation index system including natural environment, social environment, economic environment, network environment, technical environment and energy environment was established. Secondly, the improved catastrophe theory was used to build the power grid security evaluation model. Finally, a case analysis of the relevant data of a power grid company from 2012 to 2019 was carried out. Compared with the entropy weight method, the evaluation results of regional power grid security obtained from the improved catastrophe theory are more consistent with the actual statistical results of its comprehensive power supply reliability, which proves that the established index system can accurately analyze the source of regional power grid security risk, thus realizing the effective analysis of the development trend of regional power grid security under different external environments. [ABSTRACT FROM AUTHOR]

In this study, the "cradle‐to‐gate" greenhouse gas (GHG) intensities of six types of power generation in China are analyzed using a life cycle assessment approach, including wind power, solar photovoltaic power, nuclear power, hydropower, biomass power, and thermal power. According to the mix of regional power grids in China and GHG intensities of various types of power generation, the GHG intensities of hybrid power on regional power grid scale are calculated. The results show that they are closely corresponding to the grid mix of each region. Besides, the value of northeast China is the highest and the largest variation between regions is about twice. Furthermore, the efforts made by the Chinese government promoting energy shift are expected to accelerate the decrease of the electricity system GHG intensity to 376.9 gCO2‐eq/kW h by 2035, about 51.0% lower than that in 2017. And the total GHG emissions are predicted to reach 4.5 × 109 tCO2‐eq in 2035, while in "below 2°C" scenario this value will decrease to 3.6 × 109 tCO2‐eq. This study compiles the life cycle inventory of China's electricity generation on spatial and temporal scale, and can provide suggestions on the development of regional and national electricity systems. [ABSTRACT FROM AUTHOR]

Cascade hydropower plants which have good regulation performance and are managed by the dispatching center of regional power grids are usually required to simultaneously shave the peak load for multiple provincial power grids, which is an important way of relieving the growing peak shaving pressure on power grids in central and eastern China. This paper establishes an optimization model for the short-term generation scheduling of cascade hydropower plants in regional power grids. In this model, minimizing the peak-valley load difference of multiple power grids is adopted as the objective function. In addition to conventional hydraulic constraints, the operation constraints of individual hydropower units, electrical constraints and head effect on the power generation are all considered to obtain precise scheduling. The original nonlinear and non-convex model is converted into a standard mixed-integer linear programming (MILP) formulation through several linearization strategies. The results for the real-world case study indicate that: 1) the proposed model is computationally efficient with a calculation time of 326 s; 2) the peak-valley differences of the Shanghai Power Grid and Zhejiang Power Grid decreased by 9.71% and 2.29%, respectively; 3) compared with actual operation, the proposed model shows better performance in peak shaving for multiple provincial power grids. • Daily operation of cascade hydropower plants shaving peak load for multiple power grids is studied. • Operation constraints of hydropower units and head effect on power generation are considered. • An MILP based model is developed through several linearization strategies. • The proposed model has better peak shaving performance compared with actual operation. [ABSTRACT FROM AUTHOR]

During the application of traditional regional power grid load frequency control methods, the performance of load frequency control will be significantly reduced due to the influence of environmental conditions. In order to overcome this problem, in this paper, we propose a method suitable for the load frequency control of the regional power grid under extreme cold weather. In the severe environment of extreme cold weather, the resistance-capacitance unit is used to represent the equivalent simplified model of the regional power grid. Then, the energy stored in the regional power grid is calculated by defining the state of charge of the regional power grid. In addition, a control strategy for the load frequency of the regional power grid was formulated based on the introduction of the SOC value. The SOC values of regional power grids are different, so we have divided the regional power grids. By setting the load frequency of the regional power grid, the load frequency control situation of the regional power grid under different conditions is determined. According to the regulation principle of the PMW model, a model equation for the load frequency control of the regional power grid is constructed. Meanwhile, the pull transform is used to obtain the equivalent frequency domain equation for the load frequency control of the regional power grid, which in turn realizes the control of the load frequency of the regional power grid. The experimental results show that the load frequency control method of the regional power grid under extreme cold weather can not only improve the frequency control accuracy, but also improve the control efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, an increasing number of photovoltaic (PV) plants are becoming integrated into one regional power grid. Under this circumstance, the probabilistic forecast of regional PV power generation is of significance for the regional power system operation and control. This study presents a novel probabilistic forecast method for regional PV generation that integrates the convolutional neural network (CNN) with non‐linear quantile regression (QR). In this method, the CNN structure is enhanced to extract the non‐linear features of the input data and generate the non‐linear QR function. As a result, the improved CNN can effectively process high‐dimensional and complex input data and the non‐linear QR model can provide quantile forecast information of regional PV power. The validity of the proposed method is verified by using it to forecast the regional PV generation from the clustered PV plants in the Weifang region of China. [ABSTRACT FROM AUTHOR]

The quantitative evaluation of cluster wind power output volatility and source-load timing matching is vital to the planning and operation of the future power system dominated by new energy. However, the existing volatility evaluation methods of cluster wind power output do not fully consider timing volatility, or are not suitable for small sample data scenarios. Meanwhile, the existing source-load timing matching evaluation indicator ignores the impact of wind power permeability on the timing matching degree between wind power output and load. Therefore, the authors propose quantitative evaluation methods of cluster wind power output volatility and source-load timing matching in regional power grid. Firstly, the volatility-based smoothing coefficient is defined to quantitatively evaluate the smoothing effect of wind-farm cluster power output. Then, the source-load timing matching coefficient considering wind power permeability is proposed to quantitatively evaluate the timing matching degree of regional wind power output and load, and the corresponding function model of volatility-based smoothing coefficient and source-load timing matching coefficient is established. Finally, the validity and applicability of the proposed methods are verified by MATLAB software based on the actual power output of 10 wind farms and actual grid load in a certain grid dispatching cross-section of northeast China. The results demonstrated that the proposed volatility-based smoothing coefficient can accurately represent the smoothing effect of wind farm cluster power output while maintaining the volatility continuity of wind power output time series and without affect from the data sample size. The source-load timing matching coefficient can accurately characterize the difference in the timing matching degree between wind power output and grid load under different wind power permeability and the influence degree on grid load.

Abstract Recently, the booming electricity demand and intermittent energy has sharply increased the peak shaving pressure in China. However, for a majority of regional power grids in China, the installed capacity of flexible energy (like hydropower and pumped-storage) is small and the thermal plants are asked to respond the sudden load change at peak periods. The existing method based on specialist experience and historical information may generate inferior solutions and fail to reduce the peak pressure. Thus, a practical mixed integer linear programming (MILP) model is developed for unit commitment of thermal plants with peak shaving operation aspect in regional power grid lack of flexible hydropower energy, where the goal is chosen to minimize the peak-valley difference of the residual load series obtained by subtracting all the thermal generation from the original load curve while satisfying the necessary physical constraints. The MILP model is used to the thermal system in the China's largest regional power grid, East China Power Grid. The simulations show that the MILP model can effectively smooth the residual load curve by gathering power generation of thermal plants at peak periods. Therefore, an alternative tool is provided to alleviate the peak pressure of thermal-dominant regional power grid in China. Highlights • This work is from peak operation of thermal system in China's regional power grid. • A mixed integer linear programming model is firstly developed to solve this problem. • The objective is to minimize the peak-valley difference of the residual load series. • The MILP model obtains satisfying results in reducing peak loads of power system. [ABSTRACT FROM AUTHOR]

Highlights • Thermostatically controlled loads (TCLs) are used to regional power grid control. • TCLs are modeled as a simplified first-order equivalent thermal parameter model. • A novel TCLs control algorithm is proposed to maximize renewable energy usage. • The algorithm uses the state-queueing model and modified colored power algorithm. • The algorithm considers not only comfort levels but also fairness of customers. Abstract The active development and application of renewable energy, such as wind power and solar power, has become an inevitable tendency, which will promote the increase of the proportion of renewable energy generation accordingly in regional power grids. The intermission and randomness of renewable energy generation lead to enormous challenges in the operation and planning of power systems. One of the current methods to accommodate and reduce renewable energy fluctuation is conventional storage devices, e.g. battery energy storage devices, but this approach has the limitation of a high operating cost, which hinders the technology from popularizing. We present a novel load control algorithm employing the technology of residential thermostatically controlled appliances to maximize renewable energy usage in regional power grids. The thermal dynamics of the thermostatically controlled loads are modeled as a simplified first-order equivalent thermal parameter model. The new load control algorithm, based on the state-queueing model with modified colored power algorithm, is developed to manage activities of residential thermostatically controlled appliances, which allows customers to set different colors as the response levels of their appliances. Such an algorithm can ensure both the comfort level and the fairness of customers. Both a practical case and a real case are applied to demonstrate the effectiveness and practicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Energy storage is considered as an effective approach to deal with the power deviation that caused by the stochastic wind power forecast error. Because of the spatial‐temporal correlation of forecast errors for wind farms, which locate close to each other and integrate into the same regional power grid, energy storage could be deployed effectively and economically. Therefore, this study proposes an optimisation method to size the capacity of energy storage system (ESS) considering the spatial‐temporal correlation of forecast errors for multiple nearby wind farms. The copula theory based correlation model of high‐dimensional forecast errors is built to capture the spatial‐temporal characteristics of forecast error. Then, according to multiple scenarios technique, an optimal ESS sizing model is established to minimise the investment and operation costs of the ESS. Meanwhile, an operation strategy considering the trend of prediction and correlation of errors is used to implement rolling operation strategy of ESS. The case studies demonstrate the effectiveness of the new method and illustrate the significant impact of the correlation of forecast error on the capacity of ESS. [ABSTRACT FROM AUTHOR]

A regional power grid containing high‐density wind power is generally connected to a load centre through long‐distance transmission lines. Voltage problems, caused by wind speed fluctuation, in the sending‐end grid have become increasingly serious. The variable speed wind turbine has been used to compensate the deficit in reactive power. However, the available methods could not meet the requirement because the controllable range of reactive power contributed by wind farms cannot be adjusted. A new idea of reactive power control realised by the active control before wind speed fluctuation was proposed to prevent the voltage variation. Firstly, the influence of wind speed fluctuation on voltage was analysed, and the power controllable range of wind turbines was studied. The principle and strategy of the active control of reactive power were proposed based on the model predictive control theory. Then, the active control model was established according to the system dynamic demand for reactive power. According to wind speed forecasting, the reactive control capability of wind turbine was excavated to meet the grid demand by adjusting active power before wind speed variation, and the reduced active power of wind farms is optimally compensated by thermal powers. Finally, the method was proven to solve voltage problems. [ABSTRACT FROM AUTHOR]

The Chinese government has deployed ambitious plans for its power system and renewable energy development to achieve carbon neutrality. To assess the decarbonization processes of regional power grids, this study develops an inventory of the power generation structure at the provincial level based on current official power planning, and simulates the power generated and carbon emissions intensity in China's regional power grid from 2020 to 2050 using a dynamic scenario. Results found the following: First, compared with 2020, carbon emissions from the power system in 2050 decreased by approximately 1.8268 BtCO 2 , and the North China regional power grid contributes the most to emission reduction. Second, except for Tibet power grid, the life-cycle carbon emissions coefficients in China's regional power grids maintained a downward trend, with carbon emission coefficients ranging from 0.1439 to 0.2385 kgCO 2 eq/kWh in 2050. Third, cross-regional power exchange can effectively reduce the carbon emission intensity of regional power grids dominated by installed fossil energy and Northwest regional power grid is the largest power output region, reaching 1077.78 TWh in 2050. Overall, this study assesses the prospective decarbonization of China's regional power grids. The findings contribute to the development of power decarbonization planning and provide data references for electrification of energy-consuming sectors. • The projected carbon emissions intensity of each regional power grid is simulated. • Our model considers cross-regional power exchange. • Dynamic scenarios are used to achieve uncertainty in power generation. • The power system is not decarbonized fast enough under current official power planning. • Recommendations are given from CCS technology and power transmission. [ABSTRACT FROM AUTHOR]

In order to peak emissions before 2030, achieve the goal of 2 °C temperature rise and protect people’s health, China’s power sector urgently needs to increase its share of clean energy. However, high generation costs and an inadequate grid transmission capacity still impedes clean generation technology development. It is still unclear which conditions would enable the large penetration of clean generation technologies in China’s power sector. Thus, this study develops a quantitative calculation method to find the most effective regional subsidy and interregional transmission capacity levels, so that the penetration barriers can be overcome and clean energy resources in China can be fully used. The simulation results indicate that the most effective subsidy for each clean generation technology differs from region to region. Using solar power as an example, the most effective subsidy in the north, northeast and northwest regions is 0.4 RMB/kW h and in the south region it is 0.7 RMB/kW h. If these subsidies are implemented, the share of solar power in the Chinese power sector can reach 24.7% by 2050. Additionally, the results also indicate that the most effective interregional transmission capacity in 2050 is about 13 times of the current transmission capacity plan. Under the most effective regional subsidies and interregional transmission capacity, the share of clean energy generation in the Chinese power sector can reach 59.6%. [ABSTRACT FROM AUTHOR]

The self - excitation of generator operating with long line often occurs due to optimized magnetism of line distribution capacitance current. How to effectively prevent unit self - excitation during black start, therefore, is one of the key factors to determine if black start is feasible. This paper proposed the criterion for regional network hydropower self - excitation according to its cause, and studied the influence of self - excitation on Heilongjiang power grid with different plans of black start. [ABSTRACT FROM AUTHOR]

The existing regional power grid risk evaluation methods are short of comprehensive quantitative statistics and analysis of a lot of factors, such as different device types, different grid fault types,different device failure rate and so on. Aiming at this problem, this paper proposed the comprehensive quantitative method for regional power grid risk evaluation, made the evaluation on real- time and operation mode from three aspects, including power supply quality, power supply capability and power supply economy,and established a suitable index system for regional power grid risk evaluation. The practice proves that this system illustrates the statistical objects and calculation methods of the indexes, effectively solving the problems of the system,such as single mode and the lack of comprehensive statistics in the existing regional power grid risk evaluation methods. Meanwhile, the system enhances the level of regional power grid management, which enables its safe and stable operation. [ABSTRACT FROM AUTHOR]

The bulk hydropower transmission with one-fourth of its total capacity in China via UHVDC (ultra-high-voltage direct current) lines brings a new challenge for receiving power grids. There is an urgent need for coordinated operations about the receiving regional power grids which operate outside energy and their own large plants linked in the AC/DC network. A great operating concern is how to shave peak loads from subordinate provincial power grids using their load differences and various energy source characteristics. The East China Grid, a main receiver of Chinese hydropower transmission and the largest regional power grid, is taken as the example. An integrated framework for coordinating large-scale UHVDC hydropower and conventional hydro energies for power grid peak operation is presented. It divides the original problem into several subproblems according to the types of energy sources. An EPSA (exterior point search algorithm), a heuristic method, and a multi-step optimization algorithm are developed to respectively solve the UHVDC hydropower subproblem, the pumped-storage subproblem, and the conventional hydropower subproblem. These algorithms are integrated into an iterative solution procedure between all subproblems, wherein the EPSA is also applied to allocate power generation among provincial power grids. The proposed method is effective and efficient for coordinating large-scale UHVDC hydropower with conventional hydro energies and for fully responding to different peak loads among provinces, as demonstrated in two case studies. [ABSTRACT FROM AUTHOR]

The quantitative evaluation of cluster wind power output volatility and source-load timing matching is vital to the planning and operation of the future power system dominated by new energy. However, the existing volatility evaluation methods of cluster wind power output do not fully consider timing volatility, or are not suitable for small sample data scenarios. Meanwhile, the existing source-load timing matching evaluation indicator ignores the impact of wind power permeability on the timing matching degree between wind power output and load. Therefore, the authors propose quantitative evaluation methods of cluster wind power output volatility and source-load timing matching in regional power grid. Firstly, the volatility-based smoothing coefficient is defined to quantitatively evaluate the smoothing effect of wind-farm cluster power output. Then, the source-load timing matching coefficient considering wind power permeability is proposed to quantitatively evaluate the timing matching degree of regional wind power output and load, and the corresponding function model of volatility-based smoothing coefficient and source-load timing matching coefficient is established. Finally, the validity and applicability of the proposed methods are verified by MATLAB software based on the actual power output of 10 wind farms and actual grid load in a certain grid dispatching cross-section of northeast China. The results demonstrated that the proposed volatility-based smoothing coefficient can accurately represent the smoothing effect of wind farm cluster power output while maintaining the volatility continuity of wind power output time series and without affect from the data sample size. The source-load timing matching coefficient can accurately characterize the difference in the timing matching degree between wind power output and grid load under different wind power permeability and the influence degree on grid load. [ABSTRACT FROM AUTHOR]