Applied Sciences

Research

Jump to: Review

19 pages, 5082 KiB

Open AccessArticle

The Optimization of the Location and Capacity of Reactive Power Generation Units, Using a Hybrid Genetic Algorithm Incorporated by the Bus Impedance Power-Flow Calculation Method

by Insu Kim

Appl. Sci. 2020, 10(3), 1034; https://doi.org/10.3390/app10031034 - 4 Feb 2020

Cited by 11 | Viewed by 4859

Abstract

Dynamic and static reactive power resources have become an important means of maintaining the stability and reliability of power system networks. For example, if reactive power is not appropriately compensated for in transmission and distribution systems, the receiving end voltage may fall dramatically, [...] Read more.

Dynamic and static reactive power resources have become an important means of maintaining the stability and reliability of power system networks. For example, if reactive power is not appropriately compensated for in transmission and distribution systems, the receiving end voltage may fall dramatically, or the load voltage may increase to a level that trips protection devices. However, none of the previous optimal power-flow studies for reactive power generation (RPG) units have optimized the location and capacity of RPG units by the bus impedance matrix power-flow calculation method. Thus, this study proposes a genetic algorithm that optimizes the location and capacity of RPG units, which is implemented by MATLAB. In addition, this study enhances the algorithm by incorporating bus impedance power-flow calculation method into the algorithm. The proposed hybrid algorithm is shown to be valid when applied to well-known IEEE test systems. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

24 pages, 2068 KiB

Open AccessArticle

Two-Stage Optimal Scheduling of Large-Scale Renewable Energy System Considering the Uncertainty of Generation and Load

by Xiangyu Kong, Shuping Quan, Fangyuan Sun, Zhengguang Chen, Xingguo Wang and Zexin Zhou

Appl. Sci. 2020, 10(3), 971; https://doi.org/10.3390/app10030971 - 2 Feb 2020

Cited by 10 | Viewed by 2451

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Graphical abstract

16 pages, 2101 KiB

Open AccessArticle

Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model

by Jiayun Chu, Tingdi Zhao, Jian Jiao, Zhiwei Chen and Fuchun Ren

Appl. Sci. 2019, 9(24), 5528; https://doi.org/10.3390/app9245528 - 16 Dec 2019

Cited by 4 | Viewed by 2232

Abstract

Based on power adding technology, the linear transformer driver (LTD) scheme is widely used to generate high-energy pulsed outputs and adopts a hierarchical and modular structure. Although robust design and fault analysis for basic components have been conducted recently, there is still a [...] Read more.

Based on power adding technology, the linear transformer driver (LTD) scheme is widely used to generate high-energy pulsed outputs and adopts a hierarchical and modular structure. Although robust design and fault analysis for basic components have been conducted recently, there is still a lack of enough reliability analysis studies of the whole system. Taking an actual LTD system as an object, this paper presents a system reliability model based on a load-sharing mechanism. A unified load-sharing rule structure is established and four typical rules corresponding to equal, linear, exponential, and local-equal relationships are discussed in detail while evaluating the impact of the load-sharing mechanism. Subsequently, simulation experiments are performed to illustrate the effects of different load-sharing rules as well as analyzing the system reliability in which we simultaneously propose a self-adaptive Monte Carlo simulation flow to achieve the sampling probability adjustment according to the random failure sequence. The simulation results can serve as a suggestion for further improvement of the system reliability. Moreover, the model framework and the simulation analysis method described here are universal and can be applied to evaluate the reliability of other LTD-based systems with tiny modifications. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

22 pages, 3811 KiB

Open AccessArticle

Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

by Lei Fu, Tiantian Zhu, Guobing Pan, Sihan Chen, Qi Zhong and Yanding Wei

Appl. Sci. 2019, 9(22), 4901; https://doi.org/10.3390/app9224901 - 15 Nov 2019

Cited by 21 | Viewed by 2899

Abstract

Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main [...] Read more.

Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

15 pages, 3638 KiB

Open AccessFeature PaperArticle

Modelling and Stability Analysis of Wind Power Plants Connected to Weak Grids

by Esmaeil Ebrahimzadeh, Frede Blaabjerg, Torsten Lund, John Godsk Nielsen and Philip Carne Kjær

Appl. Sci. 2019, 9(21), 4695; https://doi.org/10.3390/app9214695 - 4 Nov 2019

Cited by 7 | Viewed by 2778

Abstract

It is important to develop modelling tools to predict unstable situations resulting from the interactions between the wind power plant and the weak power system. This paper presents a unified methodology to model and analyse a wind power plant connected to weak grids [...] Read more.

It is important to develop modelling tools to predict unstable situations resulting from the interactions between the wind power plant and the weak power system. This paper presents a unified methodology to model and analyse a wind power plant connected to weak grids in the frequency-domain by considering the dynamics of the phase lock loop (PLL) and controller delays, which have been neglected in most of the previous research into modelling of wind power plants to simplify modelling. The presented approach combines both dq and positive/negative sequence domain modelling, where a single wind turbine is modelled in the dq domain but the whole wind power plant connected to the weak grid is analysed in the positive/negative sequence domain. As the proposed modelling of the wind power plant is systematic and modular and based on the decoupled positive/negative sequence impedances, the application of the proposed methodology is relevant for transmission system operators (TSOs) to assess stability easily with a very low compactional burden. In addition, as the analytical dq impedance models of the single wind turbine are provided, the proposed methodology is an optimization design tool permitting wind turbine manufacturers to tune their converter control. As a case study, a 108 MW wind power plant connected to a weak grid was used to study its sensitivity to variations in network short-circuit level, X/R ratio and line series capacitor compensation (Xc/Xg). Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

21 pages, 8211 KiB

Open AccessArticle

A Typical Distributed Generation Scenario Reduction Method Based on an Improved Clustering Algorithm

by Sitong Lv, Jianguo Li, Yongxin Guo and Zhong Shi

Appl. Sci. 2019, 9(20), 4262; https://doi.org/10.3390/app9204262 - 11 Oct 2019

Cited by 11 | Viewed by 2240

Abstract

In recent years, distributed generation (DG) technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of distributed generation and to meet the challenges of [...] Read more.

In recent years, distributed generation (DG) technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of distributed generation and to meet the challenges of DG access to the power grid, the multi-scenario analysis method commonly used in DG optimal allocation method is studied in this paper. In order to solve the problems that may arise from using large-scale scenes in the planning of DG considering uncertainties by using multi-scene analysis method, the cluster analysis method suitable for large-scale scene reduction in scene reduction method is introduced firstly, and then an improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the reduction method is verified. Finally, the method mentioned in this paper is compared with other commonly used methods through IEEE-33 node system. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

15 pages, 3081 KiB

Open AccessArticle

A Data Segmentation-Based Ensemble Classification Method for Power System Transient Stability Status Prediction with Imbalanced Data

by Zhen Chen, Xiaoyan Han, Chengwei Fan, Zirun He, Xueneng Su and Shengwei Mei

Appl. Sci. 2019, 9(20), 4216; https://doi.org/10.3390/app9204216 - 10 Oct 2019

Cited by 7 | Viewed by 2289

Abstract

In recent years, machine learning methods have shown the great potential for real-time transient stability status prediction (TSSP) application. However, most existing studies overlook the imbalanced data problem in TSSP. To address this issue, a novel data segmentation-based ensemble classification (DSEC) method for [...] Read more.

In recent years, machine learning methods have shown the great potential for real-time transient stability status prediction (TSSP) application. However, most existing studies overlook the imbalanced data problem in TSSP. To address this issue, a novel data segmentation-based ensemble classification (DSEC) method for TSSP is proposed in this paper. Firstly, the effects of the imbalanced data problem on the decision boundary and classification performance of TSSP are investigated in detail. Then, a three-step DSEC method is presented. In the first step, the data segmentation strategy is utilized for dividing the stable samples into multiple non-overlapping stable subsets, ensuring that the samples in each stable subset are not more than the unstable ones, then each stable subset is combined with the unstable set into a training subset. For the second step, an AdaBoost classifier is built based on each training subset. In the final step, decision values from each AdaBoost classifier are aggregated for determining the transient stability status. The experiments are conducted on the Northeast Power Coordinating Council 140-bus system and the simulation results indicate that the proposed approach can significantly improve the classification performance of TSSP with imbalanced data. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

23 pages, 8761 KiB

Open AccessArticle

Probabilistic Energy Flow Calculation through the Nataf Transformation and Point Estimation

by Boyu Qin, Cheng Fang, Ke Ma and Jing Li

Appl. Sci. 2019, 9(16), 3291; https://doi.org/10.3390/app9163291 - 11 Aug 2019

Cited by 5 | Viewed by 3078

Abstract

With the increasing capacity of renewable energy sources, uncertainties regarding renewable energy and other dynamic loads in integrated energy systems (IESs) are increasing. Thus, it is necessary to study the probabilistic energy flow (PEF) of IESs. However, existing PEF calculation methods such as [...] Read more.

With the increasing capacity of renewable energy sources, uncertainties regarding renewable energy and other dynamic loads in integrated energy systems (IESs) are increasing. Thus, it is necessary to study the probabilistic energy flow (PEF) of IESs. However, existing PEF calculation methods such as the point estimate method (PEM) are computationally inefficient when there are many random variables and estimated points; moreover, relatively large errors can occur when the estimated points are outside their limits. Hence, this paper presents a calculation method that addresses these problems. Because there are correlations among the variables, the Nataf transformation is employed to control the correlation quickly and effectively. A model for an IES that is interconnected with natural gas and electricity systems and accounts for the uncertainties of wind plants, photovoltaic power plants, and dynamic gas loads is presented. Correlations between wind plants and photovoltaic power plants are handled using the Nataf transformation. Finally, a modified PEM is developed to solve the PEF. For situations in which the estimated points exceed their boundaries, the power transformation and equal constraint transformation methods are used. The results of time-domain simulations demonstrate the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

19 pages, 1761 KiB

Open AccessArticle

Demand Side Management Effects on Substation Transformer Capacity Limits

by Kerry D. McBee, Jacquelyn Chong and Prasanth Rudraraju

Appl. Sci. 2019, 9(16), 3266; https://doi.org/10.3390/app9163266 - 9 Aug 2019

Cited by 4 | Viewed by 3859

Abstract

In high penetrations, demand side management (DMS) applications augment a substation power transformer’s load profile, which can ultimately affect the unit’s capacity limits. Energy storage (ES) applications reduce the evening peaking demand, while time-of-use rates incentivize end-users to charge electric vehicles overnight. The [...] Read more.

In high penetrations, demand side management (DMS) applications augment a substation power transformer’s load profile, which can ultimately affect the unit’s capacity limits. Energy storage (ES) applications reduce the evening peaking demand, while time-of-use rates incentivize end-users to charge electric vehicles overnight. The daily load profile is further augmented by high penetrations of photovoltaic (PV) systems, which reduce the midday demand. The resulting load profile exhibits a more flattened characteristic when compared to the historical cyclic profile. Although the initial impact of PV and ES applications may reduce a unit’s peak demand, long-term system planning and emergency conditions may require operation near or above the nameplate rating. Researchers have already determined that a flattened load profile excessively ages a unit’s dielectrics more rapidly. The focus of this research was to identify an approach for establishing new transformer capacity limits for units serving flattened load profiles with a high harmonic content. The analysis utilizes IEEE standards C57.91 and C57.110 to develop an aging model of a 50 MVA SPX Waukesha transformer. The results establish a guideline for determining transformer capacity limits for normal operation, long-term emergency operation, and short-term emergency operation when serving systems with high penetrations of DSM applications. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

18 pages, 2957 KiB

Open AccessArticle

Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events

by Honghao Wu, Junyong Liu, Jichun Liu, Mingjian Cui, Xuan Liu and Hongjun Gao

Appl. Sci. 2019, 9(15), 3003; https://doi.org/10.3390/app9153003 - 26 Jul 2019

Cited by 13 | Viewed by 5182

Abstract

The cybersecurity of wind farms is an increasing concern in recent years, and its impacts on the power system reliability have not been fully studied. In this paper, the pressing issues of wind farms, including cybersecurity and wind power ramping events (WPRs) are [...] Read more.

The cybersecurity of wind farms is an increasing concern in recent years, and its impacts on the power system reliability have not been fully studied. In this paper, the pressing issues of wind farms, including cybersecurity and wind power ramping events (WPRs) are incorporated into a new reliability evaluation approach. Cyber–physical failures like the instantaneous failure and longtime fatigue of wind turbines are considered in the reliability evaluation. The tripping attack is modeled in a bilevel optimal power flow model which aims to maximize the load shedding on the system’s vulnerable moment. The time-varying failure rate of wind turbine is approximated by Weibull distribution which incorporates the service time and remaining life of wind turbine. Various system defense capacities and penetration rates of wind power are simulated on the typical reliability test system. The comparative and sensitive analyses show that power system reliability is challenged by the cybersecurity of wind farms, especially when the installed capacity of wind power continues to rise. The timely patching of network vulnerabilities and the life management of wind turbines are important measures to ensure the cyber–physical security of wind farms. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

18 pages, 3888 KiB

Open AccessArticle

Control Strategy Based on the Flexible Multi-State Switch for Maximum Consumption of Distributed Generations in Distribution Network

by Guopeng Zhao, Jiaxing Liu, Siyuan Liu, Xinwei Zhou, Jihong Li and Yi Lu

Appl. Sci. 2019, 9(14), 2871; https://doi.org/10.3390/app9142871 - 18 Jul 2019

Cited by 5 | Viewed by 2123

Abstract

With the aim of improving the consumption capacity of distributed generation (DG) in the distribution network, the control strategy of the flexible multi-state switch (FMSS) for the maximum consumption of distributed generation is proposed. The principle that FMSS improves the consumption capacity of [...] Read more.

With the aim of improving the consumption capacity of distributed generation (DG) in the distribution network, the control strategy of the flexible multi-state switch (FMSS) for the maximum consumption of distributed generation is proposed. The principle that FMSS improves the consumption capacity of distributed generation is analyzed and verified by simulation. The estimating method for the maximum accessible capacity of distributed generation at the access point is proposed. For complex systems, the multi-objective function for maximizing the consumption of distributed generation was established, and the analytic hierarchy process and entropy weight method were combined to obtain the weight factor. Then, combined with system constraints, the results can be obtained by optimization algorithms. Finally, the control strategy of FMSS for maximum consumption of distributed generation was realized and verified in a simulation. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

11 pages, 3814 KiB

Open AccessArticle

The Rapid Establishment of Large Wind Fields via an Inverse Process

by Shanxun Sun, Shi Liu and Guangchao Zhang

Appl. Sci. 2019, 9(14), 2847; https://doi.org/10.3390/app9142847 - 17 Jul 2019

Cited by 2 | Viewed by 2209

Abstract

Physical-approach-based wind forecasts have the merit of a heavily reduced uncertainty in predictions, but very often suffer from a prohibitively lengthy numerical computation time, if high spatial resolutions are required. To tackle this hurdle, proper orthogonal decomposition (POD) has manifested extraordinary power in [...] Read more.

Physical-approach-based wind forecasts have the merit of a heavily reduced uncertainty in predictions, but very often suffer from a prohibitively lengthy numerical computation time, if high spatial resolutions are required. To tackle this hurdle, proper orthogonal decomposition (POD) has manifested extraordinary power in reducing the number of computation grids and hence the computation time. However, POD itself suffers from difficulties in extracting basis vectors when the snapshots contain large amounts of data, when considering large areas using high spatial resolution. By means of computational simulations and inverse process analyses, in this study the authors developed a new method for rapid wind field reconstruction with high spatial resolution, while reducing the computation load to a minimum. The strategy is to establish snapshots of velocity fields in a large area, but only using a much smaller subset of the large area to extract the basis vectors. The basis vectors are then used to reconstruct the wind field of the large area with a high spatial resolution. The method can dramatically reduce the overall computation work due to the much smaller grid size in the subset area. The new method can be applied to situations where the velocity distributions for a large area need to be known with high spatial resolution. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

16 pages, 7891 KiB

Open AccessArticle

Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm

by Saijun Yuan, Zhiguo Hao, Tao Zhang, Xiaotian Yuan and Jin Shu

Appl. Sci. 2019, 9(14), 2831; https://doi.org/10.3390/app9142831 - 16 Jul 2019

Cited by 8 | Viewed by 2566

Abstract

Subsynchronous oscillation (SSO) is a critical issue for the direct-drive permanent magnet synchronous generator (D-PMSG) based wind farm integrated to a weak onshore AC grid. To analyze the mechanism of the SSO phenomenon of D-PMSG based wind farm, widely used impedance-based stability analysis [...] Read more.

Subsynchronous oscillation (SSO) is a critical issue for the direct-drive permanent magnet synchronous generator (D-PMSG) based wind farm integrated to a weak onshore AC grid. To analyze the mechanism of the SSO phenomenon of D-PMSG based wind farm, widely used impedance-based stability analysis method is utilized in this paper. First, the impedance model based on the harmonic linearization theory of grid-connected D-PMSG is proposed, and the mechanism of sub/supsynchronous currents coupling is analyzed quantitatively for the first time. Then, based on the impedance model and relative stability criterion, the influence of wind farm operating parameters and grid impedance on stability is discussed. Simulations are carried out to verify the correctness of theoretical analysis. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

24 pages, 4485 KiB

Open AccessArticle

Coordinated Secondary Frequency Regulation Strategy of Doubly-Fed Induction Generator and Electric Vehicle

by Qian Zhang, Yan Li, Chen Li, Zhuwei Ding and Wenrui Xie

Appl. Sci. 2019, 9(14), 2815; https://doi.org/10.3390/app9142815 - 14 Jul 2019

Cited by 5 | Viewed by 2489

Abstract

Wind turbines can participate in frequency regulation by controlling active power output, but the indeterminacy and volatility of wind power result in low reliability of frequency support. Therefore, as a kind of energy storage system, an electric vehicle is adopted to coordinate with [...] Read more.

Wind turbines can participate in frequency regulation by controlling active power output, but the indeterminacy and volatility of wind power result in low reliability of frequency support. Therefore, as a kind of energy storage system, an electric vehicle is adopted to coordinate with wind turbines to regulate system frequency considering its large-scale development. First, based on the reasonable division of wind speed regions and operation point selection of pitch angle, the de-loading strategy of doubly-fed induction generator for reserve capacity under continuously varying wind speed is proposed. Then, through the combination of rotor speed and pitch angle control, frequency regulation model of a doubly-fed induction generator in whole wind speed range is established. Finally, taking into account the driving demand of electric vehicle owners, through the real-time allocation of system frequency regulation task based on frequency regulation capacity, the coordinated control strategy of doubly-fed induction generator and electric vehicle cluster for secondary frequency regulation is put forward. The simulation results show that the coordinated frequency regulation strategy based on real-time allocation can suppress frequency deviation effectively, and the regulation effect is better than the situations of wind turbine coordinating with the conventional unit or coordinating with electric vehicle cluster based on fixed allocation ratio. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

15 pages, 7078 KiB

Open AccessArticle

An Islanding Signal-Based Smooth Transition Control in AC/DC Hybrid Micro-Grids

by Zhenghong Chen, Tao Zheng and Chang Liu

Appl. Sci. 2019, 9(14), 2804; https://doi.org/10.3390/app9142804 - 12 Jul 2019

Cited by 2 | Viewed by 2216

Abstract

Smooth transition is one of the most important issues of micro-grids. The resulting transition is much impacted by the state step of the regulator. To suppress this mutation, this paper proposes a smooth transition control based on an islanding signal, which updates the [...] Read more.

Smooth transition is one of the most important issues of micro-grids. The resulting transition is much impacted by the state step of the regulator. To suppress this mutation, this paper proposes a smooth transition control based on an islanding signal, which updates the state of the regulators by detecting the change of islanding signal. Pre-synchronization control is applied during the transition from islanding mode to grid-connected mode. In comparison, the proposed approach is superior over direct transition control and state follower-based transition control, with both easier regulator parameter configuration and better performance during transition time. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Graphical abstract

15 pages, 2449 KiB

Open AccessArticle

Development of a Kernel Extreme Learning Machine Model for Capacity Selection of Distributed Generation Considering the Characteristics of Electric Vehicles

by Zhongdong Yin, Jingjing Tu and Yonghai Xu

Appl. Sci. 2019, 9(12), 2401; https://doi.org/10.3390/app9122401 - 13 Jun 2019

Cited by 7 | Viewed by 2376

Abstract

The large-scale access of distributed generation (DG) and the continuous increase in the demand of electric vehicle (EV) charging will result in fundamental changes in the planning and operating characteristics of the distribution network. Therefore, studying the capacity selection of the distributed generation, [...] Read more.

The large-scale access of distributed generation (DG) and the continuous increase in the demand of electric vehicle (EV) charging will result in fundamental changes in the planning and operating characteristics of the distribution network. Therefore, studying the capacity selection of the distributed generation, such as wind and photovoltaic (PV), and considering the charging characteristic of electric vehicles, is of great significance to the stability and economic operation of the distribution network. By using the network node voltage, the distributed generation output and the electric vehicles’ charging power as training data, we propose a capacity selection model based on the kernel extreme learning machine (KELM). The model accuracy is evaluated by using the root mean square error (RMSE). The stability of the network is evaluated by voltage stability evaluation index (Ivse). The IEEE33 node distributed system is used as simulation example, and gives results calculated by the kernel extreme learning machine that satisfy the minimum network loss and total investment cost. Finally, the results are compared with support vector machine (SVM), particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to verify the feasibility and effectiveness of the proposed model and method. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

19 pages, 4202 KiB

Open AccessArticle

Cluster Control for EVs Participating in Grid Frequency Regulation by Using Virtual Synchronous Machine with Optimized Parameters

by Dongqi Liu

Appl. Sci. 2019, 9(9), 1924; https://doi.org/10.3390/app9091924 - 10 May 2019

Cited by 10 | Viewed by 2509

Abstract

In this paper, a control method for electric vehicles (EVs) participating in grid Frequency regulation is proposed. Firstly, considering dispatching large-scale electric vehicles, the K-means clustering algorithm is applied to cluster EVs with different battery state of charge and with different average vehicle [...] Read more.

In this paper, a control method for electric vehicles (EVs) participating in grid Frequency regulation is proposed. Firstly, considering dispatching large-scale electric vehicles, the K-means clustering algorithm is applied to cluster EVs with different battery state of charge and with different average vehicle daily travel miles. Then, for each class of electric vehicle group, a multi-objective optimization model considering reducing power imbalance and feeding the driving power demand for electric vehicles is proposed. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to solve the optimization model and obtain the best control parameters for “virtual synchronous machine”, which is functioned as the power controller between EVs and the power grid. At last, based on a Monte Carlo sampling, the simulation analysis of 50 EVs with the normal distribution of battery state of charge and average vehicle daily travel miles is carried out by using the proposed method. The results show that the proposed method can effectively classify the electric vehicles with different battery state of charge and different average vehicle daily travel miles. The parameters of the power converter controller for different classes of electric vehicles are optimized considering power grid frequency, their battery state of charge and their average daily travel miles, so as to maintain the balance of power grid frequency, and to meet the power needs of EV daily drive. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

18 pages, 1546 KiB

Open AccessArticle

Research on Robust Day-Ahead Dispatch Considering Primary Frequency Response of Wind Turbine

by Rong Sun, Bing Chen, Zhenhua Lv, Jianchun Mei, Haixiang Zang, Zhinong Wei and Guoqiang Sun

Appl. Sci. 2019, 9(9), 1784; https://doi.org/10.3390/app9091784 - 29 Apr 2019

Cited by 2 | Viewed by 2070

Abstract

With the large-scale integration of renewable energy sources (e.g., wind power), the system inertial response and primary frequency regulation are affected. The virtual synchronous generator technology, which makes it possible for wind power units to adjust the frequency, provides a new way of [...] Read more.

With the large-scale integration of renewable energy sources (e.g., wind power), the system inertial response and primary frequency regulation are affected. The virtual synchronous generator technology, which makes it possible for wind power units to adjust the frequency, provides a new way of approaching this problem. In this paper, we set up the related constraints that fit the optimal dispatch framework with a primary reserve representing the primary frequency response between the conventional synchronous generator and the wind power units, and the technical parameters of the virtual synchronous generator. Meanwhile, we use the robust day-ahead optimization dispatch model considering the wind power integrated primary frequency control in the real-time operation with the scenario of the wind power output showing the uncertainty of wind power. Based on the model, we identify the key set using an iterative method and obtain the maximum power loss. Through the proposed model, we could provide the day-ahead unit output and real-time primary reserve, thus ensuring the reliable operation of the system. Finally, the effectiveness of the proposed method is verified by a computational experiment. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

15 pages, 1186 KiB

Open AccessArticle

A Bilateral Tradeoff Decision Model for Wind Power Utilization with Extensive Load Scheduling

by Qingshan Xu, Yajuan Lv, Dong Wang and Pengwei Du

Appl. Sci. 2019, 9(9), 1777; https://doi.org/10.3390/app9091777 - 29 Apr 2019

Cited by 2 | Viewed by 2253

Abstract

In this paper, we present the extensive load scheduling problem with intermittent and uncertain wind power availability. A chance-constrained bilateral tradeoff decision model is established to solve the problem. Our model aims at maximizing the wind power utilization and minimizing the system operation [...] Read more.

In this paper, we present the extensive load scheduling problem with intermittent and uncertain wind power availability. A chance-constrained bilateral tradeoff decision model is established to solve the problem. Our model aims at maximizing the wind power utilization and minimizing the system operation cost simultaneously by means of responsive loads, which are precisely divided into shiftable loads and high-energy loads. The chance constraint is applied to restrict the system imbalance with a small probability. Then, a revised sample average approximation (SAA) algorithm is developed to transform the chance constraint into sample average reformulations. Furthermore, the multi-objective differential evolution (MODE) method combined with SAA is proposed to solve the problem. Experiments enabling an effectiveness analysis of the two kinds of responsive loads are performed on the power system in Yancheng. The research of parameters of MODE, the sensitivity of different risk levels and the influence of iteration numbers are discussed. Finally, computational results prove that the combination of shiftable loads and high-energy loads have a better effect than adopting shiftable loads and high-energy loads separately, and the proposed method is convergent and valid in solving the problem. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

17 pages, 825 KiB

Open AccessArticle

Unit Commitment Accommodating Large Scale Green Power

by Yuntao Ju, Jiankai Wang, Fuchao Ge, Yi Lin, Mingyu Dong, Dezhi Li, Kun Shi and Haibo Zhang

Appl. Sci. 2019, 9(8), 1611; https://doi.org/10.3390/app9081611 - 18 Apr 2019

Cited by 13 | Viewed by 3085

Abstract

As more clean energy sources contribute to the electrical grid, the stress on generation scheduling for peak-shaving increases. This is a concern in several provinces of China that have many nuclear power plants, such as Guangdong and Fujian. Studies on the unit commitment [...] Read more.

As more clean energy sources contribute to the electrical grid, the stress on generation scheduling for peak-shaving increases. This is a concern in several provinces of China that have many nuclear power plants, such as Guangdong and Fujian. Studies on the unit commitment (UC) problem involving the characteristics of both wind and nuclear generation are urgently needed. This paper first describes a model of nuclear power and wind power for the UC problem, and then establishes an objective function for the total cost of nuclear and thermal power units, including the cost of fuel, start-stop and peak-shaving. The operating constraints of multiple generation unit types, the security constraints of the transmission line, and the influence of non-gauss wind power uncertainty on the spinning reserve capacity of the system are considered. Meanwhile, a model of an energy storage system (ESS) is introduced to smooth the wind power uncertainty. Due to the prediction error of wind power, the spinning reserve capacity of the system will be affected by the uncertainty. Over-provisioning of spinning reserve capacity is avoided by introducing chance constraints. This is followed by the design of a UC model applied to different power sources, such as nuclear power, thermal power, uncertain wind power, and ESS. Finally, the feasibility of the UC model in the scheduling of a multi-type generation unit is verified by the modified IEEE RTS 24-bus system accommodating large scale green generation units. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

17 pages, 3043 KiB

Open AccessArticle

Dynamic Coordinated Active–Reactive Power Optimization for Active Distribution Network with Energy Storage Systems

by Lingling Wang, Xu Wang, Chuanwen Jiang, Shuo Yin and Meng Yang

Appl. Sci. 2019, 9(6), 1129; https://doi.org/10.3390/app9061129 - 18 Mar 2019

Cited by 10 | Viewed by 3392

Abstract

This paper proposes a coordinated active–reactive power optimization model for an active distribution network with energy storage systems, where the active and reactive resources are handled simultaneously. The model aims to minimize the power losses, the operation cost, and the voltage deviation of [...] Read more.

This paper proposes a coordinated active–reactive power optimization model for an active distribution network with energy storage systems, where the active and reactive resources are handled simultaneously. The model aims to minimize the power losses, the operation cost, and the voltage deviation of the distribution network. In particular, the reactive power capabilities of distributed generators and energy storage systems are fully utilized to minimize power losses and improve voltage profiles. The uncertainties pertaining to the forecasted values of renewable energy sources are modelled by scenario-based stochastic programming. The second-order cone programming relaxation method is used to deal with the nonlinear power flow constraints and transform the original mixed integer nonlinear programming problem into a tractable mixed integer second-order cone programming model, thus the difficulty of problem solving is significantly reduced. The 33-bus and 69-bus distribution networks are used to demonstrate the effectiveness of the proposed approach. Simulation results show that the proposed coordinated optimization approach helps improve the economic operation for active distribution network while improving the system security significantly. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

16 pages, 5188 KiB

Open AccessArticle

Multi-Resonant-Based Sliding Mode Control of DFIG-Based Wind System under Unbalanced and Harmonic Network Conditions

by Yu Quan, Lijun Hang, Yuanbin He and Yao Zhang

Appl. Sci. 2019, 9(6), 1124; https://doi.org/10.3390/app9061124 - 17 Mar 2019

Cited by 48 | Viewed by 2766

Abstract

In general, the integral sliding mode control (ISMC) with an integral sliding surface would lead to tracking errors under unbalanced and harmonic grid voltage conditions. In order to eliminate tracking errors under these conditions, multi-resonant items are added to the conventional integral sliding [...] Read more.

In general, the integral sliding mode control (ISMC) with an integral sliding surface would lead to tracking errors under unbalanced and harmonic grid voltage conditions. In order to eliminate tracking errors under these conditions, multi-resonant items are added to the conventional integral sliding surface in the proposed strategy, which can be called multi-resonant-based sliding mode control (MRSMC). A comparison of tracking precision on the ISMC and MRSMC is analyzed. In order to regulate the system powers directly, the errors of instantaneous active and reactive powers are selected as the state variables. Finally, the output current harmonics and a majority of the doubly-fed induction generator’s (DFIG) electromagnetic torque pulsations can be removed under unbalanced and harmonic grid voltage conditions. Simulation and experimental results are presented to verify the correctness and effectiveness of the proposed strategy. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

22 pages, 4921 KiB

Open AccessArticle

Coordination of Power-System Stabilizers and Battery Energy-Storage System Controllers to Improve Probabilistic Small-Signal Stability Considering Integration of Renewable-Energy Resources

by Samundra Gurung, Sumate Naetiladdanon and Anawach Sangswang

Appl. Sci. 2019, 9(6), 1109; https://doi.org/10.3390/app9061109 - 15 Mar 2019

Cited by 19 | Viewed by 5130

Abstract

This paper proposes a probabilistic method to obtain optimized parameter values for different power-system controllers, such as power-system stabilizers (PSSs) and battery energy-storage systems (BESSs) to improve probabilistic small-signal stability (PSSS) considering stochastic output power due to wind- and solar-power integration. The proposed [...] Read more.

This paper proposes a probabilistic method to obtain optimized parameter values for different power-system controllers, such as power-system stabilizers (PSSs) and battery energy-storage systems (BESSs) to improve probabilistic small-signal stability (PSSS) considering stochastic output power due to wind- and solar-power integration. The proposed tuning method is based on a combination of an analytical method that assesses the small-signal-stability margin, and an optimization technique that utilizes this statistical information to optimally tune power-system controllers. The optimization problem is solved using a metaheuristic technique known as the firefly algorithm. Power-system stabilizers, as well as sodium–sulfur (NaS)-based BESS controllers with power-oscillation dampers (termed as BESS controllers) are modeled in detail for this purpose in DIGSILENT. The results show that the sole use of PSSs and BESS controllers is insufficient to improve dynamic stability under fluctuating input power due to the integration of renewable-energy resources. However, the proposed strategy of using BESS and PSS controllers in a coordinated manner is highly successful in enhancing PSSS under renewable-energy-resource integration and under different critical conditions. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Graphical abstract

17 pages, 1148 KiB

Open AccessArticle

A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination

by Shaopeng Zhai, Zhihua Wang, Jia Cao and Guangyu He

Appl. Sci. 2019, 9(5), 829; https://doi.org/10.3390/app9050829 - 26 Feb 2019

Cited by 1 | Viewed by 2872

Abstract

Multi-objective unit commitment (MOUC) considers concurrently both economic and environmental objectives, then finds the best trade-off with respect to these objectives. This paper proposes a novel model for MOUC, and a decomposition coordination approach is presented to solve the model. The economic objective [...] Read more.

Multi-objective unit commitment (MOUC) considers concurrently both economic and environmental objectives, then finds the best trade-off with respect to these objectives. This paper proposes a novel model for MOUC, and a decomposition coordination approach is presented to solve the model. The economic objective is to reduce the fuel cost while the environmental objective is to reduce the CO

_{2}

emission. The MOUC model considers these objectives by minimizing the distance to the Utopian point, which avoids generating Pareto optimal solutions. The model is solved by a decomposition coordination approach, which decomposes the whole system into subsystems and performs an iterative process. During each iteration step, the tie-line power flow is updated based on the margin price in connected subsystems, then, each subsystem is solved by branch and bound method, and the result is improved during iterations as shown in case studies. Besides, as the process does not require uploading units parameters, it protects the privacy of generating companies. Numerical case studies conducted using the proposed multi-objective model are applied to illustrate the performance of the approach. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

15 pages, 3936 KiB

Open AccessArticle

Comparative Study of Wind Turbine Placement Methods for Flat Wind Farm Layout Optimization with Irregular Boundary

by Longyan Wang

Appl. Sci. 2019, 9(4), 639; https://doi.org/10.3390/app9040639 - 14 Feb 2019

Cited by 13 | Viewed by 3176

Abstract

For the exploitation of wind energy, planning/designing a wind farm plays a crucial role in the development of wind farm project, which must be implemented at an early stage, and has a vast influence on the stages of operation and control for wind [...] Read more.

For the exploitation of wind energy, planning/designing a wind farm plays a crucial role in the development of wind farm project, which must be implemented at an early stage, and has a vast influence on the stages of operation and control for wind farm development. As a step of the wind farm planning/designing, optimizing the wind turbine placements is an effective tool in increasing the power production of a wind farm leading to an increased financial return. In this paper, the optimization of an offshore wind farm with an irregular boundary is carried out to investigate the effectiveness of grid and coordinate wind farm design methods. In the study of the grid method, the effect of grid density on the layout optimization results is explored with 20 × 30 and 40 × 60 grid cells, and the means of coping with the irregular wind farm boundary using different wind farm design methods are developed in this paper. The results show that, depending on the number of installed wind turbines, a power output increase from 1% to 1.5% is achieved by increasing the grid density from 20 × 30 to 40 × 60. However, the computational time is more than doubled, rising from 23 h to 47 h with 40 wind turbines being optimized from the coarse grid cells to the densified grid cells. In comparison, the coordinate method is the best option for achieving the largest power increase of 1.5% to 2% (relative to the coarse 20 × 30 grid method), while the least computational time (21 h with 40 wind turbines optimized) is spent. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

16 pages, 3283 KiB

Open AccessFeature PaperArticle

Efficiency Comparison of AC and DC Distribution Networks for Modern Residential Localities

by Hasan Erteza Gelani, Faizan Dastgeer, Kiran Siraj, Mashood Nasir, Kamran Ali Khan Niazi and Yongheng Yang

Appl. Sci. 2019, 9(3), 582; https://doi.org/10.3390/app9030582 - 11 Feb 2019

Cited by 44 | Viewed by 5619

Abstract

The paper investigates the system efficiency for power distribution in residential localities considering daily load variations. Relevant system modeling is presented. A mathematical model is devised, which is based on the data from the Energy Information Administration (EIA), USA, for analysis. The results [...] Read more.

The paper investigates the system efficiency for power distribution in residential localities considering daily load variations. Relevant system modeling is presented. A mathematical model is devised, which is based on the data from the Energy Information Administration (EIA), USA, for analysis. The results reveal that the DC distribution system can present an equivalent or even better efficiency compared to the AC distribution network with an efficiency advantage of 2.3%, averaged over a day. Furthermore, the distribution systems are compared under various capacities of solar PV accounting for the effect of variation in solar irradiation over time. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

13 pages, 2666 KiB

Open AccessArticle

Analyzing the Impact of Variability and Uncertainty on Power System Flexibility

by Chang-Gi Min

Appl. Sci. 2019, 9(3), 561; https://doi.org/10.3390/app9030561 - 8 Feb 2019

Cited by 10 | Viewed by 4156

Abstract

This study investigates the impact of variability and uncertainty on the flexibility of a power system. The variability and uncertainty make it harder to maintain the balance between load and generation. However, most existing studies on flexibility evaluation have not distinguished between the [...] Read more.

This study investigates the impact of variability and uncertainty on the flexibility of a power system. The variability and uncertainty make it harder to maintain the balance between load and generation. However, most existing studies on flexibility evaluation have not distinguished between the effects of variability and uncertainty. The countermeasures to address variability and uncertainty differ; thus, applying strategies individually tailored to variability and uncertainty is helpful for more efficient operation and planning of a power system. The first contribution of this study is in separating the variability and uncertainty, and determining which is more influential in terms of flexibility in specific system situations. A flexibility index, named the ramping capability shortage probability (RSP), is used to quantify the extent to which the variability and uncertainty affect the flexibility. The second contribution is to generate various scenarios for variability and uncertainty based on a modified IEEE-RTS-96, to evaluate the flexibility. The penetration level of renewable energy resources is kept the same in each scenario. The results of a sensitivity analysis show that variability is more effective than uncertainty for high and medium net loads. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

9 pages, 345 KiB

Open AccessArticle

The Cascade Control of Natural Gas Pipeline Systems

by Kai Wen, Jing Gong and Yan Wu

Appl. Sci. 2019, 9(3), 481; https://doi.org/10.3390/app9030481 - 30 Jan 2019

Cited by 8 | Viewed by 4664

Abstract

With the boost of natural gas consumption, an automatic gas pipeline scheduling method is required to replace the dispatchers in decision making. Since the state space model is the fundamental work of modern control theory, it is possible that the classical controller synthesis [...] Read more.

With the boost of natural gas consumption, an automatic gas pipeline scheduling method is required to replace the dispatchers in decision making. Since the state space model is the fundamental work of modern control theory, it is possible that the classical controller synthesis method can be used for the complicated gas pipeline controller design. In this paper, a cascade control algorithm is proposed based on the state space model that is used for the transient flow simulation of the natural gas pipelines. A linear quadratic regulator is designed following the classical optimal control theory. Finally, the transient process with different control methods shows the effectiveness of the cascade control using information of the entire pipeline. According to the hardware configuration of natural gas pipelines, automatic scheduling process is ready to deploy as one step to the intelligent natural gas pipelines. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

14 pages, 6555 KiB

Open AccessArticle

Distributed Control Strategy for Smart Home Appliances Considering the Discrete Response Characteristics of the On/Off Loads

by De-Yin Kong, Yu-Qing Bao, Ying-Yi Hong, Bei-Bei Wang, Hong-Bin Huang, Ling Liu and Huan-Huan Jiang

Appl. Sci. 2019, 9(3), 457; https://doi.org/10.3390/app9030457 - 29 Jan 2019

Cited by 6 | Viewed by 2819

Abstract

With the development of smart home technology, more and more electrical appliances can participate in demand response, providing support for active power balance of the power grid. However, the conventional centralized control method faces vast amounts of electrical appliances, resulting in problems such [...] Read more.

With the development of smart home technology, more and more electrical appliances can participate in demand response, providing support for active power balance of the power grid. However, the conventional centralized control method faces vast amounts of electrical appliances, resulting in problems such as communication congestion and dimension curse. This paper proposes a distributed control strategy for electrical appliances based on a multi-agent consensus algorithm. Considering the discrete response characteristics of the on/off loads, a priority ranking mechanism is established, and the customer cost function is established by a fitting method. Based on the incremental cost consensus (ICC) algorithm, the optimal power allocation of customers is realized through distributed control. Simulation and analysis of the examples verify the effectiveness of the proposed strategy. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

21 pages, 2029 KiB

Open AccessArticle

Capacity Credit Evaluation of Correlated Wind Resources Using Vine Copula and Improved Importance Sampling

by Jilin Cai, Qingshan Xu, Minjian Cao and Yongbiao Yang

Appl. Sci. 2019, 9(1), 199; https://doi.org/10.3390/app9010199 - 8 Jan 2019

Cited by 5 | Viewed by 3615

Abstract

This paper concentrates on the capacity credit (CC) evaluation of wind energy, where a new method for constructing the joint distribution of wind speed and load is proposed. The method is based on the skew-normal mixture model (SNMM) and D-vine copulas, which is [...] Read more.

This paper concentrates on the capacity credit (CC) evaluation of wind energy, where a new method for constructing the joint distribution of wind speed and load is proposed. The method is based on the skew-normal mixture model (SNMM) and D-vine copulas, which is used to model the marginal distribution and the correlation structure, respectively. Then a cross entropy based importance sampling (CE-IS) is improved to enhance the efficiency of the power system reliability assessment, which is a crucial part of the CC evaluation. After that, the proposed methods are adopted to combine with the secant method to develop a complete algorithm to calculate the CC of wind energy. Numerical tests are designed and carried out based on the IEEE-RTS 79 system and wind speed data obtained from four wind farms in Northwest China. In order to show the superiority of SNMM and D-vine copula, the goodness-of-fit is quantified by different statistics. Besides, the improved CE-IS method is validated by comparison with Monte Carlo sampling (MCS) and traditional CE-IS in the efficiency of reliability assessment. Finally, the proved methods are combined with the secant method to calculate the CC of four wind farms, which can provide information for wind farm planning. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

17 pages, 2730 KiB

Open AccessArticle

Optimal Configuration of Different Energy Storage Batteries for Providing Auxiliary Service and Economic Revenue

by Lijuan Chen, Tiantian Wu and Xiaohui Xu

Appl. Sci. 2018, 8(12), 2633; https://doi.org/10.3390/app8122633 - 15 Dec 2018

Cited by 10 | Viewed by 3894

Abstract

Energy storage providing auxiliary service at the user-side has broad prospects in support of national polices. Three auxiliary services are selected as the application scene for energy storage participating in demand management, peak shaving and demand response. Considering the time value of funds, [...] Read more.

Energy storage providing auxiliary service at the user-side has broad prospects in support of national polices. Three auxiliary services are selected as the application scene for energy storage participating in demand management, peak shaving and demand response. Considering the time value of funds, the user-side energy storage economy model is built. The model comprehensively considers the delayed transformation income, the government subsidy income, the auxiliary service income and the whole-life-cycle cost factor. According to the cost and benefit analysis, an energy storage optimization configuration model is proposed. The model takes maximum revenue of industrial user in energy storage’s whole-life-cycle as the objective function. Then, the Cplex solver is employed to solve the model. In addition, four indexes are utilized to evaluate the financial effect brought by the user-side energy storage. Finally, the revenue and configuration results of the four types of battery energy storage are calculated to verify the validity of the proposed model. In comparison to the value of evaluation index, planning suggestions are provided for the user-side energy storage providing different auxiliary services. Moreover, the conditions of profit and worthwhile investment are obtained through sensitivity analysis of energy storage providing peak shaving service. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

Review

Jump to: Research

19 pages, 8978 KiB

Open AccessReview

A Comprehensive Survey of Accurate and Efficient Aggregation Modeling for High Penetration of Large-Scale Wind Farms in Smart Grid

by Fang Liu, Junjie Ma, Wendan Zhang and Min Wu

Appl. Sci. 2019, 9(4), 769; https://doi.org/10.3390/app9040769 - 22 Feb 2019

Cited by 12 | Viewed by 3849

Abstract

As one of the important renewable energies, wind power has been exploited worldwide. Modeling plays an important role in the high penetration of wind farms in smart grids. Aggregation modeling, whose benefits include low computational complexity and high computing speed, is widely used [...] Read more.

As one of the important renewable energies, wind power has been exploited worldwide. Modeling plays an important role in the high penetration of wind farms in smart grids. Aggregation modeling, whose benefits include low computational complexity and high computing speed, is widely used in wind farm modeling and simulation. To contribute to the development of wind power generation, a comprehensive survey of the aggregation modeling of wind farms is given in this article. A wind farm aggregation model consists of three parts, respectively, the wind speed model, the wind turbine generator (WTG) model, and the WTG transmission system model. Different modeling and aggregation methods, principles, and formulas for the above three parts are introduced. First, the features and emphasis of different wind speed models are discussed. Then, the aggregated wind turbine generator (WTG) models are divided into single WTG and multi-WTG aggregation models, considering the aggregation of wind turbines and generators, respectively. The calculation methods for the wind conditions and parameters of different aggregation models are discussed. Finally, the WTG transmission model of the wind farm from the aggregation bus is introduced. Some research directions are highlighted in the end according to the issues related to the aggregation modeling of wind farms in smart grids. Full article

(This article belongs to the Special Issue Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy

Share This Special Issue

Special Issue Editors

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (32 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI