Multi-Stage Statistical Approach to Wind Power Forecast Errors Evaluation: A Southern Sulawesi Case Study

In this study, wind power forecasting is performed for a Wind Power Plant (WPP) with an installed capacity of 135 MW in Turkey. The ruggedness index (RIX) of the terrain where WPP was installed is analyzed with Wind Atlas Analysis and Application Program (WAsP). According to the obtained RIX value, the terrain of WPP is found to be complex. Due to the complexity of the terrain, wind power forecasting becomes difficult. To deal with this problem, a forecasting method with fast, accurate, and high performance is needed. Therefore, Extreme Learning Machine (ELM) based method is proposed for wind power forecasting in this study. Electrical and meteorological measurements are obtained from WPP for the application of the proposed method. These measurements are provided with high quality measuring devices. Also, Global Positioning System (GPS) time synchronization is used to prevent lags between measurements. The wind speed, wind direction, and wind power data of 1-year period are obtained from WPP. These data are used to compare the proposed method with a classical Artificial Neural Network (ANN) based method in terms of two, three and four hours-ahead wind power forecast performances. In the forecast studies performed for all data related to 2, 3, and 4-hours ahead, Normalized Root Mean Square Error (NRMSE) values of ELM are obtained as 7.01, 10.12, and 12.06, respectively, while these values are found as 8.19, 12.18, and 13.09 for ANN. In addition, the values of Correlation Coefficients (R) of the proposed forecast method results regarding 2, 3, and 4-hours ahead are 0.96588, 0.93528, and 0.88984, respectively. The R values related to ANN are observed as 0.95421, 0.91373, and 0.87576, respectively. According to the obtained results, it is observed that ELM has better performance features than classic method under all forecast conditions and it is clearly seen that ELM has by far short training time than other one.

Accurate forecasting is the key factor in promoting wind power consumption and improving the stable operation of power systems. A short-term wind power forecasting (WPF) and uncertainty analysis method based on whale optimization algorithm (WOA), least squares support vector machine (LSSVM), and nonparametric kernel density estimation (NPKDE) was proposed in this paper. The advantages of WOA (fast convergence speed and high convergence accuracy) were used to optimize the penalty factor and kernel function width of the LSSVM model, and the calculation speed and forecasting accuracy of the LSSVM model were improved. The training sample set is classified according to the wind speed interval, and the WOA-LSSVM forecasting model is trained by subclass after classification to further improve the accuracy of short-term WPF. The NPKDE method is used to accurately calculate the probability density distribution characteristics of the forecasting error of wind power, and the confidence interval of the WPF is accurately calculated based on the probability density distribution characteristics. The calculation results show that the forecasting accuracy of the WOA-LSSVM model is higher than those of the LSSVM, long short-term memory (LSTM), and particle swarm optimization and least squares support vector machine (PSO-LSSVM) models, and the forecasting accuracy of the WOA-LSSVM model can be further improved after classifying the training sample set. The coverage of the confidence intervals in different time scales is higher than the corresponding confidence level, indicating that the NPKDE method can accurately describe the probability density distribution characteristics of the WPF errors.

A new intelligent method based on combination of VMD and ELM for short term wind power forecasting

Precise wind power forecasting has several benefits, such as optimized peak regulation in power systems, enhanced safety analysis, and improved energy efficiency. Considering the substantial influence of meteorological data, such as wind speed and temperature, on wind power generation, and to minimize the impact of fluctuations and complexity of wind power data on the forecast results, this paper proposes a combined wind power forecasting method. This approach is based on the long short-term memory network (LSTM) model, using the maximal information coefficient (MIC) method to select numerical weather prediction (NWP) and combining the efficiency of complete EEMD with the adaptive noise (CEEMDAN) method for nonlinear signal decomposition. Results indicate that the accuracy of the forecast results is supported by NWP. Moreover, wind power data are decomposed by the CEEMDAN algorithm and converted into relatively regular sub-sequences with small fluctuations. The MIC algorithm effectively reduces the redundant information in NWP data, and the LSTM algorithm addresses the uncertainty of wind power data. Finally, the wind power of multiple wind farms is forecasted. Comparison of the forecast results of different methods revealed that the NWP-CEEMDAN-LSTM method proposed in this paper, which considers feature extraction using MIC, effectively tracks power fluctuations and improves forecast performance, thereby reducing the forecast error of wind power.

The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

Energy storage scheduling design on friendly grid wind power

Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution’s complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas.

In Europe an increasing share of wind power is sold on the power market. Therefore more and more wind power generators become balancing responsible and face imbalance cost that reduce revenues from selling wind power. A comparison of literature illustrates that the imbalance cost of wind power varies in a wide range. To explain differences we indentify parameters influencing imbalance cost and compare them for case studies in Austria, Denmark and Poland. Besides the wind power forecast error also the correlation between imbalance and imbalance price influences imbalance cost significantly. Especially in systems with significant wind shares, an assessment of imbalance cost based on the amount of imbalance and average System Sell and System Buy Prices is therefore not reliable. While imbalance cost rather reflects cash flows within the clearing of imbalances, the presented concept of cost of imperfect forecast is better suited to reflect real cost incurred due to inaccurate wind power forecasts.

Small-signal stability analysis of power system oscillations is a well-established field within power system analysis, but not much attention has yet been paid to systems with a high penetration of wind turbines (WTs) and with large wind power plants (WPPs). A comprehensive analysis is presented, which assesses the impact of full-load converter interfaced WTs on power system small-signal stability. The study is based on a seven-generator network with lightly damped inter-area modes. A detailed WT model with all grid relevant control functions is used in the study. The WT is, furthermore, equipped with a park-level WPP voltage controller and comparisons are presented. The WT model for this work is a validated dynamic model of the 3.6 MW Siemens wind power WT. The study is based on modal analysis which is complemented with the time-domain simulations on the non-linear system.

The fluctuation and uncertainty of wind power generation bring severe challenges to secure and economic operation of power systems. Because wind power forecasting error is unavoidable, probabilistic forecasting becomes critical to accurately quantifying the uncertainty involved in traditional point forecasts of wind power and to providing meaningful information to conduct risk management in power system operation. This paper proposes a novel direct quantile regression approach to efficiently generate nonparametric probabilistic forecasting of wind power generation combining extreme learning machine and quantile regression. Quantiles with different proportions can be directly produced via an innovatively formulated linear programming optimization model, without dependency on point forecasts. Multistep probabilistic forecasting of 10-min wind power is newly carried out based on real wind farm data from Bornholm Island in Denmark. The superiority of the proposed approach is verified through comparisons with other well-established benchmarks. The proposed approach forms a new artificial neural network-based nonparametric forecasting framework for wind power with high efficiency, reliability, and flexibility, which can be beneficial to various decision-making activities in power systems.

According to the characteristics of double fed asynchronous generator rotor energy flow, research and design based on the DSP control with two-way flow of energy function of Dual PWM converter and discusses the grid side converter control method of Dual PWM converter in particular. The wind turbine simulation model is established by using the electromagnetic transient PSCAD/EMTDC software. The simulation system is used to simulate the P, Q decoupling control and maximum wind energy tracking control of the generator. Wind is a natural phenomenon generated by the solar radiation heat, the earth's surface caused by uneven heating of the atmosphere. It is estimated that only 1% of the wind on the ground will be able to meet the needs of the world's energy. With the continuous improvement of the level of wind power generation technology, the cost of wind power gradually reduced, considering environmental benefits, the cost will be close to the cost of coal-fired power generation. Therefore, great efforts to develop wind energy as the representative of the renewable energy and new energy, to achieve sustainable development, has become the consensus of the whole society, strategic choice to solve the energy crisis and environmental problems. 2.Overview of control technology of doubly fed wind power gener ation Wind power is the kinetic energy contained in the natural wind into electrical energy in the process, the wind turbine wind energy capture and convert it into mechanical energy, driven by rotary generator, and the generator for converting mechanical energy conversion into electrical energy. As countries around the world pay more attention to wind power, wind power has become the world's fastest, most widely studied and applied new energy technology. With the development of wind power technology, wind turbine, wind turbine generator, converter and its control system and the way of the wind turbine, there are different types of. Among them, the doubly fed wind power generation system due to the converter control capacity is small, the speed of operation range wide characteristics become currently the world's most widely used VSCF wind power generation system. Variable speed constant frequency generator in asynchronous generator rotor side applied three-phase low frequency current to realize AC excitation, the output power is constant frequency and constant voltage amplitude, frequency, phase of the exciting current control. At the same time, the active power and reactive power decoupling control of the generator is realized by using the vector control technology. Control active power capable of adjusting the rotation speed of the wind turbine, to capture the maximum wind power tracking control; regulation of reactive power adjustable power factor and improve the dynamic and static stability of the wind turbine and power system operation. In addition, the VSCF wind power generation technology, so to achieve good flexible connection between the generator and power grid system, compared with the traditional constant speed constant frequency generation system easier to achieve grid connected operation and operation

This study presents a modern version of the economic load dispatch (MELD) problem with the contribution of renewable energies and conventional energy, including wind, solar and thermal power plants. In the study, reduction of electricity generation cost is the first priority, while the use of multiple fuels in the thermal power plant is considered in addition to the consideration of all constraints of power plants. Two meta-heuristic algorithms, one conventional and one recently published, including Particle swarm optimization (PSO) and Equilibrium optimizer (EO), are applied to determine the optimal solutions for MELD. A power system with ten thermal power plants using multiple fossil fuels, one wind power plant, and three solar power plants is utilized to evaluate the performance of both PSO and EO. Unlike other previous studies, this paper considers the MELD problem with the change of load demands over one day with 24 periods as a real power system. In addition, the power generated by both wind and solar power plants varies at each period. The results obtained by applying the two algorithms indicate that EO is completely superior to PSO, and the solutions found by EO can satisfy all constraints. Particularly in Case 1 with different load demand values, EO achieves better total electricity production cost (TEGC) than PSO by 0.75%, 0.87%, 0.13%, and 0.45% for the loads of 2400 MW, 2500 MW, 2600 MW and 2700 MW. Moreover, EO also provides a faster response capability over PSO through the four subcases although EO and PSO are run by the same selection of control parameters. In Case 2, the high efficiency provided by EO is still maintained, though the scale of the considered problem has been substantially enlarged. Specifically, EO can save $51.2 compared to PSO for the minimum TEGC. The savings cost is equal to 0.33% for the whole schedule of 24 hours. With these results, EO is acknowledged as a favourable search method for dealing with the MELD problem. Besides, this study also points out the difference in performance between a modern meta-heuristic algorithm (EO) and the classical one (PSO). The modern metaheuristic algorithm with special structure is highly valuable for complicated problem as MELD.

This research provides an overview of Iraq's renewable energy prospects. One of the most important sorts of renewable energy resources in the world is wind energy. Wind energy is considered environmentally beneficial and very economical. These benefits are cited as the primary reasons for using wind turbines to generate power across the world. An assessment of the country's profile has been made, taking into consideration existing energy generation, crude oil production at current levels, gas flares that generate CO2 emissions, as well as industry, human activities, and grid electricity distribution. Iraq has power shortages, and several obstacles must be solved in order to satisfy projected increases in electrical demand. This analysis discovered that solar, wind, and biomass energy are now underutilized, but that they might play a key part in Iraq's sustainable energy future. Wind energy, for example, has become an important aspect in reducing pollution and improving air quality. The usage of tiny wind turbines to generate power under Iraqi meteorological conditions is examined in this study. The research evaluates the wind system that is necessary to provide electric power for highway services including lighting and parking. According to the study's findings, wind turbines can be used effectively for roadway illumination. The efficiency of a maximum wind turbine is determined by the blade design. For the planned use, the wind speed reported in Iraqi winter is pretty acceptable. Alternative and sustainable energy, wind energy, Iraqi climate are all index terms. The Iraqi government recently joined the Paris Climate Agreement and is now starting to encourage the participation of small and large consumers to generate electricity from renewable energy sources. This article analyzes the hybrid electrical system of solar and wind energy for the city of Dohuk, the northern part of Iraq, to find out the feasibility of this system compared to the local electrical network. First, access to solar and wind energy resources in Dohuk were ensured.

Optimization of wind power generation to minimize operation costs in the daily scheduling of hydrothermal systems

The rapid deployment of wind power has made grid integration and operational issues focal points in industry discussions and research. Compliance with grid connection standards for wind power plants (WPPs) is crucial to ensuring the reliable and stable operation of the electric power grid. This report compares the standards for grid-connected WPPs in China to those in the United States to facilitate further improvements in wind power standards and enhance the development of wind power equipment. Detailed analyses of power quality, low-voltage ride-through capability, active power control, reactive power control, voltage control, and wind power forecasting are provided to enhance the understanding of grid codes in the two largest markets of wind power. This study compares WPP interconnection standards and technical requirements in China to those in the United States.