Operational reliability evaluation integrating prediction models for enhanced situational awareness in wind-integrated system

An integrated development environment based situational awareness for operational reliability evaluation in wind energy systems incorporating uncertainties

Evaluation of hybrid forecasting approaches for wind speed and power generation time series

Wind power is currently the fastest growing power generation sector in the world-worldwide growth in wind power generation has been at 40% a year for the last 10 years. With this rapid growth of generation, new challenges are being introduced to power markets. Wind power is an intermittent source. For example, at a wind site in Tasmania, Australia, the average change in mean wind speed vectors over a 2.5- minute period is about 44.4% and the average change in maximum wind speeds over the same period is 55.0%. This demonstrates that the wind speed and its direction can change rapidly by a large amount over a very short period of time. Great variability in wind farm generation has both technical and commercial implications for the efficient planning and operation of power systems. Large wind farms tend to use large wind turbines. Large turbines have a significant rotational inertia when considering a short time frame. As a result, wind patterns shorter than 10 seconds usually have a negligible effect on the turbine output, and it is a standard practice in wind generation to neglect short-term gusts. However, trends over several minutes are of vital importance. In order to accommodate the increase in wind power generation, we need to change the way that wind generation is scheduled. Both utilities and electricity market operators will be required to forecast the wind generation to improve system performance. Wind generation will have to be bid into the market like any other generation. In order to handle these requirements, power system operators will need to have better wind forecasting models and tools. Forecasting is a vital part of business planning in today’s competitive environment, regardless of the field in which you work. To be ready for a situation before it occurs gives a distinct advantage over the competition. Wind prediction is complex due to the wind’s high degree of volatility and deviation. This means that standard time length definitions for power engineering do not strictly apply. The phrase shortterm in the case of wind forecasting, normally means periods from minutes out to half an hour while longterm forecasting means periods from several hours out to a few days. This difference between the two forecasting time periods is important when it comes to creating a prediction system. Three main classes of different techniques have been used for wind power forecasting. These are numerical weather prediction (NWP) methods, statistical methods, and methods based on applications of artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFISs). TheNWP methods are dominant for forecasts over 10 hours ahead. However, statistical and ANN-based methods appear to be more accurate over shorter periods (minutes to a few hours). They are also much simpler than the NWP methods.

The intermittent nature of wind power presents special challenges for utility system operators when performing system economic dispatch, unit commitment, and deciding on system energy reserve capacity. Also, participation of wind power in future electricity markets requires more systematic modeling of wind power. It is expected that the installed energy capacities from wind sources in the United States will increase by up to 20% by the year 2020. New York Independent System Operator (NYISO), General Electric (GE), and Automatic Weather Stations Inc., (AWS) conducted a project for the future of wind energy integration in the United States. They stated that NY State has 101 potential wind energy sites and it should be able to integrate wind generation up to at least 10% of system peak load without further expansion (GE report 2005). In order to integrate wind power systematically, it is necessary to solve the technical challenges as well as policy regulation designs. Some of these polices have been updated to allow increased intermittent renewable energy by settling imbalances in generation rulemakings and portfolio standards, where the most commonly used one at this time is the production tax credit portfolios. Due to intermittent nature of wind power, forecasting methods become a powerful tool and of great importance to many power system applications that include uncertainties in generation outputs. The recent work has discussed several methods to develop wind power forecasting algorithms to anticipate the degrees of uncertainty and variability of wind generation. (C. Lindsay & Judith, 2008) use an auto-regressive moving average model to estimate the next ten-minute ahead production level for a hypothetical wind farm and investigate the possibility of pairing wind output with responsive demand to reduce the variability in the net wind output. In (Kittipong M. et al., 2007), the authors develop an Artificial Neural Network (ANN) model to forecast wind generation power with 10-min resolution. Current and previous wind speed and wind power generation are used as input parameters to the network where the output from the ANN is the wind generation power. (M. S. Miranda & R. W. Dunn, 2006) predicted one-hour-ahead of wind speed using both an auto-regressive model and Bayesian approach. (D. Hawkins & M. Rothleder, 2006), discuss operational concerns with increased amount of wind energy in the Day-aheadand Hourahead-Market for CAISO in California. They emphasize the importance of forecasting accuracy for unit commitment and ancillary services and the implications of load following or supplemental energy dispatch to rebalance the system every five minutes. In (Alberto F. at el., 2005), the authors propose a probabilistic method to estimate the forecasting error for

Wind power plays a vital role in the electricity generation of many countries, including Ethiopia. It serves as a valuable complement to hydropower during the dry season, and its affordability is crucial for the growth of industrial centers. However, accurately estimating wind energy poses significant challenges due to its random nature, severe variability, and dependence on wind speed. Numerous techniques have been employed to tackle this problem, and recent research has shown that Artificial Neural Network (ANN) models excel in prediction accuracy. This study aims to assess the effectiveness of different ANN network types in estimating the monthly average daily wind power at Adama Wind Farm I. The collected data was divided into three sets: training (70%), testing (15%), and validation (15%). Four network types, namely Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg–Marquardt (LR), were utilized with seven input parameters for prediction. The performance of these networks was evaluated using Mean Absolute Percentage Error (MAPE) and R-squared (R2). The EBP network type demonstrated exceptional performance in estimating wind power for all wind turbines in Groups GI, GII, and GIII. Additionally, all proposed network types achieved impressive accuracy levels with MAPE ranging from 0.0119 to 0.0489 and R2 values ranging from 0.982 to 0.9989. These results highlight the high predictive accuracy attained at the study site. Consequently, we can conclude that the ANN model’s network types were highly effective in predicting the monthly averaged daily wind power at Adama Wind Farm I. By leveraging the power of ANN models, this research contributes to improving wind energy estimation, thereby enabling more reliable and efficient utilization of wind resources. The findings of this study have practical implications for the wind energy industry and can guide decision-making processes regarding wind power generation and integration into the energy mix.

Wind power generated by wind has non-schedule nature due to stochastic nature of meteorological variable. Hence energy business and control of wind power generation requires prediction of wind speed (WS) from few seconds to different time steps in advance. To deal with prediction shortcomings, various WS prediction methods have been used. Predictive data mining offers variety of methods for WS predictions where artificial neural network (ANN) is one of the reliable and accurate methods. It is observed from the result of this study that ANN gives better accuracy in comparison conventional model. The accuracy of WS prediction models is found to be dependent on input parameters and architecture type algorithms utilized. So the selection of most relevant input parameters is important research area in WS predicton field. The objective of the paper is twofold: first extensive review of ANN for wind power and WS prediction is carried out. Discussion and analysis of feature selection using Relief Algorithm (RA) in WS prediction are considered for different Indian sites. RA identify atmospheric pressure, solar radiation and relative humidity are relevant input variables. Based on relevant input variables Cascade ANN model is developed and prediction accuracy is evaluated. It is found that root mean square error (RMSE) for comparison between predicted and measured WS for training and testing wind speed are found to be 1.44 m/s and 1.49 m/s respectively. The developed cascade ANN model can be used to predict wind speed for sites where there are not WS measuring instruments are installed in India.

The generalization ability of artificial neural networks (ANNs) and M5 model tree (M5Tree) in modeling reference evapotranspiration (ET 0 ) is investigated in this study. Daily climatic data, average temperature, solar radiation, wind speed, and relative humidity from six different stations operated by California Irrigation Management Information System (CIMIS) located in two different regions of the USA were used in the applications. King-City Oasis Rd., Arroyo Seco, and Salinas North stations are located in San Joaquin region, and San Luis Obispo, Santa Monica, and Santa Barbara stations are located in the Southern region. In the first part of the study, the ANN and M5Tree models were used for estimating ET 0 of six stations and results were compared with the empirical methods. The ANN and M5Tree models were found to be better than the empirical models. In the second part of the study, the ANN and M5Tree models obtained from one station were tested using the data from the other two stations for each region. ANN models performed better than the CIMIS Penman, Hargreaves, Ritchie, and Turc models in two stations while the M5Tree models generally showed better accuracy than the corresponding empirical models in all stations. In the third part of the study, the ANN and M5Tree models were calibrated using three stations located in San Joaquin region and tested using the data from the other three stations located in the Southern region. Four-input ANN and M5Tree models performed better than the CIMIS Penman in only one station while the two-input ANN models were found to be better than the Hargreaves, Ritchie, and Turc models in two stations.

Purpose of the Study. The study aims to investigate various approaches to mitigating the impact of renewable energy sources on the stability of the power system and to determine the optimal combination of conventional and renewable energy sources to ensure stable and reliable energy supply. The effectiveness of different approaches to minimizing the impact of renewable energy on grid stability is evaluated. Materials and Methods. Methods of eigenvalue analysis and transient process modeling were used. Correlation and regression analysis methods were used to assess the impact of renewable energy sources (RES) on the stability of the energy system. Data from the system operator «Ukrenergo» were used, including power system load schedules (hourly, daily, seasonal). Data from the Ukrainian Hydrometeorological Center for the years 2023–2024 were used to model RES generation: data on solar radiation (hourly, daily) and wind speed (hourly, daily). Technical specifications of solar panels and wind turbines installed in Ukraine were also used. The QGIS software was utilized for geoinformation analysis. The study employed artificial neural network theory, optimization methods based on linear programming and logical production rules, as well as methods for assessing and regulating steady-state operating conditions of the system, taking into account energy storage devices. Results. The scientific novelty lies in the development of an innovative method for assessing the reduction of the impact of renewable energy sources on power system stability. The feasibility of using artificial neural networks to forecast wind and solar power generation was demonstrated, as well as the optimization of the model structure and parameters to improve forecasting accuracy. The optimal combination of conventional and renewable energy sources was determined to ensure stable and reliable operation of the power system. Practical Significance. Based on experimental and theoretical research, specific recommendations and solutions are provided aimed at ensuring the stability of the power system during the integration of renewable energy sources, including optimization of management, use of new technologies, and infrastructure development

For the purpose of mitigating carbon dioxide emissions, three renewable energy transportation systems are proposed in which methanol is synthesized by use of wind power generation energy at an oversea and is transported to Japan to be used for a power generation. The proposed systems are the following three systems: (1) wind energy and captured CO2 utilization system, (2) wind energy and coal utilization system, and (3) wind energy and biomass utilization system. The characteristic and cost of the proposed systems’ components such as a wind power generation and a methanol synthesis plants are investigated, and so are the energy and carbon flows of the systems, assuming that the wind power generation plant is constructed at the eastern coast of Russia. Major indicators such as energy efficiency, methanol cost, CO2 reduction cost, etc., of the proposed systems are evaluated together with those of a similar CO2 recycling system utilizing hydraulic power. On the basis of the evaluation results, the wind energy and biomass utilization system is shown to be the most excellent among the evaluated systems from the viewpoints of the CO2 reduction cost. When LNG cost is increased, its estimated CO2 reduction cost islower than that of a CO2 recovery system adopted to a conventional LNG-fired power plant. Consequently, the proposed system is expected to be a feasible option for CO2 reduction in the near future when the wind power generation cost is much decreased.

In the last two decades, the wind power generation has been rapidly and widely developed in many regions and countries for tackling the problems of environmental pollution and sustainability of energy supply. However, the high share of intermittent and fluctuating wind power production has also increased the burden of system operator for securing power system reliability during the operational phase. Moreover, the power system restructuring and deregulation have not only introduced the competition for reducing cost but also changed the strategy of reliability evaluation and management of power systems. The conventional long-term reliability evaluation techniques have been well developed, which have been more focused on planning and expansion rather than operation of power systems. This paper proposes a new technique for evaluating operational reliabilities of restructured power systems with high wind power penetration. The proposed technique is based on the combination of the reliability network equivalent and time-sequential simulation approaches. The operational reliability network equivalents are developed to represent reliability models of wind farms, conventional generation and reserve provides, fast reserve providers and transmission network in restructured power systems. A contingency management schema for real time operation considering its coupling with the day-ahead market is proposed. The time-sequential Monte Carlo simulation is used to model the chronological characteristics of corresponding reliability network equivalents. A simplified method is also developed in the simulation procedures for improving the computational efficiency. The proposed technique can be used to evaluate customers’ reliabilities considering high penetration of wind power during the power system operation in the deregulated environment.

Introduction: Wind energy is a kind of clean energy, and wind power generation is an important means of utilizing wind energy. However, wind power generation is highly volatile and prone to causing grid operation accidents, so it is necessary to predict wind power generation. The annual power generation of wind turbines is determined by the Turbine Power Curve (TPC), which, however, is easily affected by different meteorological conditions. The successful deployment of wind turbines requires accurate predictions of wind farm power before construction and near-real-time power predictions after construction to facilitate grid uptake. However, the existing research methods have not considered the quantitative influence of climate characteristics on the prediction accuracy of wind power, limiting the accuracy of wind power generation predictions. In order to solve this problem, this investigation applies a machine learning model that is based on decision trees to decrease the ambiguity associated with wind resource evaluations and to enhance the precision of wind energy forecasts. Method: The machine learning model, based on decision trees, was trained on four distinct classifications of vertical wind profiles to depict wind velocities necessitating complex computations across different rotor layer altitudes. Results: The findings indicated that the model after integration of rotor-equivalent wind speed and temperature lapse rate achieved a 21.48% enhancement in predictive accuracy for the dataset in question, surpassing traditional power curve techniques. Discussion: The model scrutinized the utility of incorporating parameters such as the wind speed at hub height, the wind speed equivalent to the rotor, and the rate of direct temperature decrease as variables for predicting power output. Climate feature data were also utilized to train the regression tree model, enabling the correlation of wind power with wind profile and climate features for predicting wind power based on physical relationships. Conclusion: This methodology has emerged as the optimal strategy for power forecasting across all categorized vertical wind profile types. Notably, the model incorporating the temperature lapse rate in the prediction exhibited higher accuracy than the other one, highlighting the significance of considering climatic characteristics in wind power prediction.

This work deals with assessing the reliability of wind energy sources incorporated into a microgrid system to increase grid stability. As the share of power generated from wind energy continues to increase due to environmental concerns and to reduce carbon footprints, the case study proposed is designing an effective wind energy system for a smart building environment to reduce the burden on the grid. Incorporating wind energy systems into grid-connected buildings increases the reliability of the microgrid system for smart building environments. Applying smart techniques with integrated automation in the built environment can play a key role in decreasing peak demand for load development in smart cities. This can also enhance the overall efficiency of power grids, reduce waste, and enable the effective use of energy resources. Furthermore, researchers are finding new ways to collect energy from renewable sources and power-built environments due to dwindling fossil fuel resources and increased environmental awareness. The analysis of wind-connected power systems has been initiated to satisfy the increasing demand for reliable supply systems by consumers. This has prompted the use of wind energy systems with controlled techniques to enhance the reliability of power systems and reduce carbon footprints generated by fossil fuel-based power systems. Therefore, the main purpose of this work is to understand the economic prospects of wind energy sources for smart building environments, reliability analysis of wind energy sources-based power systems, reduce carbon footprints, and balance power demand through wind energy systems. The Progressive Wind Model Taylor-Sequence Proliferation (PWMTSP) and Innovative Six Parameters Based Progressive Beta Creative Model (ISPBPBCM) approaches are proposed to understand the reliability of wind energy-based power systems in smart building environments. The work also provides an economic analysis of the proposed low-cost wind power system with different capacities for residential consumers in selected geographic location. Eight cases are considered for the proposed effective wind energy system for smart building environments ranging from 2 MW to 32 MW. Hence, the economic analysis of the proposed systems is discussed for residential users. All eight cases are eco-friendly and reduce carbon emissions as these generated units from wind energy sources need not be purchased from fossil fuel-based plants, which increases the reliability of the proposed system. The expected energy not supplied (EENS) in the planned work increases as wind power capacity increases. Loss of load expectation (LOLE), loss of load probability (LOLP), and failure rate all decrease with the installation of more wind power systems. Moreover, it can be seen that mean time to repair (MTTR) improves when wind energy capacity rises incrementally from 2 MW to 32 MW. The proposed method is anticipated to be adaptable to various types of wind energy systems where dependability is a crucial component of system design and scalable to large wind integrated power systems.

Guest Editorial: Situational awareness of integrated energy systems

Nowadays, solar and wind power generation is an important part of the power sources in many countries all over the world. However, due to the variability and uncertainty of these sources, stable operation of the power system with large solar and wind power penetration is a big challenge for system operators. In order to enhance the contribution efficiency of solar and wind power in the power market, a strong ancillary service system is essential and must be reasonable priced. The ancillary services considered in this paper are spinning reserve, non-spinning reserve. In addition, a model for calculating and comparing generation costs for energy and ancillary services, with and without penetration of solar and wind power in the power market have been proposed. Consequently, this model has been developed and validated with a case study. The results show that in a power system with large proportion integration of solar and wind power, the energy and ancillary services generation costs for the power system are high. Besides, the feed-in tariffs of solar and wind power are more expensive than the traditional electricity generation, which leads to high locational marginal prices of the electricity market.

One of the most crucial prerequisites for effective wind power planning and operation in power systems is precise wind speed forecasting. Highly random fluctuations of wind influenced by the conditions of the atmosphere, weather and terrain result in difficulties of forecasting regardless of whether it is short-term or long-term. The current study has developed a method to model wind speed data predictions with dependence on seasonal wind variations over a particular time frame, usually a year, in the form of a Weibull distribution model with an artificial neural network (ANN). As a result, the essential dependencies between the wind speed and seasonal weather variation are exploited. The proposed model utilizes the ANN to predict the wind speed data, which has similar chronological and seasonal characteristics to the actual wind data. This model was applied to wind speed databases from selected sites in Malaysia, namely Mersing, Kudat, and Kuala Terengganu, to validate the proposed model. The results indicate that the proposed hybrid artificial neural network (HANN) model is capable of depicting the fluctuating wind speed during different seasons of the year at different locations.