In this review, the information is retrieved from the scientific database, specifically from the Web of Science. Several authors recognized it as a complete and rigorous database to conduct a bibliometric investigation (Meho et al. 2007; Waltman et al. 2010; Bornmann et al. 2015; García-Orozco et al. 2022). The Core Collection Web of Science (Clarivate) features more than 21,100 journals. They are dispersed across more than 250 scientific disciplines. Furthermore, they are peer-reviewed and published worldwide, ensuring high academic quality. In total, it contains 74.8 million records and 1,500 million cited references from 1900 to date.
It is important to access reliable source and all of its information must be thoroughly documented as well as understood. Hence, Table 1 lists the search terms and their corresponding Web of Science query links.
Table 1
Query link for the search term.
| Search term | Query link |
| Wind farm + energy storage system | https://www.webofscience.com/wos/woscc/summary/99eff7c0-20a1-4d04-a26b-1f9c066b5039-334bfa1a/relevance/1 |
| Wind power forecasting | https://www.webofscience.com/wos/woscc/summary/686d68c7-5fdf-4014-9b73-f8bcc4c52c2c-334c01e2/relevance/1 |
| Wind farm + layout | https://www.webofscience.com/wos/woscc/summary/6b7683c1-e59f-4ef8-a1ad-6463b23b051e-334c0c5e/relevance/1 |
| OPF + wind farm | https://www.webofscience.com/wos/woscc/summary/d72874a0-58d2-4d99-81d3-56fab15d52b8-334c1145/relevance/1 |
In the Web of Science, the basic search function is used for this review. The data retrieval and search are conducted in April 2022. The search terms are as follows. For reviewing the battery energy storage system, the search topic is “wind farm + energy storage system”. For the review of forecasting wind power, the search topic is “wind power forecasting”. For reviewing the optimization of wind farm layouts, the search topic is “wind farm + layout”. For the review of optimal power flow, the search topic is “OPF + wind farm”.
After that, we applied a series of filters. Only the articles are included for the period from January 2018 to April 2022. The editorial, notes, letters, conference papers, and reviews are excluded. The main articles are being filtered in order to narrow down to search only the most important articles (Merigo ´ et al., 2015). In this search, the total number of documents retrieved for the search term “wind farm + energy storage system”, is 604, which are spread across 42 journals. For the search term “wind power forecasting”, 2417 documents are retrieved from 202 journals. For the search term “wind farm + layout”, 434 documents are retrieved from 114 journals. For the search term “OPF + wind farm”, 42 documents are retrieved from 24 journals.
The following subsections present a review of the papers in the final portfolio. These papers have gone through the entire stages described in Section 2. The top 10 papers on each topic are selected by the Methodi Ordinatio for review.
3.1 Energy storage system
Over the last few decades, there has been a lot of interest in wind energy. Due to many factors, the incorporation of this renewable resource is growing. These factors include the reduction of energy dependence on fossil fuels as well as the mitigation of climate change. Due to short-term power fluctuation, the power quality and reliability of electrical grids may be affected by the stochastic nature of wind energy. The energy storage systems have been analyzed by many researchers for wind power smoothing purposes. These systems are becoming less costly and perform extremely well. In this context, the important research works carried out on the topic of energy storage system are comprehensively reviewed and presented in this section.
Table 2
Top 10 most significant papers in energy storage systems since 2018.
| Rank | Paper title | Ref. | Citations | Year | IF | InOrdinatio |
| 1 | Two-Stage Optimization of Battery Energy Storage Capacity to Decrease Wind Power Curtailment in Grid-Connected Wind Farms | Dui et al. 2018 | 76 | 2018 | 6.663 | 142.663 |
| 2 | Design, thermodynamic, and wind assessments of a compressed air energy storage (CAES) integrated with two adjacent wind farms: A case study at Abhar and Kahak sites, Iran | Razmi et al. 2021 | 37 | 2021 | 7.147 | 134.147 |
| 3 | Battery energy storage sizing based on a model predictive control strategy with operational constraints to smooth the wind power | Cao et al. 2020 | 29 | 2020 | 4.63 | 113.63 |
| 4 | A new adiabatic compressed air energy storage system based on a novel compression strategy | Huang et al. 2022 | 0 | 2022 | 7.147 | 107.147 |
| 5 | Predictive Operation and Optimal Sizing of Battery Energy Storage With High Wind Energy Penetration | Moghaddam et al. 2018 | 34 | 2018 | 8.236 | 102.236 |
| 6 | Optimal sizing and technology selection of hybrid energy storage system with novel dispatching power for wind power integration | Khosravi et al. 2021 | 7 | 2021 | 4.63 | 101.63 |
| 7 | Comprehensive comparison on the ecological performance and environmental sustainability of three energy storage systems employed for a wind farm by using an emergy analysis | Yazdani et al. 2019 | 20 | 2019 | 9.709 | 99.709 |
| 8 | A Multi-Objective Planning Framework for Coordinated Generation From Offshore Wind Farm and Battery Energy Storage System | Paul et al. 2020 | 10 | 2020 | 7.917 | 97.917 |
| 9 | A Consensus Approach to Real-Time Distributed Control of Energy Storage Systems in Wind Farms | Baros et al. 2019 | 15 | 2019 | 8.96 | 93.96 |
| 10 | Multi-objective optimal sizing of hybrid energy storage systems for grid-connected wind farms using fuzzy control | Du et al. 2021 | 1 | 2021 | 2.219 | 93.219 |
In recent years, wind energy has made a significant contribution to the power system. Because of this, wind power curtailment has become an issue. The impact of wind power curtailment can be reduced through the use of battery energy storage by wind power forecast error compensation and peak shaving. When optimizing the battery energy storage capacity installed at the wind farm, it is important to take into account the uncertainty of wind power as well as the operational constraints of the power system. Dui et al. (2018) analyzed a system consisting of a thermal plant, battery energy storage, and wind farm. The authors presented a technique for determining the capacity of battery energy storage and optimal power in the system. In this research work, there is an opportunity to improve the optimization method to cooperate with multiple wind and battery energy storage system. Moreover, it is important to establish cooperation strategies as the number of battery energy storage systems installed in wind farms continues to expand. This will allow wind farms to operate more efficiently in the future.
Because of the fluctuation in wind speed at wind farms, unstable and intermittent power generation occurs, with different frequencies and amplitudes. One of the important energy storage technologies is compressed air energy storage (CAES). It is capable of dealing with the stochastic nature of wind farm power output. Furthermore, it contributes to peak shaving as well as auxiliary grid services. An environment-friendly CAES system was proposed by Razmi et al. (2021). Two nearby Iranian wind farms (Kahak and Abhar) were considered. During off-peak hours, the fluctuating and excess wind power is transmitted to the high-temperature thermal energy storage and compressor. The compressor receives constant rate power. The balance of the variable power is transformed into heat. The high-temperature thermal energy storage system stores the heat. This research can be extended by adding the amount of electricity available on the grid during peak hours.
Because of the forecast error and variability of wind power, it becomes more difficult to use wind power. Moreover, the power system faces stability and security issues. An energy storage system can be integrated into a wind farm, which is one of the potential solutions to accommodate wind power fluctuation. Cao et al. (2020) presented a control strategy for sizing energy storage system to integrate with a wind farm. For various energy storage system configuration capacities, the fluctuation rates of the wind storage systems were calculated. A sizing decision map presented the results in a visual manner. There is scope for further research. A method for coordinating the various types of energy storage devices can be developed. Another area of investigation is the determination of the optimal set-point power.
Because of the inconsistency of wind resources, wind farms frequently encounter difficulties in delivering consistent power output. A new Adiabatic Compressed Air Energy Storage (ACAES) system was proposed by Huang et al. (2022). The system is based on a rotary valve design and a compression strategy, which stores and releases energy as and when it is required. Thus, the usability and performance of wind farms were improved. Lower cost, ease of use, and less space are the advantages of this system compared to other existing ACAES systems. If this system is further optimized, it is possible to increase its efficiency.
The high penetration of wind energy demands a quick response to control energy imbalances in the power grid. One of the necessary tools for reducing energy and power imbalances is a battery energy storage system. The battery management system and control methodology have a significant impact on the efficiency of the energy storage system. The size of batteries will be reduced to a minimum when the efficiency of the battery energy storage system is increased. A control method called “predictive controller” was proposed by Moghaddam et al. (2018). for battery energy storage system. It improves the efficiency of the energy storage system by using the most recent forecast data. Accordingly, the size of energy storage required is reduced.
When large-scale wind farms are integrated with the power grid, wind power uncertainty is a major issue. A practical solution is the use of energy storage systems. Wind power requires energy capacity as well as high-power storage for power dispatching. To meet these requirements, Khosravi et al. (2021) proposed a hybrid energy storage system. A power management method was introduced for wind-hybrid energy storage system power dispatching, technology selection, and optimal sizing of the hybrid energy storage system. The prescheduled power delivery and the wind-hybrid energy storage system performance were investigated in short-term power management. This method can be further developed for long-term power management.
Table 3
Challenges, strategies, and opportunities for energy storage systems.
| Ref. | Challenges | Strategies | Opportunities |
| Dui et al. 2018 | Reducing the effects of wind power curtailment | Peak shaving and wind power forecast error compensation by a two-stage method | Improve the optimization method Develop cooperation strategies |
| Razmi et al. 2021 | Wind speed fluctuation | An environmentally-friendly compressed air energy storage system | The 91 MW, 74 MW, and 60 MW electricity can be added to the grid |
| Cao et al. 2020 | The variability of wind power and forecast error | Control strategy for integrating an Energy Storage System with the wind farm | Co-ordinate the operation of different energy storage devices choose the appropriate set-point power |
| Huang et al. 2022 | Inconsistent power output during peak demand | Compression strategy based Adiabatic Compressed Air Energy Storage system | Improve the efficiency of the ACAES system |
| Moghaddam et al. 2018 | Managing energy imbalance in the power grid | Control method for BESS | Improve the performance of BESS |
| Khosravi et al. 2021 | Wind power uncertainty in large-scale wind farms | Power management method for hybrid energy storage system | Develop a method for long-term power management |
| Yazdani et al. 2019 | The uncertainty and volatility of storing energy | Hydrogen energy storage systems | Evaluate the other ESSs |
| Paul et al. 2020 | Optimal capacity of battery energy storage system | A multi-objective planning framework | Incorporate dynamic degradation of battery capacity |
| Baros et al. 2019 | Distributed control of the energy storage systems | A consensus approach with power-sharing | Include complex communication network topology |
| Du et al. 2021 | Stable control of wind power through hybrid energy storage systems | An adaptive wavelet decomposition based smoothing strategy | Analyze the uncertainty and strategy for a forecasted period |
The use of wind energy is increasing every year due to economic and environmental factors. The penetration of wind energy is affected by the volatility, uncertainty, and difficulty of storing energy. An energy storage system is a viable option for storing energy. When there is more energy is produced than is needed, the excess is stored in the system. Likewise, when the consumption of energy is less than the produced energy, the excess is stored in the system. Three energy storage systems such as liquid air energy storage, compressed air energy storage, and hydrogen energy storage were investigated by Yazdani et al. (2019). There is a variety of modern and traditional energy storage systems available. Future research could look into these systems. Meanwhile, the materials, energy, environmental and economic impacts can be studied.
To coordinate the operations of battery energy storage system and large-scale wind farms, Paul et al. (2020) determined optimal battery energy storage capacity by developing a multi-objective planning framework. Multiple objectives such as the curtailment of wind energy, loss of load hour, expected energy not supplied, wind turbine availability, battery cost, and battery lifetime have been considered. This research can be improved in the future by integrating complex structures such as electricity market-oriented control strategy and dynamic degradation of battery capacity.
A double-fed induction generator is currently used in the most advanced wind generator. It integrates storage devices into the system. A consensus-based approach was proposed by Baros et al. (2019) for energy storage system control. This approach is used to regulate the power output of wind farms in real-time, with sharing of power among the storage devices. A more complex communication network topology may be explored in future work.
With a hybrid energy storage system, reliable wind power control is a significant measure for large-scale wind farms. Du et al. (2021) established a smoothing technique, which is based on adaptive wavelet decomposition. It matches wind fluctuations to the grid-connected power quality criteria. The strategy and uncertainty can be examined further in future research.
3.2 Wind power forecasting
This section reviews emerging and established approaches to wind power forecasting for providing an up-to-date view of the field. It is providing a current picture of the subject. The forecasting challenge is presented by the nature of the wind. The wind speed is a stochastic process because the atmosphere can never be completely known.
From local terrain to large-scale weather systems, many factors influence wind speed. Furthermore, Wind speed, rather than wind power, is frequently the most important variable in forecasting scenarios. There is a non-linear and dynamic relationship between wind power and speed. It makes the problem more complex and wind power forecasting is affected by the wind speed between rated and cut-in wind speeds. The error in wind power forecasting is generally auto-correlated and heteroscedastic. In practice, the output of a wind farm is ranging from zero to its rated capacity. The basic statistical assumptions like identically and independent normally distributed errors are violated by these properties. Hence, wind power forecasting requires sophisticated forecasting methods in order to provide careful treatment.
As the penetration of wind power into the power grids is increasing at a faster rate, wind power forecasting has become an important factor in economic and secure power system operation. Hao et al. (2019) proposed a nonlinear ensemble method for forecasting wind power. With the consideration of the error factor, a two-stage forecasting framework has been designed. A multi-objective grey wolf optimization algorithm was developed to forecast wind power.
Table 4
Top 10 most significant papers in wind power forecasting since 2018.
| Rank | Paper | Ref. | Citations | Year | IF | InOrdinatio |
| 1 | A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting | Hao et al. 2019 | 94 | 2019 | 9.746 | 173.746 |
| 2 | Hybrid machine intelligent SVR variants for wind forecasting and ramp events | Dhiman et al. 2019 | 52 | 2019 | 14.982 | 136.982 |
| 3 | An improved residual-based convolutional neural network for very short-term wind power forecasting | Yildiz et al. 2021 | 28 | 2021 | 9.709 | 127.709 |
| 4 | Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network | Wang et al. 2020 | 37 | 2020 | 9.746 | 126.746 |
| 5 | Short-term wind power forecasts by a synthetical similar time-series data mining method | Sun et al. 2018 | 52 | 2018 | 8.001 | 120.001 |
| 6 | Improved EMD-Based Complex Prediction Model for Wind Power Forecasting | Abedinia et al. 2020 | 28 | 2020 | 7.917 | 115.917 |
| 7 | A novel hybrid model based on Bernstein polynomial with mixture of Gaussians for wind power forecasting | Dong et al. 2021 | 11 | 2021 | 9.746 | 110.746 |
| 8 | A novel hybrid model based on the nonlinear weighted combination for short-term wind power forecasting | Duan et al. 2022 | 5 | 2022 | 4.63 | 109.63 |
| 9 | Online Ensemble Approach for Probabilistic Wind Power Forecasting | Von Krannichfeldt et al. 2022 | 1 | 2022 | 7.917 | 108.917 |
| 10 | Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method | Kim et al. 2018 | 39 | 2018 | 7.147 | 106.147 |
To ensure grid reliability and stability, wind power forecast is a significant factor. Dhiman et al. (2019) developed a machine-intelligent wind forecasting model. It was constructed using wavelet transform and various types of support vector regression. The forecasting of wind farms was improved by this machine intelligent hybrid methodology even in uncertain wind conditions such as ramp events.
To make sure the smooth operation of power systems and efficient economic dispatch, it is essential to have an accurate wind power forecast. A two-step deep learning model was designed by Yildiz et al. (2021) for wind power forecasting. In the CNN architecture, the spatial attention and spectral modules could be investigated in the future in order to further increase the learning capability of the architecture. Moreover, the forecast accuracy could be further improved by examining different decomposition approaches.
The incorporation of wind energy into the power grid poses a significant challenge to the power system because of the randomness and intermittency of wind energy. It increases the threat to the stability of the power system. This uncertainty can be effectively reduced by predicting the fluctuating wind power output in wind farms through wind power forecasting. A wind power forecasting method was proposed by Wang et al. (2020) to improve the forecasting accuracy. This method is based on a hybrid Laguerre neural network and singular spectrum analysis.
Sun et al. (2018) introduced a wind power forecasting method for the short term. This method consists of a wavelet-based neural network and a hybrid clustering method. To produce more accurate wind power forecasting results, variable features such as pressure, and wind direction could also be investigated in the future.
It is necessary to employ complex intelligent prediction tools in order to forecast the chaotic nature and highly volatile of wind power. An improved empirical mode decomposition was proposed by Abedinia et al. (2020) to decompose wind measurements. The application of more effective feature selection and improved forecasting engines are recommended as further works of this research. Additionally, the researchers can also extend this forecasting model to a probabilistic version.
The traditional forecasting methods often fail to accurately forecast wind power because of the nonlinearity and intermittence of wind power time series. A hybrid forecasting model was proposed by Dong et al. (2021) to improve the stability and accuracy of wind power forecasting. This hybrid model could be further studied in the future for probabilistic energy forecasting and online wind power forecasting.
Duan et al. (2022) developed a hybrid forecasting model. In order to improve wind power forecasting stability and accuracy, the nonlinear weighted combination, and decomposition strategy were used. To tackle the disadvantages of the linear weighted combination, two deep learning models were employed. In the future, the effect of non-Gaussian noise, reducing the complexity of the model, and eliminating the delay characteristics could be considered for further research.
The power grid with high wind power penetration requires probabilistic wind power forecasting in the decision-making process. Before being used to make predictions online, offline training is given to train the conventional probabilistic wind power forecasting models. However, the most recent information cannot be fully used by these models during the prediction process. Von Krannichfeldt et al. (2022) designed an online ensemble approach considering the most recent information for probabilistic wind power forecasting. The missing and faulty data is a significant challenge for online forecasting approaches. Therefore, this problem could be addressed in future work.
Wind power output is generated from natural wind resources. Unlike other traditional energy resources, these resources vary over space and time. The energy equilibrium in the electrical power system can be effectively balanced with accurate wind power forecasting. Kim et al. (2018) developed an enhanced ensemble method for short-term probabilistic wind power forecasting. Better results may be attained in future research by improving the model and selecting a probabilistic forecasting range.
Table 5
Challenges, strategies, and opportunities for wind power forecasting.
| Ref. | Challenges | Strategies | Opportunities |
| Hao et al. 2019 | Wind power forecasting to secure power system operation | A multi-objective grey wolf optimization algorithm | Develop another multi-objective algorithm |
| Dhiman et al. 2019 | Wind power forecast to ensure grid reliability and stability | Machine intelligent hybrid methodology | Improve machine intelligent hybrid methodology |
| Yildiz et al. 2021 | Wind power forecast to make sure the smooth operation of power systems | A CNN architecture | Include the spatial attention and spectral modules |
| Wang et al. 2020 | The randomness and intermittency of wind energy | A hybrid Laguerre neural network | Design other hybrid Laguerre neural network |
| Sun et al. 2018 | Wind power forecasting method for the short term | A wavelet-based neural network and a hybrid clustering method | Investigate a more accurate wind power forecasting method |
| Abedinia et al. 2020 | To forecast the high volatile of wind power | An improved empirical mode decomposition | Develop more effective feature selection and improve forecasting engines |
| Dong et al. 2021 | Intermittence of wind power time series | A hybrid forecasting model | Study probabilistic energy forecasting and online wind power forecasting |
| Duan et al. 2022 | To improve wind power forecasting stability | The nonlinear weighted combination, and decomposition strategy | Consider the effect of non-Gaussian noise, and the delay characteristics |
| Von Krannichfeldt et al. 2022 | Probabilistic wind power forecasting in the decision-making process | An online ensemble approach | Study the missing and faulty data |
| Kim et al. 2018 | Accurate wind power forecasting | An enhanced ensemble method | Select a probabilistic forecasting range |
3.3 Wind farm layout optimization
It is possible to significantly reduce the costs by determining the most optimal placement and configuration of wind turbines. In the long run, it also increases the power production of wind farms. Furthermore, in addition to better space fixation, the installation of optimally located wind turbines is another important parameter in the optimization process. The wind farm power output would be less than its real potential power when the wind turbines are not optimally allocated in the wind farm. It will have significant consequences for the proposed wind farm’s cost-benefit feasibility.
The wind farm layout design has several important aspects. The majority of previous studies have concentrated on minimizing the initial investment and maximizing the overall energy yield. In the past, a number of heuristic-based optimization methods have been successful in determining the optimal solution to these problems. Even so, comparing these methods to one another has received far too little attention. The purpose of this section is to survey the existing methods for wind farm layout optimization.
Table 6
Top 10 most significant papers in wind farm layout optimization since 2018.
| Rank | Paper title | Ref. | Citations | Year | IF | InOrdinatio |
| 1 | Investigation into spacing restriction and layout optimization of wind farm with multiple types of wind turbines | Sun et al. 2019 | 29 | 2019 | 7.147 | 106.147 |
| 2 | Optimization of wind farm layout with optimum coordination of turbine cooperations | Tang et al. 2022 | 0 | 2022 | 5.431 | 105.431 |
| 3 | Wind farm layout optimization for wake effect uniformity | Yang et al. 2019 | 27 | 2019 | 7.147 | 104.147 |
| 4 | Genetic-algorithm-based layout optimization of an offshore wind farm under real seabed terrain encountering an engineering cost model | Liu et al. 2021 | 4 | 2021 | 9.709 | 103.709 |
| 5 | Multi-objective lightning search algorithm applied to wind farm layout optimization | Moreno et al. 2021 | 4 | 2021 | 7.147 | 101.147 |
| 6 | A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner | Wu et al. 2020 | 11 | 2020 | 8.001 | 99.001 |
| 7 | 3-D Layout Optimization of Wind Turbines Considering Fatigue Distribution | Huang et al. 2020 | 10 | 2020 | 7.917 | 97.917 |
| 8 | Influence of atmospheric stability on wind farm layout optimization based on an improved Gaussian wake model | Guo et al. 2021 | 3 | 2021 | 4.082 | 97.082 |
| 9 | Continuous adjoint formulation for wind farm layout optimization: A 2D implementation | Antonini et al. 2018 | 19 | 2018 | 9.746 | 88.746 |
| 10 | Assessing the energy benefit of using a wind turbine micro-siting model | Parada et al. 2018 | 19 | 2018 | 8.001 | 87.001 |
Sun et al. (2019) reduced the gap between wind turbines by developing a directional restriction approach. The influence of wind directions is additionally considered when compared to existing restrictions. Therefore, this approach is particularly efficient for sites with obvious prevailing wind directions. The multi-population genetic algorithm was applied with the directional restriction for the wind farm optimization process. In the future, more problems such as economic cost, power transmission, and cable layout could be further investigated.
In the traditional methods for wind farm micro-siting, it is commonly assumed that individual maximum power generation is obtained by each turbine. However, farm-level control action is usually implemented during the daily operation so that the profit of wind plants is improved. Tang et al. (2022) considered the coordination of turbine co-operations as well as farm-level control operations and achieved the optimum cost per unit of energy by investigating the wind farm layout optimization. In the future, detailed control actions for layout optimization, and improvement of the algorithm efficiency could be studied.
Maximizing wind farm energy production is the primary goal of wind farm layout optimization. However, the wind turbine may be exposed to higher wake exposure, when there is limited space for wind turbine installations.
This strategy is prominent in a mixed layout with wind turbines of various hub heights and capacities. Yang et al. (2019) ensured uniform wake loss of wind turbines and increased wind farm energy output by developing a new objective function. However, minimization of wake loss standard deviation and maximization of energy were not simultaneously obtained in this study. In future studies, multi-objective optimization could be implemented to achieve these conflicting objectives.
The wind farm energy output can be increased by optimizing the wind farm layout. However, the seabed terrain was not considered in many studies. It would raise the electricity cost of wind farms as it could impact the turbine supporting structure length. Liu et al. (2021) considered different seabed terrains and optimized the offshore wind farm layouts by using a genetic algorithm. The authors made a few recommendations for future investigation. The yaw angle and the hub height of the wind turbine could be considered in the offshore wind farm optimization. The computational time cost will be high when the real application has tens or hundreds of wind turbines. The researchers could develop a graphic processing unit accelerated coding of genetic algorithm for this optimization method. Future studies may also consider the wind farm maintenance cost over its entire life, and develop a model for evaluating wind farm maintenance cost. Other optimization algorithms such as random search, local search, greedy search, particle swarm optimization, and extended pattern search can be developed in future work.
Wide knowledge is necessary to design the wind farm layout design. It is a complex and expansive task. Typically, the objective function is the maximization of energy efficiency. Other objective functions might also be considered in addition to energy efficiency. Moreno et al. (2021) designed a multi-objective lightning search algorithm for solving this kind of multi-objective optimization problem. The authors considered three minimization objective functions such as the losses of wake effect, the area of the overall wind farm, and the annual energy production cost.
The design of wind farms is an important step in realizing wind energy application. The wind farm layout optimization problem was studied by Wu et al. (2020). The authors proposed a new method to optimize the wind farm layout. In this method, wake loss, wind distribution, and power production were taken into consideration. The placements of wind turbines in the wind farms were optimized by using a genetic algorithm. The layout of cables has a significant impact on the transmission of power. The annual economic benefit is defined as the objective function. It is made up of the following components: land cost, cable cost, energy cost, and annual production benefit. There is space for additional investigation. The wind farm topographic condition was not taken into consideration in this problem. The wake loss method used in this work was unable to resolve the problems when applied to different types of wind turbines. Because wind turbines in a wind farm may have different rotor diameters. In the future, the wake loss method can be improved. It is possible to solve the problem of wind turbine layout with different wind turbine rotors. The cable route in the wind farms with complex topography is another topic for further investigation.
Due to the fast expansion of wind energy, both land resources and wind resources are being placed under increasing strain. In order to address this problem, the wind industry has become increasingly concerned about the issue of obtaining better micro-sitting for wind turbines in recent years. Huang et al. (2020) proposed a method for designing three-dimensional wind turbine layouts. The authors simultaneously optimized the vertical hub height and horizontal layout. The impact of micro-sitting of wind turbines and low-speed wind turbine technology was considered in this work. The layout optimization is performed by a harmony search algorithm. The multi-objective functions for turbine selection were solved by a sorting genetic Algorithm-II. The wake effect model based on a polar coordinate transformation was implemented in the optimization process. When the wind direction changes, it simplifies the calculations of wake effects among the multiple wind turbines with different rotor diameters and hub heights.
In recent years, atmospheric stability has received a lot of attention. It influences the performance of wind farm.
Nevertheless, most of the previous research works assumed that atmospheric stability is neutral. Guo et al. (2021) established a framework for wind farm layout optimization considering the impact of atmospheric stability. It was developed with the help of an improved Gaussian wake model. One of the important input parameters for this framework was the local atmospheric stability. Consequently, the method of calculating wind power generation was modified. To test the performance of this method, it was applied to optimize the wind farm layout of one real wind farm and three ideal wind farms. The universality of complex terrain problems can be considered for future investigation. Investigation of more common complex terrain appears to be a promising opportunity for improvement.
In the current methods for wind farm layout optimization, simple analytical models are used to estimate wake loss. Antonini et al. (2018) presented a continuous adjoint formulation to calculate the gradients in the wind farm layout optimization. The high-fidelity CFD models were integrated into the system using the developed optimization methodology. It was successful in overcoming the high computational cost associated with the CFD-based optimization technique. Prior to applying any discretization, the general adjoint equations were derived by the proposed continuous adjoint formulation. Because of this, flexible implementation in the CFD software package is possible. The adjoint formulation has been presented for different conditions of the flow equation such as turbulent flow, frozen-turbulence, and laminar. The developed formulation was tested in a two-dimensional domain. The results were compared to those obtained by gradient calculations through finite-difference approximations. The developed adjoint method based on the gradient calculations was implemented in the gradient-based optimization method. Under a diverse range of wind resource scenarios, a two-dimensional wind farm layout optimization problem was solved through the use of open-source software library resources. The application of this continuous adjoint formulation for two-dimensional wind farm layout optimization could be expanded to a more general three-dimensional formulation. In addition, it has the potential to solve the optimization problem of wind farm layout in complex terrain.
The layout of the wind farm is generally determined by a few simple rules. As a result of this, it gives rise to the regular array. Due to high wake loss, this array may be inefficient. Furthermore, the regularly arrayed layouts are compared with the wind turbine micro-siting models. Nevertheless, a fixed number of turbines and a single regular layout configuration were considered in these studies. Parada et al. (2018) proposed a method for designing highly efficient wind farms. In addition, the number of wind turbines and different spacings were considered. This method was compared with different configurations of regular array layouts. The proposed method efficiently handles the use of real wind data and irregular terrain boundaries. It also maximizes wind farm power. In the future, more realistic cases with different wind turbine types and irregular terrain may be considered to implement this method. In future approaches, additional practical objectives could also be considered.
Table 7
Challenges, strategies, and opportunities for wind farm layout optimization.
| Ref. | Challenges | Strategies | Opportunities |
| Sun et al. 2019 | Reduce the gap between wind turbines | A directional restriction approach | Investigate cable layout |
| Tang et al. 2022 | Wind farm micro-siting | Coordination of turbine co-operations and farm-level control operations | Enhance the algorithm efficiency |
| Yang et al. 2019 | Maximizing wind farm energy production | Uniform wake loss of wind turbines | Implement multi-objective optimization |
| Liu et al. 2021 | Wind farm layout in seabed terrain | A genetic algorithm | Consider the yaw angle and the hub height of the wind turbine |
| Moreno et al. 2021 | Loss of wake effect, energy efficiency, area of the overall wind farm, and annual energy production cost | A multi-objective lightning search algorithm | Develop another multi-objective optimization algorithm |
| Wu et al. 2020 | Wind farm layout optimization | A genetic algorithm | Improve the wake loss method |
| Huang et al. 2020 | Designing three-dimensional wind turbine layouts | A harmony search algorithm and a sorting genetic Algorithm-II | Develop more efficient optimization algorithms |
| Guo et al. 2021 | Impact of atmospheric stability on wind farm | An improved Gaussian wake model | Solve the universality of complex terrain problems |
| Antonini et al. 2018 | Calculate the gradients in the wind farm layout optimization | A continuous adjoint formulation | Expand the model to a three-dimensional formulation |
| Parada et al. 2018 | Designing highly efficient wind farms | Regularly arrayed layouts | Consider more realistic cases and additional practical objectives |
3.4 Optimal power flow
In optimal power flow, the best-operating conditions are selected for electric power systems. It minimizes the cost of production and satisfies demands across the power system networks. The importance of reducing greenhouse gas emissions has grown in several countries as a result of the imposition of carbon taxes and statutory legislation. Moreover, reducing the power losses implies a commercial advantage for utility, and a minimum deviation of voltage is required to maintain a high-power quality standard. As a result, in addition to the production cost, power losses in the transmission network, voltage deviation, and pollution mitigation should be considered when formulating the optimal power flow objectives. Recently several investigations of wind power impact on optimal power flow have been conducted. These studies are briefly discussed below.
Table 8
Top 10 most significant papers in optimal power flow since 2018.
| Rank | Paper title | Ref. | Citations | Year | IF | InOrdinatio |
| 1 | Risk-Based Contingency-Constrained Optimal Power Flow With Adjustable Uncertainty Set of Wind Power | You et al. 2022 | 1 | 2022 | 10.215 | 111.215 |
| 2 | Spatial Correlation Modeling for Optimal Power Flow With Wind Power: Feasibility in Application of Superconductivity | Quan et al. 2021 | 16 | 2021 | 1.704 | 107.704 |
| 3 | Wind farm incorporated optimal power flow solutions through multi-objective horse herd optimization with a novel constraint handling technique | Ida Evangeline et al. 2022 | 0 | 2022 | 6.954 | 106.954 |
| 4 | Optimal power flow using Moth Swarm Algorithm with Gravitational Search Algorithm considering wind power | Shilaja et al. 2019 | 29 | 2019 | 7.187 | 106.187 |
| 5 | Optimal placement and sizing of FACTS devices for optimal power flow in a wind power integrated electrical network | Biswas et al. 2020 | 10 | 2021 | 5.606 | 105.606 |
| 6 | A real-time multi-objective optimization framework for wind farm integrated power systems | Ida Evangeline et al. 2021 | 2 | 2021 | 9.127 | 101.127 |
| 7 | Distributionally Robust Chance-Constrained AC-OPF for Integrating Wind Energy Through Multi-Terminal VSC-HVDC | Yao et al. 2020 | 6 | 2020 | 7.917 | 93.917 |
| 8 | Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling | Sun et al. 2019 | 10 | 2019 | 10.215 | 90.215 |
| 9 | Information gap decision theory to deal with long-term wind energy planning considering voltage stability | Rabiee et al. 2018 | 22 | 2018 | 7.147 | 89.147 |
| 10 | Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system | Salkuti et al. 2019 | 11 | 2019 | 2.459 | 83.459 |
You et al. (2022) proposed an optimal power flow model with risk-based contingency constraints. Both distributionally robust optimization and an adjustable uncertainty set were included in the model. With the incorporation of network contingencies, an adjustable uncertainty wind power set was created. On the basis of this uncertainty set, this model was able to secure the network against contingencies in a probabilistic manner and wind power fluctuations. Hence, the optimal balance between risk and operation cost is maintained at all times. In the meantime, a data-driven ambiguity set based on the L1-norm was employed. Therefore, this model was robust in terms of ambiguous wind power probability distribution. Moreover, when the available wind power data was increasing, the model size was unchanged. The authors also derived a decomposition-based algorithm. Hence, this model can be solved using commercially available solvers. The performance of this model was verified by conducting experiments on IEEE 14 bus system and 118 bus system.
It is possible to expand the scope of this model in order to overcome the limitations of the current model. The computation time required to solve the low-level problem was significantly longer when more creditable contingencies were considered. Thus, some acceleration techniques may be incorporated. It includes the identification of an umbrella contingency set and high-performance parallel-computing framework. When compared to all credible contingency sets, the umbrella set can be significantly smaller. However, it is sufficient to achieve a high level of security. Moreover, it can attain higher economic performance.
It is also possible to extend the scope of this model to take into account the distributional ambiguity of contingencies. Prior to the optimization process, a high occurrence probability may be assigned to more severe contingencies to model such ambiguity.
Because of the uncertainties associated with wind energy, its integration into the power system leads to difficulty in system operation. One of the aspects of uncertainty is spatial correlation. Its origins can be traced back to a geographically dispersed distribution. Nevertheless, the majority of studies did not take into consideration of spatial correlation, which decreases the accuracy of the model. To preserve spatial correlation characteristics, Quan et al. (2021) proposed the high-dimensional t-copula model. A sufficient amount of simulated data is sampled through this model in order to form scenario generation. To decrease the number of scenarios, the K-means cluster algorithm is further employed. From the AEMO dataset, four wind farms were selected to demonstrate the formation of the spatial correlation model. Finally, optimal power flow was conducted on an IEEE 24 bus system with and without considering spatial dependence. In the future, more efficient methods for planning and operation of superconducting wind farms could be investigated.
When it comes to incorporating wind energy into electric power grids, optimal power flow plays a significant role. For the present scenario, the conventional equations are not sufficient because of the complexities. Ida Evangeline et al. (2022) studied wind farm integrated power systems and explored the multi-objective optimal power flow problem. The objective functions such as power generation cost, voltage deviation, power loss, and carbon emission were considered. The authors solved the multiple objective optimal power flow problem by developing the multi-objective horse herd optimization algorithm. The algorithm has been enhanced by the addition of a decomposition strategy. On the other hand, the prior research works did not have efficient techniques to handle the constraints. To manage the variables out of bounds, a constraint handling technique was presented. Seven case studies were examined to verify the suitability and performance of the proposed algorithm. Experiments were carried out on wind farms integrated with IEEE 30, IEEE 57, and IEEE 118 bus systems.
Optimal power flow is a significant problem in power systems. It is now necessary to use renewable energy resources in order to achieve a cost-effective power supply and to meet the power demand. Different metaheuristic approaches and artificial intelligence were used in various existing research studies to solve the optimal power flow problem in the power system. Shilaja et al. (2019) integrated the gravitational search algorithm and the moth swarm algorithm to develop a hybrid approach. The authors applied this hybrid approach to solve optimal power flow problems in the power system with wind energy resources. The fluctuating nature of the wind farm was demonstrated by the Weibull distribution function. Minimizations of fuel cost and power loss are the objective functions. The authors carried out case studies with and without wind power in order to evaluate the efficiency of the hybrid approach.
The challenging problem in the power domain is optimal power flow. When intermittent and uncertain renewable sources are incorporated into the power grid, the complexity of the problem increases. In order to mitigate congestion from the network and manage growing demand, the Flexible AC transmission system is commonly used in modern power systems. Biswas et al. (2020) examined stochastic wind power incorporated power systems with different FACTS devices such as thyristor-controlled phase shifter, thyristor-controlled series compensator, and static VAR compensator. The authors performed test cases with fixed and uncertain load demands. Appropriate probability density functions were used to model the load demand and stochastic wind energy. The objective functions namely, overestimation cost of the wind power, penalty cost for underestimation, the direct cost of wind power, and cost of thermal generation were considered. To reduce the total generation cost of the power systems, ratings and locations of the FACTS devices were optimized. The optimization task was performed by a powerful evolutionary algorithm called success history-based adaptive differential evolutionary algorithm. The superiority of the feasible solution method was used to handle the constraints of the optimal power flow problem. In the future, the power systems with FACTS devices, solar power, and wind power can be considered.
Furthermore, the complex power systems incorporating the FACTS devices such as static synchronous compensators and thyristor-controlled series reactors could be studied.
Wind power is a rapidly growing sector that is having a significant impact on the global energy environment. It contributes to the shift in the climate change paradigm by mitigating pollution. From the perspective of transmission network operators, the integration of wind power presents new challenges. With wind energy, the main problem is that it is inconsistent in nature. The operator is required to select an operation strategy on a regular basis, as the wind energy varies from time to time. To solve this issue, Ida Evangeline et al. (2021) developed a real-time framework for multiple objective optimal power flow. The control variables are simultaneously optimized, and the dynamic optimization problem associated with it is solved in real-time. A decomposition-based multi-objective particle swarm optimization was developed to assess the feasibility and performance of the algorithm. Experiments were carried out on wind farms integrated IEEE 30 and IEEE 57 bus systems.
The wind power from far-distance offshore is collected by utilizing the multiple terminal VSC-HVdc (MTDC) system. It is the appropriate transmission approach for renewable energy integration. Despite this, the system operation faces a great challenge due to the lack of power flow regulation, especially in an MTDC system with conventional fixed dc droop control. Yao et al. (2020) developed an MTDC model to tackle this issue. In which, the linear function of the control variable was used to represent the output power of the onshore converter. For the hybrid MTDC/AC system, the authors developed a robust distribution chance-constrained ACOPF model considering wind power uncertainty from forecast error. Based on the Wasserstein-Moment criterion, this model enforces chance constraints on an ambiguity set using the worst-case probability distribution. The model also ensures a secure operation without making any assumptions about the probability distribution of uncertainty. The 14-bus system and 1354-bus system are used to demonstrate the performance of the model.
Table 9
Challenges, strategies, and opportunities for optimal power flow.
| Ref. | Challenges | Strategies | Opportunities |
| You et al. 2022 | Optimal power flow model with risk-based contingency constraints | A decomposition-based algorithm | Reduce the computation time and consider the distributional ambiguity of contingencies |
| Quan et al. 2021 | Spatial correlation of the model | High-dimensional t-copula model | Investigate more efficient methods |
| Ida Evangeline et al. 2022 | Multi-objective optimal power flow | Multi-objective horse herd optimization algorithm | Develop a more efficient multi-objective optimization algorithm. |
| Shilaja et al. 2019 | Optimal power flow problem | A hybrid approach consists of the Gravitational search algorithm and the Moth swarm algorithm | Include other objective functions |
| Biswas et al. 2020 | To mitigate congestion from the network | Success history-based adaptive differential evolutionary algorithm | Consider the power systems with FACTS devices, solar power, and wind power |
| Ida Evangeline et al. 2021 | Operation strategy for power system | A real-time multiple objective optimal power flow framework | Examine more realistic power grids |
| Yao et al. 2020 | Fixed dc droop control | Multiple terminal VSC-HVdc system | Improve the performance by developing a hybrid model |
| Sun et al. 2019 | To tackle wind power uncertainties | A probabilistic optimal power flow framework | Enhance the Trun-MultiGMM |
| Rabiee et al. 2018 | Long-term wind energy planning | The information gap decision theory model | Consider a large size power grid. |
| Salkuti et al. 2019 | Multi-objective optimal power flow | The Glow-worm Swarm Optimization algorithm | Improve the performance of the algorithm |
The power system operation is influenced by the truncated probabilistic and irregular characteristics of wind power uncertainty. Sun et al. (2019) proposed a probabilistic optimal power flow framework to tackle wind power uncertainties. The correlation between the wind power generated by multiple wind farms was taken into account.
To illustrate the multimodal and irregular wind power distribution, a truncated multi-variate Gaussian mixture model was developed. To produce the wind power sample from the Trun-MultiGMM, a Markov chain quasi-Monte-Carlo model was designed. Experiments were carried out on the multiple benchmark power systems and publicly available wind generation datasets. This research has the potential to be expanded in the future. The Trun-MultiGMM can be enhanced to obtain POPF/PPF solution for extremely large power systems. based on TrunMultiGMM, it is possible to develop the load/wind forecasting model.
Rabiee et al. (2018) considered the inherent uncertainty of wind energy and proposed a method for long-term wind energy planning. The information gap decision theory (IGDT) model was employed to manage the uncertainty of wind power. In addition, because of the significance of security considerations, the power system security is ensured by applying the load margin as an index for voltage stability. In the initial operation point, the power flow equations (operation constraints) have been considered in addition to the voltage collapse point. Thus, the safe operation of the power network was ensured via the voltage stability constrained wind power planning method based on IGDT. The IGDT methodology has been evaluated to manage the uncertainty of wind power. The New England 39 bus system was used to demonstrate the performance of the method.
Salkuti et al. (2019) studied a thermal-wind power system and solved the multi-objective optimal power flow problems. In this research work, the output of wind energy was assumed to be a schedulable resource. Hence, the system operator can determine the wind power penetration limits. The Weibull probability density function was used to model the stochastic behaviour of wind power. The objective functions such as voltage stability enhancement index, transmission loss, and total generation cost were optimized. The total generation cost is the sum of the wind power over-estimating cost, wind power under-estimating cost, wind power cost, and thermal power cost. The Glow-worm swarm optimization algorithm was used to solve the single-objective optimization problems. The multi-objective Glow-worm swarm optimization algorithm was used to solve the multi-objective optimal power flow problems. On the wind farm incorporated IEEE 30 bus and IEEE 300 bus systems, the experiments were carried out. The scope for future research includes receiving subsidies/credits for local pollutant emission reduction, meeting the greenhouse gas target, and reducing carbon emission.