Wind Power Capacity Research Articles

Daily Variation and Regional Differences in Wind Power Output during Heat and Cold Wave Days in China

China currently boasts the largest installed capacity of wind power; however, its output is unstable and highly dependent on weather variability. Despite this, the influence of extreme weather events on wind energy production at the interprovincial scale in China has not been fully characterized. This study aims at investigating the daily variations and regional differences in wind power output during heat wave (HW) and cold wave (CW) days in six regions of China. In addition, the study projects the monthly changes in HW and CW days in the coming decades by utilizing a stacking ensemble machine learning method. The projections are under a real-world warming scenario that incorporates current and long-term actions or policies. The findings of the study reveal that, for most regions, the daily cumulative wind power generation on HW days is close to that on normal days; however, there is a lower output during the daytime and a higher output at night. Furthermore, the number of HW days is projected to increase by 2.3 to 21.8 days during the periods of 2031–2040, 2041–2050, and 2051–2060 in these regions. By comparison, the daily cumulative wind power generation increases significantly on CW days, and the monthly distribution of CW days is expected to undergo notable changes in the future. These findings provide valuable insights into wind resource planning and operation under extreme weather conditions in China.

Optimal capacity configuration of the hydro-wind-photovoltaic complementary system considering cascade reservoir connection

To address the critical issues on environmental pollution and energy consumption, humans began vigorously developing and employing renewable energy sources (RESs). Hydro-wind-photovoltaic(PV) complementary system plays an indispensable part in the sphere of renewable energy research, while the optimal capacity proportion serves as a foundation and premise for building hybrid energy systems. Taking the lower portion of the Jinsha River in Sichuan Province as an example, this research presents a novel capacity configuration model of hydro-wind-PV complementary systems. Based on the consideration of cascade hydropower station connection, the model aims to minimize discarded energy and maximize delivery channel utilization. A new DE based on chaotic local search is then employed to solve the model. The result shows that the optimal proportions for the complementary system with 35 GW, 40 GW and 45 GW of delivery channel capacity are (0.93,0.93), (1.18,1.18) and (1.35,0.35), respectively. Meanwhile, as the affordability of complementary systems increases, the total installed capacity of wind power and photovoltaic power that may be accessible will also rise. Consequently, this paper not only offers a novel strategy for tackling similar issues on optimal capacity configuration, but also makes a contribution to maximizing the accessed installed capacity of RESs.

Capacity investment decisions of energy storage power stations supporting wind power projects

PurposeRapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.Design/methodology/approachBased on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.Findings(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.Originality/valueThis paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.HighlightsElectricity pricing and capacity of energy storage power stations in an uncertain electricity market.Investment strategy of energy storage power stations on the supply side of wind power generators.Impact of pricing method on the investment decisions of energy storage power stations.Impact of pricing method, energy storage investment and incentive policies on carbon emissions.A two-stage wind power supply chain including energy storage power stations.

Multi-objective Capacity Planning for Expressway Microgrid Considering Photovoltaic Panel Dust Removal Maintenance

The increasing passenger volume and the rapid development of the electric vehicle industry make the electricity consumption of the expressway service area grow day by day. It is of great significance to study the planning and design method of the microgrid in the expressway service area. This paper established a wind-photovoltaic-storage capacity planning model for the microgrid in expressway service areas, which considered the dust removal maintenance of photovoltaic panels. The multi-objective optimization problem was solved by the improved fast non-dominated sorted genetic algorithm (NSGA II) with the aim of minimizing the total system investment, and operating cost and maximizing the self-consistent rate of electricity consumption of the microgrid. The Pareto optimal solution frontier with uniform distribution and wide range is obtained, and the capacities of PV, wind power, energy storage, and contact line are obtained under different self-consistent rates of electricity consumption. This method can not only make full use of distributed PV resources that can be developed on the expressway to improve the self-consistent level of energy but also comprehensively evaluate the economy of the microgrid system and meet the requirements of power supply reliability.

Modeling the Reserve Capacity of Wind Power and the Inherent Decision-Dependent Uncertainty in the Power System Economic Dispatch

The ever-increasing penetration of renewable generation is demanding additional flexibility for the operation of power systems. In this sense, it has been proved that wind power generators (WPGs) can provide part of the required operating reserve through pitch angle control (PAC) under a deloaded strategy. Unlike conventional operating reserve sources, however, the operating reserve provided by WPGs can't always be considered reliable, which renders the power system dispatch more challenging. In this research, the reserve capacity of the WPGs is modeled considering the decision-dependent uncertainty (DDU). The random wind speed introduces uncertainty to the available reserve capacity of the WPGs while the decisions on the deloading degrees of the WPGs directly affect their coefficients between wind speed and usable wind power, making the uncertainty decision-dependent. Moreover, the operational technical limits of the WPGs and the multiple possible wind speed scenarios are also considered in the wind power reserve capacity modeling. Then, a two-stage reliability constrained stochastic economic dispatch model with WPGs' reserve provision (SED-WRP) is developed to jointly dispatch the generation and reserve. A group-based multi-cut Benders decomposition method is applied to improve the convergence performance of the solution algorithm. Case studies are solved to quantify the impact of considering the reserve provision of WPGs and the inherent DDU in power system dispatch decisions.

A data-driven multi-energy economic scheduling method with experiential knowledge bases

The high penetrability of renewable energy and the increasing demand for hydrogen energy pose challenges to system scheduling and the accommodation of photovoltaic and wind power. Therefore, a data-driven multi-energy economic scheduling method with experiential knowledge bases is proposed to enhance system operational efficiency and economic performance. Firstly, the Wasserstein generative adversarial network is used to enhance wind power and photovoltaic historical samples. The K-medoids clustering algorithm and ϕ-divergence are applied to achieve scenario reduction and acquire fuzzy sets, eliminating the need for assumptions about their probability distributions. Secondly, a kernel density estimation is used to improve the accuracy of characterizing the probability distribution of energy loads. Based on fuzzy sets, the distributionally robust optimization is used to achieve optimal scheduling in an integrated electricity-hydrogen-heat system. This technique enhances the accommodation capacity of photovoltaic and wind power while reducing operational costs. Finally, a decision-making technique based on an experiential knowledge base is studied, aiming to swiftly match corresponding decision variables by evaluating the similarity of labeled state variables. Simulation results show that the proposed method improves the decision-making speed by about 0.8 times and reduces operating costs and photovoltaic or wind power curtailment by at least 1%. The method can provide fast and efficient day-ahead economic scheduling for digitized integrated energy systems.

The Frequency Control Strategy of a Wind–Storage Combined System Considering Battery SOC

The wind power capacity has increased a lot recently and the number of close energy storage systems has also rapidly increased. To enhance the frequency stability support ability of such wind–storage combined systems, this paper proposes a virtual synchronous control strategy for a wind–storage combined system considering the battery state of charge (SOC). The virtual synchronous control is used to make the wind turbine generate more active power when the system is in disturbance. Most importantly, to ensure that the stored energy is used efficiently, the wide-area SOC concept of the energy storage system is considered, which is realized by designing the batteries with additional control that is related to the other batteries’ SOCs. This means that the low-SOC energy storage system’s power shortage can be compensated for by the high-SOC batteries. The above unified control enhances the wind–storage combined system’s frequency supporting ability. According to the simulations, the frequency drop can be suppressed through the help of the virtual synchronous control and the stored energy. Additionally, the instability can also be eliminated when wide-area SOC is considered, such that the proposed efficiency and correctness are finally verified through the simulations.

Coordinated Optimization of Distributed Wind Power and Electric Energy Storage Technologies

The location and capacity of distributed wind power (DWG) and battery energy storage (BES) in the distribution network affect the operation of the distribution network. To improve the consumption capacity of wind power and reduce the impact of uncertainty of wind power output on the operation of the distribution network, the optimal allocation of DWG and BES should be coordinated in the planning stage. In this paper, the stochastic tide of the wind-storage system is used to reflect the operation status and uncertainty of the distribution network with DWG and BES, and the maximization of the integrated return of equal annual value is used as the objective function to establish the opportunity constrained planning model.

Accelerating the energy transition towards photovoltaic and wind in China

China’s goal to achieve carbon (C) neutrality by 2060 requires scaling up photovoltaic (PV) and wind power from 1 to 10–15 PWh year−1 (refs. 1–5). Following the historical rates of renewable installation1, a recent high-resolution energy-system model6 and forecasts based on China’s 14th Five-year Energy Development (CFED)7, however, only indicate that the capacity will reach 5–9.5 PWh year−1 by 2060. Here we show that, by individually optimizing the deployment of 3,844 new utility-scale PV and wind power plants coordinated with ultra-high-voltage (UHV) transmission and energy storage and accounting for power-load flexibility and learning dynamics, the capacity of PV and wind power can be increased from 9 PWh year−1 (corresponding to the CFED path) to 15 PWh year−1, accompanied by a reduction in the average abatement cost from US$97 to US$6 per tonne of carbon dioxide (tCO2). To achieve this, annualized investment in PV and wind power should ramp up from US$77 billion in 2020 (current level) to US$127 billion in the 2020s and further to US$426 billion year−1 in the 2050s. The large-scale deployment of PV and wind power increases income for residents in the poorest regions as co-benefits. Our results highlight the importance of upgrading power systems by building energy storage, expanding transmission capacity and adjusting power load at the demand side to reduce the economic cost of deploying PV and wind power to achieve carbon neutrality in China.

Dynamic thermal line rating model of conductor based on prediction of meteorological parameters

This paper developed a new algorithm to predict dynamic thermal line rating to increase the capacity of transmission lines, which can enhance the capacity of wind power integrated to the grid and reduce the curtailment. The proposed dynamic thermal line rating prediction model was trained by analyzing historical meteorological data and conductor physical parameters, and used deep learning with parameters optimized by an optimized algorithm. The prediction accuracy of the model is verified by Mean Absolute Error, R2 and comparison with other models. The simulation results show that the proposed prediction model has a good performance. The suggested dynamic thermal line rating algorithm, which bears resemblance to the actual value, boosts the static thermal line rating by varying degrees of 23% to 75% at different instances throughout the sample. At the same time, this paper designs an optimal power flow economic dispatch objective function. By comparing the economic dispatch of the power grid calculated by adding static thermal line rating and the prediction models, the method proposed in this paper can effectively increase the amount of wind power integration and reduce power generation costs.

Quantifying the impacts of synoptic weather patterns on North Sea wind power production and ramp events under a changing climate

Only a few studies on the overall impact of climate change on offshore wind power production and wind power ramps in the North Sea region have been published. This study focuses on the characteristics of expected wind power production and wind power ramps in the future climate aided by the classification of circulations patterns using a self-organizing map (SOM). A SOM is used to cluster high-resolution CMIP5-CORDEX sea level pressure data into 30 European area weather patterns. These patterns are used to better understand wind power production trends and any potential changes. An increased frequency of occurrence and extended persistence of high pressure systems lasting at least 24 h is projected in the future. Whereas a contrasting reducing tendency for low-pressure systems is estimated. No significant evidence is seen for a change in wind power capacity factor over the North Sea, though tentative evidence is seen for a reduction in wind power ramps. Annual energy production is seen to be dominated by a small number of weather patterns with westerly, south-westerly or north-westerly winds. Future wind power production is projected to become less from westerly winds and more from south-westerly and north-westerly flows. Ramp up events are primarily associated with strong south-westerly winds or weather patterns with a weak pressure gradient. Ramp down events have a stronger association with more north-westerly flow. In a future climate, a reduction in ramp up events associated with weak pressure gradients is projected.

Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors

The aim of this paper is to address the challenges regarding the safety and economics of power system operation after the integration of a high proportion of wind power. In response to the limitations of the literature, which often fails to simultaneously consider both aspects, we propose a solution based on a stochastic optimization scheduling model. Firstly, we consider the uncertainty of day-ahead wind power forecasting errors and establish a multi-scenario day-ahead stochastic optimization scheduling model. By balancing the reserve capacity and economic efficiency in the optimization scheduling, we obtain optimized unit combinations that are applicable to various scenarios. Secondly, we account for the auxiliary service constraints of thermal power units participating in deep peak shaving, and develop an intra-day dynamic economic dispatch model. Through the inclusion of thermal power units and energy storage units in the optimization scheduling, the accommodation capacity of wind power is further enhanced. Lastly, in the electricity market environment, increasing wind power capacity can increase the profits of thermal power peak shaving. However, we observe a trend of initially increasing and subsequently decreasing wind power profits as the wind power capacity increases. Considering system flexibility and the curtailed wind power rate, it is advisable to moderately install grid-connected wind power capacity within the power system. In conclusion, our study demonstrates the effectiveness of the proposed scheduling model in managing day-ahead uncertainty and enhancing the accommodation of wind power.

Optimal resilience enhancement dispatch of a power system with multiple offshore wind farms considering uncertain typhoon parameters

With the increasing penetration of offshore wind power capacity into power systems, extreme typhoon events have been severely threatening the secure operation of a power system with multiple offshore wind farms (OWFs). To alleviate this problem, this paper proposes an optimal resilience enhancement dispatch (ORED) framework consisting of three stages: preventive control, emergency response, and rapid restoration. Combining the wind field model of typhoon and the probability distribution model of typhoon parameters, a Wasserstein distance-based ambiguity set (AS) is constructed for describing the uncertainty of typhoons. In addition, a distributionally robust chance constraint-based ORED (DRCC-ORED) model, which considers uncertain typhoon parameters, is established for a power system with multiple OWFs. A conditional value-at-risk (CVaR) approximation method is used to transform the DRCCs with random variables into linear constraints, and the expectation operation of the cost function under the worst probability distribution is reformulated. Further, the proposed bi-level DRCC-ORED model is transformed into a single-level mixed-integer second-order cone programming model, which can be efficiently solved by the commercial solver GUROBI. Case studies on the modified IEEE 39-bus system with an OWF and an actual provincial power system with four OWFs demonstrate the high effectiveness of the proposed model.

One Price Fits All? On Inefficient Siting Incentives for Wind Power Expansion in Germany under Uniform Pricing

This paper evaluates investment incentives for wind power under two market designs: uniform and nodal pricing. An electricity system model is developed, that allows for investments in wind power capacities while carefully accounting for static transmission grid constraints. Wind power capacities are assumed to reach the same expansion target by 2030 under both market designs. The results show that the introduction of nodal prices leads to investments in wind power plants shifting to locations with lower wind yield. The amount of electricity fed into the grid from wind power plants, however, is higher under nodal pricing as curtailment is reduced by two-thirds. Furthermore, grid-optimal wind locations are shown to require higher direct subsidy payments but decrease yearly variable supply costs by 1.5% in 2030. Yet distributional effects present an obstacle to the introduction of a nodal pricing regime, with about 75% of German demand facing an increase in electricity costs of about 5%. To mitigate the distorted investment signals arising from uniform pricing regimes, restricting investments within grid expansion areas proves to be more promising than including latitude-dependent generator-component in the grid tariff design.

Using flexible energy system interactions amongst industry, district heating, and the power sector to increase renewable energy penetration

Sweden’s goal of 100% renewable electricity generation by 2040 requires investments in intermittent electricity production (e.g. wind power). However, increasing the share of intermittent electricity production presents challenges, including reduced flexibility of electricity production. A strategy for overcoming this issue is developing flexibility in electricity consumption. This study analyses the potential for using flexible industrial processes, heat pumps (HP), and combined heat and power (CHP) plants in Swedish district heating systems to increase the share of wind power capacity without compromising grid stability. The simulation tool EnergyPLAN was used to assess the potential contribution of these strategies. The analysis includes a range of annual wind power production between 45 and 60 TWh. The required electricity imports and critical excess electricity (that can neither be used nor exported due to transmission line limitations) were used to evaluate the system’s stability. Managing the operation of CHP plants, HPs, and industrial processes in a flexible way is challenging, but these strategies may still play a decisive role in increasing the share of renewable electricity production and reducing demand peaks in cities. Whilst HP regulation is better at reducing excess electricity production at lower wind power capacities (from 32 to 15% for the analysed interval of wind power production), CHP regulation becomes more relevant when wind power capacity increases (from 14 to 39%). Like HP regulation, flexibility in electricity demand in industrial processes had a greater percentage contribution at lower wind power capacities. Combining HP, CHP regulation, and flexible electricity demands in industry can reduce excess electricity production by 68–80% and electricity imports by 14–26%. Wind power contributing to grid stabilisation reduces excess electricity production but does not reduce electricity imports.

Recent advances in data-driven prediction for wind power

Wind power is one of the most representative renewable energy and has attracted wide attention in recent years. With the increasing installed capacity of global wind power, its nature of randomness and uncertainty has posed a serious risk to the safe and stable operation of the power system. Therefore, accurate wind power prediction plays an increasingly important role in controlling the impact of the fluctuations of wind power to in system dispatch planning. Recently, with the rapid accumulation of data resource and the continuous improvement of computing power, data-driven artificial intelligence technology has been popularly applied in many industries. AI-based models in the field of wind power prediction have become a cutting-edge research subject. This paper comprehensively reviews the AI-based models for wind power prediction at various temporal and spatial scales, covering from wind turbine level to regional level. To obtain in-depth insights on performance of various prediction methods, we review and analyze performance evaluation metrics of both deterministic models and probabilistic models for wind power prediction. In addition, challenges arising in data quality control, feature engineering, and model generalization for the data-driven wind power prediction methods are discussed. Future research directions to improving the accuracy of data-driven wind power prediction are also addressed.

Geographical balancing of wind power decreases storage needs in a 100% renewable European power sector

To reduce greenhouse gas emissions, many countries plan to massively expand wind power and solar photovoltaic capacities. These variable renewable energy sources require additional flexibility in the power sector. Both geographical balancing enabled by interconnection and electricity storage can provide such flexibility. In a 100% renewable energy scenario of 12 central European countries, we investigate how geographical balancing between countries reduces the need for electricity storage. Our principal contribution is to separate and quantify the different factors at play. Applying a capacity expansion model and a factorization method, we disentangle the effect of interconnection on optimal storage capacities through distinct factors: differences in countries' solar PV and wind power availability patterns, load profiles, as well as hydropower and bioenergy capacity portfolios. Results indicate that interconnection reduces storage needs by around 30% in contrast to a scenario without interconnection. Differences in wind power profiles between countries explain around 80% of that effect.

Electricity scenarios for Hungary: Possible role of wind and solar resources in the energy transition

The paper examines the compatibility of wind and solar energy resources with projections of future electricity demand in Hungary. For such, we model the national electricity system and estimate surplus generation. The model makes use of hourly distributions of electricity demand and power generation. Simulations for the year 2033 (last scenario year) suggest that 46–47% of the projected electricity consumption can be supplied by wind turbines and solar PV technology with a surplus of less than 5% of yearly consumption. A suitable capacity ratio of wind to solar PV and the management of electric vehicle charging may reduce surplus electricity. It is shown by our EnergyPLAN model that the solar PV capacity should be 1.1 times the wind power capacity which is a huge contrast to the current situation where solar PV is almost 10 times the wind power capacity in Hungary.

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Abstract In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.

Regulation Cost Forecasting Model for Systems with High Renewable Energy Penetration Based on Stochastic Distribution of Net Load Modeling

With the increasing installed capacity of wind power and PV, the strong randomness of power generation makes the requirements of power systems for regulation and other ancillary services increase. However, the traditional regulation requirement prediction method cannot meet the problem of randomness on both sides of the plant and the user well. Therefore, this paper designs a regulation requirement forecasting method based on netload random distribution modeling. Combined with the hypothetical typical operation scenario of the Chinese power system in 2025, the total cost of regulation is predicted. The results show that China’s total cost of regulation can reach 10.62 billion yuan in 2025. And the sensitivity test results show that the total cost of regulation increases with the decrease of sub-period and with the increase of coverage of random fluctuation of net load.