Wind Generation Research Articles

Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm.

Surrounded by the Shandong Peninsula, the Bohai Sea and Yellow Sea possess vast marine energy resources. An analysis of actual meteorological data from these regions indicates significant seasonality and intra-day uncertainty in wind and photovoltaic power generation. The challenge of scheduling to leverage the complementary characteristics of various renewable energy sources for maintaining grid stability is substantial. In response, we have integrated wave energy with offshore photovoltaic and wind power generation and propose a day-ahead and intra-day multi-time-scale rolling optimization scheduling strategy for the complementary dispatch of these three energy sources. Using real meteorological data from this maritime area, we employed a CNN-LSTM neural network to predict the power generation and load demand of the area on both day-ahead 24 h and intra-day 1 h time scales, with the DDPG algorithm applied for refined electricity management through rolling optimization scheduling of the forecast data. Simulation results demonstrate that the proposed strategy effectively meets load demands through complementary scheduling of wave power, wind power, and photovoltaic power generation based on the climatic characteristics of the Bohai and Yellow Sea regions, reducing the negative impacts of the seasonality and intra-day uncertainty of these three energy sources on the grid. Additionally, compared to the day-ahead scheduling strategy alone, the day-ahead and intra-day rolling optimization scheduling strategy achieved a reduction in system costs by 16.1% and 22% for a typical winter day and a typical summer day, respectively.

Modular wind generators for autonomous power supply to remote mining areas mineral resources

The use of renewable energy sources to supply power to energy-isolated areas is an urgent topic. The simplicity of the design and high reliability of inductor generators create good prospects for their use in wind power plants as an alternative to synchronous and asynchronous electric machines that are currently in use. An obstacle to such an application is the specifics of the design, construction, and execution of modular machines with magnetic switching, concentrated windings, and a distributed magnetic system integrated into the wind turbine design. The purpose of the study is to consider cause-effect relationships and establish relationships between magnetic flux, EMF, the geometry of electric and magnetic circuits, the structural arrangement of electric windings, and circuit solutions of an automatic electromagnetic excitation system for the design of a new type of generators. The current issues of the effective use of an exciting magnetic flux for voltage regulation in power supply systems powered by power plants, the main generators of which contain a distributed magnetic system with concentrated electric windings, are considered. The optimal structural compositions and geometry of the ratios of electric and magnetic circuits are determined from the machine's basic calculation equation, which is obtained under the condition of maximum magnetic flux. The reasons for the induction of variable EMF in the DC excitation windings, which prevent the effective magnetization of the machine, have been established. The location of the electric windings is proposed, which makes it possible to weaken or completely compensate for the pulsations of the magnetic flux coupled with the exciting circuits. It is established that in order to eliminate parasitic EMF in the exciting windings, it is necessary to use two-circuit circuits for alternating switching of the magnetic flux in the circuits, with sequential activation of the sections of the windings of the exciting circuits. To completely eliminate these EMFs, performing two-circuit magnetic flux switching circuits with a common exciting winding is advisable. The methods and features of the circuit implementation of the conditions of electromagnetic excitation and maintenance of voltage constancy at the output of generators of a new type are considered.

Wind-Induced Cooling Effects on Photovoltaic Panel Performance

This investigation evaluated the performance of a photovoltaic (PV) panel system under varied cooling speeds of a calibrated wind generator. The objectives encompassed the calibration of wind speed, integration of the wind generator with the PV panel system, monitoring the performance of the PV panel with wind-induced cooling, and analyzing overall performance under different wind generator settings. Parameters assessed included open circuit voltage, short circuit current, PV panel surface temperature, and power output. The tests were carried out both with and without wind cooling and under artificial and natural lighting conditions. Under artificial lighting conditions, the solar PV panel demonstrated suboptimal short circuit current compared to natural lighting conditions, leading to an overall decrease in power output. Moreover, the findings revealed a significant relationship between simulated wind speed and the overall performance of the PV panel, with notable variations in surface temperature, voltage level, current level, and power output. The statistical analysis supported these findings, with all dependent variables exhibiting statistically significant differences. The practical implications of these observations are pertinent to the design of more efficient and sustainable PV and PV-wind cooling systems. By comprehending the influence of wind on PV panel performance, system designers and operators can make informed decisions to maximize energy production and enhance the overall efficiency of the PV system.

Green Hydrogen Driven by Wind and Solar—An Australian Case Study

The energy transition to wind and solar opens up opportunities for green hydrogen as wind and solar generation tend to bring electricity prices down to very low levels. We evaluate whether green hydrogen can integrate well with wind and solar PVs to improve the South Australian electricity grid. Green hydrogen can use membrane electrolysis plants during periods of surplus renewable energy. This hydrogen can then be electrified or used in industry. The green hydrogen system was analysed to understand the financial viability and technical impact of integrating green hydrogen. We also used system engineering techniques to understand the system holistically, including the technical, social, environmental, and economic impacts. The results show opportunities for the system to provide seasonal storage, grid firming, and reliability services. Financially, it would need changes to electricity rules to be viable, so at present, it would not be viable without subsidy.

Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives

In China, the inversion between peak periods of wind and photovoltaic (PV) power (WPVP) generation and peak periods of electricity demand leads to a mismatch between electricity demand and supply, resulting in a significant loss of WPVP. In this context, this article proposes an improved demand response (DR) strategy to enhance the consumption of WPVP. Firstly, we use feature selection methods to screen variables related to response quantity and, based on the results, establish a response potential prediction model using random forest algorithm. Then, we design a subsidy price update formula and the subsidy price constraint conditions that consider user response characteristics and predict the response potential of users under differentiated subsidy price. Subsequently, after multiple iterations of the price update formula, the final subsidy and response potential of the user can be determined. Finally, we establish a user ranking sequence based on response potential. The case analysis shows that differentiated price strategy and response potential prediction model can address the shortcomings of existing DR strategies, enabling users to declare response quantity more reasonably and the grid to formulate subsidy price more fairly. Through an improved DR strategy, the consumption rate of WPVP has increased by 12%.

Investigation of Load, Solar and Wind Generation as Target Variables in LSTM Time Series Forecasting, Using Exogenous Weather Variables

Accurately forecasting energy metrics is essential for efficiently managing renewable energy generation. Given the high variability in load and renewable energy power output, this represents a crucial area of research in order to pave the way for increased adoption of low-carbon energy solutions. Whilst the impact of different neural network architectures and algorithmic approaches has been researched extensively, the impact of utilising additional weather variables in forecasts have received far less attention. This article demonstrates that weather variables can have a significant influence on energy forecasting and presents methodologies for using these variables within a long short-term memory (LSTM) architecture to achieve improvements in forecasting accuracy. Moreover, we introduce the use of the seasonal components of the target time series, as exogenous variables, that are also observed to increase accuracy. Load, solar and wind generation time series were forecast one hour ahead using an LSTM architecture. Time series data were collected in five Spanish cities and aggregated for analysis, alongside five exogenous weather variables, also recorded in Spain. A variety of LSTM architectures and hyperparameters were investigated. By tuning exogenous weather variables, a 33% decrease in mean squared error was observed for solar generation forecasting. A 22% decrease in mean absolute squared error (MASE), compared to 24-h ahead forecasts made by the Transmission Service Operator (TSO) in Spain, was also observed for solar generation. Compared to using the target variable in isolation, utilising exogenous weather variables decreased MASE by approximately 10%, 15% and 12% for load, solar and wind generation, respectively. By using the seasonal component of the target variables as an exogenous variable itself, we demonstrated decreases in MASE of 19%, 12% and 8% for load, solar and wind generation, respectively. These results emphasise the significant benefits of incorporating weather and seasonal components into energy-related time series forecasts.

Numerical Simulations of Ionic Wind Induced by Positive DC-Corona Discharges

This paper analyzes ionic wind production and propulsive force in various electrode configurations under atmospheric conditions. By considering the aerodynamic forces in addition to previously considered electric ones, new predictions for steady-state forces and ionic wind flow velocity are successfully compared with experimental measurements, providing convincing quantitative evidence of the predictive capabilities of drift-diffusion modeling associated with one-way Coulomb forcing of Navier–Stokes equations for ionic wind generation. Furthermore, various electrode configurations are analyzed, some of them streamlined, reducing wakes downstream collectors on the one hand and providing additional thrust on the other. The quantification of these additional thrusts is analyzed, physically discussed, and explored in various configurations.

Wind Power Forecasting Based on CNN and LSTM Models

Nowadays more and more sustainable and environmentally friendly energy sources are demanded and wind power is playing an increasingly significant role in the energy market. For example, according to the Chinese Academy of Sciences, China plans to generate more than ten times the current scale of wind power in the next decade. Accurate prediction of wind generation is essential for the scheduling, operation, and energy trading of power systems. In this study, a method combining long and short-term memory network (LSTM) and convolutional neural network (CNN) is proposed for wind power generation prediction. In this study, a wind power generation data set from Longyuan Power Group is used to adapt to the input requirements of the model. The data are entered into the LSTM model to capture long-term trends in the data. Next, this paper uses the CNN to process the local spatial features to improve the prediction accuracy. Combining the outputs of these two models, this paper obtains an efficient wind power generation prediction method. In order to evaluate the performance of the proposed method, root mean square error is used to analyze the prediction data. The results show that the combined method of LSTM and CNN is better than individual methods in prediction accuracy and stability. In conclusion, this study proposes a wind power forecasting method combining LSTM and CNN. This method helps to improve the dispatching and operation efficiency of the power system and provides strong support for the promotion of wind power in the energy market.

A comprehensive optimization mathematical model for wind solar energy storage complementary distribution network based on multi-regulatory devices under the background of renewable energy integration

In the context of global energy transformation and sustainable development, integrating and utilizing renewable energy effectively have become the key to the power system advancement. However, the integration of wind and photovoltaic power generation equipment also leads to power fluctuations in the distribution network. The research focuses on the multifaceted challenges of optimizing the operation of distribution networks. It explores the operation and control methods of active distribution networks based on energy storage and reactive power compensation equipment. The stable operation of the distribution network is analyzed under the conditions of wind and photovoltaic integration, with a particular focus on precise regulation to address the limitations of existing methods. Afterwards, the study proposes an improvement plan that combines on load tap changer transformers and reactive power compensation equipment to solve complex power balance problems through second-order cone programming relaxation method. The results of numerical analysis show that the constructed mathematical model maintains a stable voltage of 1 to 1.1 pu at distribution network nodes within 24 h. Especially during peak hours from 15:00 to 24:00, it remains normal without any abnormal fluctuations when the control equipment is not added. These results confirm that precise regulation of multiple devices ensures voltage stability and avoids low or high voltage issues.

Dynamic Patterns in the Small-Signal Behavior of Power Systems with Wind Power Generation

This paper investigates the dynamic patterns in the small-signal behavior of power systems with wind power generation. The interactions between synchronous generators and wind generators are investigated. In addition, the impact of increased wind generation penetration on the damping and frequency of the synchronous generator’s electromechanical oscillations is addressed. Wind generators of three different technologies are considered throughout this study. Very detailed dynamic models of wind generators are used and detailed.

Siting and operating incentives in electrical networks: A study of mispricing in zonal markets

The incentives electricity generators face in investment and output decisions hinge on market design. Under some zonal market designs, where profit-maximizing participants face a uniform regional price, achieving lowest-cost system-wide production can be impossible. Further, zonal designs can incentivize siting of intermittent renewables in inefficient locations behind network constraints, of concern for jurisdictions undergoing a clean energy transition. We develop measures of mispricing that compare the zonal prices generators receive to locational marginal prices that value congestion externalities from generator output. We apply these measures to show wind and solar generators are increasingly siting in constrained areas of the Australian network, and highlight sources of potential efficiency gains from adopting a nodal market design with locational marginal pricing.

Fairness and usability analysis in renewable power curtailment: A microgrid network study using bankruptcy rules

The integration of renewable energy sources globally has effectively reduced carbon emissions and enhanced community self-sufficiency through local resource use. However, challenges arise with higher renewables penetration, especially at local and distribution levels. Curtailment of renewables becomes necessary during peak generation intervals to ensure power quality and system stability, raising fairness concerns. This study examines the feasibility of using bankruptcy rules to address fairness concerns in renewable power curtailment in microgrid networks. Four bankruptcy rules - proportional, constrained equal awards (CEA), constrained equal losses (CEL), and sequential priority rules - are evaluated using the IEEE 33-bus system transformed into a network of five microgrids with solar and wind generators. Voltage profiles of microgrid networks in Detroit and Chicago under high renewable penetration are assessed, and bankruptcy rules are applied to curtail surplus renewable power. The fairness of each rule is analyzed using Jain's fairness index, while a usability index evaluates power usability across microgrids under different bankruptcy rules. Simulation results show that the proportional and CEA rules exhibit better fairness and equal usability among microgrids, with voltage profiles just below the upper bound. Conversely, the priority rule improves voltage profiles at the expense of fairness and equal usability.

Optimal sizing and power losses reduction of photovoltaic systems using PSO and LCL filters.

The integration of renewable energy systems into electricity grids is a solution for strengthening electricity distribution networks (SEDNs). Renewable energies such as solar photovoltaics are suitable for reinforcing a low-voltage line by offering an electrical energy storage system. However, the integration of photovoltaic systems can lead to problems of harmonic distortion due to the presence of direct current or non-linear feedback in networks from other sources. Therefore, connection standards exist to ensure the quality of the energy before injection at a point of common coupling (PCC). In this work, particle swarm optimization (PSO) is used to control a boost converter and to evaluate the power losses and the harmonic distortion rate. The test on the IEEE 14 bus standard makes it possible to determine the allocation or integration nodes for other sources such as biomass, wind or hydrogen generators, in order to limit the impact of harmonic disturbances (LIHs). The evaluation of the harmonic distortion rate, the power losses as well as the determination of the system size is done using an objective function defined based on the integration and optimization constraints of the system. The proposed model performs better since the grid current and voltage are stabilized in phase after the photovoltaic source is injected.

A new differential protection scheme based on Synchro-squeezing transform applied to AC Micro-Grid

In recent years, the protection of a microgrid has been a substantial challenge. This paper performs a differential protection scheme for the micro-grid based on Synchro-Squeezing Transform (SST). The proposed scheme protects the micro-grid in different modes i.e. islanded or grid-connected mode. Also, it is investigated in the ring and radial configuration. In the proposed scheme, currents on two sides of the line are measured by current transformers (CTs), and then differential current (IDiff) is calculated. This current is applied to extract the absolute value of the summation of the square root (ASSR). In this stage, transient and external faults without CT saturation and different switching such as connected or disconnected load/capacitor/distributed generation are detected from different internal faults. In the next stage, the frequency components of SST (FCSST) are extracted. By this feature, external fault and cross-country fault with CT saturation are detected. The proposed scheme is evaluated in noisy conditions, fault in different section of line with high resistance fault (HRF), and high impedance faults (HIF) of line. The micro-grid is simulated in PSCAD/EMTDC by various distributed generation (DG) like wind generation and photovoltaic systems penetrated at different buses. The proposed protection scheme is coded in MATLAB. The simulations represent the proposed protection scheme is correctly discriminate external and internal faults. At the same time, the method is immune to CT saturation and cross-country faults.

Technoeconomic Analysis of a Wind Power Generation System and Wind Resource Mapping Using GIS Tools: The Case of Twelve Locations in the Commune of Evodoula, Cameroon

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 MtCO2,eq/year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P75 and P90 are 10.08% and 19.22%, respectively, lower than the energy value in P50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas.

Lean Gas-Flare-Tip Technology Reduces Emissions in the Gulf of Thailand

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216098, “Revolutionizing Sustainability: Achieving Net-Zero Emissions With Lean Gas-Flare-Tip Technology Breakthrough in the Gulf of Thailand,” by Nunthachai Amarutanon, Boonyakorn Assavanives, and Kantkanit Watanakun, PTTEP, et al. The paper has not been peer reviewed. _ In the complete paper, the authors describe an extremely low-BTU (ELBTU) flare-tip technology designed to help the oil and gas industry achieve net-zero targets. The operator embarked on a joint research and development project to address the limitations of high-heating-value requirements by designing the technology. The prototype was proved to successfully combust lower-heating-value flare gas, significantly outperforming existing technologies. Introduction The joint research and development project began work in 2018. The development of the described technology began with a literature survey on lean-gas-combustion theory. The project included multiple iterations of prototype design, simulation, and testing, with various parameter adjustments to optimize performance while considering a thorough range of combustion criteria. The design was studied through computational fluid dynamics simulation and tested to determine its operating envelope. Confidence in the performance and mechanical integrity of the final design has been proved by a series of prototype tests. Theory and Definitions Requirements and general functions that had to be achieved by the technology, all of which are detailed in the complete paper, included the following: - Maintain flame stability - Minimize burnback - Minimize flame pulldown - Ensure mechanical integrity The design of the ELBTU flare tip, shown in Fig. 1, includes an assist fire around the tip-perimeter discharge and spokes that penetrate the flare-gas discharge. The assist fire is designed as a supplementary gas burner where assist gas and assist air are delivered. Jet pumps are used to supply assist air to the assist gas burner, minimizing the line size for the assist air. With this design, assist gas and assist air are well-mixed, producing stable combustion and providing heat to the flare gas. A top hat was introduced to mitigate wind impact, retard the mixing of ambient air, and increase the time the flare gas remains at elevated temperatures. These benefits allow combustion of the assist gas and subsequent mixing of combustion radicals with the flare gas without the involvement of wind. Two of the major components, the assist fire ring and the spokes, were borrowed from existing tip designs that have proved reliable over decades of operation. Prototype Testing The prototype test investigated flare-tip performance by varying vent-gas low heating value (LHV), vent-gas exit velocity, assist-air flow rate, and assist-gas flow rate (related to heat release) with and without a wind generator and with and without the top hat. The wind generator was an upgrade to the test facility. The wind generator created a crosswind across the tip of over 32 km/h. The performance envelope was generated based on the acceptable operating points regarding two performance aspects of the flame characteristics (stability and quality). After hundreds of tests, it was found that the ELBTU flare tip was able to combust a vent gas having an LHV as low as 110 BTU/scf with acceptable flame stability and flame quality. To ensure mechanical integrity, a crucial factor to ensure that the ELBTU flare tip will have an acceptable service life, internal and external parts of the prototype were thoroughly investigated before and after testing. It was concluded that the design has acceptable mechanical integrity for this application.

Equalizing multi-temporal scale adequacy for low carbon power systems by co-planning short-term and seasonal energy storage

Power supply faces seasonal security risks due to the large seasonal volatility of renewable energy sources (RES) generation. Power systems with high shares of RES generation are more difficult to keep seasonal and daily/hourly supply adequacy at the same level. Therefore, this paper first addresses multi-temporal adequacy detail accounting for differences in RES output and timing of production, and demand. Then a planning approach is proposed for sizing short-term and seasonal energy storage accompanying with RES to achieve multi-temporal adequacy equilibrium. Unlike existing methods based on linking typical days or 8760-h simulations, the seasonal electricity adequacy constraints are captured by orderly clustering yearly net power curves, then the energy balance constraints are decoupled into long-term (i.e., seasonal) and short-term (i.e., hourly) energy balances. At the short-term scale, the robust optimization is used to address the uncertainty and randomness of wind and solar generation, in the same time realizing the coordination of short-term and seasonal energy storage. A modified column and constraint generation algorithm is used to solve the min-max-min model with binary variables. Finally, case studies are carried out on the Garver-6 system and the Northwest China Power Grid. The results demonstrate that the proposed planning method ensures multi-temporal scale adequacy equilibrium for high-RES power systems, and improving the planned system's economic performance.

Recurrent neural network for pitch control of variable-speed wind turbine.

Wind is one of the most widely used renewable energy sources due to its cost-effectiveness, power requirements, operation, and performance. There are many challenges in wind turbines, such as wind fluctuation, pitch control, and generator speed control. When the wind speed exceeds its rated value, the pitch angle controller limits the generator output power to its rated value. In this research work, several soft computing techniques have been implemented for pitch control of variable-speed wind turbine. The data is collected for the National Renewable Energy Laboratory offshore 5 MW baseline wind turbine. Wind speed, tip speed ratio, and power coefficient are taken as inputs, and pitch angle as output. Machine learning and artificial intelligence-based techniques such as recurrent neural networks (RNNs), adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron feed-forward neural network (MLPFFNN), and fuzzy logic controller (FLC) are implemented on MATLAB, and their results are evaluated in terms of mean square error (MSE) and root mean square error (RMSE). The controllers have been implemented in MATLAB/Simulink to schedule the wind turbine blade pitch angle and keep the output power stable at the rated value. The experimental results show that RNN provided the best results for 15 neurons in hidden layers and 1000 epochs with MSE of 3.28e-11 and RMSE of 5.54e-06, followed by MLPFFNN with MSE of 2.17e-10 and RMSE of 1.56e-05, ANFIS with MSE of 8.5e-05 and RMSE of 9.22e-03, and FLC with MSE of 6.25e-04 and RMSE of 0.025. The proposed scheme is more reliable and robust and can be easily implemented on a physical setup by using interfacing cards such as dSPACE, NI cards, and data acquisition cards.

Aeroacoustic investigation of a ducted wind turbine employing bio-inspired airfoil profiles

Ducted wind turbines for residential purposes are characterized by a lower diameter with respect to conventional wind turbines for on-shore applications. The noise generated by the rotor plays a significant role in the overall aerodynamic noise. By making modifications to the blade sections of the wind turbine, we can alter the contributions of aeroacoustic noise sources. This study introduces innovative wind turbine blade designs inspired by owl wing characteristics, achieving significant noise reduction without compromising aerodynamic performance. A three-dimensional scan of an owl wing was first employed to derive a family of airfoils. The airfoils were employed to modify the blade of a referenced wind turbine airfoil section at various positions on the blade span to determine a blade operating more efficiently at the tip-speed ratio of the original one. While maintaining the same aerodynamic performance, the bio-inspired profiles show a more uniform pressure coefficient distribution, considerably decreasing in the noise level. Furthermore, this study makes considerable progress in ducted wind turbine design by obtaining an 8 dB noise reduction and a 12% improvement in sound pressure level. An in-depth aerodynamic examination shows a 6.4% rise in thrust force coefficient and optimized power coefficients, reaching a peak at a tip speed ratio of 8, demonstrating improved energy conversion efficiency. The results highlight the dual advantage of the innovative design: significant noise reduction and enhanced aerodynamic efficiency, offering a promising alternative for urban wind generation.

Evaluation and Bias Correction of the ERA5 Reanalysis over the United States for Wind and Solar Energy Applications

The applicability of the ERA5 reanalysis for estimating wind and solar energy generation over the contiguous United States is evaluated using wind speed and irradiance variables from multiple observational data sets. After converting ERA5 and observed meteorological variables into wind power and solar power, comparisons demonstrate that significant errors in the ERA5 reanalysis exist that limit its direct applicability for a wind and solar energy analysis. Overall, ERA5-derived solar power is biased high, while ERA5-derived wind power is biased low. During winter, the ERA5-derived solar power is biased high by 23% on average, while on an annual basis, the ERA5-derived wind power is biased low by 20%. ERA5-derived solar power errors are found to have consistent characteristics across the contiguous United States. Errors for the shortest duration and most extreme solar negative anomaly events are relatively small in the ERA5 when completely overcast conditions occur in both the ERA5 and observations. However, longer-duration anomaly events on weekly to monthly timescales, which include partially cloudy days or a mix of cloudy and sunny days, have significant ERA5 errors. At 10 days duration, the ERA5-derived average solar power produced during the largest negative anomaly events is 62% greater than observed. The ERA5 wind speed and derived wind power negative biases are largely consistent across the central and northwestern U.S., and offshore, while the northeastern U.S. has an overall small net bias. For the ERA5-derived most extreme negative anomaly wind power events, at some sites at 10 days duration, the ERA5-derived wind power produced can be less than half of that observed. Corrections to ERA5 are derived using a quantile–quantile method for solar power and linear regression of wind speed for wind power. These methods are shown to avoid potential over-inflation of the reanalysis variability resulting from differences between point measurements and the temporally and spatially smoother reanalysis values. The corrections greatly reduce the ERA5 errors, including those for extreme events associated with wind and solar energy droughts, which will be most challenging for electric grid operation.