Increased Wind Generation Research Articles

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.

Exploratory analysis of a real wind turbine dataset, using AI tools to cluster and classify data, for condition monitoring and fault detection

In recent years there has been an increase in wind generation, driven by environmental factors and the incentive offered for the development of clean and sustainable technologies for energy generation. However, due to the rapid growth of this technology, concerns about the safety and reliability of wind turbines are increasing, especially due to the associated risks and financial costs. Therefore, health monitoring and fault detection for wind turbines has become an important research focus. Thus, the objective of this work was to realize an exploratory study of real data from a wind turbine, using AI tools that help to group the different behaviors, according to the similarity of resources and characteristics of the data. For this, unsupervised learning methods were used to cluster the data and a model was proposed to train and test, using a multilayer perceptron network, to classify these clusters. The differential of this work is the use of real data from CHESF's wind turbines. Another important contribution is in relation to permanent magnet wind turbines, as there are not many studies in this field, therefore a great potential to be explored.

Güneş ve Rüzgar Enerjisinden Elektrik Üretiminin Gün Öncesi Piyasa Takas Fiyatlarına ve Fiyat Volatilitesine Etkisi: Türkiye Örneği

Solar and wind generation are the primary tools to prevent climate change and high carbon emissions. Due to their intermittent generation characteristics, solar and wind power plants have a different impact on the market-clearing price formation compared to conventional generation sources. The paper investigates the solar and wind generation effect on the day ahead market-clearing price in Turkey between the 2016 and 2022. We used a 2nd order polynomial learner model to analyze the impact of solar and wind generation level on the market-clearing price level and volatility. We find that Dutch TTF increases market-clearing price with a coefficient of 0.24. An increase in wind and solar generation reduce the market-clearing price. Solar generation is ineffective on the market-clearing price below a certain demand level. Wind generation reduces market-clearing price with a 37.78 coefficient at low demand levels and a 6.55 coefficient at high demand levels. Solar generation has a price-reducing effect with 5.55 at high demand levels. Finally, Dutch TTF and wind generation increased volatility with coefficients of 0.04 and 0.69; solar generation reduced volatility with a coefficient of 0.83.

Leveraging the existing German transmission grid with dynamic line rating

The integration of large shares of wind and solar power into the power system benefits from transmission network expansion. However, the construction of new power lines requires long planning phases and is often delayed by citizen protests. As an non-invasive alternative, Dynamic Line Rating (DLR) offers the potential to leverage the existing grid by dynamically adjusting the transmission line capacities to the prevailing weather conditions. In this study, we present the first investment model that includes DLR in a large-scale power system with real-world network data and a high temporal resolution. Using Germany as an example, we show that a system-wide integration of DLR improves the integration of existing and additional renewables while reducing grid congestion. The evolving synergies between DLR and increased wind generation result in total cost savings of about 3% of all system costs for a scenario with 80% renewable power production, mainly due to reduced storage and solar capacity needs. If considering a fully decarbonized electricity system, the cost savings from DLR amount to up to 5.5% of the system costs, i.e. 4 billion Euro per year. Our results underscore the importance of a rapid implementation of DLR in power systems to support the energy transition and relieve grid congestion.

Coal generator revenues and the rise of renewable generation: Evidence from Australia’s National Electricity Market

In this paper we investigate the relationship between increasing shares of renewable generation and the wholesale market revenues of coal-fired generating units in Australia's National Electricity Market (NEM). Renewable generation may influence coal generator revenues both through decreased spot market prices and reduced dispatch allocation. We use autoregressive distributed lag (ARDL) models on a high-frequency (30-min) panel dataset between July 2017 and June 2021. We find that a one MWh increase in wind and solar generation each is associated with decreases in coal generator revenues of 0.0062–0.0083 and 0.0041 to 0.0054 AUD2012 respectively, per megawatt of installed coal-fired generating capacity. These results can inform managerial and policy decisions regarding the likelihood of more premature coal plant exits in the coming decades.

Optimal sizing of hybrid wind-photovoltaic plants: A factorial analysis

This research attempts to determine the optimal size (in terms of profitability) of a photovoltaic (PV) plant that is going to be added to an existing wind installation. The analysis carried out is based on a real sample of 62 wind facilities located in 25 Spanish provinces in 2021. Given the hourly energy generated throughout the year by the wind facility and its grid capacity, the optimal power of the PV plant in the hybrid facility will be the one that allows maximising the net present value of the investment, i.e., the one that allows to better adjust (in economic terms) the PV production to the characteristics of the wind farm. For our empirical analysis we need data on the day-ahead energy market price, on the grid capacity and hourly production (in the day-ahead energy market) of the 62 wind farms analysed, and on the hourly PV production in the Spanish provinces where those wind farms are located. In average terms, to maximise the Net Present Value (NPV) per € invested, the optimal PV power to be added to an existing wind farm should be 8% of the wind power already installed. The averages of the financial indicators for optimal PV sizing are promising (discount rate of 7%): NPV per € invested is 1.89, NPV is M€ 23.5, discounted payback is 4.85 years, and the internal rate of return index is 25.6%. To conclude our empirical analysis, we estimate a multilevel regression model for the hourly wind production. The regression model shows, among other results, that one more MW of wind power translates into an increase in wind generation of 0.31 MWh. Our findings will help the design of hybrid plants without neglecting the economic aspect.

System Profit Improvement of a Thermal–Wind–CAES Hybrid System Considering Imbalance Cost in the Electricity Market

Studying a renewable energy integrated power system’s features is essential, especially for deregulated systems. The unpredictability of renewable sources is the main barrier to integrating renewable energy-producing units with the current electrical grid. Due to its unpredictable nature, integrating wind power into an existing power system requires significant consideration. In a deregulated electricity market, this paper examines the implications of wind farm (WF) integration with CAES on electric losses, voltage profile, generation costs, and system economics. Comparative research was done to determine the impact of wind farm integration on regulated and deregulated environments. Four randomly chosen locations in India were chosen for this investigation, together with real-time information on each location’s real wind speed (RWS) and predicted wind speed (PWS). Surplus charge rates and deficit charge rates were created to assess the imbalance cost arising from the discrepancy between predicted and real wind speeds to calculate the system economics. When the effect of imbalance cost is considered, the daily system profit shows a variation of about 1.9% for the locations under study. Customers are always seeking electricity that is dependable, affordable, and efficient due to the reorganization of the power system. As a result, the system security limit could be exceeded or the system might function dangerously. The final section of this paper presents an economic risk analysis using heuristic algorithms such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO). It also discusses how the CAES is used to correct the deviation of WF integration in the real-time electricity market. Economic risk analysis tools include value-at-risk (VaR) and conditional value-at-risk (CVaR). The entire piece of work was validated using a modified IEEE 30-bus test system. This works shows that with a three-fold increase in wind generation, the risk coefficient values improves by 1%.

Economic Enhancement of Wind–Thermal–Hydro System Considering Imbalance Cost in Deregulated Power Market

Studying the property of the combination of renewable energy sources in the existing power systems is of great importance, and especially in the case of deregulated systems. The uncertainty of renewable sources is the largest barrier to integrating renewable-energy-producing units into the existing electrical infrastructure. Due to its uncertainty, integrating wind power into an existing power system requires extra consideration. In this work, the impacts of wind farm (WF) integration and a pumped hydroelectric storage system (PHES) on the electric losses, voltage profiles, generation costs, and system economy in a deregulated power market were studied. A comparative study was performed to determine the impact of wind farm integration on regulated and deregulated environments. Four locations in India were chosen at random for this work, and we used the real-time statistics for the actual wind speeds (AWSs) and forecasted wind speeds (FWSs) for each chosen location. To determine the system economy, surplus charge rates and deficit charge rates were developed to evaluate the imbalance cost resulting from the mismatch between the predicted and actual wind speeds. Considering the effect of the imbalance cost, the system profit/day varies by an average of 1.6% for the locations studied. Because of the reorganization of the power system, consumers constantly look for reliable and affordable power that is also efficient. As a result, the system security limit may be breached, or the system may run in a dangerous state. Lastly, in this paper, an economic risk analysis is presented with the help of heuristic algorithms (i.e., artificial bee colony algorithm (ABC) and moth–flame optimization algorithm (MFO)), along with sequential quadratic programming (SQP), and the way in which the PHES is used to compensate for the deviation in the WF integration in the real-time electricity market is also presented. The value at risk (VaR) and conditional value at risk (CVaR) were used as the economic risk analysis tools. According to the work, with the increase in the wind generation, the system risk improves. The results show that, as the wind generation increases by three times, there is an improvement in the risk coefficient values by 1%. A modified IEEE 14-bus test system was used for the validation of the entire work.

The impact of variable renewables on the distribution of hourly electricity prices and their variability: A panel approach

This paper investigates the impact of intermittent renewable generation on the distribution of electricity prices and their variability in Denmark and Germany. We exploit hourly data from 2015 to 2020 and employ a novel panel quantile approach - the Quantiles via moments (MMQR) method. Previous research has mainly used aggregated-daily data and have applied a time-series setting. We argue that since the electricity price formation and renewable energy generation can show great variations during a day, a panel setting with 24 individuals-hours could offer higher accuracy. Therefore, we apply a panel approach that accounts for both the time and cross-sectional dimension of electricity prices. The panel allows us to control for time-invariant (hourly-specific) characteristics and can reveal hidden market dynamics that exist during a day. The combination of hourly-specific effects and the quantile approach enable us to estimate the renewable sources effect on various price quantiles while controlling for market dynamics. In this way, we investigate extreme market cases accounting for the range and distribution of the electricity prices data. The results suggest that the merit-order effect occurs in both countries, with wind and solar generation having diverse effects on the electricity price distribution. Thus, policy makers should consider this diversifying effect to develop efficient renewable support schemes. We also explore non-linearities by including different demand levels in our model and investigate price variability. The outcomes indicate that wind generation increases (decreases) the occurrence of price fluctuations for low demand (high demand) in both countries. Meanwhile, in Germany, solar power stabilizes price fluctuations for high demand levels, stronger than wind. Market risk information could be useful for organizations in recognizing beneficial investment opportunities or hedging strategies. We finally aggregate the hourly observations into daily and compare the estimation outcomes. The results prompt us to believe that aggregated time-series tend to underestimate the RES impact on prices. In addition, we estimate the same models using hourly data in a time-series approach in order to verify that the diverse effect between aggregated time-series and hourly panel data is driven by the time-invariant characteristics, and not the data resolution. We find that the hourly time-series underestimate the merit-order effect, like in the aggregated time-series case, which supports our claims that the results are steered by the cross-sectional dimension. Thus, a panel approach could provide higher accuracy estimates of the RES influence on electricity prices. In conclusion, hourly-related features seem to affect the merit-order effect and its robustness, and a panel approach should be considered when investigating electricity markets.

System integration costs and emission savings of high penetration of intermittent renewable electricity

Integrating intermittent wind and solar generation into the grid has system integration costs of spinning reserves, increased transmission, and storage. As the penetration of wind and solar generation increase, system integration costs need to be considered, but there are also emissions savings. A TIMES model for the Midcontinent Independent System Operator footprint using publicly available data was used to examine system integration costs and emissions savings benefits of high penetration of intermittent renewable electricity. TIMES is a bottom-up optimization model that minimizes the total system cost. The number of time slices was increased to capture wind and solar generation dynamics, to better understand spinning reserves costs, and to allow battery storage. Using candidate sites for solar and wind generation, transmission, investment and production costs were added. Total system costs increased as there were higher transmission and spinning reserve costs. The emissions savings of Carbon Dioxide equivalent, Sulphur Dioxide, and Particulate Matter were converted to economic benefits using the Social Costs of Emissions. As the penetration of wind and solar generation increases, emissions savings and economic benefits increase. The system integration costs are greater than the benefits of emissions savings. The economic benefits do not justify the system integration cost increases.

Impact Analysis of a Battery Energy Storage System Connected in Parallel to a Wind Farm

Increasing wind generation insertion levels on electrical grids through power converters may cause instabilities in the AC grid due to the intermittent wind nature. Integrating a Battery Electric Energy Storage System (BESS) in wind generation can smooth the power injection at the Common Coupling Point (PCC), contributing to the power system voltage and frequency stability. In this article, it is proposed to analyze the operation of a lithium-ion battery technology based 1 MW/1.29 MWh BESS connected in parallel with wind generation with a capacity of 50.4 MW. The main characteristics investigated are power smoothing and power factor correction. Experimental results show that BESS contributes to smoothing the active power and correcting the power factor of wind generation, improving the quality of electrical energy at the PCC.

An evaluation of the consumer-funded renewable obligation scheme in the UK for wind power generation

Wind generation increased dramatically in the UK over the last decade, partially contributed by the consumer-funded Renewable Obligation (RO) scheme introduced in 2002 as part of the government policy to support renewable energy. Despite its success in stimulating electricity generation from renewable sources, the RO scheme was closed in 2017 and succeeded by an alternative scheme, raising questions on the policy change. This paper makes the first attempt to evaluate the RO scheme by comparing the reduced spending on electricity and the costs of the scheme, focusing on wind generation. Using daily average hourly data from April 2009 to March 2021, our results from the Prais-Winsten estimation suggest that a marginal increase of 1 GW in hourly wind generation reduced the wholesale electricity price by £1.28/MWh. However, we show that the reduced spending on electricity resulted from the lower price was not enough to offset the costs of the RO scheme attributed to wind farms. Therefore, the RO scheme brought negative gain to consumers through increased wind generation, indicating that its closure was in consumers' best interest.

A Wind Power Accommodation Capability Assessment Method for Multi-Energy Microgrids

With the increase in wind generation installed capacity, the uncertainty of wind power has brought great challenges to wind power accommodation. The accurate assessment result of wind power accommodation capability (WPAC) has important guiding significance to the formulation of the day-ahead wind power trading plan for the multi-energy microgrid (MEMG). This paper proposes a WPAC assessment method for the MEMG. A liner energy flow model is first derived based on the equivalent infinitesimal replacement and the mild assumption that supply temperatures of different heat load nodes are nearly the same. Then, the operational risk model easily embedded into the assessment model is established based on the probability distributions of prediction error corresponding to multiple wind power output ranges. Finally, the WPAC assessment model is constructed as a two-stage robust optimization problem and solved by the column-and-constraint generation (C&CG) algorithm. Simulations on a MEMG composed of the IEEE 33-bus power system and the 23-node district heating network (DHN) verify the effectiveness of the proposed method.

Impact of wind-hydro dynamics on electricity price: A seasonal spatial econometric analysis

Hydro and wind are both commonly deployed renewable sources of electricity. Wind is intermittent and unpredictable to a large degree. Hydro depends on seasonal rainfall patterns, although storage may offer scope to spread the risk associated with dry seasons. We investigate the impact of intermittent wind generation, coupled with a given hydro capacity, on wholesale electricity prices, accounting for both spatial and seasonal effects. Results from a spatial Durbin model provide evidence of significant negative spill-over effects of wind generation on wholesale nodal prices. After robustness analysis with alternative spatial weight matrices, we provide estimates of the seasonal impact of wind generation on nodal prices using a transmission weight matrix. We evaluate seasonal nodal price effects during dry periods and wet periods in New Zealand's hydro-dominated electricity market. Results show that the price reduction associated with a 10% increase in wind penetration varies across seasons, ranging from $0.48 per MWh in winter in 2012 to $3.05 per MWh in spring. The largest price reduction effect of 3.05 is found for the wet season in spring 2012, but the variance change is 3.6 which is less than 5.24 for the dry season in summer 2012. This evidence reveals that increased wind generation reduces nodal prices, as expected, but also increases the variance in nodal prices, particularly during a dry season. Thus, while a wind-hydro system is shown as a favourable low-carbon combination during wet seasons, without backup generation the system remains susceptible to price volatility. We further suggest policy recommendations to ensure the reduction of price volatility and the continuity of electricity supply during dry periods.

Wind generation and the dynamics of electricity prices in Australia

Australia’s National Electricity Market (NEM) is experiencing one of the world’s fastest and marked transitions toward variable renewable energy generation. This transformation poses challenges to system security and reliability and has triggered increased variability and uncertainty in electricity prices. By employing an exponential generalized autoregressive conditional heteroskedasticity (eGARCH) model, we gauge the effects of wind power generation on the dynamics of electricity prices in the NEM. We find that a 1 GWh increase in wind generation decreases daily prices up to 1.3 AUD/MWh and typically increases price volatility up to 2%. Beyond consumption and gas prices, hydro generation also contributes to an increase in electricity prices and their volatility. The cross-border interconnectors play a significant role in determining price levels and volatility dynamics. This underscores the important role of strategic provisions and investment in the connectivity within the NEM to ensure the reliable and effective delivery of renewable energy generation. Regulatory interventions, such as the carbon pricing mechanism and nationwide lockdown restrictions due to COVID-19 pandemic, also had a measurable impact on electricity price dynamics.

An Improved Integrated Cumulant Method by Probability Distribution Pre-Identification in Power System With Wind Generation

The increase of wind generation (WG) has challenged the conventional way of probabilistic load flow (PLF) calculation. A reliable and efficient PLF method is required to face the stochastic nature of various power systems with WG. Firstly, the paper analyzes several typical cumulant methods (CMs) for PLF, such as Gram-Charlier expansion of type A (GCA), Gram-Charlier expansion of type C (GCC), and maximum entropy (ME). Then, an improved integrated CM by probability distribution pre-identification is proposed for power systems with WG based on doubly fed induction generations (DFIGs). The skewness and kurtosis are used as probability distribution pre-identification indices in the CM framework. Meanwhile, the influence of the DFIG control strategy on reactive power is considered in the load flow model and the moment calculation. Finally, the accuracy and efficiency of the proposed method are validated with the IEEE test system. In various scenarios, suitable CM is selected and applied to the PLF based on pre-identifying distribution characteristics. Results reveal that probabilistic density functions (PDFs) of bus voltages and line flows obtained by the proposed method have both accuracy and efficiency.

Operating reserve demand curve, scarcity pricing and intermittent generation: Lessons from the Texas ERCOT experience

Resolving the resource adequacy problem has been usually entrusted to the imposition of some kind of long-term capacity requirements or to forward markets. The Operating Reserve Demand Curve (ORDC), which is linked to short-term market conditions and does not require central planning, has been presented as an alternative system with which to ensure long-term resource adequacy in the market. Using hourly data from the Texas ERCOT market between January 2015 and February 2019, we empirically show that ORDC prices are significantly negatively affected by wind generation. We find that, if wind generation is relatively low, a 1% increase in wind generation decreases the ORDC price by around 0.15–0.1%. This fact may preclude the ORDC from providing long-term price signals and price stability to generators. Moreover, we also find that if wind generation is greater than 9000 MW, the ORDC price is expected to be zero, which may further disincentive to increase generation capacity –especially dispatchable capacity that may be needed as a backup if the wind is not blowing.

Wind Generation and the Dynamics of Electricity Prices in Australia

Australia's National Electricity Market (NEM) is experiencing one of the world's fastest and marked transitions toward variable renewable energy generation. This transformation poses challenges to system security and reliability and has triggered increased variability and uncertainty in electricity prices. By employing an exponential generalized autoregressive conditional heteroskedasticity (eGARCH) model, we gauge the effects of wind power generation on the dynamics of electricity prices in the NEM. We find that a 1 GWh increase in wind generation decreases daily prices up to 1.3 AUD/MWh and typically increases price volatility up to 2%. Beyond consumption and gas prices, hydro generation also contributes to an increase in electricity prices and their volatility. The cross-border interconnectors play a significant role in determining price levels and volatility dynamics. This underscores the important role of strategic provisions and investment in the connectivity within the NEM to ensure the reliable and effective delivery of renewable energy generation. Regulatory interventions, such as the carbon pricing mechanism and nationwide lockdown restrictions due to COVID-19 pandemic, also had a measurable impact on electricity price dynamics.

Coordinated Control Design for Reconnection of Islanded Generation

In countries with large hydroresources, generation formed by small hydropower plants may have a significant participation in distributed power generation. Small hydropower plants are connected at subtransmission or distribution level and usually are disconnected after contingencies and islanding. Islanded operation requires adequate system dynamics after disconnection and smooth reconnection. The increase in wind generation and photovoltaic sources requires that utilities consider these sources in islanded operation. In this paper, the design of coordinated controllers for reconnection of distributed generation including hydro- and wind power plants after islanding is considered. The design method is based on constrained optimal control, which allows the design of structured controls such as PI, PID and decentralized. The proposed method is applied to a test system with two small hydropower plants and a wind farm. The results show that the proposed method provides a systematic design approach to ensure smooth reconnection of the islanded network to the main network.

Spatial Effects of Wind Generation and Its Implication for Wind Farm Investment Decisions in New Zealand

Spill-over effects on electricity nodal prices associated with increased wind generation have not been examined in the literature. To examine these effects, we use spatial econometric models to estimate the direct and indirect effects of wind generation on nodal wholesale electricity prices. Spatial econometric models allow us to provide quantitative estimates of spill-over magnitudes and statistical tests for significance. Results show negative and significant effects are associated with increases in wind penetration, and the effect is stronger during peak hours and weaker during off-peak hours. Simulation results demonstrate net savings of NZ$8 million per MW of additional wind capacity installed at the CNI2 wind site. The findings provide valuable information on the evaluation of wind farm development in terms of site location, wholesale prices, and financial feasibility. Our approach also contributes to forecasting location specific wholesale electricity prices, and provides a better understanding of the implications of locating wind sites.