Value Of Wind Power Research Articles

Wind Value in Hydropower Dominated Electricity Market

This paper studies the interaction between hydro and wind power in hydro-thermal-wind power system. Socially efficient and monopoly hydropower allocation solutions are presented. Market power utilization potential for hydropower is reduced during low demand periods when residual demand crosses the elastic part of thermal supply curve. Conversely, in high demand periods when the residual demand crosses the inelastic part of thermal supply curve, real potential for market power utilization exists. Compared to thermal power systems, wind value factors are high as efficiently allocated hydropower buffers wind power shocks. Monopoly hydroallocation decreases wind power value and the gap to wind values under socially efficient hydropower widens as wind market share increases.

The combined value of wind and solar power forecasting improvements and electricity storage

As the penetration rates of variable renewable energy increase, the value of power systems operation flexibility technology options, such as renewable energy forecasting improvements and electricity storage, is also assumed to increase. In this work, we examine the value of these two technologies, when used independently and concurrently, for two real case studies that represent the generation mixes for the California and Midcontinent Independent System Operators (CAISO and MISO). Since both technologies provide additional system flexibility they reduce operational costs and renewable curtailment for both generation mixes under study. Interestingly, the relative impacts are quite similar when both technologies are used together. Though both flexibility options can solve some of the same issues that arise with high penetration levels of renewables, they do not seem to significantly increase or decrease the economic potential of the other technology.

Flexible Demand and Flexible Supply As Enablers of Variable Energy Integration

This article analyses the impact of the interplay between the demand response, wind penetration and hydropower in the Nordic power market. We develop a long-term equilibrium model in which the thermal power capacity adjusts to the zero-profit level over varying real-time pricing adoption rates and wind power shares. Our results show that an increased demand response decreases the amount of thermal power capacity and improves the utilization rate of thermal power and the value of wind power. We show a decreasing marginal gain from an increasing demand response adoption with respect to the thermal utilization and wind value factor. However, the demand response itself does not lead to lower CO2 emissions or reduced variation in hydropower generation.

Value of wind power – Implications from specific power

This paper investigates the marginal system value of increasing the penetration level of wind power, and how this value is dependent upon the specific power (the ratio of the rated power to the swept area). The marginal system value measures the economic value of increasing the wind power capacity. Green-field power system scenarios, with minimised dispatch and investment costs, are modelled for Year 2050 for four regions in Europe that have different conditions for renewable electricity generation. The results show a high marginal system value of wind turbines at low penetration levels in all four regions and for the three specific powers investigated. The cost-optimal wind power penetration levels are up to 40% in low-wind-speed regions, and up to 80% in high-wind–speed regions. The results also show that both favourable solar conditions and access to hydropower benefit the marginal system value of wind turbines. Furthermore, the profile value, which measures how valuable a wind turbine generation profile is to the electricity system, increases in line with a reduction in the specific power for wind power penetration levels of >10%. The profile value shows that the specific power becomes more important as the wind power penetration level increases.

Wind Power Prediction Using a Hybrid Approach with Correction Strategy Based on Risk Evaluation

With the rapid development of renewable energy applications and for managing wind power farms connected to power grid, in this work, is proposed evaluation risk based hybrid strategy for very short-term wind power prediction (VSTWPP) method, it has become more important. VSTWPP is essential in the electricity market for producers and consumers. Power balance of network at any moment must be maintained due to security and quality, and the uncertainty of wind power fluctuation can be reduced by accurate prediction method. This paper focuses on a hybrid approach with correction (HWC) strategy for VSTWPP. In this study, Gaussian model is used to calculate the probability distributions of wind power value and its error for different time periods and different methods. The WPP is achieved in three phases: 1) Ratios of wind power were predicted using hybrid approach of multiple linear regression and least squares; 2) Transformation of these ratios was performed to obtain predicted wind power values; 3) Correction strategy was implemented to obtain the final results of WPP. Comparisons between HWC, hybrid approach without correction (HWoC), autoregressive moving average (ARMA) model and autoregressive integrated moving average (ARIMA) model were done to observe prediction performance. Observation shows that the error of WPP with the proposed HWC was significantly lower than those with other methods.

Spinning Reserve Requirements Optimization Based on an Improved Multiscenario Risk Analysis Method

This paper proposes a novel security-constrained unit commitment model to calculate the optimal spinning reserve (SR) amount. The model combines cost-benefit analysis with an improved multiscenario risk analysis method capable of considering various uncertainties, including load and wind power forecast errors as well as forced outages of generators. In this model, cost-benefit analysis is utilized to simultaneously minimize the operation cost of conventional generators, the expected cost of load shedding, the penalty cost of wind power spillage, and the carbon emission cost. It remedies the defects of the deterministic and probabilistic methods of SR calculation. In cases where load and wind power generation are negatively correlated, this model based on multistep modeling of net demand can consider the wind power curtailment to maximize the overall economic efficiency of system operation so that the optimal economic values of wind power and SR are achieved. In addition, the impact of the nonnormal probability distributions of wind power forecast error on SR optimization can be taken into account. Using mixed integer linear programming method, simulation studies on a modified IEEE 26-generator reliability test system connected to a wind farm are performed to confirm the effectiveness and advantage of the proposed model.

Renewable Energy Support in Germany: Surcharge Development and the Impact of a Decentralized Capacity Mechanism

The German support for renewable energies in the electricity sector is based on the feed-in tariff for investors that grants guaranteed revenues for their renewable energy supply. Corresponding to differences of granted tariffs and respective market values, a surcharge on consumption covers differential costs. While granted tariffs are bound to fall with advances in renewable energy technologies, the market design and the flexibility of the system influence the expected market values of renewables and the necessary surcharge. We apply the European electricity market equilibrium model EMELIE-ESY to investigate this relationship. We find a crucial dependence of market values of renewables on a high system flexibility and the current so-called energy-only market design. Under these conditions, the market values of renewables sequentially recover with increasing market prices by 2024 and 2034. This allows to limit the increase of the core surcharge to below a quarter of its 2013 value by 2024 despite a doubling of renewables, and to introduce substantial surcharge reductions through 2034. However, the introduction of a capacity market would erode market values of renewable energies and induce a pronounced growth of the core surcharge. Under inflexible supply structures and a capacity market, we find an increase of the core surcharge of more than 50 percent by 2024, a respective loss of the market value of wind power of the same magnitude, and an increase of the generation induced part of the consumer prices of more than a quarter.

Capacity value assessments of wind power

This article describes some of the recent research into the capacity value of wind power. With the worldwide increase in wind power during the past several years, there is increasing interest and significance regarding its capacity value because this has a direct influence on the amount of other (nonwind) capacity that is needed. We build on previous reviews from IEEE and IEA Wind Task 25a and examine recent work that evaluates the impact of multiple‐year data sets and the impact of interconnected systems on resource adequacy. We also provide examples that explore the use of alternative reliability metrics for wind capacity value calculations. We show how multiple‐year data sets significantly increase the robustness of results compared to single‐year assessments. Assumptions regarding the transmission interconnections play a significant role. To date, results regarding which reliability metric to use for probabilistic capacity valuation show little sensitivity to the metric. WIREs Energy Environ 2017, 6:e226. doi: 10.1002/wene.226This article is categorized under: Wind Power > Economics and Policy Wind Power > Systems and Infrastructure Energy Infrastructure > Economics and Policy

Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting

Wind power forecasting is an important tool in power system operations to address variability and uncertainty. Accurately doing so is important to reduce the occurrence and length of curtailment, enhancing market efficiency, and improving the operational reliability of the bulk power system. This research quantifies the value of wind power forecasting improvements in the IEEE 118-bus test system as modified to emulate the generation mixes of Midcontinent, California, and New England independent system operator balancing authority areas. To measure the economic value, a commercially available production cost modeling tool was used to simulate the multitimescale unit commitment (UC) and economic dispatch process for calculating the cost savings and curtailment reductions. To measure the reliability improvements, an in-house tool, Flexible energy scheduling tool for integrating variable generation, was used to calculate the system's area control error and the North American Electric Reliability Corporation Control Performance Standard 2. The approach allowed scientific reproducibility of results and cross validation of the tools. A total of 270 scenarios were evaluated to accommodate the variation of three factors: generation mix, wind penetration level, and wind forecasting improvements. The modified IEEE 118-bus systems utilized 1 year of data at multiple time scales, including the day-ahead UC, 4-h-ahead UC, and real-time dispatch. The value of improved wind power forecasting was found to be strongly tied to the conventional generation mix, existence of energy storage devices, and the penetration level of wind energy. The simulation results demonstrate that wind power forecasting brings clear benefits to power system operations.

The benefits of flexibility: The value of wind energy with hydropower

Several studies have shown that the revenue of wind power generators on spot markets (“market value”) diminishes with increasing deployment. This “value drop” is mostly observed in power markets that are dominated by thermal power plants, such as in Germany. This paper assesses the wind market value in power systems where hydroelectric stations with large reservoirs prevail, such as in Sweden. Due to their dispatch flexibility, such hydropower compensates for wind power output variability and thereby mitigates the wind power value drop. The market value of electricity from wind declines with penetration in both types of power systems, but it tends to decline at a slower rate if hydropower is present. This paper presents empirical evidence on the relevance of this effect derived from market data and numerical model results. Our results indicate that when moving from 0% to 30% wind penetration, hydropower mitigates the value drop by a third. As a result, 1 MWh of wind energy is worth 18% more in Sweden than in Germany. Sensitivity analyses indicate high robustness despite large parameter uncertainty: in 80% of all sensitivities, wind energy is valuable 12–29% more in Sweden than in Germany. The benefits of hydropower seem to level off at around 20% wind penetration. This suggests that the hydro flexibility is “exhausted” at this level. Low wind speed wind turbines, carbon pricing, and upgrades of hydropower generation capacity can lever the added value of hydro flexibility further. Not only is wind energy more valuable in the presence of hydropower, hydroelectricity also becomes more valuable if paired with wind power.

Dispatchability Maximization for Co-Optimized Energy and Reserve Dispatch With Explicit Reliability Guarantee

In this paper, we consider dispatchability as the set of all admissible nodal wind power injections that will not cause infeasibility in real-time dispatch (RTD). Our work reveals that the dispatchability of the affine policy based RTD (AF-RTD) is a polytope whose coefficients are linear functions of the generation schedule and the gain matrix of affine policy. Two mathematical formulations of the dispatchability maximized energy and reserve dispatch (DM-ERD) are proposed. The first one maximizes the distance from the forecast to the boundaries of the dispatchability polytope subject to the available production cost or reserve cost. Provided the forecast value and variance of wind power, the generalized Gauss inequality (GGI) is adopted to evaluate the probability of infeasible RTD without the exact probability distribution of wind power. Combining the first formulation and the GGI approach, the second one minimizes the total cost subject to a desired reliability level through dispatchability maximization. Efficient convex optimization based algorithms are developed to solve these two models. Different from the conventional robust optimization method, our model does not rely on the specific uncertainty set of wind generation and directly optimizes the uncertainty accommodation capability of the power system. The proposed method is also compared with the affine policy based robust energy and reserve dispatch (AR-ERD). Case studies on the PJM 5-bus system illustrate the proposed concept and method. Experiments on the IEEE 118-bus system demonstrate the applicability of our method on moderate sized systems and its scalability to large dimensional uncertainty.

A simple hourly wind power simulation for the South-West region of Western Australia using MERRA data

A simple simulator capable of generating synthetic hourly values of wind power was developed for the South West region of Western Australia. The global Modern Era Retrospective Analysis for Research and Applications (MERRA) atmospheric database was used to calibrate the simulation with wind speeds 50 m above ground level. Analysis of the MERRA data indicated that the normalised residual of hourly wind speed had a double exponential distribution. A translated square-root transformation function yn=((1.96+ye)−1.4)/0.302 was used to convert this to a normal-like distribution so that autoregressive (AR) time series analysis could be used. There was a significant dependency in this time series on the last 3 h m so a third order AR model was used to generate hourly 50 m wind speed residuals. The MERRA daily average 50 m wind speed was found to have a Weibull-like distribution, so a square root conversion was used on the data to obtain a normal distribution. The time series for this distribution was found to have a significant dependency on the values for the last two days, so a second order AR model was also used in the simulation to generate synthetic time series values for the square root of the daily average wind speed. Seasonal, daily, diurnal, and hourly components were added to generate synthetic time series values of total 50 m wind speed. To scale this wind speed to turbine hub height, a time varying wind shear factor model was created and calibrated using measured data at a coastal and an inland site. Standard wind turbine power curves were modified to produce an estimate of wind farm power output from the hub-height wind speed. Comparison with measured grid supervisory control and data acquisition (SCADA) data indicated that the simulation generated conservative power output values. The simulation was compared to two other models: a Weibull distribution model, and an AR model with normally distributed residuals. The statistical fit with the SCADA data was found to be closer than these two models. Spatial correlation using only the MERRA data was found to be higher than the SCADA data, indicating that there is still a further source of variability to be accounted for. Hence the simulation spatial correlation was calibrated to previously reported findings, which were similar to the SCADA data.

Comprehensive Value Assessment of Wind Power by Layer

Great emphasis has been placed on rational value assessment of wind power in system in recent times. Because there are different properties in various output levels of wind power, there should also be different values. This paper proposes a novel method for value assessment of wind power to get a more comprehensive and accurate evaluation. Wind power outputs are divided horizontally based on the wind layer model. Then comprehensive studies of cost and benefit are conducted layer by layer. Furthermore, the detailed characteristics in layers, storage benefit, and value allocation are analyzed. After that, the sensitivities about the chosen system, parameters, region size, and demand-side participation are discussed at length. The effectiveness and rationality of the model are proved by the case study. The model assesses the value of wind power more precisely and profoundly, which would provide significant guidance in the setting of price policy for wind power and market supervision.

Assessment of Wind Energy Potential in Golestan Province of Iran

Renewable energy sources are estimated to have a thriving future in many countries as well as Iran. The aim of this work is the evaluation of wind energy potentiality for the five counties of Golestan province in the northern region of Iran. A long term data source, consisting of 30 years in Gorgan, 22 years in Gonbade-e Qabus, 21 years in Maraveh Tappeh, 9 years in Aliabad, and 7 years in Bandar-e Turkaman of eight-hourly mean wind data, was adopted and analyzed. Mean wind power based on quantified data, Weibull distribution function, the relative percentage error (RPE) and wind direction between obtaining values of wind power has been considered. According to these data, it was found that the numerical values of the shape parameter and scale parameter for Golestan varied a tight range. Annual values of ‘‘k’’ ranged from 2.7 to 4.7 where it is constant in different elevation because of better performance of this method in estimating wind energy potential, while annual values of ‘‘c’’ were in the range of 2.6 m/s in 10 m and 7.6 m/s in 40 m. Wind power densities have been estimated and relatively low for large wind turbines. The consequences indicate that in some months Maraveh Tappeh and Bandar-e Turkaman has best wind energy potential, as class 2, in order to establish some small wind turbine models for the sustainable development of Golestan province. Article History: Received Sept 13, 2015; Received in revised form Dec 27, 2015; Accepted January 17, 2016; Available online How to Cite This Article: Babayani, D., Khaleghi, M. and Hashemi-Tilehnoee, M. (2016) Assessment of Wind Energy Potential in Golestan Province of Iran. Int. Journal of Renewable Energy Development, 5(1), 25-31. http://dx.doi.org/10.14710/ijred.5.1.25-31

The Market Value of Wind and Solar Power: An Analytical Approach

Several studies have shown that the revenues of wind and solar power generators on spot markets (“market value”) decline with increasing deployment. This “value drop” is often discussed quantitatively but infrequently analytically, a gap that this paper aims to fill. We derive a formal expression of the market value as a function of the penetration rate. At low deployment, the market value is driven by the covariance over time between winds or sunshine and electricity consumption. In countries where power demand peaks at noon during summer, the value of solar power is initially high; the equivalent is true for wind power in those regions where stormy winters coincide with periods of high demand for heating. As deployment increases, however, we show that the market value declines linearly with the penetration rate in energy terms (market share). The slope of the decline is determined by the relative variance of wind or sun: the more the output is concentrated in a few hours of the year, the steeper the drop in value. It is in this sense that variability (intermittency) “causes” the value drop. A drop in market value is also a feature of a power generation technology that operates constantly, but the drop is smaller in size.

Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Singular Spectrum Analysis (SSA), this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm), to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons.

Application and economic viability of wind turbine installation in Lutak, Iran

There is a lack of studies on evaluating the economic feasibility of large-scale wind turbine development in Iran. Thus, this study aims at analyzing the feasibility of large wind turbines installation in Lutak region, situated in the southeast of Iran. For this aim, the 10-min average recorded wind speed data at 40 m height collected from January 2008 to December 2009 in Lutak are analyzed. Based on three reliable statistical indicators, it is found that the Weibull function enjoys excellent capability to analyze the wind data in Lutak. The wind data analysis reveals that the period of May to September is the windiest time of the year for which the wind power classification falls into class 6 or 7. Nevertheless, due to the lower wind potential in the early and late days of year, very high differences are observed between daily mean wind speed as well as wind power values throughout the year. The highest and lowest mean wind speed and wind power occur in July and January. The wind speed lies between 3.85 and 11.26 m/s while the wind power ranges from 78.79 to 1210.32 W/m2. It is also observed that wind blows predominantly from northwest and north directions in Lutak. The performance and economic feasibility of installing four different types of wind turbines with the rated power of 600–900 kW are examined for installation at 40 m height. The attained results indicate that the EWT 52/900 kW wind turbine is a more appropriate economical option.

Factors influencing calculation of capacity value of wind power: A case study of the Australian National Electricity Market (NEM)

Calculation of wind power capacity values for risk assessment of power system adequacy has attracted great attention in the literature. And the most popular approach has been the Effective Load Carrying Capability (ELCC) method which allows for the consideration of key factors such as wind capacity factor, forced outage rates (F.O.R) of conventional power stations, system reliability targets, and the correlation between wind availability and system load. However, comparatively little attention has been paid to analysing the effects of other factors such as the number of wind farms and wind installed capacity, the length of historic time series data on demand and wind resources. This paper provides an in-depth analysis of how these factors influence the calculation of capacity value for the Australian National Electricity Market (NEM) power system using metered half-hourly wind and load data for 2006 to 2013. The analysis incorporates the periods with extreme risk events. Our results show that capacity values depend greatly on the design of the simulation model used, and highlight the importance of capturing wind and load data points relating to extremely high demand periods. We compare our NEM-wide estimates to recent estimates for the State of South Australia.

Assessing Wind Energy Uncertainty Impact on Joint Energy and Reserve Markets by using Stochastic Programming Evaluation Metrics

Wind energy is considered as a challenging problem for power systems because of its variable nature. As a result of accommodating wind resources with conventional thermal units in the system, performing of electricity markets will be more complicated. The results of the market clearing problem can be improved by proper wind uncertainty modeling. One of the best methods in order to handle wind uncertainty is to use stochastic programming. The expected value of the perfect information (EVPI) and value of the stochastic solution (VSS) are two well-known indices defined to analyse stochastic programming results compared to a case with having full information and a case disregarding uncertainty, respectively. In this paper, impact of the wind uncertainty on the EVPI and VSS indices is investigated in the joint energy and reserve market clearing problem. Wind uncertainty is characterized by two measures, namely wind penetration level and wind forecasting accuracy level. The wind penetration levels are modeled as a fraction of a basic wind power value while various forecasting accuracy levels are modeled by normal probability distribution function with different variances. The problem is investigated and results are analyzed under different levels of the wind penetration and forecasting accuracies. Based on obtained results, the value of the EVPI metric increases with increment of the wind penetration level or forecasting error variance. Also, because of the higher uncertainty level of the wind power arising from penetration level or variance increment, the value of the VSS metric increases.

Wind power potential assessment for three buoys data collection stations in the Ionian Sea using Weibull distribution function

ABSTRACTThe present article utilizes wind measurements from three buoys data collection stations in Ionian Sea to study the wind speed and power characteristics using the Weibull shape and scale parameters. Specifically, the site dependent, annual, and monthly mean patterns of mean wind speed, Weibull parameters, frequency distribution, most probable wind speed, maximum energy carrying wind speed, wind power density and wind energy density characteristics have been analyzed. The Weibull distribution was found to represent the wind speed distribution with more than 90% accuracy, in most of the cases. Moreover, the correlation between the percentages of times the wind speed was above cut-in-speed and the measured mean wind speed for the three selected sites, as the correlation between the aforementioned percentages and the scale parameter c were examined and were found linear. At all these sites, no definite increasing or decreasing trends in annual mean wind speed values could be detective over the data reporting period. The mean values of wind speed, scale parameter, most probable wind speed, maximum energy carrying wind speed, wind power and wind energy density values showed higher values during winter time and lower in summer time in Pylos and Zakynthos. Moreover, Pylos and Zakynthos were found to be the best sites from wind power harnessing point of view.