Wind Power Generation Scenarios Research Articles

A Wind Power Scenario Generation Method Based on Copula Functions and Forecast Errors

The scenario of renewable energy generation significantly affects the probabilistic distribution system analysis. To reflect the probabilistic characteristics of actual data, this paper proposed a scenario generation method that can reflect the spatiotemporal characteristics of wind power generation and the probabilistic characteristics of forecast errors. The scenario generation method consists of a process of sampling random numbers and a process of inverse sampling using the cumulative distribution function. In sampling random numbers, random numbers that mimic the spatiotemporal correlation of power generation were generated using the copula function. Furthermore, the cumulative distribution functions of forecast errors according to power generation bins were used, thereby reflecting the probabilistic characteristics of forecast errors. The wind power generation scenarios in Jeju Island, generated by the proposed method, were analyzed through various indices that can assess accuracy. As a result, it was confirmed that by using the proposed scenario generation method, scenarios similar to actual data can be generated, which in turn allows for preparation of situations with a high probability of occurrence within the distribution system.

Adversarial Representation Learning for Intelligent Condition Monitoring of Complex Machinery

Condition monitoring (CM) of machinery is important for ensuring the reliability of industrial processes. To adapt to the rareness of data from faulted machinery, semi-supervised CM can be implemented by training on only healthy samples. However, the performance of CM can be impaired by the variability of operating data acquired from complex machinery. Additionally, the accuracy of results is limited by the impractical assumption that samples under different health conditions are naturally separable. To address these problems, an adversarial representation learning method is developed herein. The method is trained by reconstructing operating data in both signal and latent spaces, and adversarial evolution is adopted to avoid the convergence at local optima. In this case, data representations of health conditions can be obtained to suppress the volatility of measurements, and redundant information can be reduced by latent codes. Moreover, a strategy of representation embedding is developed to impose constraints on unhealthy data, guaranteeing separable samples under distinct health conditions in the monitoring stage. Furthermore, feature fusion is conducted to avoid missing detailed information on health conditions. The satisfactory performance of the proposed method is demonstrated by experiments in test benches and actual scenarios of wind power generation.

Wind Energy and the Energy Transition: Challenges and Opportunities for Mexico

We present a review of wind energy development in Mexico, factors hampering this development, and proposals for solutions to address this hampering. This review is relevant in the context of climate change mitigation strategies and the achievement of the United Nations’ sustainable development goals. Wind energy can be harvested at competitive costs to solve society’s energy poverty and climate change problems. Firstly, we present the current wind energy installed capacity and wind power generation status globally and in Mexico and discuss why Mexico is lagging behind, particularly since 2020. Despite this lag, several state governors are still considering wind energy developments. The current economic context is then considered, with community wind energy as a solution forward for wind energy development, using a successful case study from the UK that has addressed energy poverty and provided an additional income source for an island community. Any community energy project using wind as its main energy resource relies on accurate wind energy assessment in its feasibility analysis. Thus, an evaluation of different wind energy atlases for Mexico was performed, which showed that models considering microscale processes could lead to a relative difference of more than 50% when compared to those that do not consider them. This led to the conclusion that microscale effects must be considered in wind energy characterization models. Furthermore, it is acknowledged that wind faces other challenges, such as the effect of future climate change scenarios, grid planning, and vulnerability and risk associated with tropical storms, which can be substantial in Mexico. Solutions are proposed in the form of possible wind power generation scenarios, planning and implementation of centralized and distributed transmission lines, and possible wind siting and technological choices to reduce the vulnerability and risk to tropical storms. Finally, we close with some future perspectives for researchers and decision-makers. The main conclusions are that sustainable growth can only be compatible with a transition to renewable sources of energy, energy community projects can address energy poverty and achieve sustainable development goals, wind energy feasibility studies need to include microscale effects, return of investment can be improved by siting the wind farms in regions of low vulnerability and risk to extreme events, and high-voltage transmission lines are crucial for sustainable development, even with the important role that distributed systems play. Finally, turbine growth and materials recycling, among other factors, must be considered when assessing the environmental impacts of wind farm decommissioning.

Forecasting methods for wind power scenarios of multiple wind farms based on spatio-temporal dependency structure

In this paper, we first proposed a new wind power generation scheme for multiple wind farms that considers both spatial and temporal dependence of wind power time series based on copula theory in a computationally efficient way. Different vine copulas were studied and compared. Then a novel two-stage spatio-temporal sampling method is used to generate wind power scenario samples. Finally, we propose a conditional quantile sampling method that converts the samples into specific wind power generation scenarios. A case study was conducted on Wind Integration National Dataset (WIND) of National Renewable Energy Laboratory (NREL) to benchmark the performance of the new method with existing methods. The results show that the proposed method can generate scenarios with more accuracy and efficiency, which is conducive to subsequent operations.

Effects of sizing on battery life and generation cost in PV–wind battery hybrid systems

Battery energy storage system (BESS) is a crucial part of standalone renewable hybrid power systems. Dynamic battery degradation analysis and life prediction are essential for better techno-economic estimation of standalone PV–wind battery hybrid power systems. With this viewpoint, this paper aims to study battery degradation using a physics-based pseudo-two-dimensional (P2D) thermal battery model integrated with renewable PV–wind hybrid power systems and investigates the impact of BESS size variation on its degradation and its effect on the energy generation costs A power management and control strategy is developed to ensure continuous power flow with two regulation modes; (a) maximum power point tracking and (b) controlled power generation. Yearly real-world load data, operating, and ambient conditions are used to study five different percentage mixes of PV and wind power generation scenarios. For example, a mix of 70% PV-30% wind, 350 kWh BESS is needed (base case) based on the demand. The yearly degradation rate for this case is calculated to be 3.80%. The degradation rates vary from 3.80 to 2.33% per year for every 10% increment in the BESS size from the base case. Performing a techno-economic analysis reveals that the least energy generation cost is achieved when increasing the BESS size by 20%. This increases BESS life from 5.3 years to 7.3 years and reduces the generation cost from 35.19 ₹ kWh−1 (0.482 $ kWh−1) to 34.34 ₹ kWh−1 (0.470 $ kWh−1). These results provide essential insights to analyse the impact of BESS sizing on degradation and energy generation cost in a standalone PV–wind battery hybrid power system framework. The oversized BESS provides extended life and reduces the energy generation cost for a standalone PV–wind–battery hybrid power system.

Strategic bidding of an energy storage agent in a joint energy and reserve market under stochastic generation

This work presents a bi-level optimization model for a price-maker energy storage agent, to determine the optimal hourly offering/bidding strategies in pool-based markets, under wind power generation uncertainty. The upper-level problem aims at maximizing storage agent's expected profits, whereas at the lower-level problem, a two-stage sequential market clearing procedure takes place. The first stage refers to a jointly cleared energy and reserve day-ahead market, deriving the optimal quantities for energy dispatch and reserve procurement, while at the second stage, a real-time energy-only market settlement is realized, considering plausible wind power generation scenarios. Real-time decisions include activated upward and downward reserves, already procured in the day-ahead market. The bi-level model is initially transformed into a mathematical program with equilibrium constraints -by recasting the lower-level problem using its Karush-Kuhn-Tucker optimality conditions-, which is further reduced into a mixed integer linear program. The proposed mathematical framework is applied to a 6-bus power grid, incurred by network transmission constraints. Numerical simulations illustrate an explicit perspective of the potential arbitrage opportunities for the storage agent, when acting strategically in energy and reserve markets, under wind generation increment scenarios and plausible transmission line congestions.

An overview of performance evaluation metrics for short-term statistical wind power forecasting

Wind power forecasting has become an essential tool for energy trading and the operation of the grid due to the increasing importance of wind energy. Therefore, estimating the forecast accuracy of a WPF model and understanding how the accuracy is calculated are necessary steps to appropriately validate WPF models. The present study gives an extensive overview of the performance evaluation methods used for assessing the forecast accuracy of short-term statistical wind power forecast estimates, and the concept of robustness is introduced to determine the validity of a model over different wind power generation scenarios over the testing set. Finally, a numerical study using decomposition-based hybrid models is presented to analyse the robustness of the performance evaluation metrics under different conditions in the context of wind power forecasting. Data from Ireland are employed using two different resolutions to examine its influence on the forecast accuracy.

Regionalisation of islanded microgrid considering planning and operation stages

To harmonise the operation of renewable and conventional power generation, the radial distribution network of islanded microgrids (MGs) has been regionalised into two types of regions namely conventional generation dominant regions (CGRs) and renewable generation dominant regions (RGRs). RGRs will operate on constant frequency control methodology and CGRs will operate using conventional control techniques. A framework starting from the planning stage where sensitivity analysis-based approach has been implemented for regionalisation and then optimisation of distributed generation (DG) units has been carried out for regionalised MGs (RMGs) is proposed in this study. At operation stage, a short-term dispatch framework has been proposed based on the forecasted wind power generation scenarios with a battery energy storage system in the RMGs. A power exchange strategy with a scheduled pattern between the regions has also been developed to make the scheme more dispatchable and reliable. Multi-objective optimisation algorithms have been developed for DGs planning and power dispatch in RMGs. The performance of the proposed RMGs has been studied by implementing it with four different modes of operation by converting IEEE 15-bus radial distribution system into an islanded MG.

Wind Speed Clustering Using Linkage-Ward Method: A Case Study of Khaaf, Iran

The use of renewable energy for providing electricity is growing rapidly. Among others, wind power is one of the most appealing energy sources. The wind speed has direct impact on the generated wind power and this causes the necessity of wind speed forecasting. For better power system planning and operation, we need to forecast the available wind power. Wind power is volatile and intermittent over the year. For getting better insight and a tractable optimization problem for different decision making problems in presence of wind power generation, it is required to cluster the possible wind power generation scenarios. This article presents probabilistic wind speed clustering prototype for wind speed data of Khaaf, Iran. This region is known as one of the high potential wind sites in Iran and several wind farm projects is planned in this area. The average speed of wind for a ten-minute period measured at height of 40m over a year (2008) is used for clustering. From the result of this research, the most appropriate probabilistic model for the wind speed can be obtained.

Clustering based unit commitment with wind power uncertainty

Wind power generation is continuously increasing around the world, but due to uncertainty in wind power generation, the unit commitment problem has become complex. In this paper, scenario generation and reduction techniques are used to consider wind power uncertainty on system operation. Also, a new approach is developed for creating clusters of unit status associated with a probability of occurrence from an initial set of large wind power generation scenarios. And then a model of wind-hydro-thermal coordination problem along with the pumped storage plant is established. Combination of proposed weighted-improved crazy particle swarm optimization along with a pseudo code based algorithm and scenario analysis method is utilized to solve above problem. The effectiveness and feasibility of the proposed method is tested on systems with and without pumped storage plant integration. The results are analyzed in detail, which demonstrate the model and the proposed method is practicable in solving the unit commitment.

Coordinated Operational Planning for Wind Farm With Battery Energy Storage System

This paper proposes a coordinated operational dispatch scheme for a wind farm with a battery energy storage system (BESS). The main advantages of the proposed dispatch scheme are that it can reduce the impacts of wind power forecast errors while prolonging the lifetime of BESS. The scheme starts from the planning stage, where a BESS capacity determination method is proposed to compute the optimal power capacity and energy capacity of BESS based on historical wind power data; and then, at the operation stage, a flexible short-term BESS-wind farm dispatch scheme is proposed based on the forecasted wind power generation scenarios. Three case studies are provided to validate the performance of the proposed method. The results show that the proposed scheme can largely improve the wind farm dispatchability.