A Wind Power Probabilistic Model Using the Reflection Method and Multi-Kernel Function Kernel Density Estimation

Chapter 9 - Integrated Planning of Energy and Water Supply in Islands

In order to improve the computational efficiency of power system time series simulation, the redundancy of wind power and photovoltaic time series data should be reduced. At present, the time series data of wind-photovoltaic are aggregated separately, wind and pv correlation is not involved, reducing the accuracy and reliability of high-proportion renewable energy power system simulation. For this reason, This paper presents a aggregation method involving wind power and photovoltaic correlation. Based on wind and photovoltaic power historical data, firstly, based on kernel density estimation method and Copula theory, generate the wind-photovoltaic kernel density function and the joint probability density function, by sampling the joint probability density function to obtain N groups of typical daily curves. Take a provincial power grid as an example, a comparative analysis was made between the proposed wind-photovoltaic correlation aggregation method and the separate wind-photovoltaic aggregation method, The results show that the aggregation series can not only accurately reflect the characteristics of original wind-photovoltaic output power, but also maintain the correlation between wind-photovoltaic output power, which has certain engineering application value.

With renewable energy sources (RESs) highly penetrating into the power system, new problems emerge for the independent system operator (ISO) to maintain and keep the power system safe and reliable in the day-ahead dispatching process under the fluctuation caused by renewable energy. In this paper, considering the small hydropower with no reservoir, different from the other hydro optimization research and wind power uncertain circumstances, a day-ahead scheduling model is proposed for a distributed power grid system which contains several distributed generators, such as small hydropower and wind power, and energy storage systems. To solve this model, a two-stage stochastic robust optimization approach is presented to smooth out hydro power and wind power output fluctuation with the aim of minimizing the total expected system operation cost under multiple cluster water inflow scenarios, and the worst case of wind power output uncertainty. More specifically, before dispatching and clearing, it is necessary to cluster the historical inflow scenarios of small hydropower into several typical scenarios via the Fuzzy C-means (FCM) clustering method, and then the clustering comprehensive quality (CCQ) method is also presented to evaluate whether these scenarios are representative, which has previously been ignored by cluster research. It can be found through numerical examples that FCM-CCQ can explain the classification more reasonably than the common clustering method. Then we optimize the two stage scheduling, which contain the pre-clearing stage and the rescheduling stage under each typical inflow scenario after clustering, and then calculate the final operating cost under the worst wind power output scenario. To conduct the proposed model, the day-ahead scheduling procedure on the Institute of Electrical and Electronics Engineers (IEEE) 30-bus test system is simulated with real hydropower and wind power data. Compared with traditional deterministic optimization, the results of two-stage stochastic robust optimization structured in this paper, increases the total cost of the system, but enhances the conservative scheduling strategy, improves the stability and reliability of the power system, and reduces the risk of decision-making simultaneously.

The short-term wind power scenarios have a significant impact on the operation cost and power system reliability due to the stochastic generation scheduling of wind-integrated power systems. In order to obtain the scenarios containing the information of forecast error distribution and fluctuation distribution for short-term wind power, a scenario generation method is proposed. This paper characterizes forecast error via empirical distributions of a set of forecast bins and assumes that wind power fluctuations over unit interval follow t location-scale distribution. An inverse transform sampling from a multivariate normal distribution is adopted to generate a large number of wind power scenarios. The covariance matrix of the multivariate normal distribution is estimated to fit the distribution of historical wind power fluctuations. The proposed scenario generation method is applied to the actual aggregate wind power data in the whole regions of Ireland's Power System. The results indicate that the variability of wind power scenarios can be adjusted by estimating the key range parameter in the exponential covariance structure of a multivariate normal distribution.

The high penetration of inverter-fed renewable energy sources (RESs) in modern energy systems has led to a reduction in the system’s inertial response. This reduction in the rotational inertial response is associated with synchronous generation and might result in a deteriorated frequency response following a power disturbance. This paper investigates the frequency stability of the Kingdom of Saudi Arabia’s (KSA) grid. It includes a description of the changing energy landscape of the KSA’s electricity grid and an investigation of the impact of high penetration levels of inverter-fed RESs on the dynamic behavior of the KSA grid. The impact of RESs has been studied through a simulation of case studies of the future KSA power system using the MATLAB/Simulink simulation software. The frequency stability of the KSA’s power system has been evaluated with various RES levels under peak and base load conditions. The simulation results show that the high penetration levels of RESs dramatically affect the system’s frequency response, especially under off-peak conditions. In addition, the significance of battery energy storage systems (BESSs) for compensating the reduction in the system inertial response has been addressed. The results show the effectiveness of aggregated BESSs for enhancing the system frequency control of the KSA grid.

Currently, with the increasing interest in renewable energy sources (RES), their penetration in the power system is expected to increase considerably. South Korea plans to increase the proportion of renewable energy to 20% by 2029 based on the 7th basic plan for long-term electricity supply and demand. On the other hand, expanding the penetration of renewable energy sources is a new challenge for conventional power systems. Most renewable energy sources, such as photovoltaic and wind power, are intermittent, and thus difficult to control without a loss of power. In addition, the intermittent characteristics leads to an imbalance of power and load. This can be solved by combining renewable energies because the output characteristics of wind power and solar are different. A reliability assessment is an appropriate method for achieving the optimal combination of renewable energy sources. Therefore, this paper proposes the optimal sizing and location of renewable energy sources in a microgrid based on a reliability assessment. In addition, a stochastic method for LOLE (Loss of Load Expectation) is also proposed to evaluate the reliability. In this case study, optimal sizing and locations are determined by the minimum LOLE and power loss in an IEEE 34-bus system.

Wind power prediction accuracy is closely related to the quality of wind power data. However, most of traditional prediction methods do not consider the cybersecurity of raw data. So, it would be important to study the prediction accuracy under false data injection attack (one type of cybersecurity attacks) and its influence on the economy and security of power system. In this paper, very short-term forecasting approach of wind power generation is studied for choosing the optimal prediction model. Different scenarios of false data injection attack are then simulated upon wind power data. Wind power prediction accuracy after being affected by false data injection is observed. The robustness of wind power prediction model is evaluated. Finally, wind power prediction information is applied to day-ahead unit commitment to evaluate the impact of cybersecurity of wind power forecasting on power system economy and security.

Optimization modeling to support renewables integration in power systems

Introduction: To date, a variety of studies have been published on the topic of long term energy system transition. Most studies on future energy systems, however, have a shorter time frame or adopt a supranational focus (e.g. the Energy Roadmap, 2011 or the World Energy Outlook, 2015). It then constitutes a sincere challenge to perform a national energy system transition study with as time horizon 2050 and covering a far-reaching transformation of the energy system. VITO, together with the FPB (Federal Planning Bureau) and ICEDD, performed a study to scrutinise the transition of the Belgian national energy system towards a future mix entirely based on renewable energy sources. The focus on renewable energy sources and on building a national energy system completely running on renewable energy can be traced back to three main concerns: – Climate change: Renewable energy sources (RES) are a major instrument in the fight against climate change as RES do not release (net) greenhouse gas emissions. – Security...

Calculation of the aeolian sediment flux-density profile based on estimation of the kernel density

Precise wind power forecasting has several benefits, such as optimized peak regulation in power systems, enhanced safety analysis, and improved energy efficiency. Considering the substantial influence of meteorological data, such as wind speed and temperature, on wind power generation, and to minimize the impact of fluctuations and complexity of wind power data on the forecast results, this paper proposes a combined wind power forecasting method. This approach is based on the long short-term memory network (LSTM) model, using the maximal information coefficient (MIC) method to select numerical weather prediction (NWP) and combining the efficiency of complete EEMD with the adaptive noise (CEEMDAN) method for nonlinear signal decomposition. Results indicate that the accuracy of the forecast results is supported by NWP. Moreover, wind power data are decomposed by the CEEMDAN algorithm and converted into relatively regular sub-sequences with small fluctuations. The MIC algorithm effectively reduces the redundant information in NWP data, and the LSTM algorithm addresses the uncertainty of wind power data. Finally, the wind power of multiple wind farms is forecasted. Comparison of the forecast results of different methods revealed that the NWP-CEEMDAN-LSTM method proposed in this paper, which considers feature extraction using MIC, effectively tracks power fluctuations and improves forecast performance, thereby reducing the forecast error of wind power.

This paper presents a novel literature survey on leveraging electrolysis for grid frequency stabilization in power systems with high penetration of renewable energy sources (RESs), uniquely integrating global research findings with specific insights into the Polish energy context—a region facing acute grid challenges due to rapid RES growth and infrastructure limitations. The intermittent nature of wind and solar power exacerbates frequency fluctuations, necessitating dynamic demand-side management solutions like hydrogen production via electrolysis. By synthesizing over 30 studies, the survey reveals key results: electrolysis systems, particularly PEM and alkaline electrolyzers, can reduce frequency deviations by up to 50% through fast frequency response (FFR) and primary reserve provision, as demonstrated in simulations and real-world pilots (e.g., in France and the Netherlands); however, economic viability requires enhanced compensation schemes, with current models showing unprofitability without subsidies. Technological advancements, such as transistor-based rectifiers, improve efficiency under partial loads, while integration with RES farms mitigates overproduction issues, as evidenced by Polish cases where 44 GWh of solar energy was curtailed in March 2024. The survey contributes actionable insights for policymakers and engineers, including recommendations for deploying electrolyzers to enhance grid resilience, support hydrogen-based transportation, and facilitate Poland’s target of 50.1% RESs by 2030, thereby advancing the green energy transition amid rising instability risks like blackouts in RES-heavy systems.

On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting

The Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.

An improved residual-based convolutional neural network for very short-term wind power forecasting