Uncertainty Of Wind Power Generation Research Articles

A Hybrid Approach Based on Unequal Span Segmentation-Clustering for Short-Term Wind Power Forecasting

Because the fluctuation and uncertainty of wind power generation bring severe secure and economic challenges to power systems, wind power forecasting becomes the critical part of the management in power systems. In this paper, a hybrid approach based on unequal span segmentation-clustering is proposed to mine the variation trend information in the wind speed series for improving forecasting performance. Firstly, the wind speed series representation is proposed to accurately represent the major variation trend of the wind speed series. It reduces the influence of the non-stationary and irregular behaviors of the wind speed series on the unequal span segmentation-clustering. Secondly, the shape-position comprehensive evaluation is proposed to combine shape and position measures to evaluate the clustering of the trend segments with unequal length. It identifies the trend segments which have comprehensive similarity on both shape and position to better construct forecasting models. Thirdly, novel bayesian optimization mutation operators are proposed to optimally move or add cut points in the unequal span segmentation. It enhances the local search capability of the unequal span segmentation. By comparing different approaches using wind datasets from two wind farms, the effectiveness and advantages of the proposed approach are demonstrated.

Probabilistic Analysis of Small-Signal Stability of Power Systems Affected by Wind Power Generation Uncertainties Considering Wake Effects

Probabilistic Analysis of Small-Signal Stability of Power Systems Affected by Wind Power Generation Uncertainties Considering Wake Effects

Nataf-KernelDensity-Spline-based point estimate method for handling wind power correlation in probabilistic load flow

Modern power systems integrated with renewable energies (REs) contain many uncertainties. The proposed method introduces a novel approach to address the challenges associated with wind power generation uncertainty in probabilistic load flow (PLF) studies. Unlike conventional methods that use wind speed as an input, the paper advocates for utilizing wind generator output power (WGOP) as an input to the point estimate method (PEM) in solving PLF. The uniqueness lies in recognizing the distinct behavior of wind power uncertainty, where not all random samples of wind speed contribute to actual wind power production. The paper suggests a Nataf-KernelDensity-Spline-based PEM, combining the Nataf transformation, Kernel density estimation (KDE), and cubic spline interpolation. This innovative integration effectively manages wind power correlation within the analytical framework. By incorporating spline interpolation and kernel density estimation into the traditional PEM, the proposed method significantly enhances accuracy. To validate the effectiveness of the proposed approach, the method is applied to IEEE-9 and IEEE-57 bus test systems, considering uncertainties related to load, wind power generation (WPG), solar power generation (SPG), and conventional generator (CoG) outages. Comparative analysis with Monte Carlo simulation (MCS) results demonstrates that the proposed method outperforms the conventional PEM in terms of accuracy. Overall, the paper contributes a pioneering solution that not only highlights the importance of using WGOP as an input in PLF but also introduces a sophisticated method that surpasses traditional approaches, improving accuracy in power system studies involving renewable energy integration. The accuracy of the proposed method is validated by comparing its results with those obtained through Monte Carlo simulation (MCS), where the proposed method yields more accurate results than the conventional PEM.

Cross-regional green certificate transaction strategies based on a double-layer game model

With the implementation of the renewable energy quota system and the development of the green certificate market, the diversification of cross-region trading subjects has brought about the problem of complicating the competition and cooperative relationship between multiple subjects. This problem makes it difficult to balance the conflict of interests of multiple participating subjects in green certificate transactions, and reduces the enthusiasm for trading. To this end, this paper proposes a multi-participant decision model of cross-regional green certificate trading behavior that takes into account the uncertainty of renewable energy generation. In the model, in order to explore the potential full set of strategies of the participating subjects, the cooperative game theory is utilized to analyze the complex interaction between multiple participating subjects in the cross-regional transaction, and the cooperative game model based on green certificates is established. As the participating subjects' information about other participants and their knowledge of the game environment are limited, the decision-making behavior is uncertain. In order to more reasonably portray the subject's game behavior in the actual decision-making scenario and to solve the defect that the traditional evolutionary game model can not reflect the influence of uncertain factors, we use the stochastic evolutionary game theory to dynamically deduce the cooperative strategies of the participating subjects under the limited rationality, and at the same time, introduce the strategy influencing factors to explore the dependence relationship between strategies. In addition, in order to cope with the impact of power generation uncertainty on cross-regional trading and to solve the defects of distributionally robust fuzzy set construction which is subjective and only for the analysis of uncertainty in a single time section, it is proposed to use Bayesian multivariate nonlinear model and mixed-effects model to construct probability distribution fuzzy sets of wind and light power generation uncertainty on the basis of distributionally robust theory. Finally, based on the arithmetic example, the cooperative strategy of multi-participants in cross-regional transactions under finite rationality is deduced, and the impact of uncertainty factors on the cooperative strategy is analyzed.

How do uncertain renewable energy induced risks evolve in a two-stage deregulated wholesale power market

Integrating renewable power into power market transactions is significant in terms of achieving carbon peaking and carbon neutrality. However, the integration of intermittent renewable energy sources (RES) introduces instability into power generation and brings certain risks to deregulated power market transactions. This study aims to identify the risks arising from RES power integration and analyze how these risks transmit and evolve in deregulated power markets. A Cournot game model is constructed to analyze the generation decisions of a thermal power generation company (GENCO) and a wind power GENCO in a two-stage power market. According to the results, in the mid-to-long-term power market, an increase in the uncertainty of wind power generation (WPG) could reduce wind power supply, increase the clearing price and decrease the power demand. Then, in the spot power market, the increased uncertainty related to WPG might decrease the amount of wind power traded, increase the marginal generation cost of the thermal power GENCO, raise the clearing price, and reduce the market demand for power. Finally, the uncertainty related to wind power generation could result in diminished revenue for the wind power GENCO and a loss in overall social welfare in the two-stage power market transaction.

Integrated Assessment of the Reliability and Frequency Deviation Risks in Power Systems Considering the Frequency Regulation of DFIG-Based Wind Turbines

Recently, frequency regulation strategies have been widely adopted in the operation and control of doubly fed induction generator-based wind turbines (DFIG-WTs). Thus, wind power, which is becoming an increasingly important energy source, is expected to play a significant role in both power generation and frequency regulation in modern power systems. Under such circumstances, maintaining the reliability and frequency of power systems at a designated level may be more challenging due to inherent uncertainties in wind power generation. In this paper, the integrated assessment of the reliability and frequency deviation risks of power systems with a high penetration level of wind power is investigated. A multi-time scale analytical framework is proposed to calculate the integrated reliability and frequency deviation indices. The coupling between the reliability and frequency deviation is further addressed by developing a novel frequency-sensitive reliability model of the electric generator. Frequency deviations under supply/demand fluctuations and device failures are analyzed, and the power system frequency regulation process is modeled with the fuzzy adaptive virtual inertial response of DFIG-WTs and energy storage system (ESS). Furthermore, IEEE-RTS79 is used to verify the validity of the proposed model and solution method.

Optimal configuration for the wind-solar complementary energy storage capacity based on improved harmony search algorithm

With the increase in the permeability of renewable energy, the randomness and uncertainty of photovoltaic power generation and wind power generation have an impact on the stable operation of the power grid. Energy storage technology can effectively improve the controllability of new energy, but the optimal allocation of energy storage capacity has always been a difficult problem limiting the development of energy storage technology. In this paper, the capacity optimization model of the complementary energy storage system is established based on the analysis of the wind-solar energy storage principle and the energy balance criterion. Then, with the minimum investment cost, the highest economic benefit, low wind and light abandonment rate and low load power shortage rate as the optimization objectives, the harmony search algorithm was used to solve the optimization model under the constraint of stable operation of the system, and the optimal capacity allocation scheme was obtained. The results of the example show that the optimization strategy in this paper can reduce the investment cost of the system and improve the reliability of the power supply and energy efficiency of the system.

Smart City Facility Location Recommendation Algorithm Using Multimedia Data and Improved Generative Adversarial Networks

Internet of Things, low energy consumption, and intelligent and functionally integrated urban infrastructure construction are crucial elements in the development of smart cities. Distributed generation (DG) and electric vehicle charging infrastructure play a vital role in the planning and construction of smart cities. However, the uncertainty associated with the power output of distributed generation significantly impacts the planning of distribution networks. To address this issue, this paper proposes a site-selection recommendation algorithm that leverages urban multimedia data and improved generative adversarial networks. The proposed algorithm begins by modeling the uncertainty of wind power and photovoltaic (PV) generation using an enhanced conditional generative adversarial network model. To generate multimedia datasets with time-series characteristics for wind power and PV generation scenarios, monthly multimedia data labels are incorporated into the model. These multimedia datasets, representing a wide range of scenarios, are then clustered using the K-means clustering method. Furthermore, a distributed generation planning model is established, aiming to minimize the annual integrated cost. The planning problem is efficiently solved using CPLEX, a mathematical programming solver. In the simulation experiments, the proposed scheme is compared with alternative schemes. The results demonstrate that the proposed scheme achieves a significant total cost saving of 21.95% compared to the comparison scheme. Moreover, the experimental comparison reveals that the proposed scheme exhibits higher stability. Additionally, in terms of algorithm efficiency, the proposed algorithm outperforms the other three algorithms tested in terms of the number of iterations and speed. The experimental results highlight the effectiveness of the proposed planning model in improving the economy and stability of the distribution network. Furthermore, it enhances the computational efficiency of the planning problem associated with distributed power supply and electric vehicle charging stations. The findings of this research hold substantial research significance for the site selection planning of distributed power supply.

A Review of Modern Wind Power Generation Forecasting Technologies

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

Expected value model of microgrid economic dispatching considering wind power uncertainty

New energy microgrid is one of the development trends of the future power grid. As there are wind turbines and other new energy equipment in the microgrid, its output is easily affected by unstable weather factors. Moreover, the complexity of dispatching caused by the storage battery in the microgrid and the non-linear factors of the diesel generator have brought great challenges to the economic dispatching of the microgrid. To tackle these problems, this paper establishes the expected value model of microgrid economic dispatching considering the uncertainty of wind power generation and the factors such as battery and diesel generator and proves that this expected value model is convex. On this basis, the piecewise linearization method is proposed to find the global optimal solution of the economic dispatch. Finally, a representative microgrid is optimized to verify the effectiveness of expected value model.

A combination predicting methodology based on T-LSTNet_Markov for short-term wind power prediction

ABSTRACT Wind power has been valued by countries for its renewability and cleanness and has become most of the focus of energy development in all countries. However, due to the uncertainty and volatility of wind power generation, making the grid-connected wind power system presents some serious challenges. Improving the accuracy of wind power prediction has become the focus of current research. Therefore, this paper proposes a combined short-term wind power prediction model based on T-LSTNet_markov to improve prediction accuracy. First, perform data cleaning and data preprocessing operations on the original data. Second, forecast using T-LSTNet model in original wind power data. Finally, calculate the error between the forecast value and the actual value. The k-means++ method and Weighted Markov process are used to correct errors and to get the result of the final prediction. The data that are collected from a wind farm in Inner Mongolia Autonomous Region, China, are selected as a case study to demonstrate the effectiveness of the proposed combined models. The empirical results show that the prediction accuracy is further improved after correcting errors.

Reinforcement learning-based composite differential evolution for integrated demand response scheme in industrial microgrids

The fourth industrial revolution is being propelled by the “Energy Internet,” which aims to encourage the integration of industrial multi-energy microgrids (MEMG) and renewable energy sources. The conventional demand response (DR) schemes only utilize a single energy source, which limits the industrial users (IUs) and prevents them from making full use of the demand side's communication capabilities. However, smart industrial multi-energy microgrids (MEMGs) give IUs additional options for meeting their energy needs by integrating diverse energy sources, including electricity, natural gas, and thermal power. This new approach to DR programs is known as “Integrated Demand Response” (IDR). This research work proposes a smart IDR program for a novel grid-connected industrial MEMG framework consisting of exhaust air wind turbines, concentrated photovoltaic-thermal panels, an electrical energy storage system (EES), a thermal energy storage (TES), a diesel generator, an indirect customer-to-customer energy trading platform, and typical electrical and thermal industrial loads. The proposed smart IDR considers the uncertainties of both wind and solar power generation and the buying and selling costs of electrical and thermal energy to automatically reduce the industry's total power consumption and battery EES degradation costs. A novel State-Action-Reward-State-Action (SARSA)-based composite different evolution (DE) method is proposed to solve a complex scenario-based non-convex optimization problem. It uses two selection strategies, three mutation strategies, and a positive feedback mechanism to change the states of the individuals. The strategies are coupled in pairs, resulting in a total of six distinct actions that may be performed by the SARSA agents. This allows an individual to not get stuck at a local optimum and adaptively benefit from all the mutation and parameter selection methods. Moreover, SARSA has introduced two more factors, the discount factor and the learning rate, which further improve the optimization performance. The proposed method is also compared with five other state-of-the-art methods to prove its effectiveness in minimizing industrial energy bills and battery degradation costs. The simulated results confirmed that the proposed SARSA-based composite DE algorithm has achieved the lowest total energy cost and battery degradation costs when compared with other state-of-the-art algorithms.

Study on the Optimal Configuration of GSHP in Regional Integrated Energy Systems Considering the Uncertainty of New Energy

The study of regionally integrated energy system optimization problems is of great significance and usefulness for the development of the energy Internet. Regional integrated energy system planning and optimization modeling is the basis for rational system configuration and economical operation. It is one of the key issues in the development of integrated regional energy systems. The optimization of a regionally integrated energy system is studied in this paper. The uncertainty of wind power generation and photovoltaic power generation and the characteristic of CHP is considered. Were established. On this basis, Considering the uncertainty of new energy, a GSHP optimal allocation model based on two-layer optimization is established. A two-level optimization model is introduced. A genetic algorithm is applied to the upper layer, and a genetic algorithm is applied to the lower layer to analyze the nonlinear programming problem. Finally, different system configuration schemes are proposed and computationally solved, combined with examples for case studies to verify the feasibility of the established regional integrated energy system optimization model.

Learning-Based Coordinated Operation of Multiple Microgrids With Hydrogen Systems: A Novel Bilevel Framework

This article provides a framework for coordinating the operation of multiple microgrids with hydrogen systems in a distribution network considering the uncertainties of wind and solar power generation as well as load demands. The model is based upon a bilevel stochastic programming problem. On the upper level, the distribution system is the leader with a profit-maximization goal, and the microgrids are followers with cost-minimization goals on the lower level. The problem is solved by transforming the model to a single-level model using Karush–Kuhn–Tucker (KKT) conditions and linearized using McCormick’s relaxation and Fortuny–Amat techniques. Unlike previous studies, both levels are modeled as scenario-based stochastic problems. Moreover, the scenarios associated with uncertain variables are obtained from a real data set. After preparing the data set, scenarios are reduced using a machine learning-based clustering approach. An application of the coordinated operation model is developed for a distribution network containing several microgrids. By solving the problem, the optimal amount of power exchange and the clearing price between microgrids and distribution systems are determined. Moreover, the proposed bilevel model made 13% more profit for the distribution system than the centralized model. Also, the effects of integrating hydrogen systems with microgrids on increasing the flexibility of operators are investigated.

Combined Emission Economic Load Dispatch with Renewable Energy Sources Employing Hybrid Statistical Multiswarm Particle Swarm Optimizer-Sine Cosine Algorithm

Due to the increase in environmental concerns, it becomes mandatory now to focus on energy management and the planning of resources. An integration of distributed generation resources with conventional sources is gaining popularity. The hybrid modeling of the power system poses a complex and challenging optimization problem without getting trapped in a local optimum. A novel Hybrid Statistical Multiswarm Particle Swarm Optimizer-Sine Cosine Algorithm (HSMPSO-SCA) is developed in this paper, and its performance is evaluated on various test benchmark functions. The real operating conditions such as valve point loading, prohibited zones, ramp rate limits and uncertainties in solar and wind power generation for the IEEE 30 bus system are considered for combined emission economic load dispatch (CEELD). The performance of the HSMPSO-SCA is also analyzed for the solution of CEELD for various standard test systems with interaction of solar and wind plants. The efficacy of the proposed HSMPSO-SCA is found to be much better than various metaheuristics, hybrid and multi-agent algorithms in minimizing cost along with emissions optimally and achieves better accuracy and a faster convergence rate.

A wind power smoothing strategy based on two-layer model algorithm control

The inherent uncertainty of wind power generation leads to a mismatch between actual wind power generation and electric grid demand power, while the occurrence of sudden loads makes real-time scheduling of wind farms difficult. To address these issues, a cooperative compensation strategy of battery is proposed to compensate for the unbalanced power and sudden loads. Positive unbalanced power is used directly to compensate for sudden loads, and the energy storage absorbs the remaining positive unbalanced power to indirectly compensate for negative unbalanced power and sudden loads when the energy storage has excess power. The fluctuation rate of wind power after compensation is considered, and a smoothing model based on two-layer model algorithm control (MAC) is established. A variable mode decomposition (VMD) adaptive frequency division method is proposed to decompose the compensated power in order to smooth the high-frequency and low-frequency parts of the compensated power using different components of the hybrid energy storage to achieve accurate compensation.

A Stochastic Approach to Integrating Electrical Thermal Storage in Distributed Demand Response for Nordic Communities With Wind Power Generation

Demand response and distributed energy storage play a crucial role in improving the efficiency and reliability of electric grids. This paper describes a strategy for optimally integrating distributed energy storage units within a forward market to address space heating demand under a Stackelberg game in isolated microgrids. The proposed strategy performs distributed management in an offline fashion through proximal decomposition methods. It leverages stochastic programming to consider user flexibility degree and wind power generation uncertainties. Also, flexibility for demand response is realized through electric thermal storage (ETS). The performance of the proposed strategy is evaluated via simulation studies carried out through a case study in Kuujjuaq. Ten residential agents compose the demand side, each with flexibility levels and economic preferences. Economic benefits are evaluated on both sides across different ETS acceptance levels. The simulation results demonstrate significant benefits for the coordinator and customers. The proposed strategy reveals that ETS, in the presence of dynamic tariffs, reduces diesel consumption, maximizes renewable production and reduces grid stress.

Robust multi-objective optimization for islanded data center microgrid operations

Electricity cost has become a critical concern of data center operations with the rapid increasing of information processing demand. Data center microgrid (DCMG) is a promising way to reduce electric energy consumption from traditional fossil fuel generators and the billing cost, by effectively utilizing local renewable energy, e.g., wind power. However, uncertainties of wind power generation and real-time workload of data center would have significant impacts on the operational efficiency of DCMG, especially when it is in the island mode. For this reason, a novel affinely adjustable policy based robust multi-objective optimization model under flexible uncertainty set is proposed in this paper, which simultaneously optimizes wind power curtailment, the operation cost, and the over-plus level of computation resource, while considering uncertainties of both the wind power and real-time workload. Through numerical simulation studies, the validity of robust multi-objective optimization model for the island operation of DCMG is verified. Besides, the effectiveness of the proposed methods, i.e., the novel affinely adjustable policy and the flexible uncertainty set, in handling uncertainties are evaluated. Compared to the conventional robust multi-objective optimization model, the proposed approach reduces the operating costs of about 10% in average while maintaining similar reliability in numerical simulations. Moreover, the complex quantitative relationship among these multiple objectives is further investigated. Simulation results indicate the minimization of wind power curtailment and over-plus level of computation resource increases about 25% of the operation cost. These quantitative relationships can well support the decision making of DCMG operation management.

Optimization of Static Voltage Stability Margin Considering Uncertainties of Wind Power Generation

An optimal static voltage stability margin (SVSM) control model considering the uncertain wind farm output is proposed, in which the probability distribution of SVSM is required to satisfy the chance and variance constraints. A probabilistic distribution control method is proposed to solve the model. First, a linear weighted sum of B-spline basic functions is introduced to approximate the analytical function between the probability density function (PDF) of SVSM and control variables. Next, the analytical expression of the PDF of SVSM corresponding to a control variables’ sample is derived, and the sample of corresponding B-spline function weights is obtained, thus forming a series of ‘control variables-weights’ samples. Then, the analytic functions between weights and control variables are obtained using the generated samples to train a radial basis function neural-network, and the analytical function between the PDF of SVSM and control variables is obtained. After that, the chance and variance constraints are transformed into general nonlinear constraints with respect to control variables. Moreover, a method of selecting key control variables by calculating sensitivities is proposed to increase computational efficiency. Test results on the IEEE-39 bus system and an actual provincial power grid demonstrate the feasibility and effectiveness of the proposed method.

Collaborative Resilience to Extreme Weather in Decentralized Co-operation of Electricity and Natural Gas Distribution Systems

• A model to enhance the resilience of the integrated electrical distribution and gas networks. • A min-max robust optimization model considering worst-case scenarios. N-k and wind power generation • A multi-stage and multi-zone model to capture the spatial-temporal dynamic of extreme hurricane. • A hybrid column-and-constraints optimization solution. The flexibility of the power-to-gas (P2G) technologies and natural gas units (NGUs) can enhance the resilience of the electric distribution system (EDS) considering the high penetration of renewable energy resources (RERs). The decentralized collaborative operation (co-operation) of electric distribution systems and natural gas systems (EDSs&NGSs) considering information privacy preserving can enhance the resilience during an extreme hurricane. To address this issue, this paper proposes a three-level hierarchal solution to decompose the centralized co-operation of the EDS&NGS framework in which an independent decision-making strategy and information privacy preserving for both network operators are simultaneously addressed. Furthermore, in this paper, a min–max robust resilience-constrained co-optimization model is presented to enhance the resilience of the integrated EDS&NGS against worst-case N − k contingencies and wind power generation uncertainty under extreme hurricane events. To attain the spatial dynamics of extreme hurricanes, a the multi-zone and multi-time extreme hurricanes model is considered. Then, a column-and-constraints generation (C&CG) algorithm is used to solve the proposed robust resilience-constrained co-optimization model. To verify the effectiveness of the model, we conducted experiments on a modified IEEE-33-bus-10-node/123-bus-20-node EDS&NGS. The numerical results show that the proposed robust resilience-constrained co-operation of EDS&NGS problem is an effective model for enhancing EDS resilience.