Impact Of Wind Power Uncertainty Research Articles

GCNInformer: A combined deep learning model based on GCN and Informer for wind power forecasting

AbstractWind power energy is green, clean, and renewable, which is random and volatile. The integration of unstable wind energy severely threatens the security and constant operation of the power system. The need to enhance the reliability of wind power grid integration, mitigate the impact of wind power uncertainty, and develop a robust prediction model has become a pressing issue. However, only some people have considered the correlations among the power of multiple adjacent wind turbine arrays. In this paper, we propose GCNInformer to construct these relationships. Furthermore, we analyze the relationships among multiple features of individual wind turbines. GCNInformer is composed of two main components. The first component employs a graph convolutional network (GCN) to establish relationships among multiple wind turbine arrays, enhancing the correlation of the data. The second part employs Informer to extract the time information from the data and predict long‐term sequences. For training and testing, GCNInformer utilizes two data sets: Data_CQ and Data_DL. The evaluation of the model's performance is conducted using various metrics such as mean absolute percentage error, mean absolute error, root mean square error, and mean square error. Numerous experimental findings have validated the effectiveness of the GCNInformer.

Consider Carbon Emission Flow Analysis of Power System with Regards to Power Loss and Wind Power Uncertainty

A method for analyzing the impact of wind power uncertainty and active power loss on carbon emission flow in power systems is proposed. Based on the stochastic power flow calculation of wind power injection, the correlation function between wind power output and the total carbon injection rate into the system is determined. Meanwhile, the reverse power flow method is used to analyze the contribution of each unit to the active power flow in different parts of the system and calculate the corresponding carbon emissions. Under the assumption that wind power fluctuations are borne by balancing units, this method combines a conventional unit and a system node, branch, load, and active network loss correlation matrix calculation model to solve the average impact factor of wind power injection on other nodes and branches, thereby determining the uncertain characteristics of carbon emission flow in power systems under stochastic and intermittent wind power injection. The correctness and effectiveness of the method are proved by analyzing the IEEE 14-node standard example, providing new theoretical support and ideas for quantitatively evaluating the low-carbon contribution of wind power generation to the system.

Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model

Uncertainty in wind power is often unacceptably large and can easily affect the proper operation, quality of generation, and economics of the power system. In order to mitigate the potential negative impact of wind power uncertainty on the power system, accurate wind power forecasting is an essential technical tool of great value to ensure safe, stable, and efficient power generation. Therefore, in this paper, a hybrid intelligent model based on isolated forest, wavelet transform, categorical boosting, and quantile regression is proposed for deterministic and probabilistic wind power prediction. First, isolated forest is used to pre-process the original wind power data and detect anomalous data points in the power sequence. Then, the pre-processed original power sequence is decomposed into sub-frequency signals with better profiles by wavelet transform, and the nonlinear features of each sub-frequency are extracted by categorical boosting. Finally, a quantile-regression-based wind power probabilistic predictor is developed to evaluate uncertainty with different confidence levels. Moreover, the proposed hybrid intelligent model is extensively validated on real wind power data. Numerical results show that the proposed model achieves competitive performance compared to benchmark methods.

A tri‐level optimization strategy incorporating wind power against coordinated cyber‐physical attacks

AbstractWith the fast development of cyber‐physical power systems, coordinated cyber‐physical attacks have become one of the main threats to the security of modern power grid. Considering that it is difficult for attackers to obtain full information of the entire power grid, the coordinated cyber‐physical attack problem for partitioned power grid is studied in this paper, and a tri‐level defender‐attacker‐dispatcher optimization model with consideration of wind power uncertainty in a specific coordinated cyber‐physical attack mode is proposed. The Karush‐Kuhn‐Tucker (KKT) condition and an improved implicit enumeration algorithm are applied to obtain the solution of the complicated optimization problem effectively. In addition, two risk assessment indices pertinent to the physical lines and load measurements are proposed according to the obtained total amount of optimized load shedding and coordinated attack‐defense strategy under valid attack‐defense scenarios. Taking a partitioned IEEE 39‐bus test system as example, the risks of physical transmission lines and load measurements in different areas under attacks are elaborately studied, and the impact of wind power uncertainty on attack‐defense strategies is discussed as well. Numerical simulation results illustrate the effectiveness of the proposed optimization model and risk assessment indices, and some significant conclusions are drawn.

Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm

Dynamic economic emission dispatch (DEED) in combination with renewable energy has recently attracted much attention. However, when wind power is considered in DEED, due to its generation uncertainty, some additional costs will be introduced and the stability of the dispatch system will be affected. To address this problem, in this paper, the energy-storage characteristic of electric vehicles (EVs) is utilized to smooth the uncertainty of wind power and reduce its impact on the system. As a result, an interaction model between wind power and EV (IWEv) is proposed to effectively reduce the impact of wind power uncertainty. Further, a DEED model based on the IWEv system (DEEDIWEv) is proposed. For solving the complex model, a self-adaptive multiple-learning multi-objective harmony-search algorithm is proposed. Both elite-learning and experience-learning operators are introduced into the algorithm to enhance its learning ability. Meanwhile, a self-adaptive parameter adjustment mechanism is proposed to adaptively select the two operators to improve search efficiency. Experimental results demonstrate the effectiveness of the proposed model and the superiority of the proposed method in solving the DEEDIWEv model.

Impact of wind power uncertainty on cascading failure in cyber–physical power systems

The increasing penetration of wind power may have a profound effect on the cascading dynamics of power delivery systems. In this study, we consider the impacts of packet traffic congestion, power overloading and network interaction on the failure evolution, and investigate the impacts of wind uncertainty and penetration level on the vulnerability of the cyber–physical power system to cascading failure. The proposed model takes into account the modularity of the coupled system, and incorporates both uncertainty analysis and stochastic approach into the line trip mechanism. The discrete packet traffic model is adopted to describe the influence of communication delay on the cascading failure of the power grid. Moreover, we investigate the effect of community structure on the robustness of the modular coupled system. Results indicate that increased wind uncertainty can lead to the occurrence of cascading events and higher wind power penetration increases the vulnerability of the coupled system to cascading failure. In addition, the robustness of the modular coupled system can be enhanced by dissolving the community structure of the communication network, and the timely and efficient data transmission in the communication network can make the cyber-coupled power system more robust.

Edge‐cloud collaborative architecture based multi‐time scales rolling optimization of regional integrated electrical and natural gas energy system considering wind power uncertainty

With the serious energy crisis and speedy development of natural gas utilization, regional integrated electrical and gas energy system is an efficient way to improve energy potency. This paper studies the energy scheduling of integrated electrical and gas energy system with bi-directional energy conversion at distribution network level, considering wind power uncertainty. To realize independent autonomy of energy networks, meantime adjusting scheduling strategies of integrated energy system from system-level perspective, innovative edge-cloud collaborative operation architecture is proposed. In edge servers, electrical and gas networks are optimized severally for scheduling strategies. In cloud server, the uncertainty of wind power are processed with robust optimization method, strategies of energy conversion units are updated for re-optimization. Cooperated with edge-cloud architecture, a novel multi-time scales rolling optimization framework is proposed, electrical and gas networks are optimized under day-ahead time scale in corresponding edge servers, and electrical network is further adjusted under intraday time scale in electrical edge server. Case studies show that edge-cloud collaborative operation architecture can ensure the practical feasibility of optimization, which is more suitable for energy scheduling of integrated electrical and gas energy system. Multi-time scales rolling optimization framework have significant efficiency on reducing the impact of wind power uncertainty.

Robust and opportunistic scheduling of district integrated natural gas and power system with high wind power penetration considering demand flexibility and compressed air energy storage

Coordinating integrated energy system and clean energy sources, particularly wind energy, is deemed as a desirable means to alleviate the energy and environmental crisis. However, the nature of uncertainty and intermittency of wind energy is imperative to be considered for the stable operation of the system. The gas-fired generation units with fast start-up and high ramp-rate can deepen the coupling of multiple energy sources and increase system flexibility to deal with uncertainties. This paper proposes a novel day-ahead scheduling for the district integrated natural gas and power system (DIGPS) at the presence of severe uncertainty caused by high wind power penetration. A linear and flexible energy flow equation is presented to build the energy conversion and coupling of the proposed DIGPS. Moreover, information gap decision theory (IGDT) is employed to better depict the inherent uncertainty of wind power output. Two different decisions including risk averse and opportunity seeker strategies are formulated to implement the co-optimization operation of the DIGPS. Also, compressed air energy storage (CAES) and demand response program (DRP) are introduced to reduce system operation costs and the impact of wind power uncertainty. An illustrative example and a modified IEEE 33-bus distribution system are tested to demonstrate the performance of the proposed model. Numerical testing results show that the total cost has reduced 3.63% with considering DRP. Besides, compared with stochastic programming and error-based interval forecasting method, the proposed IGDT approach can obtain cost reductions of 3% and 14%, respectively. The calculation time has been reduced by 91% in comparison with stochastic programming.

Dynamic dependence modelling of wind power uncertainty considering heteroscedastic effect

The inherent stochastic nature of wind power poses significant challenges to the safe and economic operation of power systems, and an accurate model of wind power forecast error can give operators great help in dealing with the negative impact of wind power uncertainty on the power system. To obtain accurate forecast error intervals, a wind power forecast error (WPFE) model based on dynamic Copula theory is proposed in this paper. By combining Autoregressive integrated moving average (ARIMA) method and generalized autoregressive conditional heteroscedasticity (GARCH) model, the marginal distribution of WPFE model with the ability to describe its time-varying feature is achieved. In this proposed model, 4 dynamic Copula functions are estimated and evaluated by the combination method of Inference Functions for Margins (IFM) and Akaike Information Criterion (AIC), and the best-fitted one is selected to be applied in a set of synchronous data of wind power and its forecast. The result shows that compared with the conventional method, the proposed method can provide more accurate forecast error intervals. This advantage can benefit the scheduling of the power system with high wind power penetration rate, which is also verified by a stochastic unit commitment case in modified IEEE 118-Bus system.

Distributionally-robust chance constrained and interval optimization for integrated electricity and natural gas systems optimal power flow with wind uncertainties

With increasing penetrations of gas-fired generation in power systems because of reducing gas prices and emissions regulations, the interdependent and coordinated operation of integrated electricity and natural gas systems (IEGSs) is becoming an urgent research topic. Meanwhile, the significantly increasing deployment of wind power necessitates that IEGSs operation considers wind power output uncertainty. How to model the impact of wind power uncertainty on IEGSs power and gas flows dispatch is challenging. In this paper, a hybrid distributionally-robust chance-constrained and interval optimization (DRCC-IO) based model is proposed to consider the influence of wind power uncertainty and its spatial-temporal correlation on IEGSs operation. First, the DRCC-OPF model is proposed to obtain reliable economic dispatch solutions for the electricity network considering the wind power forecast errors. The spatial-temporal correlation of the wind power plant (WPP) forecasts is considered with a sparse correlation covariance matrix. Then, the interval optimization (IO) model is used to model the impacts of the power variations of gas-fired units on the natural gas network. Finally, the proposed model considers the impacts of wind power uncertainty on both the electricity and natural gas networks. Case studies performed on a six-bus power system coupled with a seven-node gas system and an IEEE 118-bus power system with a 14-node gas system verify the effectiveness of the proposed method to improve system security and reduce costs of the IEGSs. The robustness of the wind power forecast errors can be controlled in the proposed model to trade off the security and costs of the IEGSs.

Two-stage energy generation schedule market rolling optimisation of highly wind power penetrated microgrids

Considering increasingly scale of renewable energy resources penetration in modern power system, especially the wind power generation, this paper constructs a joint operation system which combined the wind turbine with the battery energy storage system (BESS) in a highly wind power penetrated microgrid, in order to weaken the impact of wind power uncertainty. Simultaneously, the demand response (DR) is optimally incorporated into system operation as flexible resources to adjust the imbalance of supply-demand in the microgrid. The battery investment capital and operation maintenance cost are also modeled as components of the optimisation. A novel two-stage energy generation schedule market rolling optimisation framework, which consists of the day-ahead energy generation schedule market and intraday energy generation reschedule market, is presented to pursue the best energy profits for the microgrid. A mutation-based artificial bee colony (MABC) algorithm is designed for the day-ahead market optimisation due to the nonlinear of battery operation model in this stage, while without considering the dispatch of battery, the intraday market rolling optimisation is solved as a mixed integer programming. Simulations are carried out on a small-scale microgrid with high penetration of wind power, the results show that the constructed joint operation system can effectively reduce the effect of wind power uncertainty and the proposed two-stage energy generation schedule market rolling optimisation framework can adjust the generation schedule along with the updated wind power predicted data, and thus increase the total energy profits for the joint operation system prominently.

Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain

The growing share of electricity production from variable renewable energy sources increases the stochastic nature of the power system. This has repercussions on the markets for electricity. Deviations from forecasted production schedules require balancing of a generator’s position within a day. Short term products that are traded on power and/or reserve markets have been developed for this purpose, providing opportunities to actors who can offer flexibility in the short term. The value of flexibility is typically modelled using stochastic scenario extensions of dispatch models which requires, as a first step, understanding the nature of forecast uncertainties. This study provides a new approach for determining the forecast errors of wind power generation in the time period between the closure of the day ahead and the opening of the first intraday session using Spain as an example. The methodology has been developed using time series analysis for the years 2010–2013 to find the explanatory variables of the wind error variability by applying clustering techniques to reduce the range of uncertainty, and regressive techniques to forecast the probability density functions of the intra-day price. This methodology has been tested considering different system actions showing its suitability for developing intra-day bidding strategies and also for the generation of electricity generated from Renewable Energy Sources scenarios. This methodology could help a wind power producer to optimally bid into the intraday market based on more accurate scenarios, increasing their revenues and the system value of wind.

Probabilistic assessment of oscillatory stability margin of power systems incorporating wind farms

With the increase in penetration of wind power which is essentially intermittent and random, the dynamic performance of the power system will change significantly, and so will the characteristics of the oscillations and their stability margins. This paper provides an attempt to include probabilistic character of wind power into the power system oscillatory stability margin (OSM) analysis. Nataf transformation approach is applied to generate the wind speed samples, which models the correlation between the various wind farms. The mathematical model of OSM for wind farm integrated power system is formulated and is calculated by the integration-based eigenvalue tracing approach. Considering the uncertainties of the wind power, several statistical indices are presented to evaluate OSM. Monte Carlo simulation (MCS) is used to calculate these statistics. The impact of wind power uncertainty on OSM restricted by inter-area mode is investigated in four-machine two-area test system and 16-machine five-area test system, respectively, for different wind farm locations, wind power penetration levels and wind speed correlation (WSC) degrees. Appropriate conclusions are finally drawn.

Security Constrained Dispatch Based on Wind Power Concentrated Access

Due to wind resources distribution characteristics and current situation of power grid, wind power is mainly large-scaled and centralized developed in China. In order to meet the security requirements of power system under this circumstance, a real-time security constrained dispatch method considering the impact of wind power uncertainty on N-1 line contingency safety is proposed. Key N-1 line contingency security constraints are identified by sensitivity analysis and added into dispatching model. Robust optimization approach is employed to solve this optimal problem. Simulation results of the IEEE-30 nodes power system validated the rationality and feasibility of the proposed method.