Wind Power Uncertainty Research Articles

A Multilevel Model of Energy Market Considering Coupon Incentive Based Demand Response with Wind Power Uncertainty

Demand response (DR) can decrease or increase energy consumption and has the ability to use generating assets more efficiently. Because of the increased power consumption, it has become necessary for the power system networks either to update or upgrade the already existing power system or to inculcate some schemes to minimize the load through effective customer engagement. To address this issue incentive-based demand response proved to be an effective tool to change the pattern of consumption. This work proposes a novel multilevel energy market considering coupon incentive based DR (C-IBDR). The proposed model is operated on a modified PJM 5-Bus system where the independent service operator (ISO) using economic dispatch (ED) computes the localized marginal pricing (LMP) and the load serving entity (LSE) issues coupons to consumers and procures the demand reduction from them. Two wind generators are incorporated into the test system and their effect on the localized marginal pricing with and without demand response is analyzed. The optimization problems are modeled using particle swarm optimization (PSO) and linear programming (LP) in MATLAB. The results demonstrate that the proposed C-IBDR reduces peak demand from 900 to 860 MW through the utilization of wind and DR. This alteration modifies the load curve, increases the power factor from 0.524 to 0.555, raises the revenue of LSEs from $106,646 to $106,946, reduces generation costs from $473,291 to $214,651, and procures a total of 262 MW of demand from the consumer side in the form of demand reduction. The system is further tested for scalability. The practicality of the suggested approach in handling more extensive systems is vividly demonstrated through its application to the modified IEEE 118-bus system.

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.

Nonparametric probabilistic forecasting based stochastic optimal scheduling of integrated electricity and gas systems

The volatility and sporadic availability of renewable energy create significant challenges to the optimal scheduling of integrated electricity and gas systems (IEGS). This paper develops a nonparametric probabilistic forecasting based stochastic scheduling approach of IEGS. The quantile at a series of quantile levels can be generated by direct quantile regression method. Given the set of predicted quantiles, a set of representative scenarios for wind power uncertainty can be obtained by using Monte Carlo simulation method and scenario reduction approach. Based on the implicit finite difference scheme, the original partial differential equations of the gas network are discretized to establish an algebraic model, which provide possibility for efficient solution. Then, the nonconvexity caused by the momentum equation is eliminated by the second-order core relaxation. Finally, the stochastic optimal scheduling model is reformulated as a second-order core programming problem. Numerical simulations are performed to showcase the superiority of the established stochastic optimal scheduling model.

Small-signal stability constrained optimal power flow of power system with DFIGs considering wind power uncertainty

When substantial wind power generation is integrated into the power system, the low rotational inertia and uncertain power output of wind turbines pose a great challenge to the small-signal stability (SSS) of power system operation. In this paper, considering the dynamic model of a doubly fed induction generator (DFIG) and the uncertainty of wind power, we establish a bilayer robust optimization of the SSS constrained optimal power flow (SSSCOPF-RO) model for a power system with DFIGs. It uses Lyapunov's second theorem to describe the SSS constraint. By the semi-definite relaxation and McCormick envelope relaxation techniques, we transform the inner-layer optimization of the SSSCOPF-RO model into a semi-definite program (SDP) model to improve the convergence reliability and computational efficiency of solving the model. Based on the dual optimization theory, the inner-layer SDP model is converted into its dual SDP model, and the bilayer robust optimization model is transformed into a single-layer optimization model, which can be solved using the solver MOSEK in the software CVX. Finally, case studies were conducted on IEEE 9-, 39-, and 118-bus systems to validate the proposed method. The obtained optimal solution of the transformed single-layer optimization model could ensure the SSS of the system under the uncertain fluctuation of DFIGs’ active power output and effectively reduce the network loss cost, and the results indicate the effectiveness and robustness of the proposed model.

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.

Efficient Operation of Integrated Electrical-Water System for Wind Power Accommodation

This paper investigates the operation of an integrated electrical-water system for wind power accommodation. The efficient operation can be considered as how well to handle the binary variables in a water distribution system (WDS) model. The optimization problem of WDS is NP-hard, and it often takes hours to find the optimal solution. Since wind power varies in minutes, long computation time may hinder the WDS from being fully used by power system to deal with the wind power fluctuation and uncertainties. This paper develops a three-step optimization model to answer the challenges of the WDS optimization problem. The solution hinges on the calculation of the binary variable values in advance. The first step is to construct a linear power-WDS co-optimization (LPWCO) model to find the optimal electrical demand of WDS. The second step develops a tank flow allocation based method and a greedy-based algorithm to determine the binary variable values of WDS. This allows the WDS optimization model to be efficiently solved by commercial solvers in the third step. The proposed three-step model converts the NP-hard optimization problem of WDS into a polynomial-time problem which can be solved quickly. This allows the wind power system to leverage the energy flexibility provided by WDS in the real-time operation. Results of case study show that, compared with existing methods, the proposed method can reduce the wind power curtailment and the operation cost by about 55% and 53%. More importantly, the solution time is reduced dramatically from hours to seconds, suitable for practical application.

Calculation and optimization of inter-regional available transfer capability considering wind power uncertainty and system improvement measures

The intermittency and uncertainty of wind energy make transmission congestion management increasingly complex. In this paper, we investigate the impact of wind energy on available transmission capacity (ATC) and then explore ways to mitigate transmission congestion from the grid and load side, respectively. The impact of a stochastic variable is considered by applying Latin Hypercube Sampling (LHS) and backward curtailment techniques to generate typical scenarios. ATC is improved by the optimal allocation of Thyristor Controlled Series Compensation (TCSC) on the grid side and the use of Demand Response (DR) on the load side. The source-grid-load cooperative optimisation model for ATC is solved by an improved particle swarm optimisation (PSO) algorithm. Based on the IEEE-30 bus system, an experimental scheme is designed and analytical calculations are performed. The results show that the joint application of TCSC and DR can help to improve ATC in a comprehensive way. The work done in this paper can achieve the purpose of promoting renewable energy consumption by improving the utilisation efficiency of the transmission network without changing the network structure.

Distributionally robust optimization for virtual power plant clusters considering carbon emission-based dynamic dispatch priority

A virtual power plant (VPP) is an effective way to manage distributed energy resources. Virtual power plant cluster (VPPC) is constructed due to the increasing number of VPPs. It has great potential in carbon emission reduction through coordination between VPPs. Therefore, a dynamic dispatch priority model is proposed. The model adjusts the dispatch sequence of VPPs based on carbon emissions. The higher the carbon emissions, the lower the dispatch priority. The VPPC participates in carbon trading and the carbon trading cost is included in the optimization objective. There are still many uncontrolled distributed energy resources, e.g., wind power, out of the VPPC. The distributionally robust optimal method is used to deal with the uncertainty of wind power. Finally, this paper proposes a two-stage distributionally robust optimal model of VPPC considering carbon emission-based dynamic dispatch priority. This model can be solved by the column-and-constraint generation (C&CG) algorithm. A VPPC with four VPPs is used in case studies, which verify the economic benefit and low carbon of the proposed model.

Distributionally robust optimal scheduling of integrated energy systems including hydrogen fuel cells considering uncertainties

The economic operation of the integrated energy system faces the problems of coupling between energy production and conversion equipment in the system and the imbalance of various energy demands. Therefore, taking system safety as the constraint and minimum economic cost as the objective function, including fuel cost, operation, and maintenance cost, this paper proposes the operation dispatching model of the integrated energy system based on hydrogen fuel cell (HFC), including HFC, photovoltaic, wind turbine, electric boiler, electric chiller, absorption chiller, electric energy storage, and thermal energy storage equipment. On this basis, a distributionally robust optimization (DRO) model is introduced to deal with the uncertainty of wind power and photovoltaic output. In the distributionally robust optimization model, Kullback–Leibler (KL) divergence is used to construct an ambiguity set, which is mainly used to describe the prediction errors of renewable energy output. Finally, the DRO economic dispatching model of the HFC integrated energy system (HFC-IES) is established. Besides, based on the same load scenario, the economic benefits of hybrid energy storage equipment are discussed. The dispatching results show that, compared with the scenario of only electric energy storage and only thermal energy storage, the economic cost of the scenario of hybrid electric and thermal storage can be reduced by 3.92% and 7.55% respectively, and the use of energy supply equipment can be reduced, and the stability of the energy storage equipment can be improved.

Collaborative optimal scheduling of shared energy storage station and building user groups considering demand response and conditional value-at-risk

Energy storage devices can improve the utilization of clean energy and reduce the operating costs of building users. However, traditional energy storage is limited by its relatively low resource utilization and high cost. Firstly, to fully utilize the advantages of energy storage, a shared energy storage station (SESS) is introduced into the building user groups (BUGs). Second, this paper considers the incentive effect of flexible load participation in demand response (DR) on BUGs, quantifies the risky scheduling cost of wind uncertainty using conditional value-at-risk (CVaR), and then proposes a collaborative optimal scheduling model for BUGs and SESS considering DR and CVaR. Finally, the proposed model is verified by examples to achieve efficient complementation of SESS and BUGs, reduce the risk scheduling cost due to wind power uncertainty, and significantly reduce the capacity value of SESS and the electricity purchase of BUGs; meanwhile, this study carries out multi-perspective sensitivity analysis and CVaR analysis,which is informative for the refinement of the operating model of SESS and BUGs.

Short-term coordinated hybrid hydro-wind-solar optimal scheduling model considering multistage section restrictions

With the large-scale integration of wind and solar power in China, the consumption of these intermittent renewable energies is severely restricted by the capacity of the transmission channel, which leads to massive renewable energy curtailment. Therefore, it would be beneficial to use limited transmission channels to absorb as much renewable energy as possible. In this paper, we propose a chance constraint-based multistage nested hydro-wind-solar coordinated optimal scheduling model to aid peak shaving while ensuring maximum power generation. First, multistage partitioned section chance constraints are introduced to mitigate the power congestion of the transmission network. Then, based on the principle of using hydropower to complement the uncertainty of wind and solar power, the compensation chance constraints are considered. To quantify the uncertainty of wind and solar power, their prediction errors are analyzed using the Gaussian mixture model. Finally, the model is recast and linearized into a mixed integer linear programming model. A case study of a hydro-wind-solar base in Southwest China demonstrates that the proposed model can effectively leverage the regulation ability of hydropower to coordinate multiple power sources with the restrictions of multistage transmission sections, effectively alleviating the congestion of transmission channels and reducing the curtailment of renewable energy.

Improving the economic and environmental benefits of the energy system: A novel hybrid economic emission dispatch considering clean energy power uncertainty

New energy power generation plays an important role in adjusting energy structure, reducing environmental pollution, and promoting sustainable social development. However, new energy power generation represented by solar and wind power has significant volatility, meanwhile, the wind and solar curtailment caused by uncertainty reduce utilization rate. In this context, considering the impact of uncertainty in new energy generation on system scheduling and developing new algorithms that can efficiently solves such complex scheduling problems are necessary. Therefore, this study constructed a hybrid dynamic economic emission model (HDEED) with the new energy generation penalty mechanism, and proposed a novel multi-objective tunicate swarm algorithm (MTSA)-based solution method. First, based on the unit operation characteristic equations, considering wind and solar power uncertainty, the HDEED model with the new energy generation penalty mechanism was constructed. Second, on the basis of the single objective tunicate swarm algorithm, MTSA algorithm was proposed by adding the running operators, and the feasibility of the proposed algorithm was verified. The test results showed that the Pareto fronts obtained by MTSA algorithm presented good distribution and diversity compared with existing algorithms. Finally, the proposed model and algorithm were proved by modified test system, meanwhile, the impacts of clean energy power on system operation stability, economic and environmental benefits were discussed. The results revealed that the penalty costs caused by uncertainty were higher than the operation and maintenance costs of new energy power stations, which accounted for about 2.1% of the total costs. The outcomes obtained in this study contribute to improving the utilization rate of clean energy and promoting the sustainable development of energy system.

An optimal capacity allocation method for integrated energy systems considering uncertainty

The randomness and fluctuation of wind power output will cause certain waste in capacity allocation of integrated energy system. Therefore, a robust chance constrained optimization model is proposed to solve the shortcomings of the traditional model in wind power output analysis, such as weak reliability and conservative results. First, the uncertainty of wind power output is analyzed. Second, a robust chance constrained optimization model combining stochastic programming and robust optimization is established to deal with the uncertainty of the output power of the integrated energy system objectively. Finally, the results are compared with the existing integrated energy system capacity configuration, and the validity of the model is verified. The results show that the robust chance constrained optimization model proposed in this paper can effectively reduce the capacity cost by 38.2% while ensuring the robustness of the system in wind power uncertainty analysis.

Wasserstein‐metric‐based distributionally robust optimization method for unit commitment considering wind turbine uncertainty

AbstractThe penetration of wind turbines in the power grid is increasing rapidly. Still, the wind turbine output power has uncertainty, leading to poor grid reliability, affecting the grid's dispatching plan, and increasing the total cost. Thus, a distributionally robust optimization method for thermal power unit commitment considering the uncertainty of wind power is proposed. For this method, energy storage and interruptible load are added to simulate increasingly complex electricity consumption scenarios. Furthermore, the amount of load cutting reflects the satisfaction level of electricity consumption on the user side. Based on Wasserstein metric, an ambiguity set is established to reflect the probabilistic distribution information of the wind power uncertainty. An ambiguity set preprocessing method is proposed to depict the probability distribution of ambiguity set more clearly, to minimize the operation cost under the condition that the uncertainty of wind turbine output power obeys the extreme probabilistic distribution of the ambiguity set. The test case in a modified version of the IEEE 6‐bus system shows that the proposed method can flexibly adjust the robustness and economy of optimization decisions by controlling the sample size and the confidence of Wasserstein ambiguity set radius. In addition, the proposed ambiguity set preprocessing method can obtain more economical dispatching decisions with a smaller sample size.

Robust stochastic optimal dispatching of integrated electricity-gas-heat systems with improved integrated demand response

In recent years, integrated energy systems with deep coupling of power, natural gas, and heat energy have gradually attracted extensive attention. The increasing issue of uncertainty in the generation and load of an integrated electric-gas-heat system (IEGHS) is a growing prominent problem. The effective implementation of demand response (DR) programs is an important way to solve this problem in the IEGHS. In this paper, a robust stochastic optimal dispatching method for an IEGHS with integrated DR (IDR) under multiple uncertainties is proposed. A robust adjustable uncertainty set is adopted to deal with the uncertainty of wind power. The Wasserstein generative adversarial network based on gradient normalization is proposed to generate load-side demand scenarios. Furthermore, an improved IDR model that considers the peak-valley difference cost of the electricity-gas-heat load is proposed. Finally, the simulation analysis successfully demonstrates the efficacy of the proposed model. By utilizing this approach, the system can obtain a scheduling scheme with the lowest operating cost even under worst-case scenarios.

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.

Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors

The aim of this paper is to address the challenges regarding the safety and economics of power system operation after the integration of a high proportion of wind power. In response to the limitations of the literature, which often fails to simultaneously consider both aspects, we propose a solution based on a stochastic optimization scheduling model. Firstly, we consider the uncertainty of day-ahead wind power forecasting errors and establish a multi-scenario day-ahead stochastic optimization scheduling model. By balancing the reserve capacity and economic efficiency in the optimization scheduling, we obtain optimized unit combinations that are applicable to various scenarios. Secondly, we account for the auxiliary service constraints of thermal power units participating in deep peak shaving, and develop an intra-day dynamic economic dispatch model. Through the inclusion of thermal power units and energy storage units in the optimization scheduling, the accommodation capacity of wind power is further enhanced. Lastly, in the electricity market environment, increasing wind power capacity can increase the profits of thermal power peak shaving. However, we observe a trend of initially increasing and subsequently decreasing wind power profits as the wind power capacity increases. Considering system flexibility and the curtailed wind power rate, it is advisable to moderately install grid-connected wind power capacity within the power system. In conclusion, our study demonstrates the effectiveness of the proposed scheduling model in managing day-ahead uncertainty and enhancing the accommodation of wind power.

Two-stage mobile emergency generator dispatch for sequential service restoration of microgrids in extreme conditions

A fast and reliable sequential service restoration (SSR) methodology is necessary to enhance the resilience of the distribution system (DS) after an outage. However, traditional SSR strategies ignore the utilization of mobile emergency generators (MEGs) to reduce the system outage duration. This paper exploits the enhanced resilience of DSs by dispatching MEGs as backup sources to form microgrids (MGs) and sequentially restore out-of-service loads. A two-stage dispatching model is proposed for the SSR of MGs that takes into consideration the preventive control stage (PCS) and the emergency control stage (ECS). Moreover, SSR decisions are optimized depending on stage-based uncertainties. Specifically, in PCS, MEGs are pre-positioned prior to event strikes. Considering contingency uncertainty, a scenario-based stochastic model is proposed to minimize the expected outage duration of loads, and a progressive hedging algorithm is applied to improve computational efficiency. With the realization of contingency in ECS, MEGs are rerouted from pre-positions to target locations, and provisional MGs are sequentially formed to restore critical loads. Considering wind power uncertainty, a robust model is formulated, and the convex hull relaxation method is customized to reduce the computational complexity. The resulting SSR scheme can be adaptively and securely adjusted in response to random contingencies and wind power fluctuation. The effectiveness of the proposed method is validated by numerical simulations.

Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation

The exponential growth of unpredictable renewable power production sources in the power grid results in difficult-to-regulate reactive power. The ultimate goal of optimal reactive power dispatch (ORPD) is to find the optimal voltage level of all the generators, the transformer tap ratio, and the MVAR injection of shunt VAR compensators (SVC). More realistically, the ORPD problem is a nonlinear multi-objective optimization problem. Therefore, in this paper, the multi-objective ORPD problem is formulated and solved considering the simultaneous minimization of the active power loss, voltage deviation, emission, and the operating cost of renewable and thermal generators. Usually, renewable power generators such as wind and solar are uncertain; therefore, Weibull and lognormal probability distribution functions are considered to model wind and solar power, respectively. Due to the unavailability and uncertainty of wind and solar power, appropriate PDFs have been used to generate 1000 scenarios with the help of Monte Carlo simulation techniques. Practically, it is not possible to solve the problem considering all the scenarios. Therefore, the scenario reduction technique based on the distance metric is applied to select the 24 representative scenarios to reduce the size of the problem. Moreover, the efficient non-dominated sorting genetic algorithm II-based bidirectional co-evolutionary algorithm (BiCo), along with the constraint domination principle, is adopted to solve the multi-objective ORPD problem. Furthermore, a modified IEEE standard 30-bus system is employed to show the performance and superiority of the proposed algorithm. Simulation results indicate that the proposed algorithm finds uniformly distributed and near-global final non-dominated solutions compared to the recently available state-of-the-art multi-objective algorithms in the literature.

A two-stage demand response strategy for multiple scenarios based on deviation compensation

Integrated demand response (IDR) is an integral part of the park-level integrated energy system (PIES). Most existing IDR strategies depend heavily on explicit forecasts of future uncertainties. Due to the randomness of wind power generation and load demands, these approaches are limited by forecasting response time or accuracy. A two-stage demand response strategy for multiple scenarios based on deviation compensation is proposed to consider prediction uncertainties arising in distributed wind power generation and multi-energy load. In the day-ahead stage, considering the impact of energy prices on the economy of PIES operation, it is proposed to divide PIES operation scenarios based on energy prices. It is also proposed to develop a day-ahead response plan considering the demand response capability of the load side. The real-time stage considers the uncertainty of wind power and load forecasting as well as the real-time response speed of the strategy, and proposes to use deviation compensation to change some day-ahead plans in order to solve the uncertainty of prediction. Finally, considering the synergy between energy storage and demand response strategies, the above demand response strategy is embedded into multi-objective Harris Hawk optimization (MOHHO) to work out the energy storage capacity. The simulation results show that the strategy considers multiple operational scenarios, is more economical, and only changes a portion of the day-ahead plan in the real-time phase, so it has faster real-time response speed.