Uncertainty Of Wind Power Output Research Articles

Multi-source coordinated low-carbon optimal dispatching for interconnected power systems considering carbon capture

The overall electricity consumption of electrolytic aluminum and ferroalloy loads is significant, and some of these loads have dispatch potential that can be used to locally absorb wind power while reducing dependence on conventional thermal power. To characterize the uncertainty of wind power, a fuzzy set of wind power forecasting error probability distribution based on the Wasserstein distance was first established, and the approximate radius of the fuzzy set was corrected under extreme scenarios. By introducing joint chance constraints, the inequalities of uncertain variables were established at the lowest confidence level to improve the reliability of the model. Next, a two-stage distributed robust optimal scheduling model for source-load coordination was developed. In the first stage, wind power forecasting information was fully utilized to schedule the electrolytic aluminum load and optimize unit commitment. In the second stage, the uncertainty of wind power output was considered to schedule the ferroalloy load and optimize unit output. The model was approximately transformed into a mixed-integer linear programming problem and solved using a sequential algorithm. The IEEE 24-bus system was used for case validation. The validation results show that the model can effectively improve wind power absorption capacity, reduce overall operating costs, and achieve a balance between low carbon emissions and robustness.

Neural network‐based integrated reactive power optimization study for power grids containing large‐scale wind power

AbstractThe high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network‐based comprehensive reactive power optimization method for large‐scale wind power grids, effectively addressing the challenges of rapid reactive power optimization in power systems. Firstly, by constructing typical wind‐power‐load scenarios, the generalization ability of the neural network is improved. Then, focusing on the comprehensive reactive power optimization problem after integrating typical wind‐power‐load scenarios into the system, the improved Harris hawks optimization algorithm (HHO) is compared with the particle swarm optimization algorithm and traditional HHO algorithm, highlighting its advantages. Finally, HHO is utilized for solving, thereby constructing a comprehensive reactive power optimization strategy tag set. Furthermore, through deep fitting of the neural network between the power grid operating state and the comprehensive reactive power optimization strategy, the computational complexity and decision‐making time of reactive power optimization are reduced.

Robust optimization scheduling of microgrid considering a tiered carbon trading mechanism and flexible loads

Abstract Considering the comprehensive demand response, stepwise carbon trading, and uncertainty of wind power output for cogeneration microgrids, relevant optimization scheduling research will be carried out. The electric heating load is divided into non-dispatchable electric heating load and dispatchable electric heating load. By introducing electric heating demand and tiered carbon trading, the economic and low-carbon operation of microgrids can be achieved. The fluctuation of the output of new energy units is reflected using probability scenario sets in statistics, and a robust optimization model with related distributions is constructed using the C&CG algorithm to calculate and solve the established model. The simulation and comparison of numerical examples show that the microgrid energy management scheme formulated by the model established in this paper has good economic and environmental friendliness, and it also simultaneously validated the superiority of distributed robust optimization methods over traditional optimization methods

Multi-objective capacity configuration optimization of the combined wind - Storage system considering ELCC and LCOE

The optimal capacity configuration of combined wind-storage systems (CWSSs) serves as a foundation and premise for building new electricity system. This paper proposes a novel capacity configuration model of CWSS. The model simultaneously considers economy, stability and low carbon by respectively minimizing Levelized cost of electricity (LCOE) and maximizing effective load carrying capacity (ELCC). The multi-objective optimization model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA-II). And Multi-Attributive Border Approximation Area Comparison (MABAC) method is used to score the alternative solutions on the Pareto front and make decisions under different scenarios. The results of the case study show that: (1) The evaluation of the ELCC for CWSS can quantify its ability to replace the installed capacity of thermal power, thus greatly reducing the uncertainty of wind power output. (2) Under low-carbon scenario, economic scenario and standard scenario, the results of optimal capacity configuration for the CWSS show that LCOE and ELCC are negatively correlated. When the weights of ELCC and LCOE are equal, the three goals of economy, low carbon and safety and stability are achieved. (3) The results of a single sensitivity analysis on LCOE show that the sensitivity coefficient of electricity generation is the highest, exerting the greatest impact on the capacity configuration decision of the CWSS. A 20 % increase in power generation can reduce LCOE by 0.1203RMB/kWh. This paper provides energy planning recommendations for decision-makers in CWSSs, contributing to the development of a reliable new electricity system.

Multi time scale optimal dispatch of distribution network considering load demand response

Considering the uncertainty of wind power output and load as well as the differences in the operating characteristics of various adjustable resources over different time scales, a multitime scale optimal dispatching model based on stochastic optimization and distributed robust optimization is designed for the distribution network. Firstly, the demand response of the user load side is divided into price-based load and incentive-based load, and price elasticity coefficients are introduced to describe it. Then, with the minimum operation cost of the distribution network, the loss cost, and the penalty cost of abandoned wind as the objective function, various active and reactive regulation elements as well as the system power flow formulas are taken as constraints. In the day-ahead and real-time stages, a stochastic optimization method based on scenarios and opportunity constraints is introduced. In the intraday stage, a self-regressive sliding window prediction model is used to predict wind power and load processing, and a Wasserstein distance-based prediction error uncertainty set is established to establish an intraday distributed robust rolling optimization model. The improved IEEE 33-node case is used for simulation verification, and the results show that the model can effectively reduce system costs and improve system operation stability.

Enhancing envrionmental sustainability via interval optimization for low-carbon economic dispatch in renewable energy power systems: Leveraging the flexible cooperation of wind energy and carbon capture power plants

In response to pressing environmental challenges, the power system is gradually developing towards a more efficient, clean, and sustainable direction. Carbon capture power plants (CCPPs) and wind power generation will play a significant role in this eco-friendly transformation. However, the high energy consumption of CCPPs and the anti-peak regulation characteristics of wind power lead to contradictions in their coordinated operation during peak shaving, restricting the system's low-carbon economic development. Aiming at the above challenges, an interval optimization method is proposed to coordinate wind power and CCPPs. Firstly, a flexible operation model of CCPPs is established, and the coordinated scheduling mechanism of wind power joint carbon capture operation and load-side demand response is introduced to optimize the system carbon emission through low carbon scheduling on both sides of the source and load. Secondly, interval numbers are used to manage the uncertainty of wind power output, and the interval-optimized scheduling model of wind power combined CCPPs is established. To solve the proposed model, the improved interval possibility method is adopted to transform the uncertain model into a deterministic one, which is solved by the CPLEX solver. Finally, the simulation outcomes using the IEEE 30-bus system framework reveal that the proposed approach significantly enhances the coordination between CCPPs and wind power. Notably, when compared to scenarios that solely focus on CCPPs, the proposed method achieves a substantial reduction in total costs by 16.67 % and decreases carbon emissions by 26.53 %. This sets a significant reference for low-carbon economic dispatch within power systems, providing key insights and foundational guidelines for future implementations, as well as novel strategies and technological support for the sustainable transformation of the power system towards lower carbon emissions.

Two-stage Robust Optimization Scheduling Considering Wind Uncertainty and Multi-area Frequency Dynamic Safety

The large-scale wind power grid connection exacerbates the problem of uneven distribution of inertia between different regions, posing challenges to the frequency security defense of multi-area systems. This article proposes a two-stage robust optimization scheduling model that considers both the uncertainty of wind power output and the dynamic safety of multi-area frequencies. Firstly, according to the conditional value at risk theory, the operating cost and operating risk of the multi-region system are optimized, and the acceptable safety boundary of wind power is obtained. Secondly, multi-zone frequency dynamic safety verification is carried out in harsh wind power scenarios. Decouple the reserve capacity into non-accident reserve and accident reserve, and nest them separately in a two-stage model to achieve clear solution ideas. Finally, the two-stage optimization problem is solved through the column and constraint generation algorithm. The simulation results of numerical examples show that the model and method can effectively improve the frequency response capability of the multi-region system, so as to ensure the safe and economical operation of the system.

Data-driven optimization for wind farm siting

Both increasing power output and minimizing the variability of that output are key factors in a highly utilized wind farm. Finding a location that offers this type of wind resource requires the use of stochastic methods to explicitly model the uncertainty of wind power output. While the literature is rich with wind speed resource assessments, this work moves the field to wind farm power assessment at undeveloped locations. A wind farm power prediction is achieved with improved spatial process representation. The power prediction results are utilized directly in a wind farm location siting optimization model which endogenously incorporates uncertainty in wind power output. The overall framework for siting new wind farms, termed the Farm Finder tool, is demonstrated in a case study in the Electric Reliability Council of Texas (ERCOT). It is shown that the formulation of this model will find an optimal solution to the siting problem on the Pareto front of high power output, with high reliability. Such location decisions improve the overall social welfare in the electricity market.

A two-stage SCUC model for distribution networks considering uncertainty and demand response

Demand response (DR) is one of the most effective and economical methods for power operators to improve network reliability in face of uncertainty and emergencies. In this paper, considering the uncertainty of wind power output and the failure of power system components, a two-stage security-constrained unit commitment (SCUC) model is established by optimizing the real-time pricing mechanism to indirectly adjust the DR. Firstly, the uncertainty of wind power output is modeled based on self-organizing map (SOM), and the component failure of the power system is modeled based on Monte Carlo. Then, a pricing scheme is proposed to stimulate users' electricity consumption behavior. Finally, the marine predator algorithm is used to solve the problem. Simulation results on IEEE-RTS system show that the proposed method can reduce the total operating cost by 10%, effectively stimulate the electricity consumption behavior of users, to improve the peak load shifting and valley filling capacity of the power system.

Low-carbon economy dispatching of integrated energy system with P2G-HGT coupling wind power absorption based on stepped Carbon emission trading

To improve the renewable energy consumption capacity of integrated energy system (IES) and reduce the carbon emission level of the system, a low-carbon economic dispatch model of IES with coupled power-to-gas (P2G) and hydrogen-doped gas units (HGT) under the stepped carbon trading mechanism is proposed. On the premise of wind power output uncertainty, the operating characteristics of the coupled electricity-to-gas equipment in the system are used to improve the wind abandonment problem of IES and increase its renewable energy consumption capacity; HGT is introduced to replace the traditional combustion engine for energy supply, and on the basis of refined P2G, a part of the volume fraction of hydrogen obtained from the production is extracted and mixed with methane to form a gas mixture for HGT combustion, so as to improve the low-carbon economy of the system. The ladder type carbon trading mechanism is introduced into IES to guide the system to control carbon emission behavior and reduce the carbon emission level of IES. Based on this, an optimal dispatching strategy is constructed with the economic goal of minimizing the sum of system operation cost, wind abandonment cost, carbon trading cost and energy purchase cost. After linearization of the established model and comparison analysis by setting different scenarios, the wind power utilization rate of the proposed model is increased by 24.5%, and the wind abandonment cost and CO2 emission are reduced by 86.3% and 10.5%, respectively, compared with the traditional IES system, which achieves the improvement of renewable energy consumption level and low carbon economy.

Low-carbon economic dispatch of integrated energy system containing electric hydrogen production based on VMD-GRU short-term wind power prediction

The integration of energy systems (IES) enhances the interaction between the electricity, gas, and heat systems, and the concept of low-carbon development can further reduce the carbon emissions of IES. However, the uncertainty of wind power output and the complexity of different energy chains pose significant challenges to the low-carbon operation of IES. Therefore, this paper proposes a low-carbon economic dispatching strategy for IES containing electric hydrogen production based on short-term wind power prediction. Firstly, a variational mode decomposition- gate recurrent unit network prediction model is employed to enhance the accuracy of ultra-short wind power forecasting, which reduces the impact of wind power output uncertainty on wind power grid connection. Secondly, a refined two-stage P2G operation model for refining the electric hydrogen production device is constructed to decrease the carbon emissions of IES. Thirdly, a thermoelectric integrated demand response model is established to adjust the heat and power demand proportion, further reducing the IES's carbon emissions. Finally, the case study is performed based on IEEE standard system to verify the effectiveness of the proposed strategy. Simulation analysis shows that introducing electrolysis for hydrogen production can reduce IES carbon emissions by 12.90%. In addition, introducing an adjustable thermal-electric comprehensive demand response can reduce IES carbon emissions by 1.543% while lowering the overall cost by 5.24%. The proposed strategies can simultaneously consider the low-carbon and economic aspects of IES.

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.

Coordinated Optimization of Distributed Wind Power and Electric Energy Storage Technologies

The location and capacity of distributed wind power (DWG) and battery energy storage (BES) in the distribution network affect the operation of the distribution network. To improve the consumption capacity of wind power and reduce the impact of uncertainty of wind power output on the operation of the distribution network, the optimal allocation of DWG and BES should be coordinated in the planning stage. In this paper, the stochastic tide of the wind-storage system is used to reflect the operation status and uncertainty of the distribution network with DWG and BES, and the maximization of the integrated return of equal annual value is used as the objective function to establish the opportunity constrained planning model.

Distributionally robust comprehensive declaration strategy of virtual power plant participating in the power market considering flexible ramping product and uncertainties

The volatility and randomness of renewable energy output make the flexible ramping ability demand of power system more urgent, and flexible ramping product (FRP) can effectively improve the flexibility of the power system. Virtual power plant (VPP) can regulate distributed generation, which is conducive to the regulation potential of flexible resources. To solve the problems of FRP and electric energy markets synergy and the wind power output uncertainty faced by the VPP in the declaration process, this paper proposes a comprehensive declaration strategy for the VPP to participate in the power market considering FRP and uncertainties. Firstly, this paper designs a synergistic trading mode covering flexibility resource demand determination and the virtual bidding curve formation for electric energy and FRP markets, in which the virtual bidding curve can reasonably compensate for the unit opportunity cost. Secondly, a comprehensive declaration-dispatching strategy decision-making model for VPP is constructed, and a two-stage distributed robust optimization (DRO) technology is used to deal with the wind power output uncertainty in the model, and flexible resources such as energy storage are used to mitigate energy deviation in the VPP. Finally, simulations implemented on a typical VPP are delivered to show that: 1) Virtual bidding curve realizes the accurate compensation for the units providing FRP. 2) Compared with the single market, VPP can increase the expected profit by 20.44% and reduce wind curtailment cost by 59.68% in the joint declaration of multi-markets.3) VPP can effectively suppress output uncertainty through energy storage system. 4) DRO model has significant advantages in data-driven, less conservative results and stable running time.

Pricing Strategy for a Virtual Power Plant Operator with Electric Vehicle Users Based on the Stackelberg Game

With the popularity and promotion of electric vehicles (EVs), virtual power plants (VPPs) provide a new means for the orderly charging management of decentralized EVs. How to set the price of electricity sales for VPP operators to achieve a win–win situation with EV users is a hot topic of current research. Based on this, this paper first proposes a Stackelberg game model in which the VPP participates in the orderly charging management of EVs as a power sales operator, where the operator guides the EV users to charge in an orderly manner by setting a reasonable power sales price and coordinates various distributed resources to jointly participate in the power market. Furthermore, taking into account the impact of wind power output uncertainty on VPP operation, a robust optimization method is used to extend the deterministic Stackelberg game pricing model into a robust optimization model, and a robust adjustment factor is introduced to flexibly adjust the conservativeness of the VPP operator’s bidding scheme in the energy market. The model is then transformed into a robust mixed-integer linear programming (RMILP) problem solved by Karush–Kuhn–Tucker (KKT) conditions and strong dyadic theory. Finally, the effectiveness of the solution method is verified in the calculation example, which gives the optimal pricing strategy for the VPP operator, the optimal charging scheme for EV users, and the remaining internal resources’ contribution plan, providing an important idea for the VPP to centrally manage the charging behavior of EVs and improve its own operating revenue.

Day-ahead and real-time market bidding and scheduling strategy for wind power participation based on shared energy storage

• Day-ahead and real-time market bidding and scheduling strategy for wind power participation. • Shared energy storage is used to reduce the real-time market deviation penalty of wind power. • Analyze the influence of deviation penalty coefficient on wind power bidding. • Analyze the impact of energy storage capacity on the income of wind farms and energy storage operator. In order to reduce the impact of wind power output and electricity price uncertainty on the income of wind power participating in the electricity market, this paper proposes a day-ahead and real-time market bidding and scheduling strategy for wind power participation based on shared energy storage. In the first stage, considering the uncertainty of wind power output and electricity price, aiming at the maximum income of wind farms in the day-ahead market, the optimal bidding power of each wind farm in the day-ahead market is obtained by using quantum genetic algorithm. In the second stage, based on the day-ahead market winning bid volume and actual output, by coordinating each wind farm to use the charging and discharging services of the shared energy storage power station, the net income of multiple wind farms in the day-ahead and real-time market is maximized. Case studies are carried out with three wind farms and one shared energy storage operator in this paper. Compared with the modes that wind farms not configuring energy storage or configuring energy storage independently, it is verified that the shared energy storage can significantly reduce the deviation penalty of wind farms in the real-time market and improve wind farm income.

Day-Ahead Scheduling of Wind-Hydro Balancing Group Operation to Maximize Expected Revenue Considering Wind Power Output Uncertainty

Day-Ahead Scheduling of Wind-Hydro Balancing Group Operation to Maximize Expected Revenue Considering Wind Power Output Uncertainty

Scenario Generation for Wind Power Using Generative Adversarial Networks

Scenarios generation is a critical part in planning and operation in high renewable energy penetratied power systems. However, the statistical assumptions of traditional parametric methods may not hold for all types of wind farms. In this paper, a data-driven artificial intelligence approach is presented to generate wind power output scenarios based on generative adversarial networks (GANs). Unlike traditional probabilistic model-based techniques which are typically difficult to scale or sample, the proposed method is data-driven and captures patterns of wind power generation. First, the GAN network structure is constructed, and the Wasserstein distance is employed as the discriminator’s loss function. The GAN training then enables the generator to learn random noise and actual history data. Finally, the scenario generation approach based on Monte Carlo simulation and GANs are compared. It shows that the scenarios generated by proposed method can accurately describe the uncertainty of wind power output.

Partitioning method of reserve capacity based on spectral clustering considering wind power

Abstract With the rapid development of the power system, the complexity of power system continues to increase. The large-scale of clean energy increases the complexity of the power system structure, which makes the power system’s operating status complex and changeable. To ensure the safety, stability, and economic operation of the power system, the power system needs to maintain a certain spinning reserve capacity and ensure that the reserve is available. The spinning reserve optimization method based on fixed partitions cannot effectively deal with the source-net-load volatility and the line congestion problems caused by wind power. To this aim, this paper proposes a power system reserve capacity partitioning algorithm based on the spectral clustering algorithm considering wind power. First, the energy-reserve joint optimization unit commitment problem is built. Secondly, considering the uncertainty of wind power output and the line N − 1 failure, a risk assessment method for line congestion is established; then the power transfer distribution factor (PTDF) of the congested line is considered as the similarity measure. A method based on the iterative partition method based on spectral clustering is proposed. Finally, the reserve capacity configuration based on the dynamic partition results is determined. The IEEE-118 bus system is used in this paper to verify the proposed method. The results show that the reserve capacity dynamic partition method proposed in this paper determines the number of partitions automatically. Reserve capacity partitions are built reasonably. After the reserve capacity partitions, the availability of spinning reserve capacity is effectively guaranteed.

The Bi-Level Optimization Model Research for Energy-Intensive Load and Energy Storage System Considering Congested Wind Power Consumption

Due to the uncertainty of wind power output, the congestion of wind power has become prominent. Exactly how to improve the capacity of wind power consumption has become a problem that needs to be studied urgently. In this paper, an energy storage system and energy-extensive load with adjustable characteristics are used as an important means of consuming wind power. Firstly, we analyze the reasons for the congestion according to the characteristics of wind power output, and establish a model of the grid’s ability to integrate wind power based on the concept of a wind power admissible interval. Secondly, we analyze the energy-extensive load regulation characteristics and establish an energy-extensive load dispatch model. Thirdly, on the basis of considering the energy-extensive load and energy storage system adjustment constraints, a bi-level optimization model is established. The upper level determines the configured capacity of the energy storage system with the goal of minimizing the total economic investment of the energy storage system, and the lower level coordinates the dispatching with the goal of maximizing wind power consumption and minimizing system operating costs. Finally, a certain region is taken as an example to verify the validity of the proposed method.