Wind Uncertainty Research Articles

Robust frequency risk-constrained unit commitment model for AC-DC system considering wind uncertainty

With the increasing penetration of volatile renewable energy sources (RES) in power systems, the formulation of RES uncertainty set in the unit commitment (UC) problem poses significant challenges to the operators especially those in AC-DC systems. To overcome the over-conservativeness and computational redundancy in conventional uncertainty formulation methods, this paper induces the stochastic rationale into the robust dispatch model and proposes a two-stage robust frequency risk-constrained unit commitment (FRCUC) model to excavate the system flexibility. Based on the typical characteristics of AC-DC dispatch system, the frequency violation risk (FVR) factors are proposed to enhance the adaptability of uncertainty sets, wherein the HVDC regulation is activated to cope with the extreme uncertain scenarios with lower probability. As a result, being aware of the wind power distribution, the proposed model succeeds in managing the operational risks by fully exploiting the controllability of HVDC system, and the over-conservativeness from conventional robust model is handled by the proposed FVR factors. The outperformance of our model is validated by the numerical experiments on a modified AC-DC system, it is validated that the operational risks are evaluated when considering the violated wind power distribution, and therefore the model economic benefits are promoted when ensuring the reliability.

RETRACTED: Stochastic scheduling of a power grid in the presence of EVs, RESs, and risk index with a developed lightning search algorithm

- Due to the rise in environmental and economic limitations in today's world, the use of dispersed generation that often applies renewable energies, and electric vehicles is markedly increasing. On a large scale, either of these technologies can have damaging effects on the electricity grid; however, with proper energy management and programming, technologies, and energy storage resources can reduce these effects. Accordingly, the impact of the integrated aggregation of EVs to the grid for the charge/discharge process and the resulting grid instability, especially at the load peak time, is the main challenge usig of these vehicles. A challenge associated with these resources is their intrinsic uncertainty and stochastic scheduling. Herein, the stochastic scheduling of a power system in the presence of electric vehicles (EVs) and renewable energy sources (RESs) is solved via the optimization algorithm developed based on lightning search. The proposed model uses the probabilistic method to generate different scenarios for wind speed and solar irradiation coefficient; then, it modifies them via the scenario reduction method. In the proposed method, the conditional value at risk (CVaR) method is adopted to evaluate and manage the risk of the problem uncertainties. The use of the capacity stored in the EV batteries for overcoming the uncertainty of generation by wind and solar energy sources is also evaluated. Finally, the proposed method is tested on a modified sample system. The simulation results show that the power capacity stored in the vehicle-to-grid (V2G) stations greatly contributes to covering the uncertainty of wind and solar farms. The optimization algorithm is superior to other methods in local and global search.

Hybrid Scenario-IGDT-Based Congestion Management Considering Uncertain Demand Response Firms and Wind Farms

Demand response resources (DRRs) have been recently introduced as one of the most economic tools of congestion alleviation in power systems. Nevertheless, the severe uncertainty of multiple DRRs constituting a demand response firm (DRF) is an indispensable issue to be considered for the resilient operation of future power systems. Consequently, the information gap decision theory (IGDT) technique is utilized for addressing the uncertainty of consumers’ participation in demand response programs. This article presents a novel hybrid scenario-IGDT-based framework, designated as SIGDT, for corrective transmission congestion management (CM) in the presence of large-scale uncertain wind farms and DRFs as well as the uncertainty of conventional generating units. A reliability network modeling of a repairable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -component wind turbine (WT) is presented, considering failure and repair rates of turbines’ components, and then the uncertainty of wind farms’ generation is handled using the scenario-based approach. The proposed framework is applied to the IEEE-reliability test system (RTS) system to demonstrate its accuracy and capability. The results discuss the impact of failure and repair rates of WTs components on the proposed SIGDT-based CM and emphasize the application of the proposed framework for decision-makers to ensure the optimal operation of power systems under uncertainties of DRFs, wind farms, and conventional units.

Economic withholding under high penetration of wind power production: A robust equilibrium method

This paper presents the behavior of bidding above marginal cost, called economic withholding, for price-maker generating companies under wind uncertainty. Economic withholding prevents some lower cost resources from supplying the demand, and compels the ISO to buy more costly resources to meet the load, therefore result in higher market clearing prices than what the competitive outcome would have been. We formulate the underlying economic withholding problem under uncertainty as a robust bilevel optimization problem, where the generating company makes a robust decision in the presence of continuous uncertain variables in order to maximize its overall profit and guarantee the revenue security in the worst-case scenario. This formulation provides an alternative distribution-free equilibrium concept. A practical solution methodology is proposed to drive the exact reformulation of the robust bilevel optimization resulted in Mixed Integer Linear Programming (MILP) problem which is scalable and can be easily implemented in practical portfolio optimization and operation. Simulation results confirm the effectiveness of the proposed framework to provide optimum economic withholding decision.

Distributionally Robust Multi-Energy Dynamic Optimal Power Flow Considering Water Spillage with Wasserstein Metric

This paper proposes a distributed robust multi-energy dynamic optimal power flow (DR-DOPF) model to overcome the uncertainty of new energy outputs and to reduce water spillage in hydropower plants. The proposed model uses an ambiguity set based on the Wasserstein metric to address the uncertainty of wind and solar power forecasting errors, rendering the model data-driven. With increasing sample size, the conservativeness of the ambiguity set was found to decrease. By deducing the worst-case expectation in the objective function and the distributed robust chance constraints, the exact equivalent form of the worst-case expectation and approximate equivalent form of the distributed robust chance constraints were obtained. The test results of the IEEE-118 and IEEE-300 node systems indicate that the proposed model could reduce water spillage by more than 85% and comprehensive operation cost by approximately 12%. With an increasing number of samples, the model could reduce conservativeness on the premise of satisfying the reliability of safety constraints.

Review of failure risk and outage prediction in power system under wind hazards✰

Given that wind hazards pose a considerable damage to power system in various countries, researchers have developed methods for disaster prevention to diminish the vulnerabilities. This paper attempts to review the failure analysis procedure under the impact of wind hazards, considering the following attributes: wind field generation method, wind-induced electrical component failure model in transmission system, and building outage prediction model from data-driven view in distribution system. First, we compare the calculation of circulation and translation wind speed, with geographical, temporal and uncertainty factors of wind speed considered. Then, we introduce the wind-induced component failure models derived by wind load calculation and fragility curves. The application of the series reliability model in multiple cluster failures is briefly described. Finally, we summarize the applications of data analysis in building outage prediction models. The reviewed literature contains the statistical learning methods and machine learning methods. It is concluded that the frameworks for component-level failure model and user-level outage prediction model are well established. However, most of the studies still develop two models, separately. Future researchers may target at the hybrid application of both models to better provide fault analysis, prediction and emergency in power system under wind hazards.

AFIS based Advanced Power Control Scheme for Grid-Connected DFIG-Driven WECS

In wind energy conversion system, extraction of optimum power and efficient operation are two major challenges. In grid connected mode, a doubly fed induction generator (DFIG) control unit is designed to operate at optimum speed to deliver maximum output power in the grid while the voltage, frequency and harmonic regulations need to be fulfilled. Vector control associated with proportional-integral (PI) controllers has been widely applied in wind farms for reliable power regulation of DFIG. As DFIG based wind energy conversion system (WECS) experiences strong nonlinearity and uncertainties originated from the aerodynamics of the wind turbine and magnetic saturation of the generator, different adaptive and nonlinear control schemes have been proposed to resolve the problems associated with fixed-gain PI controllers.

Machine learning derived dynamic operating reserve requirements in high-renewable power systems

Accurately forecasting wind and solar power output poses challenges for deeply decarbonized electricity systems. Grid operators must commit resources to provide reserves to ensure reliable operations in the face of forecast errors, a process which can increase fuel consumption and emissions. We apply neural network-based machine learning to expand the usefulness of median point forecast data by creating probabilistic distributions of short-term uncertainty in demand, wind, and solar forecasts that adapt to prevailing grid conditions. Machine learning derived estimates of forecast errors compare favorably to estimates based on incumbent methods. Reserves derived from machine learning are usually smaller than values derived using incumbent methods, which enables fuel savings during most hours. Machine learning reserves are generally larger than incumbent reserves during times of higher forecast error, potentially improving system reliability. Performance is tested using multistage production simulation modeling of the California Independent System Operator system. Machine learning reserves provide production cost and greenhouse gas (GHG) emission reductions of approximately 0.3% relative to historical 2019 requirements. Savings in the 2030 timeframe are highly dependent on battery storage capacity. At lower levels of battery capacity, savings of 0.4% from machine learning reserves are shown. Significant quantities of battery storage are expected to be added to meet California's resource adequacy needs and GHG reduction targets. The addition of these batteries saturates reserve needs and results in minimal within-hour balancing costs in 2030.

IGDT-Based Realistic Scheduling of Thermal Power Generators Under Integration of Wind Turbine Generators

When Unit Commitment (UC) is considered with the integration of renewable energy, it becomes a big issue to be emphasized because of its uncertain natural impacts. The awareness on the lack of sufficient and accurate data about uncertainty and approaching towards the realistic deployment of thermal power generators is a challenge for researchers and planners in optimization works. This work proposes a framework for UC scheduling of thermal power generators using renewable energy sources with a high degree of penetration that closely matches real-time operation despite the lack of accurate data. Applying the Information Gap Decision Theory (IGDT) to deal with uncertainty, power-based UC model in which generation trajectories act as piecewise linear models was extended to consider the ramping capability and energy output during starting up and shutting down of generating units. Simulations were performed on a standard IEEE 10 unit system. Comparing with the proposed framework, the conventional energy-based UC method cannot obtain wind uncertainty values (allowable horizon of WTG forecast error) in realistic operation as much as the expected results from its scheduling and, it can also approach more serious infeasibility conditions in the realistic operation than its expected results. The load shedding and energy curtailment amounts that occur with respective wind uncertainty values can exist as maximum or minimum levels when unit scheduling is conducted with these wind uncertainty values.

Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids

Large-scale introduction of new energy could effectively alleviate energy shortage and environmental pollution. However, the uncertainty of wind and solar energy brings serious random disturbance problems to microgrids. This paper proposes lightweight actor-critic generative adversarial networks based on ensemble empirical mode decomposition and evolutionary strategy for increasing the robustness and adaptability of microgrids. Firstly, to improve the training speed and stability of generative adversarial networks, the complex power data is properly decomposed into more regular and simpler sub-data by the ensemble empirical mode decomposition; the generative adversarial networks are optimized by the evolutionary strategy with a set of different loss functions. Secondly, fully connecting the generative adversarial networks with the actor-critic framework, the lightweight actor-critic generative adversarial networks can realize dynamic learning in the random environment and store the sample for online training by the empirical replay mechanism. Thirdly, the multi-path lightweight method is proposed to reduce the consumption of time and storage resources of lightweight actor-critic generative adversarial networks. Eventually, the lightweight actor-critic generative adversarial networks are compared with comparison algorithms in two-area and real-life four-area systems. Case study results reveal that lightweight actor-critic generative adversarial networks have better dynamic performance, online learning capabilities, and higher control performance with lower economic costs.

Near-zero carbon stochastic dispatch optimization model for power-to-gas-based virtual power plant considering information gap status theory

PurposeThis study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.Design/methodology/approachIn view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.FindingsGVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.Research limitations/implicationsThis study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.Originality/valueThis study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

Robust Unit Commitment for Minimizing Wind Spillage and Load Shedding With Optimal DPFC

The distributed power flow controller (DPFC) has a positive effect of UC problem on the network side based on its ability to manage capacity of power flow. This study presents a novel two-stage robust model to optimize the status of the generator and location–allocation of the DPFC, while simultaneously considering wind and load uncertainties. The column-and-constraint generation (CCG) method is utilized to solve the two-stage problem into the master problem and the subproblem iteratively. The optimal status of the generator and location of the DPFC can be easily obtained with the master problem, and the dispatch solution and compensation level of the DPFC are solved in the subproblem. We conduct the IEEE 24 bus system to verify the performance of the proposed procedure. There are effects on wind spillage/load shedding and generator dispatch scheduling planning once the DPFC is injected. Detailed simulation results illustrate the effect of the proposed approach.

Risk-aware two-stage stochastic programming for electricity procurement of a large consumer with storage system and demand response

In this paper, a two-stage stochastic model is put forward for electricity procurement in large consumers (LCs) with storage system, photovoltaic, wind and geothermal units, slow demand response (SDR) and fast demand response (FDR), bilateral contracts and pool market. The model considers the uncertainties of pool market prices, demands, wind and photovoltaic power. Conditional-value-at-risk (CVaR) as a risk metric is used to take the variability of procurement cost into account. Major findings indicate that SDR and FDR collectively decrease the expected procurement cost by 9.4% and CVaR by 10.5% and also show that FDR is more efficient than SDR in decreasing the electricity procurement cost and its associated risk. The superiority of FDR over SDR is attributed to its flexibility which enables it to adjust its shift-ups and shift-downs according to the occurred scenario. The results imply that demand response programs and in particular, FDR decrease the purchased electricity from pool market. According to the results, the increase in confidence level increases CVaR, but does not significantly change the expected procurement cost. The results also show that at low weight factors, the increase in weight factor increases expected procurement cost and decreases CVaR, but at weight factors beyond 8, the increase in weight factor changes neither expected procurement cost nor CVaR.

Three-stage relaxation-weightsum-correction based probabilistic reactive power optimization in the distribution network with multiple wind generators

The uncertainty of wind powers complicates the reactive power optimization in the distribution network. In this paper, the authors proposed a three-stage relaxation-weightsum-correction (TSRWC) based probabilistic reactive power optimization method to deal with the uncertainty and correlation of DFIG based wind generators in the distribution network. Firstly, by the inverse NataF transformation, an improved point estimate method is proposed to transform the uncertain wind speeds into a series of data samples with weights. The digital integral technology and root search method are employed to calculate the correlation matrix in the standard normal space. Then, with the consideration of active utilization of reactive power capability of DFIG based wind generators, the reactive power optimization problem in the distribution network corresponding to each data sample is formulated. Further, the three-stage relaxation-weightsum-correction solution method is proposed to solve the probabilistic reactive power optimization problem. Finally, the numerical simulation experiments are conducted on the real 19-node distribution network and the modified PG&E 69-node distribution network, respectively. The experimental results verified that by the proposed TSRWC based probabilistic reactive power optimization method, the expectation and deviation of power loss are more adjacent to the results from the Monte Carlo Simulation experiments than the scenario without the consideration of correlation. Meanwhile, no obvious increasement of consumed time appeared.© 2017 Elsevier Inc. All rights reserved.

Dynamic Combined Economic Emission Dispatch Including Wind Generators by Real Coded Genetic Algorithm

With the growing environmental depletion, the shift in the focus towards minimizing the emissions of gases released in the conventional generators and further incorporation of a cleaner alternate renewable source of energy such as wind or solar to the existing system is of utmost importance. The research paper aims to build an environmentally resilient electric power system. Real coded genetic algorithm- powerful optimization technique is employed to solve the dynamic combined economic emission dispatch i.e. DCEED strategy for two proposed algorithm. The first proposed DCEED algorithm includes fuel cost of only conventional generators while in the second algorithm along with conventional generators, wind powered generators with varying power output characteristic is added. A comparative analysis of both the algorithms in terms of total combined cost, emission level and fuel cost is taken into account and it is observed that in spite of wind uncertainty the proposed method is more economical.

Two-Stage Optimal Location Allocations of DPFC Considering Wind and Load Uncertainty

Distributed power flow controller (DPFC) has a considerable potential to regulate the power flow and generator rescheduling continuously. This study presents a novel two-stage stochastic model for optimal location allocations of the DPFC coupled with the interactions of DPFC to search for the optimal solutions. The Benders decomposition is utilized to reformulate the two-stage problem into the master problem and the subproblem. The optimal solution can be easily obtained with the master problem and subproblem iteratively. The relaxed DC power flow with a DPFC in the master problem accelerates the efficiency of optimal locations under a base condition. Slack variables are incorporated in the subproblem to check the feasibility of relaxed AC power flow. The optimal compensation levels of DPFC at different load/wind scenarios are optimized in the subproblem. The IEEE 118 bus system is conducted to verify the performance of the proposed procedure. The DPFC has positive impacts on unit costs, voltage performance, wind absorption, and power losses. Detailed simulation results illustrate the effect of the proposed approach.

An information gap decision theory-based decision-making model for complementary operation of hydro-wind-solar system considering wind and solar output uncertainties

As large-scale wind and solar power sharing hydropower transmission channels are put into operation in river basins to lower investment costs in China, joint operations of hydro-wind-solar energy sources have become an effective operating strategy in power systems. However, wind and solar output with uncertainties and volatilities are problematic and pose challenges for power grid optimal scheduling, especially for peak shaving of grid system. In this paper, a novel decision-making model based on the information gap decision theory method is formulated to optimize the complementary operation of multi-energy systems. First, with the objective of minimizing the variance of the residual load, a deterministic short-term peak shaving model for a hydro-wind-solar system as a base case is established. Then, to model uncertainties of wind and solar output in the hybrid system and analyze the impact of grid peak shaving, the risk averse and opportunity seeker strategies of information gap decision theory-based technique are developed. Finally, as a proficient multi-objective optimization method, the normalized normal constraint method is exploited to solve the information gap decision theory-based bilevel optimal problem, and a set of Pareto solutions is obtained for the decision maker. A hydro-wind-solar hybrid system in Guizhou Province in China, with seven hydropower plants, a virtual wind power plant and a virtual solar power plant, is taken as an example to demonstrate the effectiveness of the proposed model. The results indicate that, by considering the uncertainties of wind and solar output, the presented model not only reduces the load fluctuations but also increases the flexibility response to the system against operational risk. In addition, the calculation time and load variance of the system are greatly reduced in comparison with stochastic programming.

Two-stage robust optimization of thermal-ESS units scheduling under wind uncertainty

The uncertainties of wind turbine (WT) output have large impacts on the optimal operation of integrated energy systems. This paper investigates the two-stage robust optimization (RO) of hybrid energy integration regarding wind volatility. In RO method, the WT output is modeled with an uncertainty budget, which represents the degree of conservatism. Simulation results validate that when the uncertainty budget rises to a maximal value, ESS degradation cost increases 25.7%, and total cost decreases 2.0%. Additionally, demand response program (DRP) exhibits elastic and voluntary merits. With the implementation of DRP, 6139.9$ total savings are achieved compared with the ordinary RO method. Meanwhile, the merits of stochastic optimization (SO) have also been studied. Despite the ESS degradation cost and total cost in SO being lower than those in RO, SO requires accurate probability distribution of uncertain variables in real situations. RO can shortly provide worst-case schemes which are highly appreciated by power utility companies.

Many-objective charging optimization for electric vehicles considering demand response and multi-uncertainties based on Markov chain and information gap decision theory

This paper constructs an electric vehicle (EV) charging optimization model considering demand response and the uncertainties of source and load. First, after obtaining the charging load by using the Markov chain, the charging time is divided into peaks, flats, and valleys, and the charging load is adjusted through price-based demand response. Secondly, aiming at minimizing user charging cost, greenhouse gas (GHG) emissions, and load fluctuation, and maximizing the revenue of power supplier, a deterministic EV charging optimization model is established. Third, by modeling information gap decision theory (IGDT), the uncertainty of wind and photovoltaic powers (WP and PV) is introduced into the charging optimization model to analyze the impact of WP and PV's fluctuations on risk aversion decision makers. Finally, a joint algorithm for solving a many-objective problem based on ε-constraint and NSGA-II algorithm is proposed. The results of case study show that: (1) rolling forecast based on Markov chain has a better effect on dealing with the uncertainty of charging load than Monte Carlo; (2) the price-based demand response combined with time division model can be used to shave peaks and fill valleys and reduce user charging cost; (3) optimized by using the deterministic model, the charging load curve is smoother; (4) with the uncertainty of power generation involved in the many-objective optimization, the PV's fluctuation is greater, and the power supplier revenue increases compared with the deterministic model, but the GHG emissions also increase; (5) In the same range of expected benefit changes, the fluctuation values acceptable to decision makers are different for different objective functions; (6) the user response, the normalized number of the selected sub-objective, and the maximum acceptable benefit deviation have different influences on the ordered charging strategy; (7) the joint algorithm proposed in this paper is an effective method to solve many-objective problems that contain conflictive aims.

A multi-objective probabilistic approach for smart voltage control in wind-energy integrated networks considering correlated parameters

Renewable and sustainable energies such as wind power generation are increasingly integrated into electric grids due to their various technical, environmental, and economic advantages. However, these resources impose more uncertainties on the power system in addition to the uncertainty of load demands. These uncertainties and correlations among them confront operation decisions with serious challenges. In this paper, a new multi-objective probabilistic approach is proposed for smart voltage control of wind-energy-integrated systems through tap adjustment of power transformers and voltage regulation of generators. The objectives include improving voltage stability margin, voltage profile improvement, and power loss reduction in the presence of wind and demand uncertainties. A data clustering algorithm is employed to handle the uncertainty of the random input variables, and a Cholesky decomposition method is used for modeling the correlation between them. The proposed multi-objective optimization is solved using NSGA-II. The power system has been modeled in DIgSILENT-Powerfactory linked with MATLAB, where the NSGA-II is programmed. The IEEE 14-bus system has been employed to evaluate the performance of the conducted approach. The results demonstrate the effectiveness of the proposed approach in decreasing the losses, increasing the voltage stability margin, and improving the voltage profile of wind-energy-integrated power systems.