Uncertainty Of Wind Energy Research Articles

Modelling and analysis of resource scheduling in restructured power systems considering wind energy uncertainty

ABSTRACTConcerns over rapid transformation of global climate patterns resulted in wide deployment of renewable energy sources especially wind energy. However, intermittent nature of wind energy makes resource scheduling aspect of system operator more complex. The emerging deregulation policies have added to the complexity of scheduling problem. Therefore, treating and scheduling of wind energy on deterministic basis would result in non-optimal energy dispatch and increased operation cost over scheduling horizon. This paper focuses on optimal scheduling methodology of wind energy in day ahead market (DAM) considering expected costs due to wind uncertainty in real time (RT) spot market under deregulated market structure. This paper introduces various possible costs, namely, spot market compensation cost, additional reserve cost and rescheduling cost applicable to deviation in scheduled power from DAM to spot market. The costs can be derived from front-end information of DAM, historical and statistical estimate of RT market conditions. The effectiveness of proposed methodology is examined using IEEE 30 bus system with thermal and wind energy generators. In addition, sensitivity analysis is performed to examine the effect of incentive margin on DAM scheduling. Simulation results are presented, discussed and affirmed the effectiveness of proposed scheduling methodology.

Symbiotic organisms search algorithm for automatic generation control of interconnected power systems including wind farms

Automatic generation control (AGC) plays an important role in power systems, where it maintains the system frequency and tie-line power flow at their desired values. This study presents a novel symbiotic organisms search (SOS) algorithm in order to optimally design the proportional–integral–derivative (PID) controllers in the AGC of interconnected multiple-area power systems including wind farms uncertainty and system non-linearity. The PID controllers’ parameters define the design variables of the optimisation problem. The SOS algorithm is applied to different objective functions such as integral square error and integral absolute of error. The efficacy of the proposed controller is compared with other competing evolutionary algorithms-based proportional–integral or PID controller. The system performance is evaluated under different operating conditions such as different loading, variation of system parameters, and an addition of wind energy uncertainty. For obtaining realistic responses, real wind speed data that extracted from Zafarana location in Egypt is used in this study. The validity of the proposed controller is extensively confirmed using the simulation results. With the SOS-based PID controllers, the dynamic responses for the AGC of interconnected power systems, including wind farms uncertainty can be further enhanced.

Fully Parallel Stochastic Security-Constrained Unit Commitment

The increasing size and complexity of modern power systems and the integration of volatile renewable energy bring great challenges to the existing security-constrained unit commitment (SCUC) solution engines. This paper presents a fully parallel stochastic SCUC approach to obtain an efficient and fast solution for a large-scale power system with wind energy uncertainty. Variables duplication and auxiliary problem principle (APP) techniques are adopted to fully decompose the original stochastic optimization problem into three major solution modules: the unit commitment (UC) module solves multiple single UC problems; the optimal power flow (OPF) module handles multiple hourly DC-OPF problems; and the bridge module builds a connection between the UC and OPF modules. These three modules are conducted for both base case and scenarios, and can be totally solved in a parallel manner. Numerical case studies on a modified IEEE 118-bus system and a practical 1168-bus system demonstrate the effectiveness and efficiency of the proposed approach which will offer the power system a secure and economic operation under various uncertainties.

Stochastic analysis of wind energy uncertainty impact on ISO risk-taking in joint energy and reserve markets using conditional value at risk

Uncertainty management of the large scale wind power resources is mainly known as one of the most challenging aspects in future electricity market operations, where network operation may put at risk due to the wind power forecasting errors. Therefore, it is necessary to evaluate the impact of the wind power uncertainty on the operator risk-taking by using validated tools. This paper proposes a new mixed integer linear programming model for joint energy and reserve market clearing taking into account the co-optimization of the market clearing and operation cost of the thermal units. Also, wind power uncertainty is handled by means of two-stage stochastic programming and risk concept is introduced and formulated via a well-known measure to account for operator risk taking level. The proposed model is formulated as a two-stage stochastic programming where it clears the joint energy and reserve markets considering different penetration and forecasting accuracy levels of the wind power. Furthermore, supplementary terms are added to the objective function and constraints of the problem in order to measure the risk related to the wind power uncertainty.

On estimating uncertainty of wind energy with mixture of distributions

Accurate estimation of wind speed frequency distribution will be beneficial to predict accurately wind energy potential and select optimal wind energy conversion systems. Due to the complexity of wind regime, it has been reported that mixture models, constituted by multiple distributions (components), perform better than single models, which are commonly used to model wind speed frequency distribution. The underlying assumption in the existing mixture models is that each component is a unimodal distribution. In this work, we propose a more general model, named hierarchical mixture of multiple distributions. To deal with the bimodal or multimodal case in components, we assume that each component may also be a mixture model. In order to estimate the parameters in the proposed model, a nested expectation maximization algorithm is introduced. The experimental results indicate that, the proposed model outperforms single and mixture models. Moreover, due to the complex expression of the probability density function derived by the proposed model, we also develop a technique to estimate wind energy potential. Accordingly, the performances of different wind energy conversion systems are evaluated via four indicators, which provide comprehensive basis for selecting optimal wind energy conversion system.

Assessing Wind Energy Uncertainty Impact on Joint Energy and Reserve Markets by using Stochastic Programming Evaluation Metrics

Wind energy is considered as a challenging problem for power systems because of its variable nature. As a result of accommodating wind resources with conventional thermal units in the system, performing of electricity markets will be more complicated. The results of the market clearing problem can be improved by proper wind uncertainty modeling. One of the best methods in order to handle wind uncertainty is to use stochastic programming. The expected value of the perfect information (EVPI) and value of the stochastic solution (VSS) are two well-known indices defined to analyse stochastic programming results compared to a case with having full information and a case disregarding uncertainty, respectively. In this paper, impact of the wind uncertainty on the EVPI and VSS indices is investigated in the joint energy and reserve market clearing problem. Wind uncertainty is characterized by two measures, namely wind penetration level and wind forecasting accuracy level. The wind penetration levels are modeled as a fraction of a basic wind power value while various forecasting accuracy levels are modeled by normal probability distribution function with different variances. The problem is investigated and results are analyzed under different levels of the wind penetration and forecasting accuracies. Based on obtained results, the value of the EVPI metric increases with increment of the wind penetration level or forecasting error variance. Also, because of the higher uncertainty level of the wind power arising from penetration level or variance increment, the value of the VSS metric increases.

Wind Power Dispatch Margin for Flexible Energy and Reserve Scheduling With Increased Wind Generation

With the significant penetration of wind generation, the variability and uncertainty of wind energy poses new challenges to power system operations. In particular, more rapid reserve is required, which may result in the scarcity of balancing services. With the increasing penetration of renewable generation, it is envisaged that renewable resources will be required to partake in the system balancing tasks. In this paper, a combined flexible dispatch and reserve scheduling policy is proposed by determining a flexible wind dispatch margin. In order to provide a flexible dispatch margin, wind generators underschedule in the hour-ahead energy market, so as to hold some expected output for reserves. Additional wind energy is then available for mitigating forecast errors and other system uncertainties. This paper presents a framework to find the optimal policy to incorporate the flexible wind dispatch margin into the hour-ahead market. A finite-state Markov chain wind power forecast model, based on spatio-temporal analysis, is utilized. The presented framework is used to find the appropriate level of wind dispatch margin. The proposed approach is tested and the wind generation data are used to analyze the effectiveness of the presented model in coping with forecast errors and achieving a more secure system operation.

Risk Assessment for Power System Operation Planning With High Wind Power Penetration

Integration of wind power generation (WPG) is increasing rapidly worldwide. The variability and uncertainty of wind energy may lead to significant load-generation imbalances resulting in large frequency deviations, hence increasing system operational risks, especially in small and isolated power systems with low inertia and limited capabilities of providing frequency responses. This raises the need for investigating alternatives to current power system operation planning approaches to cope with the uncertain nature of the intermittent generation. This paper presents a risk assessment approach to analyze power system security for operation planning under high penetration of wind power generation. The proposed approach deals with not only steady-state voltage and overload evaluations, but also frequency response adequacy. For fast identification of operational limit violations in the proposed risk assessment method, we develop an analytical procedure for approximating frequency response and assessing the consequences of limit violations without performing dynamic simulations. As a result, the frequency response adequacy assessment can be run simultaneously with the steady-state voltage and overload evaluations. The proposed risk assessment approach is illustrated via its application to a model of a power system with high wind power penetration.

Assessing Wind Energy Uncertainty Impact on Joint Energy and Reserve Markets by using Stochastic Programming Evaluation Metrics

Assessing Wind Energy Uncertainty Impact on Joint Energy and Reserve Markets by using Stochastic Programming Evaluation Metrics

Expert energy management of a micro-grid considering wind energy uncertainty

Recently, the use of wind generation has rapidly increased in micro-grids. Due to the fluctuation of wind power, it is difficult to schedule wind turbines (WTs) with other distributed energy resources (DERs). In this paper, we propose an expert energy management system (EEMS) for optimal operation of WTs and other DERs in an interconnected micro-grid. The main purpose of the proposed EEMS is to find the optimal set points of DERs and storage devices, in such a way that the total operation cost and the net emission are simultaneously minimized. The EEMS consists of wind power forecasting module, smart energy storage system (ESS) module and optimization module. For optimal scheduling of WTs, the power forecasting module determines the possible available capacity of wind generation in the micro-grid. To do this, first, an artificial neural network (ANN) is used to forecast wind speed. Then, the obtaining results are used considering forecasting uncertainty by the probabilistic concept of confidence interval. To reduce the fluctuations of wind power generation and improve the micro-grid performances, a smart energy storage system (ESS) module is used. For optimal management of the ESS, the comprehensive mathematical model with practical constraints is extracted. Finally, an efficient modified Bacterial Foraging Optimization (MBFO) module is proposed to solve the multi-objective problem. An interactive fuzzy satisfying method is also used to simulate the trade-off between the conflicting objectives (cost and emission). To evaluate the proposed algorithm, the EEMS is applied to a typical micro-grid which consists of various DERs, smart ESS and electrical loads. The results show that the EEMS can effectively coordinate the power generation of DERs and ESS with respect to economic and environmental considerations.

Managing Uncertainty of Wind Energy With Wind Generators Cooperative

Power systems around the world have set ambitious targets for renewable energy integration. Several jurisdictions incentivize renewables such as the Feed-in Tariff in Ontario, Canada. These incentives shall eventually run out and renewables would have to competitively sell energy into electricity markets, overcoming uncertainty and variability in their output. This paper proposes a Wind Generators Cooperative (WGC) model for competitive integration of renewables into energy markets, in the future, overcoming challenges posed by their uncertain and variable nature. The proposed model minimizes the effect of uncertainty and maximizes returns for wind generators. In the proposed WGC model, uncertainty of the total wind power output is reduced by the smoothing effect and using pumped-hydro facilities. Using these pumped-hydro facilities, WGC stores wind energy produced during low marginal price hours and releases it during high marginal price hours. In this paper, a case study with actual data from Ontario, Canada is presented with detailed sensitivity analyses. Analyses clearly demonstrate that the WGC increases returns to wind generators and reduces their exposure to uncertainty. The study and sensitivity analyses show that the WGC model is financially viable and minimizes output uncertainty.

Short-Term Energy Balancing With Increasing Levels of Wind Energy

Increasing levels of wind energy are adding to the uncertainty and variability inherent in electricity grids and are consequently driving changes. Here, some of the possible evolutions in optimal short-term energy balancing to better deal with wind energy uncertainty are investigated. The focus is mainly on managing reserves through changes in scheduling, in particular market structure (more regular and higher resolution scheduling), reserve procurement (dynamic as opposed to static), and improved operational planning (stochastic as opposed to deterministic). Infrastructure changes including flexible plant, increased demand side participation, more interconnection, transmission, larger balancing areas, and critically improved forecasting can also be significant and are dealt with in the discussion. The evolutions are tightly coupled, their impact is system-dependent and so no “best” set is identifiable but experience of system operators will be critical to future developments.

Wind Integration Cost Impact Study for the Arizona Public Service Company: Modeling Approach and Results

The objectives of this study were to analyze operating impacts and costs of integrating wind energy in the Arizona Public Service (APS) balancing area, due to the variability and uncertainty of wind energy. Wind energy penetration levels from 1% to 10% were considered, corresponding to wind power levels of 100 MW to 1400 MW. A production cost simulation was conducted using APS system modeling software to assess the costs, by comparing the total production cost of the system with wind energy with a reference case that excludes the variability and uncertainty of wind energy. For APS's system, with an anticipated peak load of 7,905 MW in 2010, the integration costs were $0.91, $3.25, $3.57, and $4.08 per MWh for 1%, 4%, 7% and 10% wind energy. The geographic diversity of locating wind power plants over a large area was considered and found to have an important influence on the total integration cost.

Probabilistic capacity of a grid connected wind farm based on optimization method

This paper proposes a new method to find the capacity of a future wind farm regarding several limits of the grid system: voltage stability limits, thermal limits, voltage limits, load tap changing limitation and generator power output limitation. The method combines the optimization method and a probabilistic analysis to maximize the number of the wind turbines subject to those limits. Two types of wind farms are modeled in this paper: fixed speed wind farms and variable speed wind farms. It is concluded that the proposed method is a fast and accurate method to deal with the uncertainty of wind energy in the planning of wind farm capacity.