Uncertainty Of Wind Power Generation Research Articles

A new self-scheduling strategy for integrated operation of wind and pumped-storage power plants in power markets

Competitive structure of power markets causes various challenges for wind resources to participate in these markets. Indeed, production uncertainty is the main cause of their low income. Thus, they are usually supported by system operators, which is in contrast with the competitive paradigm of power markets. In this paper, a new strategy for increasing the profits of wind resources is proposed. In the suggested strategy, a Generation Company (GenCo), who owns both wind and pumped-storage plants, self-schedules the integrated operation of them regarding the uncertainty of wind power generation. For presenting an integrated self-schedule and obtaining a real added value of the strategy, participation of the GenCo in energy and ancillary service markets is modeled. The self-scheduling strategy is based on stochastic programming techniques. Outputs of the problem include generation offers in day-ahead energy market and ancillary service markets, including spinning and regulation reserve markets. A Neural Network (NN) based technique is used for modeling the uncertainty of wind power production. The proposed strategy is tested on a real wind farm in mainland, Spain. Moreover, added value of the strategy is presented in different conditions of the market.

A system dynamics approach for investigating impacts of incentive mechanisms on wind power investment

Large integration of intermittent wind generation in power system has necessitated the inclusion of more innovative and sophisticated approaches in power system investment planning. The intermittent nature and uncertainties of wind power generation will cause the investors encounter risk in their investment decisions. Main factors such as uncertainties in demand, fuel price, wind power generation which affect long-term planning have been considered in the proposed model. To overcome this problem i.e. risk impacts on decisions, this paper presents improved incentive mechanisms for wind power investment. The mechanism has been investigated based on system dynamics modeling. In the proposed mechanism, wind power generations will recover a part of their investment cost through incentive mechanisms. In this paper, different incentive methods some of which are dependent to market conditions are being proposed. The functionality of these incentive mechanisms on wind generation investment are illustrated by simulation studies.

An investigation on the impacts of regulatory interventions on wind power expansion in generation planning

Large integration of intermittent wind generation in power system has necessitated the inclusion of more innovative and sophisticated approaches in power system investment planning. This paper presents a novel framework on the basis of a combination of stochastic dynamic programming (SDP) algorithm and game theory to study the impacts of different regulatory interventions to promote wind power investment in generation expansion planning. In this study, regulatory policies include Feed-in-Tariff (FIT) incentive, quota and tradable green certificate. The intermittent nature and uncertainties of wind power generation will cause the investors encounter risk in their investment decisions. To overcome this problem, a novel model has been derived to study the regulatory impacts on wind generation expansion planning. In our approach, the probabilistic nature of wind generation is modeled. The model can calculate optimal investment strategies, in which the wind power uncertainty is included. This framework is implemented on a test system to illustrate the working of the proposed approach. The result shows that FITs are the most effective policy to encourage the rapid and sustained deployment of wind power. FITs can significantly reduce the risks of investing in renewable energy technologies and thus create conditions conducive to rapid market growth.

Dynamic model for market-based capacity investment decision considering stochastic characteristic of wind power

This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market with significant wind power generation. In such an environment, investment and construction decisions are based on price signal feedbacks and imperfect foresight of future conditions in electricity market. System dynamics concepts are used to model structural characteristics of power market such as, long-term firms’ behavior and relationships between variables, feedbacks and time delays. For conventional generation units, short-term price feedback for generation dispatching of forward market is implemented as well as long-term price expectation for profitability assessment in capacity investment. For wind power generation, a special framework is proposed in which generation firms are committed depending on the statistical nature of wind power. The method is based on the time series stochastic simulation process for prediction of wind speed using historical and probabilistic data. The auto-correlation nature of wind speed and the correlation with demand fluctuations are modeled appropriately. The Monte Carlo simulation technique is employed to assess the effect of demand growth rate and wind power uncertainties. Such a decision model enables the companies to find out the possible consequences of their different investment decisions. Different regulatory policies and market conditions can also be assessed by ISOs and regulators to check the performance of market rules. A case study is presented exhibiting the effectiveness of the proposed model for capacity expansion of electricity markets in which the market prices and the generation capacities are fluctuating due to uncertainty of wind power generation.

Stochastic unit commitment of wind farms integrated in power system

Integration of wind power generation creates new concerns for operation engineers in a power system. Unlike conventional power generation sources, wind power generators supply intermittent power due to uncertainty in parameters of wind such as its velocity. This paper presents probabilistic model for load and wind power uncertainty which can be used in operation planning (with durations up to one or two years). A stochastic model is proposed to simulate the status of units that are directly affected by the load and wind power generation uncertainties. This paper develops a solution method for generation scheduling of power system, while taking into account the stochastic behavior of the load magnitude and the wind power generation. The stochastic trend in uncertainty of these parameters has been simulated by creating scenarios that can be solved by deterministic methods. Mixed integer nonlinear programming (MINLP) is used for solving deterministic unit commitment problems, the reserve power requirement, load–generation balance, and available wind power constraints. The proposed approach is developed to make decision on fixed state of units operation in different scenarios which can be employed efficiently in unit scheduling of power system. The proposed approach is applied to a 12-unit test system (including 10 conventional units and 2 wind farms). The performance of the proposed approach is more investigated through analysis of its results for two other test systems.

Characterizing Wind Power Forecast Uncertainty with Numerical Weather Prediction Spatial Fields

Successful integration of wind power into power systems can be facilitated through better understanding of future uncertainty in wind power generation. This paper explores a new approach to characterizing this uncertainty using measures of the variability in the wind speeds predicted at multiple grid points in a Numerical Weather Prediction (NWP) system. This approach is compared to the traditional approach of using the spread from an NWP ensemble by using two measures of uncertainty; forecast errors in single time-series forecasts and observed temporal variability. Results show that the multiple grid point approach has a comparable skill level to NWP ensembles for predicting these uncertainty measures and in particular, demonstrates very good skill in predicting large forecast errors. These results also provide a positive evaluation of a terrain standardization method described in a companion paper. A possible extension of this work is to combine the multiple grid point approach with NWP ensembles to improve uncertainty characterization.

A two-layered neighborhood tabu search-based approach to optimal allocation of D-FACTS in distribution systems

This paper proposes two-layered neighborhood tabu search for determining the optimal allocation and output of D-FACTS device in a distribution system with uncertain wind power generation. In recent years, wind power generation has been introduced into distribution systems for clean and inexhaustible distributed generation. However, a distribution system with wind power generation often encounters the reverse flow due to the uncertain output performance of wind power generation. As a result, the voltage variation may become much larger. In this paper, two-layered neighborhood tabu search is proposed to determine the optimal allocation and output of D-FACTS devices. To consider the uncertainty of wind power generation, this paper carries out Monte Carlo simulation to evaluate the probabilistic voltage assessment in the distribution system with uncertain wind power generation. The proposed method is successfully applied to a sample system. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 155(3): 29–37, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20103

Simulating the Influence of Wind Power Generation on the Electricity Market Price

Wind power has recently become quite popular among power generation companies all over the world. In the year 2003 alone 8,133MW of new installations have been built up.When integrating these huge amount of wind power into an existing power grid and the trading process of a power utility on the electricity market, the uncertainty in wind power generation becomes a critical point. There are basically two viewpoints on this issue. First, operating wind power generation in a power grid needs a high amount of regulation reserve. Therefore wind generation is expensive. On the other hand, wind power generation does not consume fuel and therefore there are no fuel cost.To estimate the consequences of high wind power generation we propose in this paper a simulation model that is capable of simulating spot and ancillary markets, as well as the physical aspects of electricity generation. By simulating the interaction between spot and ancillary market we calculate the development of the electricity prices on the spot market and the ancillary market with growing wind power generation.

Dead calm [requirement for wind power generation storage devices

While wind generation is fast proving to be a valuable option in combating so-called greenhouse effects, hidden costs exist. Extra costs due to additional reserve capacity as well as inefficiencies during conventional plant operation are often ignored. This article analyses wind power production figures for the UK and suggests that economically efficient storage devices could be used to mitigate the uncertainties in wind power generation. Such devices could shift generation from low demand periods to peak demand periods, reduce or even remove the need for additional reserve and change the nature of wind generation from 'reserve sinker' to 'reserve provider'.

２層近傍タブサーチに基づく配電系統におけるＤ－ＦＡＣＴＳの最適配置解法

This paper proposes two-layered neighborhood tabu search for determining the optimal allocation and output of D-FACTS device in a distribution system with uncertain wind power generation. In recent years, wind power generation is positively introduced into distribution systems as clean and inexhaustible distributed generation. However, the distribution system with wind power generation often encounters the reverse flow due to the uncertain output performance of wind power generation. As a result, it is afraid that the voltage variation becomes much larger. In this paper, two-layered neighborhood tabu search is proposed to determine the optimal allocation and output of D-FACTS devices. To consider the uncertainty of wind power generation, this paper carries out the Monte-Carlo simulation to evaluate the probabilistic voltage assessment in the distribution system with uncertain wind power generation. The proposed method is successfully applied to a sample system.