Evaluation and Decision Making of Wind Power Integration Considering the Possibilistic Uncertainty of Wind Resources

Abstract

In the modern society, the power supply has been expected to be continuously available on demand because of the social pattern and working habits. Fueled by energy crisis and environment protection issue, wind power achieves a great development over the last decade. However, the variability and uncertainty of the wind power bring financial and technological challenges to grid integration.

To date, the literature on wind power integration (WPI) to power system is quite large. People hope to achieve the optimal WPI, aiming to provide an ideal wind capacity at the lowest overall cost. Two issues are involved in this problem: one is how to describe the wind speed uncertainty; the other one is to propose an appropriate framework to evaluate WPI comprehensively. Previous research studied wind speed from the perspective of probabilistic uncertainty, ignoring the possibilistic uncertainty. Many studies of investigating the wind power integration have been reported in the literature by considering different factors. However, there are no standard and unified methods to study this problem. This thesis did some work in wind speed uncertainty model, wind power integration evaluation method, WPI index system, wind curtailment under different wind capacity and optimal wind capacity integration:

1) Wind speed uncertainty model: A random fuzzy model and a fuzzy copula model are proposed to propagate the probabilistic and possibilistic uncertainties of wind speed simultaneously while the possibilistic uncertainty comes from incomplete knowledge of wind speed or insufficient historical data. In these models, the Weibull parameters and wind speed correlation coefficients are represented by fuzzy numbers. A complete decision rule and interval estimation method are proposed to determine the format of parameters based on cumulative probability and probability distributions of wind speeds. To validate the availability of the proposed models, they are used in wind curtailment evaluation and calculation of wind capacity factor. The results demonstrate that the models with any crisp value parameters are not suitable for describing the wind speed distributions in some cases. In comparison, the fuzzy models can cover the cumulative distribution and probability distribution of wind speeds. Compared with previous research, the proposed methods provide more interval results by calculating the membership function of wind speed parameters.

2) Framework to evaluate WPI: A Monte Carlo simulation based algorithm is proposed to evaluate WPI in a hybrid power system. This algorithm is capable of modeling different factors including economic operation, peak regulation, frequency adjustment, power system flow, maintenance schedule and time varying system state. A WP restriction analysis model is proposed to estimate the wind curtailment and identify factors restricting the WPI. A maximum WPI analysis model is developed to assess the potential of WPI based on an existing power grid. To obtain more insights from WPI simulation results, a group of indices are proposed to represent the average power, energy, duration time, frequency of wind curtailment and the potential to integrate more wind energy, including WPCC, WPAC, MWPCC, EWEU and so on. Simulation results demonstrate that the wind curtailment is assessed and corresponding reasons are discovered. It is valuable for decision makers to recognize the bottleneck of wind power integration. The cumulative probability of wind power absorption capacity indicates the potential of the wind power absorption. We can see that the proposed method describes the wind power integration effectively and comprehensively, giving a reference for power system operation and planning.

3) Wind curtailment under different wind capacity: This thesis aims to study the wind curtailment under different wind capacity while considering the uncertainty of wind resources. Fuzzy wind capacity factor is proposed to model and propagate the uncertainty of wind resources. A fuzzy linear programming problem, wind capacity integration allocation (WCIA) model, is proposed to decide the required wind capacity considering the possibilistic uncertainty of wind resources. After executing the WCIA model in different system states for a long enough simulation time, the cumulative distribution function of wind capacity is acquired. It reflects the relationship between wind capacity and wind curtailment. The results show that the proposed model is capable of describing the possibilistic uncertainty of wind resources. By transferring the cumulative probability to acceptance probability of wind capacity, we can get the wind curtailment under different wind capacity. It is useful for policy makers to set a target of wind penetration level or power system planners to achieve an overall optimal wind capacity. Moreover, the proposed method is capable of allocating wind capacity to different nodes, which is helpful in decision making of ratings and location of new wind farms.

4) Optimal wind capacity integration: This thesis presents an optimization model to determine the optimal wind capacity in an existing power system. The objective is to minimize the overall cost including the fuel cost of coal-fired generation, start-up/shutdown cost of generation units, capital investment of wind farm, operational cost of wind power and wind power subsidy. The results illustrate that the proposed method can recognize the possibilistic uncertainty and provide the power system planners the optimal the optimal wind capacity, which is meaningful in wind power expansion planning. The sensitivity analyses demonstrate that, in wind generation planning period, the capital cost of wind farm should be given more attentions. In comparison, allowed wind curtailment ratio has little effect on the results.

I take part in the joint PhD program between CityU and XJTU. This theis is the English version. This work is supported by the National High Technology Research and Development Program of China (2012AA050201) and a grant from City University of Hong Kong (9380058). This study will provide reference data for wind capacity planning, operation of the system, wind curtailment scheduling, improving power system economy and comprehensive utilization of wind energy resources.

Date of Award	22 Feb 2017
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Min XIE (Supervisor) & Zijun ZHANG (Supervisor)