Dynamic investment model for the restructed power market in the presence of wind source

The short term and long term revenues of the wind firm encounters more uncertainties which include the forecasted load, fuel price and output power of wind units. Furthermore, in short and medium terms the wind firm faces with Market clearing price‟s fluctuations, which is affected by some uncertain parameters including the demand, fuel price, wind fluctuations and the operational strategic behaviour of other investors. These uncertainties increase the risk of investment. Therefore, the private wind investors require a set of decision tools to evaluate and analyse the investment strategies in the long term planning by considering the existence of uncertainties, regulatory policies, which include the incentive policies for wind power plants and CO2 tax, and the realities in the restructured power market such as the bilateral contracts. The main objective of this thesis is to determine the long term optimal investment strategies of the hybrid wind-thermal investor in the restructured power market. To accomplish this purpose, three main steps have been conducted in this thesis. In the first step, the hybrid Autoregressive Moving Average – Monte Carlo method proposes to simulate the hourly wind speed as well as the hourly wind turbine generators. The uncertainties of the output power of wind turbine generators are modelled based upon the scenario-based method and data mining techniques. In the second step, a model developed in this work is proposed to simulate the medium term restructured power market. The scenarios of the output power of wind turbines, which are generated through the first step in terms of the outputs power of wind farm together with their occurrence probability, are used to estimate the maximum profit of investors as well as the average Market clearing price with the proposed model in the restructured power market. The stochastic uncertainties include the demand and fuel price fluctuations in the restructured power market simulated based on the Monte Carlo method. In addition to the stochastic uncertainties in the medium term power market which are considered in the proposed model, the operational strategic behaviour of other investors considered in this study is based upon the game theory concepts using the Cournot game. In the third step, the long term optimal investment strategies of the hybrid wind-thermal investor are determined based on the dynamic programming algorithm by considering the long term states of demand growth and fuel price uncertainties. The proposed framework has been implemented in the hypothetical restructured power market using the IEEE Reliability Test System. Well-established renewable energy-based power generation in the countries like Spain and Germany for instance,can provide strong and useful information for this study. With that justification, fixed Feed in Tariff‟s incentive policy was adopted in the case of wind investor. Conducted case studies have confirmed that this framework provides robust decisions and precise information about the restructured power market for hybrid wind-thermal investors.

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