Risk Analysis And Control Models For Power Generation Companies

Power Generation companies face not only the common risks of ordinary business, but also some unique risks and threats because of the special nature of electricity production and operation. Electricity cannot be stored in large quantity economiclly. Electricity supply and demand should be in balance in real-time. Generation?transmission, distribution and retail should be done instantaneously. Therefore, Power generation companies are confronted with many uncertainties in the operation process. With the reform of power sector, power generation companies have been unbundled from the vertical integrated enterprises. They have become independent companies. Power generation companies will confront competition with other market players. There will be transactions between generation companies and coal producers, grid enterprises and electricity consumers. With the deepening of reform, there will be more uncertainties in operation and management of power generation companies.In this paper, models of risk identification, analysis, evaluation and risk measurement and control of power generation companies are developed on the basis of the latest results of risk management theory and methods, optimization theory, system engineering theory and methods under the environment of energy conservation, environmental protection and market-oriented reform.First, a multi-dimensional risk identification model for power generation companies is built based on the Hierarchical Holographic Modeling (HHM).This risk identification model consists of three layers:risk dimensions from different perspectives, different risks under the same scenario, different risk factors within the same risk. It consists of eight risk dimensions including nature, society, technology, industrial chain, enterprise life cycle, management, administration. A multi-dimensional risk identification model of power generation companies is formed through risk identification and filtering. The framework model is the foundation for further risk analysis and assessment.Second, The structure of risk system of power generation companies is analyzed applying the classical interpretative structural modelling method. The linking ralationship model of risk is constructed. Through structural analysis to the risks and risk factors, the relationship between risks and risk factors is identified and the risk chain and the source of risks is found. The classical interpretative structural modelling method is extended to fuzzy sets, then the impact degree model of risk chain is developed. This model considers the effect of the risk impact degree when analyzing the relationship. It gives different linking relationship of the risk chain based on different risk impact degrees. Through comparative analysis, the relation between the linking ralationship model and the impact degree model is obtained. Third, using the Analytic Network Process method (ANP), the overall risk of power generation companies is evaluated comprehensively. The evaluation is based on three criteria of the risk loss, the risk probability and the risk uncontrollability. The weighting of each risk of the risk indicator system is calculated using ANP model, and three schemes of thermal power, hydropower and renewable energy are evaluated.Finally, Conditional Value at Risk (CVaR) techniques are used to establish models of risk measurement for power generation companies.Two models of risk control for power generation companies are derived under decision criteria of risk minimization and benefit maximization. These optimization models of risk control can measure and optimize the risks of power generation companies under the environment of multi-markets and multi-types of power generation units. For examining and illustrating the effectiveness of the model, case study is performed under four scenarios of general, environmental protection, energy-saving dispatching and bidding for generation.This work has theoriotic contribution on the models of risk analysis, assessment and control of power generation companies. It provides theoretical and strategic support for the management and decision making of power generation companies in an uncertain environment.