|Title:||Evaluation of probabilistic production costing simulation methods|
|Subject:||Electric power systems|
Digital computer simulation
Monte Carlo method
Hong Kong Polytechnic University -- Dissertations
Department of Electrical Engineering
|Pages:||ix, 65, ii leaves : ill. ; 30 cm|
|Abstract:||In the past decade, with the increasing financial and regulatory pressures, and changes in the structure of the electricity supply industry which lead to the open access of transmission systems and introduce competition between different electric utilities and power producers, electric power utilities are increasing their concern on operating their systems more efficiently and economically. As the major operating cost in a power system is the fuel cost, detailed and realistic fuel forecasts are required for efficient and effective planning. To accurately estimate the fuel requirements of a power system, the random forced unavailabilities of generating units must be considered. Hence, probabilistic production costing simulation methods, which can recognise the stochastic events of the generating units' forced outages, are indeed required to estimate the expected energy production of individual generating units and their corresponding fuel requirements for cost analysis. In this dissertation, various probabilistic production costing simulation methods are generally discussed, and the major type of the analytical technique, the cumulant method and the Monte Carlo simulation technique are studied in details. To appreciate these techniques, computer programs are written and case studies are performed to compare their accuracies and computing time requirements. In addition, the flexibility of the methods in practical applications such as in handling multi-state representation, unit commitment and economic load dispatch of generating units, limited energy hydro generating units, demand side management modelling, and their extensibility to cater for interconnected systems are also examined and discussed. In general, the Monte Carlo simulation method has high flexibility and extensibility in practical applications. In simulating complicated systems especially for a sophisticated interconnected system, system characteristics and models can be accurately represented and simulated. While the analytical method may be infeasible or can only provide very crude results based on over-simplified assumptions. However, huge computational effort is required in the Monte Carlo simulation method as a large number of sampled scenarios have to be studied for the convergence of the results. Hence, the Monte Carlo simulation method is more efficient in handling complex systems like those include several interconnected areas and/or require detailed system modelling. While the analytical method is more efficient in handling simple systems. It can simulate the forced outages of generating units with high computational efficiency, and works well for simple studies which involve a large number of study cases and detailed accurate modelling is not essentially required.|
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