Study On Optimal Operation Of GENCOs Considering Ancillary Service

As the deregulation of power industry, the gencos have been one of the independent market entities. It is a very important subject that how the gencos obtain the maximal profits considering their operation costs, electricity prices, and ancillary services prices. It is more practical under the background of deregulation in China.Optimal operation problems of power systems are allocating the load among the generators to minimize the total running costs of the systems subject to the demand, spinning reserve, power output limit, minimum down-time and up-time constraints, and so on. The problems are difficult due to their inherent high-dimensional, non-linearity, and complex-constraints nature. Many methods have been developed, however the problems are still consistent research interests. With the opening of power industry to competition, the power system structure is changing. According to these changes, power system operation, planning and control need some modifications. The objective of the operation changes from minimal total running costs to maximal total profits. The constraints have also changed, for example, the load demand constraints change from equality to inequality. Ancillary services such as reserve play a more important role, so that the models of the optimal operation have to be modified. Moreover, various emerging computing technologies provide new tools to solve the problems. The progressing of the IT makes some methods time-wasted before become easier to be used in engineering.The paper studies on the optimal operation problem of gencos in power markets. The optimal operation models of gencos with the consideration of ancillary services are established. The impacts of several market patterns on the models are discussed. Considering the features of the problems, some new PSO-based algorithms are proposed.This paper comprises seven chapters. In chapter I, the significance of the thesis, current research works of domestic and foreign, the contents of PSO algorithm and the original works of the paper are introduced. In chapter II, the definition and patterns of the ancillary services supplied by the gencos are discussed. The gencos'optimal operation models considering ancillary services for various market patterns are established. In chapter III, the gencos'economic dispatch model with ancillary services (reserve) and various constraints is analyzed. An improved PSO algorithm is developed to solve the model. In chapter IV, the gencos'dynamic economic dispatch model with ancillary services (reserve) and various constraints is discussed. A modified PSO algorithm is presented to solve the problem. In chapter V, the gencos'unit commitment problem considering ancillary services (reserve) is discussed and a new algorithm is proposed. In chapter VI, a uniform algorithm to solve the gencos'optimal operation models is put forward. Some extension discussions, including price strategy model, asset valuation model, and so on, are also performed. The main works of the paper are summarized in the last chapter.The innovations of the paper are as follows.1. How the gencos achieve the maximal profits considering ancillary services is studied. The models are constructed, and the affect of different market patterns on the models are also discussed.2. Most literature which applying PSO to solve the constrained optimal problems discard or regenerate the particles that violate the constraints. A more active repair strategy is proposed in the thesis. The strategy, modifying the infeasible solution to nearest feasible solution, improves the efficiency of the search procedure, which can upgrade the accuracy and speed of the algorithm.3. In solving the time-span optimal problem such as dynamic economic dispatch and unit commitment, particles may only find some sections of better solutions at some dispatch interval. Merely comparing total fitness of the particles may waste the valid information. Therefore, a selection-by-period strategy, making full use of the information of the particles at each dispatch period, is proposed to improve the accuracy and convergence characteristic of the algorithm.4. Many methods have been used to solve the unit commitment problem. However, they all have some deficiencies. Incorporating priority list into particle swarm optimization, combined with heuristic rules, a new method is put forward in the paper.5. A uniform PSO-based algorithm for solving optimal operation models of gencos considering ancillary services is proposed. The particles search not only the unit status but also the assigned value of power output and ancillary services, which shortens the calculation time.6. Based on the optimal operation models of gencos, the real options model for short-term generation asset valuation with the consideration of ancillary services. The model handles the uncertainty of prices of electricity, ancillary services and fuel. Unit constraints and bilateral contract are also taken into account.