Advanced Genetic Algorithm Based Innerplant Economical Operation Model For Hydroelectric Power Station

The innerplant economical operation in hydroelectric power station is a problem of selecting hydraulic units to be in service during a scheduling period, and to optimally allocate the load demand and the spinning reserve among the running units while satisfying a variety of contains. This calculated schedule is proposed in order to get maximum economical benefit. The simplification model of innerplant economical operation was proposed commonly while neglecting a great deal of constraints when scholars at home and abroad have been studying the problem. So this thesis presents a model, aiming at the key questions for innerplant economical operation. In this model, besides traditional constraints such as power balance and capacity limits constraint etc., many constraints such as the cost for start-up/shut-down status of units, compulsory constraints for unit start/shut, minimum up/down time limits and spinning reserve requirements are also considered. According to the characteristics of the model, this model can be divided into two sub-models ,namely the "unit commitment"(UC) decision and the "economic dispatch"(ED) decision. An advanced genetic algorithm approach to the problem is discussed. The major research works are as follows:1. ED problem. The units minimum and maximum output operating limits were only consider commonly in the past optimization models. But in the actual ED problem, there are a large number of units operating limits (e.g. vapor eroding, or vibration etc,) which units must avoid running. If not considering the units operating limits above, the efficiency and the life are reduction; moreover, it could lead to danger for units. So this thesis divides the units operating limits into safe and unsafe range. The safe range of units is a set of sub-ranges and interrupted by unsafe one. There is greater improvement on the model, which only considering minimum and maximum output operating limits of units previous. According to the character of the above ED model, this thesis abstracts a discrete feasible constraints model with a constraint of equation. In the proposed algorithm, genetic algorithm solution is coded as decimal representation. Genetic operators are designed to coincide with discrete feasible constraints that every individual is a feasible solution to the operating processes. Case study also showed that the model is reasonable and the proposed algorithm is feasible and efficient, comparing with the most common penalty function method handling constraints.2. UC problem. Owing to the cheap cost for start-up/shut-down of hydraulic units and few operating limits, the time for start-up/shut-down of units was not considered in the previous optimal models during a scheduling period. But in a long time range, start-up/shut-down of hydraulic units leads to not only consume water, but also wear and tear. If the status of units were changed frequently in short time, the life and the efficiency of units were reduced. So the thesis presents the optimal model, adding minimum up-time and minimum down-time constraints and insisting on the cost for start-up/shut-down of hydraulic units in order to avoid the phenomenon above.Furthermore, in the actual operation, it is believed that unit's availability must be considered (e.g. must run, unavailable, available, or fixed output etc.). To summarize above, this thesis present a UC optimal model, insisting on the cost of start-up/shut-down of hydraulic units and considering compulsory constrains for units start/shut, minimum up/down time limits, or other constrains for unit's availability. According to the characteristics of above unit commitment model, some heurist techniques are designed to guide producing initial population, which every individual is a feasible solution and it spends less time on producing initial population than the random method. Furthermore, in the proposed algorithm, genetic algorithm solution is coded as a mix of binary and decimal representation to save the storage size for population.3. The proposed model is...