Unit Commitment Via Particle Swarm Optimization Algorithm

The task of unit commitment is to arrange the up/down and load dispatch of generating units rationally such that the load demand is met and the production cost is minimized under all generator and system constraints. Recently, climate change caused by pollutants emission attracts widely attention, which brings new challenges to the traditional unit commitment. With the full development and interconnecting to power grid of renewable power, particularly the mature technology in wind power, it is in dire need of models and algorithms which can better satisfy the characteristic of renewable power. There still remain lots of difficult issues in some domains of the available methods, such as considering the constraints in its entirety, multi-objective optimization including energy-saving and emission-reducing, renewable power integration and so on. For this reason, considering the features and advantages of intelligent algorithms, some research has been done and new modified methods have been presented to mathematical models of unit commitment in different periods, such as traditional unit commitment, optimizing schedule of energy-saving and emission-reducing power system, and solution to stochastic programming with wind power. Furthermore, some typical cases are simulated, which verify the superiority of the new methods. The main research contents are as follows.1. Regarding to the disadvantage of some available methods in solving traditional unit commitment problem, such as delay in handling up/down time constraints, a modified dual particle swarm optimization algorithm, which includes two hierarchies-discrete particle swarm and continuous particle swarm, is presented based on the mathematical model and the performance of each algorithm. The discrete particle swarm algorithm in external layer is used to optimize the up/down states of all units for each time periods one after another. Here critical operators are applied to ensure the particles’diversity, and the criteria condition of feasible solutions is strengthened. Among different time periods, the up/down time constraint is considered through inheriting historically and constraining in future. The continuous particle swarm algorithm in internal layer is used to compute the load dispatch, so as to lead the evolution of outer layer. In addition, several modifications, which benefit converging to global optimal solution, are adopted. The proposed approach is applied to two cases and the experimental results demonstrate the validity of the method.2. With the increasingly growing focus on environmental protection, the objective of unit commitment is not only energy-saving but also emission-reducing. Obviously, this is a multi-objective optimization problem. An objective weight factor is defined and on this base, an objective-weight oriented multi-objective particle swarm algorithm is proposed. While choosing leader for a certain particle from the Pareto solutions during evolution, how similar the objective weight factor of one Pareto solution is to the one of this particle, is one of the important indices, which can be benefit to maintain the diversity of population. Several typical testing functions are simulated and the optimization performances are compared with other methods. It is shown that the proposed approach is effective because it not only computes Pareto optimal solution under various objective weights, but also produces more uniformly distributed Pareto fronts, which provides wide choice space for decision makers. On this base, the standard IEEE-30 testing system is simulated considering energy-saving and emission-reducing simultaneously. From the experimental results, it is concluded that whether or not the power loss is taken into account, satisfactory effects are gained both in economy and environmental protection.3. As the fluctuation and stochastic characteristics of wind power are concerned, traditional deterministic economic scheduling model is no longer appropriate for the hybrid power system including wind farm. Chance-constrained programming is adopted to establish the model for the system optimization scheduling including wind power and thermal power. To solve this problem, a quantum-inspired dual particle swarm optimization algorithm, where quantum computation is combined with particle swarm optimization is produced. The load demand and constraints are met under a certain confidence probability, and more attentions are paid to the coupling among all constraints. Several measures are adopted to ensure the validity of proposed approach. For example, all units are sequenced by a certain index representing their relative merits, and the units can be started forward or backward under different circumstances. While the spinning reserve is excessive or inadequate, it is necessary to adjust the up/down states repeatedly. Dispatching schedule under certain confidence probability for the given case is produced by the new approach. From the experimental results, it is obviously that both economy and reliability of wind-thermal power hybrid system can be taken into consideration through the proposed model and method.By working with unit commitment problem, new solving ideas are proposed in traditional economic dispatch, optimization scheduling considering energy-saving and emission-reducing, and unit commitment decision including wind power. Theoretically, achievements have been made at optimizing schedule for electric power system using intelligent algorithms, which lay theoretical foundation of further engineering application.