Optimal Operation Of Cascaded Hydropower Stations And Its Interconnected Power System

Under low-carbon economic circumstances, cascade hydropower stations and interconnected power system are intertwined with various mapping relationships between material flow and information flow. The joint optimization of this system affected by rainfall, runoff, electricity, uncertainty factors of water uses, power generation characteristics of energy and power transmission capacity, is a kind of large-scale, multi-scale, strong coupling nonlinear constrained optimization problem. Traditional joint scheduling mode due to the presence of long, medium and short term scheduling is difficult to converge, optimal operation of hydropower and thermal power plants with no tight, power balance in relative isolation and with little regard to pollutant’s emissions because of economic efficiency, has been unable to adapt to the actual needs of the energy-saving generation scheduling model in the new era. An urgent need is to study new theories and methods to meet the requirements of regional power system security, economic operation and hydroelectric energy resource optimization management This paper focuses a number of key issues of joint optimization operation of watershed cascade hydropower stations and its interconnected power systems. This paper set the efficient use of hydropower energy security as the goal, combined with the evolution theory of system dynamics, modern swarm intelligence algorithms and multi-objective optimization theory, and carry out the research on the theory and methods of cascade hydropower stations and its joint optimization operation of its interconnected power system. Some theoretical significance and practical value of the research results have been acquired by this research. The main contents and innovation of this paper include:(1) Because traditional optimization methods are difficult to solve the problem named "Curse of dimensionality" and easy to fall into local optimum, an adaptive chaotic artificial bee colony algorithm based on reservoir’s optimization scheduling method has been proposed. An improved employment bee operator, selection probability calculation method and the adaptive modification rate parameters coordinated method was proposed. The research introduced a local search strategy based on a chaotic logistic map in the algorithm effectively improve the efficiency of the algorithm and optimization capabilities. Adaptive chaotic artificial bee colony algorithm is applied to solve the standard test functions and optimal generation of hydropower stations. The results show that the proposed method can obtain more effective solutions compared to traditional methods.(2) Optimal operation of hydropower stations and its interconnected power system faces the problems such as multiple space-time variables, complex constraints, and various optimization goal. This research combines artificial colony algorithm and multi-objective optimization theory and presents a Pareto based adaptive multi-objective artificial colony algorithm. Algorithm applied the microhabitat technique to keep the optimal Pareto front in the evolutionary processes and proposes a progressive optimal algorithm based local search strategy. The proposed adaptive multi-objective artificial colony algorithm is tested by the benchmark functions and applied to solve the issues of multi-objective long-term optimal scheduling of Three Gorges cascaded hydropower stations and multi-objective optimal scheduling of the complex hydrothermal power system. Results show that the proposed method can effectively solve the problem of scheduling goal conflict, and get the Pareto optimal solution set which satisfied the constraints in one-time optimization. Comparing with the method of literature listed in the dissertation, the proposed method can obtain more widely the Pareto optimal front and better convergence effective.(3) Considering the hydraulic and electrical nested constraint problem of the hydrothermal system, this paper proposes a multi-scale circulative constraint handling method. The method effectively implemented the modification from infeasible solution to feasible solution in the evolution process through assigning the equality constraint violation into each corresponding variable by circulative constraint handling method. The proposed constraint handling method was applied to the optimization of the hydrothermal system. The results show that this method can effectively obtain the feasible solution.(4) In order to solve the issue that optimization scheduling influenced by the runoff uncertainness, this dissertation proposed a runoff simulation method of Three Gorges inflow. And applied chance constrained optimization modeling the long-term scheduling problem. In addition, in order to solve the different scheduling period scheduling is difficult to link up. This research proposed a period length variation nested optimization method. This method set the result of long-term scheduling as the input of medium-term scheduling and applied the Monte Carlo artificial bee colony to solve the problem. The method was used in the scheduling of Three Gorges cascade hydropower stations. Results show that the proposed method can obtain the detailed results in a water year under the different realiability and quantitative describes the relationship between constraint violation and optimization efficiency. The result put forward significant theoretical guidance in actual operation.