Research On Active And Reactive Power Optimization Scheduling Of The Power System In Iron And Steel Enterprise

The iron and steel industry is energy intensive industry. In the structure of energy consumption in the iron and steel industry in China, electricity is one of the important energy sources second only to coal. Therefore, optimal scheduling the electric power system will play an important role in energy saving, production costs reduction and improvement of the overall efficiency of the iron and steel enterprises. However, few research works related with the optimal scheduling of the electric power system in the iron and steel enterprises have been reported. As a result, this paper first reviews the research and application status of the optimal scheduling problem of the electric power system in iron and steel enterprises at home and abroad. Then, the characteristics of the electricity production and consumption are analyzed, and a comprehensive investigation on the optimal active power dispatch and reactive dispatch of electric power system in iron and steel enterprises is made. Finally, the application of the optimal scheduling models is realized in a real iron and steel enterprise in China, and the optimal results are gained. The main achievements of this paper are listed as follows:(1) The links of electricity production, transmission, utilization, etc of the iron and steel enterprise are introduced briefly and the current operation status of the electric power system is analyzed. After surveyed the production and utilization of electricity, the process principles and characteristics of the existing power generation technology in the iron and steel enterprise are analyzed, the principle of electricity generation are put forward, and then the utilization characteristics of electricity are analyzed briefly.(2) The optimal load dispatch model for the self-generation power plant of the iron and steel enterprise is established, with the objective to minimize the total operation cost including fuel cost, equipment maintenance cost and the charge of exchange power with the main grid during the entire scheduling period. The model takes into account the varying nature of surplus byproduct gas flows, self-generation multi-fuel feature and the impact of time-of-use (TOU) power price. In addition, the generation capacity limits, ramp rate limits, fuels requirements and other technical constraints are also included. Consequently, the resulting model becomes a challenging non-linear multiperiod optimization model subjected to various constraints. In this way, a new chaotic adaptive particle swarm optimization (PSO) algorithm is proposed to find the optimal solution for this complex problem. The proposed optimization model is applied in a real iron and steel industry in china. The case study shows that the proposed approach can effectively solve the problem, and bring significant cost savings, achieving up to 5% cost reduction with respect to other predefined scheduling strategies.(3) The integrated optimal scheduling model which considers steam system and power system of iron and steel enterprise simultaneously is established. Considering the coupling relationship between the power system and the steam system, an integrated optimization model is presented. The optimization model takes into account the varying nature of surplus byproduct gas flows, steam and power balance equations, generation capacity limits and other constraints. All major types of utility equipments, viz. boilers, steam turbines, combined heat and power (CHP) units and waste heat and energy recovery generators (WHERG) are separately modeled using thermodynamic balance equations and regression method. The model is implemented by the proposed adaptive particle swarm optimization approach and the optimal production planning and scheduling of steam and power is obtained. The case study show that the proposed model can provide optimal response to the given TOU power price and fuels prices, make full use of byproduct gas, residual heat and energy and TOU power price, and achieve the optimal load dispatch between the outsource and self-supply sources, hence effectively reduce the electricity cost and improve the economic efficiency of the enterprise.(4) The multiobjective optimal reactive power dispatch model for the power system of iron and steel industry is formulated. Aiming at the problems of unreasonable reactive power compensation, low power factor and serious transmission losses, the mathematic model of optimal reactive power dispatch is presented by considering four objectives including minimization of active power losses, maximization of tie-line power factor, improvement of the voltage profile, and enhancement of voltage stability. The model also takes into account the load flow equation, equipment regulation capability limits, security operation constraints and other constraints. In order to solve this complex problem, a novel dynamic adaptive multiobjective particle swarm optimization algorithm (DAMOPSO) is proposed. The optimization model and approach are applied to the power network of a real large iron and steel industry. The optimal results shows that the proposed approach can make full use of the existing voltage regulation and var compensation equipments, achieve the goals of improving voltage stability and power quality and reducing network losses, and hence provide a new effective method for the optimal operation of the industrial power svstem.