(723f) A Receding Horizon Optimization Approach for Energy Management in the Chlor-Alkali Process with Hybrid Renewable Energy Generation
The Chlor-alkali process produces chlorine, caustic soda and hydrogen through the electrolysis of sodium chloride solution. The corresponding industry is one of the major sectors of the chemical industries, the products of which are used in over 50% of all industrial chemical processes . The Chlor-alkali industry is also among the highest energy consuming processes due to the high electricity utilization . Energy consumption of the plant is so large that it becomes the key issue to determining profitability. While significant research work has been conducted on how to reduce the environmental impact and improve the energy efficiency of the process (e.g., see [3-5]), a number of research problems remain open at this stage, including, for example, the direct integration of fuel cells in the electrochemical plant for hydrogen recovery and the integration of renewable energy sources to supply power to the process.
Combining fuel cells and electrolyzers to form a complementary system is an efficient means of energy resource utilization that can substantially reduce investment and maintenance costs . Furthermore, the integration of renewable energy resources within existing infrastructures is an appealing solution to sustainable energy supply that is becoming increasingly feasible, especially with the accelerating decline in the cost of solar and wind energy in recent years. Another important approach to increasing the energy efficiency of the chlor-alkali process is to operate the electrolyzer under an optimal temperature. Based on this objective, solar energy, as a renewable resource, is of significance due to its ability to supply heat and electricity at the same time from an integrated photovoltaic/thermal hybrid solar system (PVT system). While the benefits of some of these solutions have been recognized previously, further research is needed to address the fundamental problems of economic system design, energy supply and optimal operational strategies for the plant, especially when hybrid renewable energy systems are integrated.
A Hybrid Renewable Energy System (HRES) combining solar, wind and other energy generation and storage units is an appealing solution to dealing with the intermittent generation and scarce supply of a single renewable resource. Various methods for control and optimization have been applied to HRES . Compared with traditional approaches, the combination of receding horizon optimization and hybrid renewable generation proves to be an effective approach to improving the economic and environmental performance . Receding horizon optimization is also an effective way of managing uncertainties in the system and its environment.
In this work, we focus on the optimal design and operation of a grid-connected HRES that combines PVT systems, wind energy conversion systems and fuel cells, to supply power to a chlor-alkali plant, with focus on the supply and recovery of power, heat and materials. The HRES is connected to the grid and allows for buying or selling electricity from and to the grid. Initially, detailed models of each system component are developed as the basis for the simulation study. Strategies for optimally managing the energy flows and dispatching the generation sources are then developed to realize the objectives of meeting production requirements while minimizing the overall operating and environmental costs. The problem formulation brings together receding horizon optimization of energy generation, forecasting and demand-side management. Sensitivity and uncertainty analyses are carried out to elucidate the key parameters that influence the energy management strategies. Finally, production demand response is integrated into the proposed methodology.
The strength of the proposed receding horizon strategy lies in the fact that it is able to achieve an overall optimization for a certain time period, and at the same time with sufficient information on future conditions available, optimal decisions about energy management can be made in advance. Compared with traditional operation policies for energy systems, the on-line process that combines real-time prediction, optimization and demand-responsive schemes can improve the system efficiency and take better advantage of all the available resources.
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