Operating Schedule of a Combined Energy Network System

Operating Schedule of a Combined Energy Network System

DOI: 10.4018/978-1-4666-5796-0.ch001


This chapter consists of two sections, ‘Operating Schedule of a Combined Energy Network System with Fuel Cell’ and ‘Fuel Cell Network System Considering Reduction in Fuel Cell Capacity Using Load Leveling and Heat Release Loss’. The chromosome model showing system operation pattern is applied to GA (genetic algorithm), and the method of optimization operation planning of energy system is developed in the 1st section. In the case study, the operation planning was performed for the energy system using the energy demand pattern of the individual residence of Sapporo, Japan. Reduction in fuel cell capacity linked to a fuel cell network system is considered in the 2nd section. Such an energy network is analyzed assuming connection of individual houses, a hospital, a hotel, a convenience store, an office building, and a factory.
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Operating Schedule Of A Combined Energy Network System With Fuel Cell


Until now, various energy devices with individual controls have been used in buildings. However, renewable energy and unused energy are positively utilized from the viewpoint of global environment problems from now on. In order to utilize renewable energy and unused energy, it is necessary to use active energy device for stabilization of an energy output. The object of study is to develop the method of the operation plan and optimum design of the combined system of active energy device and unutilized energy. The energy network is structured using an electric power system, a hot water system, and a fuel system. For the operational plan of the energy network that conducts the cooperative operation of complex energy devices, it is necessary to solve the nonlinear problem of many variables with objective functions provided beforehand. In the optimization calculations of system operational planning of a complex energy system, linear approximation calculations based on the mixed-integer plan-making method was used (Ito, Shibata, & Yokoyama, 2002). However, to analyze the operational planning of a complex energy system with high accuracy, it is necessary to solve the nonlinear problem with many variables. Until now, the operation planning of an energy system has been managed as a linear problem. So, in this Section, the method of analyzing a compound energy system by many variables and nonlinearity is developed.

A genetic algorithm (Goldberg, 1989) is therefore introduced to analyze operational planning in this Section. Previously, an analysis method of a large-scale energy system that combined a genetic algorithm and an annealing algorithm (Hongmei et al., 2000) was developed (Srinivas & Patnaik, 1994; Fujiki. et al., 1997; Yu et al., 2000). However, an analysis method that optimizes the operational pattern with the application of a genetic algorithm (GA) to the compound system built using an active energy device, a renewable enegy device, and an unutilized energy device has not yet been developed. In this Section, a GA analysis method for a compound energy system is developed as a preliminary survey of the energy network that conducts cooperative operation. The analysis software using GA developed in this Section is introduced in an individual house in Sapporo, Japan, which is a cold, snowy area, and the operational plan is investigated. The operational planning of the compound energy system is analyzed using the minimization of operational costs and the maximization of renewable energy use, and the operational planning of an active energy device is considered. Although operation costs and facility costs need to be considered for a feasibility study of the system, the facility costs of a proton exchange membrane (PEFC) fuel cell are changing greatly. Since estimating facility costs is difficult, the analysis in this Section only considers operation costs. Furthermore, the device capacity for the accumulation of electricity and thermal storage is estimated.

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