Summary

From deregulation of energy business, and an environmental problem, the installation spread of the small-scale distribution power due to a fuel cell and a heat engine is expected. Under the objective function set up by the designer or the user, optimisation planning that controls small-scale distribution power is required. In dynamic operation planning of the energy plant, the analysis method using mixed integer linear programming is developed. For the compound energy systems of solar modules and fuel cell cogeneration, there have been no reports of the optimisation of operation planning. Therefore, there are no results showing the relationship between the objective function given to the combined system and operation planning. Such as a solar modules or wind power, green-energy equipment is accompanied by the fluctuation of an output in many cases. Almost all green energy equipment requires backup by commercial power, fuel cells, heat engines, etc. Operation planning of the system that utilises renewable energy differs by the objective function and power and heat load pattern. Thus, this book investigates the operation planning of the compound energy system composed of proton exchange membrane fuel cell cogeneration with methanol steam-reforming equipment, a solar module, geo-thermal heat pump, heat storage, water electrolysis equipment, commercial power, and a kerosene boiler. In such a complex energy system, facility cost is expensive. However, in this book, it investigates as a case of the independent power source for backlands with renewable energy. This book considers the operation planning of a system, and the optimisation of equipment capacity. The Genetic Algorithm (hereafter described as GA) applicable to a non-linear problem with many variables is installed into the optimisation calculation of the operation planning of the system. In the operation analysis of a complex energy system, Mixed Integer Programming (MIP) other than GA can be used. Because the non-linear analysis using MIP is made to approximate using a linear expression of relations, it is considered that an error is large. On the other hand, GA is applicable to the analysis of the non-linear problem of many variables. The range of the analysis accuracy obtained by calculation with GA is understood that it can use industrially. In GA, the design variable of energy equipment is shown with many gene models. In this book, the objective functions given to the system were set up as (1) Minimisation of error in demand-and-supply balance, (2) Minimisation of the operation cost (fuel consumption) of energy equipment, (3) Minimisation of the carbon dioxide gas emission accompanying operation, and (4) Minimisation of the three objective functions described above. The load pattern in winter (February) and summer (August) of the average individual house in Sapporo, Japan, is used for the energy demand model shown with a case study. This chapter described the operation plan of the independence energy system when installing a methanol steam-reforming type fuel cell and renewable energy into a cold region house. Such complex operation optimization of the energy system did not have a report until now. Consequently, the method of installing and analysing the GA apply to the non-linear problem of many variables was proposed. In points of equipment cost, it is difficult for a proposed system to spread generally. However, the installation to the area where the commercial power is not fixed is possible.