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.
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