An optimal planning method for a marine heat and power generation plant by considering its operational problem

Abstract

The objective of this paper is to develop a computer‐aided optimal planning method for designing a marine heat and power generation plant which is composed of combining Diesel and turbogenerators, an exhaust gas economizer, auxiliary boilers and so forth. As there exist many alternative combinations concerning generators and boilers in constructing the abovementioned plant, it is important to determine the plant construction properly by selecting total numbers and scales of respective generators and boilers so as to optimize the long‐term economy of the plant. In this planning method, several design conditions are first set which are necessary for the planning of the objective plant, i.e. several patterns of ship's operational condition are set first, and both energy demands and steam output from an exhaust gas economizer are estimated for each pattern. Next, setting an alternative plant composed of combining generators and boilers adequately, the optimal operational policy for each pattern is determined by minimizing the fuel cost under several physical constraints based on linear and quadratic programming methods. Considering the results obtained above hierarchically, annual capital and operational costs of each plant are calculated, and the best plant construction is lastly determined by comparing the economy of all alternative plants. A numerical study has been carried out on a plant of a high speed container ship actually planned, and it is ascertained that to find the best construction of the plant the optimal planning method proposed here is advantageous compared with the conventional trial and error method.

Two-Level Evolutionary Multi-objective Optimization of a District Heating System with Distributed Cogeneration

“…The simultaneous optimization of both the configuration and the operation of distributed cogeneration plants is, in a mathematical sense, a non-linear problem (because of the non-linearity of the interconnection among the relations that express the partial-load behavior of each engine. To reduce the complexity of the problem, it is convenient though to assume the existence of a discrete number of intervals on which these relations are well approximated by a linear form (see, for instance, [26,27] and [25,28] for a single, or multiple energy user, respectively). In this way, a MILP problem is obtained.…”

A Comparison of Different District Integration for a Distributed Generation System for Heating and Cooling in an Urban Area

“…The mathematical problem of optimizing the synthesis, design and operation of a DG energy system has to be generally regarded as a variational calculus problem because several decision variables related to the components are time dependent. However, a realistic description of the system may be represented by a MILP formulation by properly discretizing all dynamic variables in quasi-stationary variables and approximating all non-linear relations in a set of linear functions [5,[21][22][23][24]. To solve the issue of synthesizing the configuration of the energy system, a reducible structure (known also as a superstructure) which embeds several possible configurations and interconnections is defined.…”

Two-Level Evolutionary Multi-Objective Optimization of a District Heating System with Distributed Cogeneration

“…The mathematical problem of optimizing the layout and operation of such a distributed cogeneration plant can be generally formulated as a MINLP problem, because the optimization variables expressing partial load operation of each CHP engine are involved in non-linear relations. However, a realistic description of the system may be formulated as a MILP problem by properly discretizing the load curves, so that in each time interval the thermal and electrical demands can be assumed as constants, and approximating the performance maps of energy producing components with a set of linear functions (see, for instance, [22,23] for a single energy user and [24,25] for multiple energy users). Notice that, in the discretized problem, each time interval is solved as the power and heat demand of each building were constant, therefore neglecting also the effect of dynamic load variation on the performance of the energy supplying components, but considering only their performance evaluated at different level of constant load.…”