Fuel Resource Scheduling -The Long-Term Problem

Abstract

Long-term fuel scheduling addresses the problem of optimum allocation of fuels to various generating units using mixed and shared fuels subject to yearly and monthly constraints, inventory constraints, generation availability constraints and load require ments (both energy and capacity).This problem is formulated as a network flow optimization problem. Feasibility of the resulting fuel consumption plan with respect to fuel constraints and its optimality with respect to cost of fuel purchases are assured by the network flow algorithm. Feasibility with respect to the load duration and generation availability are assured by defining appropriate fictitious branches in the network. The limits on these branches are calculated by the method of cumulants.The unit priorities in each month are modified by an iterative procedure, on the basis of both fuel price and fuel availability as seen by the network flow algorithm.Results for a 17 unit/17 contract test system over one year are presented to illustrate various aspects of the long-term fuel resource scheduling problem.

A computational study of a stochastic optimization model for long term hydrothermal scheduling

“…Several models have been proposed for solving the LTHS problem [15][16][17][18][19][20][21][22]. However, most of them [15][16][17][18][19][20] do not consider the specific characteristics of the Brazilian hydrothermal power system.…”

“…However, most of them [15][16][17][18][19][20] do not consider the specific characteristics of the Brazilian hydrothermal power system. Furthermore, NEWAVE has been continuously improved in order to comply with the growing requirements of the industry.…”

Long-term thermal power planning at VEW ENERGIE using a multi-interval Bloom and Gallant method

“…Prior to Bloom and Gallant, Ranjit Kumar et al [5] optimized long-term fuel procurement and use with fuel limits through changes in the loading order when matching an LDC. They used probabilistic production costing methods with a given loading 0885-8950/01$10.00 © 2001 IEEE order to determine the maximum limits on the energies generated by each unit in each interval, and then a network flow solution for the entire period was employed to generate a new loading order for each interval, being corrected according to capacity factors in the network solution.…”

Augmented Lagrange Hopfield Network for Combined Economic and Emission Dispatch with Fuel Constraint

“…The objective of the CEED problem with fuel constraint is to minimize both total fuel cost and emission from thermal units while satisfying power balance, fuel delivery, fuel storage constraints together with fuel delivery, fuel storage, and generator operating limits. The schedule time horizon for this problem can be decomposed into long-term (weeks to year) [4], shortterm (days to week) [5], daily (hours to day) [6], and real-time (minutes to hour) [7] problems. In the longterm schedule problem, the schedule time is divided into sub-periods (months or weeks) to obtain optimal fuel use strategy.…”