  Reducing the fuel consumption within a power network is crucial to enhance
the overall system efficiency and minimize operating costs. Fuel consumption
minimization can be achieved through different optimization techniques where
the output power of the generators is regulated based on their individual
efficiency characteristics. Existing studies primarily focus either on
maximizing the efficiency function or minimizing the operating cost function of
the generators to minimize fuel consumption. However, for practical
implementation, it becomes imperative to incorporate a function within the
optimization framework to represent the fuel consumption rate directly. This
study introduces a novel approach by formulating a minimization problem with a
sum-of-ratios objective function representing the fuel consumption rate.
However, optimization problems with sum-of-ratios objective functions or
constraints are extremely challenging to solve because of their strong
nonlinearity. To efficiently solve the formulated problem, a fractional
programming (FP) approach is adopted in this study. This reformulation
technique significantly reduces the solution time of the optimization problem
and provides a better solution than nonlinear programming (NLP). In addition,
the reformulated problem can also be applied to large-scale systems where the
NLP fails to converge. The proposed methodology of this study is tested on the
notional MVAC ship system, modified IEEE 30-bus and IEEE 118-bus systems. The
results demonstrate that the model successfully minimizes fuel consumption by
effectively scheduling the generator and ESS dispatch.
