An Advanced Fuel Efficiency Optimization Model with Fractional Programming

TL;DR

This paper presents a novel fractional programming-based optimization model for minimizing fuel consumption in power systems, effectively scheduling generators and energy storage to improve efficiency and reduce costs.

Contribution

It introduces a new sum-of-ratios objective function for fuel consumption minimization and applies fractional programming to solve it efficiently, outperforming traditional nonlinear programming methods.

Findings

Successfully minimizes fuel consumption in test systems

Reduces solution time compared to nonlinear programming

Effective for large-scale power system optimization

Abstract

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.