Abstract
The main goal of this paper is to prove that bi-objective optimization of high-pressure gas networks ensures grater system efficiency than scalar optimization. The proposed algorithm searches for a trade-off between minimization of the running costs of compressors and maximization of gas networks capacity (security of gas supply to customers). The bi-criteria algorithm was developed using a gradient projection method to solve the nonlinear constrained optimization problem, and a hierarchical vector optimization method. To prove the correctness of the algorithm, three existing networks have been solved. A comparison between the scalar optimization and bi-criteria optimization results confirmed the advantages of the bi-criteria optimization approach.
Highlights
The introduction of a Third Party Access (TPA) regime has been a central element of liberalization of the gas industry
The bi-criteria optimization objective function value was lower than the value of arithmetic average of the objective function of minimizing power and maximizing gas capacity
The costs of compressor stations electric energy consumption were calculated for each scenario assuming that 1 kWh of electricity costs 0.55 Polish zloty (PLN)
Summary
The introduction of a Third Party Access (TPA) regime has been a central element of liberalization of the gas industry. The objective is to foster competition in the gas market, improve supply efficiency and bolster infrastructural investments, thereby strengthening energy security. TPA provisions have a game-changing impact on the gas supply market by introducing competition, breaking the incumbent’s monopoly, lowering prices and adding complexities to service provision by the system operator. The main responsibility of the gas network operator is to maintain safe and efficient operation of the gas supply system in real time and to ensure security of supplies in the short, medium and long term. This paper is concerned with the high-pressure gas networks under steady-state conditions. If we assume that the goal is to minimize the operational costs, the steady-state optimization algorithm determines the pressure values in the network nodes, flow values in the pipes, flows in the compressor stations and the compression ratio of compressor stations, minimizing the objective function
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