The current high costs of district heating systems set limits regarding the minimum heat demand density
required for economic network expansions. Optimized routing with ideal pipe sizing offers a potential
for cost reduction. Therefore, this paper introduces a two-phase method for district heating network
expansion planning. This method consists of consecutive optimizations, starting with a mixed-integer
linear programming followed by a nonlinear optimization. During the mixed-integer linear
programming, the district heating system is optimized with continuous diameters, and the nonlinear
pressure and temperature dependencies must be linearized. The resulting topology and the continuous
diameters are afterward handed over to a nonlinear sparse sequential quadratic programming. During
this second phase, the continuous diameters have to be discretized. This study investigates a rational
approximation of material properties and a tangent hyperbolic penalization method. Different versions
of these methods with varying diameter ranges (two and three available diameters) and penalization
directions are studied. The results of this study indicate that methods without changing penalization
methods within the same optimization problem provide higher accuracy in the discretization of the
pipe’s diameter. Moreover, if the accuracy of the discretized diameter should be emphasized during the
optimization, penalization methods considering only two diameters should be preferred over
penalization methods considering three diameters. Vice-versa, if obtaining the lowest possible
diameters is prioritized over the accuracy of the discretization, methods considering three diameters
should be preferred.
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The current high costs of district heating systems set limits regarding the minimum heat demand density
required for economic network expansions. Optimized routing with ideal pipe sizing offers a potential
for cost reduction. Therefore, this paper introduces a two-phase method for district heating network
expansion planning. This method consists of consecutive optimizations, starting with a mixed-integer
linear programming followed by a nonlinear optimization. During the mixed-integer li...
»