District heating networks transport thermal energy from one or more sources to a plurality of con-sumers. Lowering the operating temperatures of district heating networks is a key research topic to reduce energy losse...
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District heating networks transport thermal energy from one or more sources to a plurality of con-sumers. Lowering the operating temperatures of district heating networks is a key research topic to reduce energy losses and unlock the potential of low-temperature heat sources, such as waste heat. With an increasing share of uncontrolled heat sources in district heating networks, control strategies to co-ordinate energy supply and network operation become more important. This paper focuses on the modeling, control, and optimization of a low-temperature district heating network, presenting a case study with a high share of waste heat from high-performance computers. The network consists of heat pumps with temperature-dependent characteristics. In this paper, quadratic correlations are used to model temperature characteristics. Thus, a mixed-integer quadratically-constrained program is pre-sented that optimizes the operation of heat pumps in combination with thermal energy storages and the operating temperatures of a pipe network. The network operation is optimized for three sample days. The presented optimization model uses the flexibility of the thermal energy storages and thermal inertia of the network by controlling its flow and return temperatures. The results show savings of electrical energy consumption of 1.55%e5.49%, depending on heat and cool demand. (c) 2021 Elsevier Ltd. All rights reserved.
This paper presents a Python library to model pooling problems, a class of network flow problems with many engineering applications. The library automatically generates a mixed-integer quadratically-constrained quadra...
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This paper presents a Python library to model pooling problems, a class of network flow problems with many engineering applications. The library automatically generates a mixed-integer quadratically-constrained quadratic optimization problem from a given network structure. The library additionally uses the network structure to build 1) a convex linear relaxation of the non-convex quadratic program and 2) a mixed-integer linear restriction of the problem. We integrate the pooling network library with GALINI, an open-source extensible global solver for quadratic optimization. We demonstrate GALINI's extensible characteristics by using the pooling library to develop two GALINI plug-ins: 1) a cut generator plug-in that adds valid inequalities in the GALINI cut loop and 2) a primal heuristic plug-in that uses the mixed-integer linear restriction. We test GALINI on large scale pooling problems and show that, thanks to the good upper bound provided by the mixed-integer linear restriction and the good lower bounds provided by the convex relaxation, we obtain optimality gaps that are competitive with Gurobi 9.1 on the largest problem instances. (c) 2022 Elsevier Ltd. All rights reserved.
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