A two-stage polynomial approach to stochastic optimization of district heating networks

Abstract

In this paper, we use stochastic polynomial optimization to derive high-performance operating strategies for heating networks with uncertain or variable demand. The heat flow in district heating networks can be regulated by varying the supply temperature, the mass flow rate, or both simultaneously, leading to different operating strategies. The task of choosing the set-points within each strategy that minimize the network losses for a range of demand conditions can be cast as a two-stage stochastic optimization problem with polynomial objective and polynomial constraints. We derive a generalized moment problem (GMP) equivalent to such a two-stage stochastic optimization problem, and describe a hierarchy of moment relaxations approximating the optimal solution of the GMP. Under various network design parameters, we use the method to compute (approximately) optimal strategies when one or both of the mass flow rate and supply temperature for a benchmark heat network. We report that the performance of an optimally-parameterized fixed-temperature variable-mass-flow strategy can approach that of a fully variable strategy. systems tend to have variable mass flow control [2]. Each strategy is subject to a trade-off in terms of losses; higher supply and return temperatures lead to increased heat losses, whereas higher mass flow rates increase the hydraulic losses in the pipes. This trade-off has been studied in different contexts. A comparison of strategies for primary networks 1 can be found in [3]. Hydraulic control strategies for the primary and secondary network were optimized in [4]. New mass flow regulation schemes using pumps were compared to the traditional strategy of controlling the consumer side heat flow using valves in [5]. The performance of district heating networks with multiple sources was studied in [6].Significant effort has also been invested in simplified models of these systems. The steady-state thermal losses of a network can be modelled as an exponential temperature drop along a pipe segment [6,7,8,9].By replacing the exponential by its first order Taylor approximation, the authors of [7] and [9] obtain a polynomial representation of the pipe output temperature. The hydraulic losses, namely the pressure drop along pipes and substations, are mass flow rate dependent and can be characterised implicitly by the nonlinear Colebrook-White equation [6]. To simplify this representation, it is often assumed that a pipe segment has a constant coefficient of resistance [7,9,10], making the absolute pressure losses quadratic in the mass flow rate. Thus, both types of network loss can be modelled using polynomial functions. Two-stage stochastic programsIf the operating strategy keeps a control variable fixed (either temperature or mass flow rate), it is desirable that the fixed choice leads to acceptable performance over a range of demand conditions. Here we define this performance as the expected operational cost incurred by hydraulic and thermal losses with respect to a probability distribution of the heat de...

Simultaneous optimisation of temperature and energy in linear energy system models

“…To tackle this problem, the nonlinear problem can be modelled (e.g. [9,10,11]), but there are further options:…”

Modified finite volumes method for the simulation of dynamic district heating networks

“…A method to optimize the parameters used by the Function Method is proposed in [27], where the authors use measured data to find the equivalent pipe length that better approximates the output temperature of the pipes. A method using the polynomial approximation for the steady state model of a DH network is presented in [28] to find strategies to operate DH when there is uncertain or variable demand. The authors use this model to minimize the cost of generation in a DH network under different operation strategies.…”