Optimal Investment and Dispatch of District Heating Technologies with Participation in Electricity Day-Ahead, Intraday, and Balancing Markets

Abstract

This thesis studies the cost–optimal operation and expansion of a district heating portfolio under electricity–market uncertainty. A two–stage stochastic optimization model with hourly resolution over the planning horizon 2025–2035 is developed to assess the role of power-to-heat technologies-electric boilers (EB) and heat pumps (HP)-when participating in the day-ahead (DA), intraday (ID), and balancing markets. The technology set comprises HP, EB, combined heat and power (CHP), geothermal (GT), solar thermal (ST), waste-to-energy (WtE), and tank thermal energy storage (TTES).Market uncertainty is represented by an exogenous-input autoregressive model (AR-X) that generates price scenarios conditional on wind and photovoltaic feed-in. Five discrete levels ”trajectories” (low, lower, base, higher, high) are constructed for each market; their Cartesian product yields 25 joint DA/ID price scenarios. First-stage decisions determine investments and capacities; second-stage problems optimize hourly dispatch scenario-wise. To manage downside risk, the objective augments expected cost with a Conditional Value-at-Risk (CVaR) term, enabling a tunable trade-off between mean performance and tail losses. Key findings are threefold. First, explicit access to ID and reserve provision (mFRR-down capacity reservation only; physical activation is not modeled) enables more opportunistic electrification, shifting HP operation into low-price hours and monetizing EB’s flexibility. Second, the optimal portfolio exhibits a robust two-block structure: WtE provides dependable baseload, while HP/EB coordinated by TTES supply the flexible share. Third, in a stylized case study calibrated to an existing physical district heating network in Salzburg, the stochastic approach can reduce total system costs relative to a deterministic baseline by ≈ 9%, illustrating the value of scenario-based market participation and risk-aware planning.