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District heating is expected to play an essential role in the cost-effective decarbonisation strategy of many countries. Resource-optimised management of district heating networks depends on a wide range of factors, including demand forecasting, operational flexibility, and increasingly volatile market conditions. However, traditional operations often still rely on static models and rather simple heuristics, while holistic optimisation requires dynamic cross-domain interoperability to allow the consideration of all these factors. This paper demonstrates a proof-of-concept for a knowledge graph based optimisation problem to minimise total heat generation cost for a district heating provider. The optimisation follows a hierarchical approach based on a merit-order principle and is embedded in a model predictive control framework to allow the system to incorporate most recent information and react to disturbances promptly. A detailed sensitivity study is conducted to identify key model parameters and assess the impact of anticipated changes in regulation and market conditions. Simulation-based optimisation is used to determine the short-term heat generation mix based on data-driven gas consumption models and day-ahead forecasts for the network's energy demand and grid temperatures. Seasonal autoregressive integrated moving average models with exogenous predictor variables are found to be sufficiently accurate and precise. The effectiveness of the approach is demonstrated for a case study of an existing heating network of a midsize town in Germany, where a reduction of approximately 20% and 40% compared to baseline operational data is obtained for operating cost and CO 2 emissions, respectively. [Display omitted] • Developed heat generation optimisation framework for district heating providers. • Identified key cost drivers and market dynamics using a detailed sensitivity analysis. • Developed high accuracy time series forecasting models for key input variables. • Assessed optimisation effectivity using a German municipal utility case study. • Improvement potential for operational cost and CO 2 emissions both exceed 20%. [ABSTRACT FROM AUTHOR]

• A novel metric (DOC) for bidirectional low temperature networks (BLTNs). • DOC describes the balancing potential of heating and cooling demands in a district. • DOC helps to identify building clusters with complementary demand profiles. • Correlations between DOC and KPIs are elaborated based on 63 demand scenarios. • Performance comparison of BLTNs with state-of-the-art building energy systems. Bidirectional low temperature networks (BLTNs) are a promising technology to drive decarbonization of the heating and cooling sector. The energy efficiency of a BLTN strongly depends on the heating and cooling demands of the connected buildings and their simultaneity. In order to evaluate the efficiency of a BLTN, a novel metric, called Demand Overlap Coefficient (DOC), is presented in this paper. The DOC can be calculated for individual buildings or a district and describes which proportion of heating and cooling demands can be balanced inside buildings and between buildings. The DOC is calculated with time series for heating and cooling demands, taking special account of their simultaneity. For a real-world use case in Germany, it is shown that 25% of heating and cooling demands can be balanced in buildings and 45% can be balanced by the BLTN between buildings. The DOC is evaluated for 63 demand scenarios. Correlations show that for districts with a DOC larger than 0.45, BLTNs have lower annualized costs than a state-of-the-art heating and cooling system. BLTNs have higher exergy efficiencies for districts with DOCs larger than 0.3. With the derived correlations, the DOC serves as a practical metric in the planning process of BLTNs in order to identify and assemble building clusters with complementary demand profiles. [ABSTRACT FROM AUTHOR]