Long-term models are commonly used to identify cost-optimal transformation pathways in strategic energy planning. Their results, however, depend on the adopted temporal representation of demand, renewable generation and system flexibility. If aggregated time slices or simplified profiles are applied, some short-term operating conditions may be insufficiently reflected. These include hours of low wind and solar generation, peak residual load, storage cycling and the interaction between electricity and district heating. This paper proposes an iterative soft-linking approach that improves the operational credibility of long-term energy system planning by coupling TIMES-PL with an hourly unit commitment and economic dispatch model. The long-term model determines the development of generation, storage and district-heating technologies, while the hourly model verifies dispatch under varying renewable availability and unit-commitment constraints. Particular attention is paid to gas-fired power plants, energy storage, power-to-heat technologies (P2H) and combined heat and power (CHP) units, as these technologies define the flexibility of a system with increasing shares of variable renewables. The results show that a higher temporal resolution leads to different utilisation patterns. Gas-fired units are used less as generic generation capacity and more as residual-load balancing resources. Storage operation becomes more dependent on short-term renewable surplus and scarcity periods. In the district-heating sector, heat production is shifted from P2H toward CHP units. The study demonstrates that hourly operational validation can reveal critical operating periods and sector-coupling effects, supporting an iterative planning workflow in which long-term capacity expansion results are corrected using operational feedback. This approach improves the reliability and policy relevance of strategic energy planning by testing whether long-term pathways remain feasible under realistic hourly operating conditions.