Limited visibility of distribution network power flows at the low voltage level presents challenges to both distribution network operators from a planning perspective and distribution system operators from a congestion management perspective. More representative loads are required to support meaningful analysis of LV substations; otherwise, such analysis risks misinforming future decisions. Traditional load profiling relies on typical profiles, oversimplifying substation-level complexity. Generative models have attempted to address this through synthesising representative loads from historical exemplars; however, while these approaches can approximate load shapes to a convincing degree of fidelity, analysis of the co-behaviour between substations is limited, which ultimately impacts higher voltage level network operation. This limitation will become even more pronounced with the increasing integration of low-carbon technologies, as estimates of base loads fail to capture load diversity. To address this gap, Conditional Diffusion models for synthesising daily active and reactive power profiles at the low voltage distribution substation level are proposed. The evaluation of fidelity is demonstrated through conventional metrics capturing temporal and statistical realism, as well as power flow modelling. Multiple models are proposed to handle varying levels of data availability, ranging from unconditional synthesis to an informed generation driven by metadata and daily statistics. The results show synthesised load profiles are plausible both independently and as a cohort in a wider power systems context. The Conditional Diffusion model is benchmarked against both naive and state-of-the-art models to demonstrate its effectiveness in producing realistic scenarios on which to base sub-regional power distribution network planning and operations.

翻译：低压层面配电网潮流的有限可见性，从规划角度给配电网运营商和从阻塞管理角度给配电系统运营商均带来了挑战。需要更具代表性的负荷来支持对低压变电站的有意义分析；否则，此类分析可能误导未来决策。传统负荷曲线建模依赖于典型曲线，过度简化了变电站层面的复杂性。生成模型试图通过从历史样本中合成代表性负荷来解决这一问题；然而，尽管这些方法能以令人信服的保真度近似负荷形态，但对变电站间协同行为的分析有限，这最终影响了更高电压等级的网络运行。随着低碳技术集成度的不断提高，这一局限性将变得更加突出，因为基础负荷的估计未能捕捉负荷多样性。为弥补这一不足，本文提出了用于在低压配电变电站层面合成日有功和无功功率曲线的条件扩散模型。通过捕捉时间和统计真实性的常规指标以及潮流建模，展示了保真度评估。提出了多种模型以处理不同级别的数据可用性，范围从无条件合成到由元数据和日统计量驱动的信息生成。结果表明，合成的负荷曲线无论是独立地还是在更广泛的电力系统背景下作为整体都是合理的。将条件扩散模型与朴素模型和最先进模型进行了基准测试，以证明其在生成现实场景方面的有效性，这些场景可作为次区域配电网规划和运营的基础。