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In this study, the significant effects of the load models used to analyse low‐voltage power systems are presented and a systematic process for selecting appropriate load compositions to evaluate the performance of distribution systems containing distributed generation is proposed. The driving force behind, and key reasons for, voltage instability are analysed by evaluating different load models subjected to grid disturbances. The stability conditions of distribution systems are also checked through time‐domain simulations performed on various network configurations, such as radial, loop, and mesh topologies. The system's responses to changes in the substation's voltage and contingency events are also demonstrated. It is found that the types and order of load models used greatly affect a system's voltage stability and therefore, an accurate load modelling is necessary to support the renewables integration in distribution systems. As the system's response is found to be sensitive to the network's topology and actual load composition, an approach that selects appropriate load compositions which, in turn, reduces the huge investment costs of improper planning, is proposed. The effectiveness of the proposed approach is verified by connecting static synchronous compensators to the network under varying load compositions.

Industrial loads (ILs), characterized by their large scale and high automation levels, offer significant potential to mitigate supply-demand imbalances in smart grids with high penetration of renewable energy generation. However, research on modeling the controllable characteristics of industrial loads remains relatively limited. Existing models are often overly simplistic, failing to account for transient processes—which are non-negligible during regulation—as well as potential parameter variations, leading to substantial regulation errors and an inability to meet precision requirements. This paper focuses on adjustable industrial loads and establishes precise regulation response models based on their production characteristics and transient processes, including continuously adjustable industrial load models, discrete parameter-fixed adjustable industrial load models, and discrete parameter-variable adjustable industrial load models. Building on these models, an improved approximate dynamic programming (IADP) algorithm is proposed, which transforms the traditional iteration-based value function approximation method into a numerical fitting approach. This method is utilized to derive a day-ahead optimal scheduling strategy. Finally, the effectiveness of the proposed approach is validated through multiple case studies, where comparisons with optimal scheduling strategies from other modeling approaches and optimization techniques further demonstrate its superiority.