Summary Distributed generation (DG) has attracted significant attention due to its great potential for enhancing economical and technical performance of power systems and reducing dependence on fossil fuels. Optimal sizing and placement are critical for stimulating such potential, about which a considerable number of models and algorithms have been proposed in past literature. This paper attempts to undertake a comprehensive review on optimal sizing and placement of DG via a systematic methodology procedure, including definition and classifications of DG, modelling and problem formulation with different technical and economic criteria, and summary of optimization algorithms. Common features and distinctive characteristics of both models and methods are identified, followed by evaluations and comparisons based on their practical performance in various test systems. Selection of DG techniques with respect to application scenarios, indispensable and optional considerations in DG planning models, and pros and cons of algorithms are listed in tables for a clearer understanding. Lastly, a total of 107 algorithms are addressed, which are classified into five categories. Particular, hybrid methods can deal with complex engineering problems with multiple objective functions and constraints most effectively and robustly. Future research trends are also highlighted with the aim of providing a comprehensive and state‐of‐the‐art survey for researchers, engineers, and other stakeholders.

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2018 Row current, execution time, total income per year, standard deviation (SD) Modified Harris hawks algorithm (Yousri et al., 2020a) 2020 Row current, mismatch loss, fill factor (FF), percentage power loss, power enhancement, execution time Marine predators algorithm (Yousri et al., 2020b) 2020 Row current, mismatch loss, FF, percentage power loss, power enhancement, execution time Grey wolf optimization algorithm (Balraj and Stonier, 2020) 2020 Global shade dispersion index, total irradiance value of each row, power enhancement Butterfly optimization algorithm (Fathy, 2020) 2020 Row current, mismatch loss, FF, power enhancement, PR Artificial ecosystem-based optimization (Yousri et al., 2020c) 2020 Row current, power enhancement, mismatch loss, execution time, FF Democratic political algorithm (Yang et al., 2021b) 2021 Power enhancement, mismatch loss, execution time, FF, SD FIGURE 1 | Structure of the proposed switching matrix in literature.

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