Optimal placement and sizing of microgrid with electric vehicle considering demand response: Optimization strategies, future prospects and recommendations

MG design is becoming a vital aspect of power system planning because to the integration of DERs, EVs, and DR into power systems. However, it becomes challenging to optimize MG size and placement. Since there is strong interdependence between placement and size of the MG, an individual approach results in inferior designs, while placement and sizing should be solved together to reduce cost and power losses, increase voltage profile quality, and improve reliability. Nonetheless, most of the reviews available in the literature are dedicated to the analysis of placement and size individually, do not consider EVs as flexible loads and DR as a shaping of demands, and are limited to heuristics and meta-heuristics techniques. Therefore, novel approaches such as AI algorithms, problems of interpretation, policy, cybersecurity, and communication latency, which are becoming more important, are mostly ignored by the existing literature. In this study, it reviews the literature on the integration of the placement and sizing of microgrids, EVs, and DR within the classification of the optimization techniques used in all these areas, comparing these techniques in terms of convergence rate, complexity, scalability, robustness, and optimality of the solutions. Also, it assesses the contemporary AI-based techniques for their interpretability, generality, and suitability in real-time mode, as well as it analyses the technical and regulatory issues related to the implementation of the techniques, distinguishing price-based and incentive-based DR mechanisms. It shows that the placement and sizing of the microgrids are mostly associated with the use of the metaheuristic optimization, being characterized by the particle swarm optimization as the most widely used optimization algorithm and hybrid metaheuristic as the most efficient technique for the combined task, while the techno-economic assessment of microgrids is mainly performed by means of specialized software tools like HOMER, and the role of AI- and policy-aware techniques increases for large-scale microgrids. Overall, the review guides researchers, designers, decision-makers, and network operators, and establishes a reference for future research on reliable and economically viable microgrid placement and sizing.