First industrial-scale Power-to-Methanol plants are starting to be deployed in various geographic locations to tackle the problem of high greenhouse gas emissions of the predominantly fossil-based production of methanol. With the aim to speed up the deployment by streamlining their engineering and construction, we explore the potential of reducing the complexity of designs to be distributed across locations with different renewable energy conditions. A multi-objective optimization-based method incorporating a broad process network for early-stage process synthesis is proposed, which by determining the installed capacities of technologies from the chemical production, utility and storage subsystems, identifies alternative designs with different levels of complexity along two dimensions: 1) the number of different technologies used, 2) standardization of designs across different locations. The method was applied to case studies, which paired together design locations with either wind- or solar-dominant renewable resource conditions in the US and Chile for standardization. As per the method, the increases of methanol production costs due to reduced design flexibility, inherently bound to complexity reduction, were quantified and Pareto fronts were constructed. These uncovered the possibility to significantly reduce the complexity of the designs with only small increases of the production costs. By comparing the results of the case studies under different cost and operation scenarios, we characterized general aspects, which need to be considered for such design simplification. One of the main outcomes were the quantified cost-increases due to standardization, which were around 7 and 15 % relative to the specifically designed plants for each location in the US and Chile case studies respectively. A subsequent analysis of the economies of numbers through learning rates reported in academic literature suggested that the proposed standardization, even across extremely different locations, could compensate these cost-increases and be economically beneficial. Yet, more specific data on achievable cost-reductions are needed, requiring more interaction with the industry and further research, for which we highlighted promising directions.