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Abstract The insertion of new distributed energy resources, such as distributed generation (DG), energy storage systems (ESS), demand response (DR), and microgrids (MG), is emerging, bringing new challenges to the current distribution network. In this regard, the active distribution networks (I) with multi-microgrids concept appears. The present paper proposes a hierarchical (master-slave problem) computational model to achieve optimal coordinated operation of multi-microgrids connected to an I. Day-ahead operation planning of an I was formulated as a multiperiod non-linear optimal power flow model, resulting in a non-linear optimization problem, additionally, the day-ahead operation planning of MGs was formulated as a multiperiod linearized optimal power flow resulting in a mixed-integer linear optimization problem. Numerical results on four different test-system microgrids connected to a 359-nodes ADNs test-system belonging to a Brazilian distribution company show the effectiveness of the proposed model and solution strategy. Three cases have been tested: with a maximum load-shedding restriction, without this restriction, and considering insertion of DG. Besides, the hierarchical model can evaluate how much losses and load shedding take effect without integrated operation and expansion planning of emerging distributed networks. This study showed the importance of analyzing the systemic impact of integrating multi-MGs and I synergistic operation interactions, resulting in improvements in the voltage quality levels, operation costs, and power losses. The results showed that, including DG in the system, the costs were reduced by 13,48% compared to the case base.

Recent global trends have increasingly focused on sustainable and green energy solutions,transitioning away from fossil fuels. Green hydrogen (GH) and green ammonia (GA) have emerged as promising contenders for energy carriers in international supply chains. Our manuscript delivers a thorough comparative analysis of GH and GA, acknowledging the complex interrelations among synthesis, storage, distribution, and conversion processes. This evaluation also incorporates the variability of weather, energy demand, and the delays inherent in shipping, on both hourly and seasonal scales. The study features a detailed case analysis of nine international trading routes, utilizing a hierarchical optimization framework and conducting comparative economic evaluations. Additionally, the study delves into a sensitivity analysis, exploring factors such as economies of scale and the operational constraints of water electrolyzer. One of the key findings of our research is the identification of GA as a more favorable option for an international energy carrier, particularly when considering renewable energy's seasonality and intermittency. GA's superior storage and transportation properties significantly mitigate uncertainties within the supply chain, offering a more robust solution in the context of green energy transportation and logistics. Furthermore, the techno-economic analyses survey their impact on each supply chain and reveal the interconnectedness between each supply chain element – power, conversion, storage, and transportation. [Display omitted] • Comparative analysis of green hydrogen and green ammonia as energy carriers. • Supply chain design to address weather/demand fluctuations, and logistics. • Investigation of dynamics of production, storage, conversion, and transportation. • Evaluation of international routes for green hydrogen and green ammonia trading. • Green ammonia offers enhanced resilience for international energy transport. • Analysis of variability, economies of scale, minimum load, and lead times. [ABSTRACT FROM AUTHOR]

• A quantitative evaluation index of the complementary characteristics of renewable energies is introduced. • Multi-objective model for optimal capacity planning of DES with renewable energies is established. • A hierarchical optimization framework is presented to implement the multi-objective optimization. • Case studies considering the CROF and total economic cost objectives are conducted. Distributed energy system (DES) integrated with renewable energy (RE) has attracted extensive attentions and developed rapidly for its high energy utilization rate and sustainability. Both solar and wind, as typical renewable energy, are usually considered in researches on DES while the complementary fluctuation characteristic of solar and wind is seldom taken into optimization objective during the planning process. This paper introduces a quantitative evaluation index of the complementary characteristics of renewable energies and proposes a multi-objective capacity planning method for DES to minimize the economic cost and to maximize the complementarity rate of fluctuation (CROF) simultaneously. To solve the established multi-objective model, a hierarchical optimization framework is employed, which remodels the multi-objective model into multi-layer optimization model by reconstructing new constraints. The nonlinear part of optimization model is linearized by linearization technique. Finally, branch and bound algorithm is employed to obtain the solution to optimization model. Finally, case studies based on typical daily load characteristic curves in three kinds of seasons were performed and analyzed. The results verify the feasibility and effectiveness of proposed method, indicate that the complementarity rate of fluctuation can be improved by 53%, and the average relative fluctuation rate of power purchased from grid can be reduced by 58.7% compared with single economic objective model. [ABSTRACT FROM AUTHOR]