@inproceedings{moshi_optimization_2016,
	title = {Optimization of integrated design and operation of microgrids under uncertainty},
	doi = {10.1109/PSCC.2016.7540870},
	abstract = {We present two Mixed-Integer Linear Programming (MILP) models for a complete microgrid planning problem which consider uncertainties in the main input data (hourly solar irradiance, wind speed and electricity demand). The first model adopts a Two-Stage Stochastic Integer Programming (2SSIP) formulation with discrete scenarios, whereas the second model adopts a Robust Optimization (RO) formulation with polyhedral uncertainty sets. The aim is to determine the optimal combination, capacities, and number of components to install in the microgrid considering long-term operations and uncertainty in the main input data. The 2SSIP model offers the possibility to obtain a planning solution using discrete scenarios sampled from appropriate probability distributions. The RO model gives a planning solution which is guaranteed to be feasible for any realization of input data within specified uncertainty sets. To show and compare the effectiveness of these models, we present a case study in which we apply the two models to plan a standalone microgrid in Singida, Tanzania. The proposed models can be applied for planning and detailed feasibility studies on generic microgrids with renewables, storage batteries and diesel generators.},
	booktitle = {2016 {Power} {Systems} {Computation} {Conference} ({PSCC})},
	author = {Moshi, G. G. and Bovo, C. and Berizzi, A. and Taccari, L.},
	month = jun,
	year = {2016},
	keywords = {2SSIP formulation, Batteries, Biological system modeling, Linear programming, MILP models, Microgrids, Optimization, Planning, RO formulation, Uncertainty, distributed power generation, integer programming, linear programming, microgrid design optimization, microgrid operation, microgrid planning, microgrid planning problem, mixed-integer linear programming, optimization, polyhedral uncertainty sets, power distribution planning, probability distributions, robust optimization, robust optimization formulation, statistical distributions, stochastic integer programming, two-stage stochastic integer programming, uncertainty},
	pages = {1--7}
}

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The first model adopts a Two-Stage Stochastic Integer Programming (2SSIP) formulation with discrete scenarios, whereas the second model adopts a Robust Optimization (RO) formulation with polyhedral uncertainty sets. The aim is to determine the optimal combination, capacities, and number of components to install in the microgrid considering long-term operations and uncertainty in the main input data. The 2SSIP model offers the possibility to obtain a planning solution using discrete scenarios sampled from appropriate probability distributions. The RO model gives a planning solution which is guaranteed to be feasible for any realization of input data within specified uncertainty sets. To show and compare the effectiveness of these models, we present a case study in which we apply the two models to plan a standalone microgrid in Singida, Tanzania. 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The first model adopts a Two-Stage Stochastic Integer Programming (2SSIP) formulation with discrete scenarios, whereas the second model adopts a Robust Optimization (RO) formulation with polyhedral uncertainty sets. The aim is to determine the optimal combination, capacities, and number of components to install in the microgrid considering long-term operations and uncertainty in the main input data. The 2SSIP model offers the possibility to obtain a planning solution using discrete scenarios sampled from appropriate probability distributions. The RO model gives a planning solution which is guaranteed to be feasible for any realization of input data within specified uncertainty sets. To show and compare the effectiveness of these models, we present a case study in which we apply the two models to plan a standalone microgrid in Singida, Tanzania. 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