Comparison among deterministic methods to design rural mini-grids: effect of operating strategies

Optimal Design of Isolated Mini-Grids with Deterministic Methods: Matching Predictive Operating Strategies with Low Computational Requirements

“…The OS model is then compared to an equivalent formulation that was developed with the PSO algorithm, based on previous work of the same authors [35,39]. Figure 1 depicts the main flowchart of the optimization procedure.…”

“…This activity is an advancement of other works published by the same authors [35,39] and this paper aims at establishing a solid baseline for researchers and developers to tackle the microgrid sizing, combined with advanced operating strategies.…”

“…In sizing methodologies, MILP is often used with a deterministic approach, where an assumption for the yearly profiles of load and renewable generation is done beforehand the simulation of the operation; stochastic approaches have also been proposed, but they require simplifications to handle a large time horizon [35,36]. Computational requirements are a challenge of MILP: as reported by Li and colleagues [33], in order to optimally design of a stand-alone energy system with provision of electricity and heat/cool, only 12 representative days instead of a full yearly profile had to be used.…”

“…Heuristic methodologies rely on an iterative process, where a given number of different sizing cases is drawn, then their yearly operation is simulated; the target function to be minimized is evaluated for all cases, selecting the cheapest ones, which are then considered in the next round of sizing cases generated, until the termination criteria are met [17,18,23,25,37,38]. As an example of heuristic methodology, Particle Swarm Optimization (PSO) has been used in the field of mini-grids for rural electrification [35,39]. In [39], the authors proposed two interesting approaches to reduce computational requirements in heuristic approaches for a hybrid system, also including the simulation of the fuel logistics.…”

“…In [39], the authors proposed two interesting approaches to reduce computational requirements in heuristic approaches for a hybrid system, also including the simulation of the fuel logistics. The paper [35] proposed an interesting comparison between different sizing methodologies, including MILP and heuristic approaches; however, we believe that the comparison could be improved, also including the methodologies developed in [39]. The benefits of predictive approaches is showcased also by authors in [40], which obtained, with respect to LFS and CCS strategies, notable savings in the annualized costs.…”