Abstract
There is a need to look for alternative sources of renewable energy, especially in zones where people continue to live under energy poverty conditions. Consequently, to enhance the performance of energy systems, algorithms to support planning decisions are required. This article proposes a simulation-optimization framework to solve the stochastic version of the integrated energy dispatch and unit commitment problem for a solar radiation system operating in non-interconnected zones. Our study was motivated by challenges faced by a rural school located in Cundinamarca, Colombia. Particularly, a simulation with optimization-based iterations approach is used, modeling solar radiation as a random variable. The optimization phase uses a heuristic procedure that enables good solutions to be found in short computational times. To test our method, computational experiments were conducted using a set of randomly generated cases. The results suggest that our approach is useful and able to handle the random nature of the process for the school “Volcanes”. Additionally, we were able to quantify the impact that using a deterministic approach has on service levels for such systems. The novelty of the article lies in the proposed method and its application to a rural school with a low-budget system.
Highlights
Published: 12 May 2021Renewable sources have been proven to be an efficient solution to increase the available energy in isolated zones [1,2,3] and reduce energy poverty [4,5]
The Word Health Organization estimates that approximately seven million people die from air pollution every year and fossil fuels are the main source of air pollutants [9,10,11]
We argue that the use of hybrid simulation-optimization approaches to model uncertainty in solar radiation is a promising research area
Summary
Renewable sources have been proven to be an efficient solution to increase the available energy in isolated zones [1,2,3] and reduce energy poverty [4,5]. We argue that the use of hybrid simulation-optimization approaches to model uncertainty in solar radiation is a promising research area This method is a very effective mechanism to tackle variability in combinatorial optimization problems and its applications have been widely documented [38]. In Roberts et al [41], the authors incorporate a probabilistic simulation optimization approach to finding the optimal design of an energy system, taking into account the uncertainties in the sources, the load demand, and the unavailability of the components subjected to failure. Our deterministic solution approach, embedded in the simulation-optimization framework, is able to compute good solutions in much less time than exact optimization methods This is a very important feature when taking into account the variability in solar radiation and the computing constraints of an isolated setting.
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