Two-Stage stochastic optimization for operating a Renewable-Based Microgrid

Carbon emissions are increasing as a result of urbanization and population growth all over the world. Scientists agree that these emissions are one of the main causes of climate change and global warming. The power industry is shifting to renewable energy sources (RES) such as solar power (PV) and employing different Energy Storage Systems (ESS) to enable a clean energy future and compensate for the scarcity of fossil fuel. However, the intermittent behaviour of renewable resources causes some obstacles such as power fluctuation, committing extra reserve units, and load shedding. Microgrid (MG) technology is introduced as a promising solution for integrating different RES and loads into the grid. Operating the MG in islanded mode with a limited ESS capacity requires a sophisticated scheduling method. Previous studies on MG addressed the operation issue, neglecting the supply-demand uncertainty or adopting the worst-case scenario. Uncertainty is an inherent characteristic in power systems; on the other hand, considering the worst-case scenario may unnecessarily increase the operation and planning costs. To address the uncertainty and fluctuant characteristics of RES-based MG, this paper proposes a two-stage stochastic optimization integrated with a novel ANN-based prediction model. A new model for PV power prediction is proposed by which the predicted data is in a probability density function (PDF) form. A stochastic optimization (SO) method is proposed to minimize the operation cost and load shedding during the islanding mode. In the proposed SO, the optimal scheduling decision is made in the current moment taking into account the probability of potential supply, load, and ESS capacities in the near future. For this purpose, an ANNbased prediction model is developed to represent the PV output uncertainty in the SO problem. The proposed prediction model proves efficient in the prediction with nRMSE of 9.7% and nMAE of 9.1%. The proposed method is applied to a real Microgrid designed by the Natural Energy Laboratory of Hawaii Authority (NELHA) and compared to a conventional optimization method. The proposed scheduling method reduces both the average Energy Not Supplied (ENS) and operation costs by 19.4% and 18.8%, respectively, with no additional investment cost. A sensitivity study is also conducted to assess the performance of the proposed method in terms of ENS, cost, and simulation time.

Automated summary

Carbon emissions resulting from urbanization and population growth worldwide are leading to climate change and global warming. To address the challenges posed by the intermittent behavior of renewable energy sources (RES), microgrid (MG) technology is introduced and studied. This paper proposes a two-stage stochastic optimization method integrated with a novel artificial neural network (ANN)-based prediction model to solve the scheduling issue of MG in islanded mode.