Abstract
This paper presents an energy management system for the microgrid present at Wroclaw University of Science and Technology. It has three components: a forecasting system, an optimizer and an optimized electrical vehicle charging station as a separate load for the system. The forecasting system is based on a deep learning model utilizing a Long Short-Term Memory (LSTM) - Autoencoder based architecture. The study provides a statistical analysis of its performance over several runs and addresses reliability and running time issues thereby building a case for its adoption. A MIDACO - MATPOWER combined optimization algorithm has been used as the optimization algorithm for energy management which intends to harness the speed of MATPOWER and the search capabilities of Mixed Integer Distributed Ant Colony Optimization (MIDACO) in finding an appropriate global minimum solution. The objective of the system is to minimize the import of power from the main grid resulting in improved self-sufficiency. Finally, an optimized electrical vehicle charging station model to maximize the renewable energy utilization within the facility is incorporated into the same.
Highlights
In the current political and societal scenario there has been substantial mobilization towards mitigation of climate change
The aim of this study is to develop an energy management systems (EMS) for the microgrid at Wroclaw University of Science and Technology that is based on a forecasting system constructed from deep learning models and a combined optimizer which is a combination of Mixed Integer Distributed Ant Colony Optimization (MIDACO) and MATPOWER with the objective function of minimizing power imported from the main grid
It can be seen that this demand is almost fully met by importing power from the main grid defeating the objective of establishing self-reliance and minimizing the use of power from the main grid
Summary
In the current political and societal scenario there has been substantial mobilization towards mitigation of climate change. In order to achieve the goals mentioned above and face the challenges they pose, one of the solutions proposed is microgrids They can be defined as a decentralized power network consisting of numerous distributed energy sources, located close to the end-users and provide the flexibility of being operated either in grid connected mode or in isolation [4]–[8]. They have a defined boundary and are considered as a single entity by the distribution and transmission system operators.
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