An Energy Management System for Microgrid Using Pelican Optimization Algorithm with Demand Side Management Based on Load Shifting
This paper proposes an Energy Management System for microgrids that utilizes the Pelican Optimization Algorithm combined with demand-side load shifting to achieve optimal generation scheduling and minimum operating costs, outperforming benchmark algorithms across four scenarios involving hybrid renewable sources and battery storage.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the modern world, electricity is no longer just a steady stream flowing from a distant power plant to a wall socket. It is becoming a dynamic, two-way conversation between the grid and the people who use it. At the heart of this shift are microgrids, small, localized networks that can generate their own power using a mix of sources like solar panels and wind turbines, alongside traditional generators and batteries. The challenge for engineers is not just building these systems, but managing them. An energy management system acts as the brain of the microgrid, constantly deciding when to generate power, when to store it, and when to draw from the main grid. The goal is always the same: to keep the lights on at the lowest possible cost while relying as much as possible on clean, renewable energy. However, because the sun doesn't always shine and the wind doesn't always blow, and because electricity prices change throughout the day, finding the perfect schedule for these resources is a complex puzzle. If the timing is off, the system wastes money or relies too heavily on expensive, polluting backup generators.
A team of researchers set out to solve this puzzle by testing a new, nature-inspired method for making these decisions. They focused on a specific type of microgrid that combines solar power, wind energy, a diesel generator, and a battery storage system. To manage this mix, they developed a strategy based on the Pelican Optimization Algorithm. This is a computer program that mimics the hunting behavior of pelicans. In nature, pelicans work together to herd fish toward the surface of the water, coordinating their movements to maximize their catch. Similarly, the algorithm uses a group of virtual "pelicans" to explore millions of possible schedules for the power system, searching for the single best arrangement that minimizes cost. The researchers also introduced a second layer of control called demand-side management, specifically a technique known as load shifting. This involves encouraging the microgrid to use electricity during cheaper, off-peak hours rather than during expensive peak times, effectively moving the demand curve to match the availability of cheap, renewable energy.
To see if their new approach worked, the team ran detailed computer simulations of the microgrid over a full twenty-four-hour cycle. They tested four different versions of the system to see how each component contributed to the final bill. The first scenario used only the generators and the grid, with no battery and no shifting of loads. The second added the load-shifting strategy but still had no battery. The third introduced the battery storage but kept the loads fixed. The final, most complex scenario combined the battery storage with the load-shifting strategy. In every single test, the new pelican-based algorithm proved to be the most effective tool for finding the cheapest solution. When the researchers compared their results against two other well-known computer methods, the pelican algorithm consistently found lower costs and reached the solution faster.
The most striking results came from the fourth scenario, where the system had both a battery and the ability to shift loads. In this setup, the total operating cost for the day dropped to 11.889 US dollars. This was the lowest cost achieved across all tests. The battery played a crucial role by storing excess energy when it was cheap or abundant and releasing it when prices were high, a process known as energy arbitrage. However, the battery alone was not enough to reach the absolute minimum. It was the combination of the battery and the load-shifting strategy that unlocked the full potential of the system. By moving flexible electricity usage away from expensive peak hours and toward times when solar and wind power were plentiful, the system reduced its need to buy power from the main grid or run the diesel generator. The simulations showed that this coordinated approach not only saved money but also aligned the system's needs more closely with the natural availability of renewable energy.
The study also highlighted that the new algorithm was particularly good at handling the added complexity of the battery and load-shifting variables. As the system became more complicated, the difference in performance between the pelican algorithm and the other methods grew larger. This suggests that as microgrids become more sophisticated, with more moving parts and stricter sustainability goals, this nature-inspired approach will become increasingly valuable. The researchers concluded that while the battery provides flexibility, the true efficiency comes from combining that storage with the ability to shift demand. This dual approach allows the microgrid to operate more smoothly, relying less on fossil fuels and more on clean energy. The findings offer a clear path forward for making these small, local power networks not just technically feasible, but economically superior, helping to move the world toward a future where energy is both affordable and clean.
Drowning in papers in your field?
Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.