Optimal fractional order PID-load frequency controller for multi-interconnected microgrids including renewable energy and storage system

Keeping the Lights Steady in a Renewable World

As more homes and industries draw power from wind farms, hydropower dams, and advanced batteries, keeping the electric grid steady becomes harder. When supply and demand fall out of balance, the grid’s frequency drifts, which can damage equipment and trigger blackouts. This paper explores a new way to keep many small, interconnected power networks—called microgrids—running smoothly, even when renewables and customer demand are highly unpredictable.

Why Grid Frequency Matters to Everyday Life

Electric power systems are designed to operate at a very specific frequency (50 or 60 hertz, depending on the region). If too many appliances are turned on at once, or if the wind suddenly slows and turbines produce less power, that frequency can dip or rise. Small deviations are normal, but large or prolonged ones can overload lines, confuse protection devices, and shorten the life of sensitive electronics. The challenge becomes even greater when several countries or regions are linked together: a disturbance in one area can ripple through tie-lines and destabilize neighbors. Traditional methods for “load frequency control” work well on simple, fossil-fueled grids, but they struggle as renewable sources and storage devices multiply.

From Single Big Grids to Many Smart Microgrids

To meet rising electricity demand while cutting fossil-fuel use, power systems are evolving from a few huge plants into networks of smaller microgrids. Each microgrid in this study combines conventional thermal plants, hydropower, wind turbines, and two advanced storage technologies: redox flow batteries, which store energy in liquid electrolytes, and hydrogen systems that convert surplus electricity into hydrogen and then back into power through fuel cells. These microgrids are tied together so they can share power. The upside is flexibility and resilience; the downside is a web of interactions that makes it much harder to keep frequency and exchanged power within safe limits under sudden load changes.

A Smarter Way to Tune the Grid’s “Autopilot”

Engineers often rely on PID controllers—automatic systems that continuously nudge generators up or down—to correct frequency errors. This work uses a more flexible version called a fractional-order PID controller, which adds extra tuning “knobs” and can better shape how the system responds over time. The catch is that tuning these controllers in large, renewable-rich networks is a complex search problem with many local dead ends. To handle this, the authors refine a so-called political optimizer, a search algorithm inspired by multi-party elections. Their new memory-based version, mPO, lets virtual “candidates” remember their best past positions and use that experience to guide future moves, while a special exploration step keeps the search diverse so it does not get stuck too early.

Testing the Algorithm Before Touching the Grid

Before applying mPO to real power problems, the authors test it on a suite of standard mathematical benchmarks used to judge optimization methods. Across 12 of these test functions, mPO consistently converges faster and more reliably than several popular nature-inspired algorithms, including grey wolf, sand cat swarm, and sine–cosine approaches, as well as the original political optimizer. It shows strong accuracy, good robustness, and less tendency to get trapped in local optima, indicating that the memory and exploration tweaks genuinely improve the search process.

Stabilizing Networks of Renewable-Rich Microgrids

The heart of the paper is a series of simulations on two interconnected microgrids and then on a larger system of four. In each case, the microgrids include thermal, hydro, and wind units plus storage, and they are subjected to sharp load changes and realistic nonlinear effects. The mPO algorithm is used to tune the fractional-order PID controllers so that a combined error measure—tracking both frequency deviations and unwanted power exchanges—is minimized. Compared with the traditional political optimizer and other methods, mPO cuts this error by roughly 8% when hybrid hydrogen–battery storage is present in the two-area system and by about 20% in the four-area system. It also shortens settling times and reduces overshoot, meaning the microgrids return to normal operation more quickly and with fewer swings.

What This Means for Future Power Systems

In simple terms, this study offers a smarter “autopilot” for tomorrow’s complex, renewable-heavy grids. By combining an advanced type of controller with a memory-enhanced search algorithm, the authors show that multi-interconnected microgrids can ride out sudden demand spikes and renewable fluctuations with smaller frequency deviations and smoother power flows. While the work is based on detailed simulations, it suggests that such intelligent tuning methods could help real-world operators integrate more clean energy without sacrificing stability, paving the way for larger, greener, and more reliable power networks.

Citation: Alshahir, A., Fathy, A., A. Hashim, F. et al. Optimal fractional order PID-load frequency controller for multi-interconnected microgrids including renewable energy and storage system. Sci Rep 16, 14342 (2026). https://doi.org/10.1038/s41598-026-43080-z

Keywords: microgrids, renewable energy, frequency control, optimization algorithm, energy storage