Fuzzy Control and Adaptive Droop Tame Solar-Hydrogen Microgrid Chaos

Hydrogen has long been pitched as the missing link in the renewable energy transition: a clean, dense energy carrier that can soak up surplus solar power and hand it back hours or days later. But a new study from Frontiers in Electronics reveals just how unruly that link can be when it is bolted onto a real microgrid, and how a clever pairing of fuzzy logic and adaptive droop control may finally keep it in line.

The research, led by Yan Wang of the Guangdong Electric Power Design Institute in Guangzhou, China, tackles a problem that has quietly plagued engineers building electrically-hydrogen coupled DC microgrids: how to coordinate a lithium-ion battery, an alkaline electrolyzer, a high-pressure hydrogen tank, and a proton exchange membrane fuel cell so that none of them misbehaves. These systems promise to maximize renewable hosting capacity while preserving power quality, yet coordinating storage technologies with wildly different dynamics and constraints remains a formidable technical challenge.

The core difficulty lies in the hydrogen tank itself. The study defines the state of hydrogen, or SoH, as the normalized fill level of the tank, analogous to a battery’s state of charge but representing chemical rather than electrochemical storage. When photovoltaic generation surges or loads spike, the SoH can swing dramatically. Large excursions trigger frequent start-stop cycling of the electrolyzer and fuel cell, accelerate equipment aging, and degrade the system’s ability to maintain DC bus voltage stability during transients. Conventional fixed-parameter droop control, widely adopted for its simplicity and decentralized implementation, simply fails to account for the distinct dynamic characteristics and operational constraints of hydrogen storage.

Wang’s solution is a hierarchical framework built on three synergistic innovations. First, a fuzzy logic algorithm optimizes dynamic power allocation between hydrogen and lithium battery storage. Unlike existing fuzzy controllers that use only battery state of charge as the primary input, this controller accepts both SoC and SoH as dual inputs, enabling cross-storage coordination that accounts for the disparate dynamics of electrochemical and chemical storage. The controller dynamically adjusts power distribution according to real-time conditions, including power imbalance magnitude, battery SoC, and hydrogen SoH, ensuring that batteries handle high-frequency fluctuations while hydrogen storage manages low-frequency components.

The fuzzy inference system partitions all three variables into five fuzzy sets designated NB, NS, M, PS, and PB, corresponding to Negative Big, Negative Small, Medium, Positive Small, and Positive Big. Triangular membership functions parameterize the fuzzy sets, and the controller architecture bifurcates into two modules activated conditionally based on power flow direction: an EL-BAT module for surplus conditions and an FC-BAT module for deficit conditions. This modular decomposition enables specialized rule bases optimized for charging and discharging scenarios, reducing rule complexity compared to a unified controller handling both power flow directions simultaneously. Following fuzzification and rule evaluation using the Mamdani inference method with minimum implication and maximum aggregation operators, the output fuzzy set is defuzzified using the center-of-gravity method to obtain a crisp allocation coefficient that determines power distribution.

Second, the microgrid’s operating states are classified into normal and extreme conditions based on hydrogen SoH thresholds, partitioning the operational domain into five distinct regimes. This classification enables differentiated control strategies that enhance equipment protection under critical conditions. Unlike mode-triggered methods that switch abruptly, the proposed framework employs smooth transitions between operating regimes through continuous droop coefficient adaptation, avoiding the power oscillations that can occur during boundary crossings in prior approaches.

Third, a variable-parameter droop control strategy for hydrogen storage dynamically regulates the hydrogen tank’s SoH. The innovation lies in formulating the droop coefficients as exponential functions of the real-time hydrogen storage state rather than constants, with a rate-of-change feedback term that provides a predictive correction mechanism. During stable operating periods with gradual SoH evolution, the adjustment factor remains near baseline, maintaining normal droop characteristics. But when significant renewable transients or sustained imbalances cause rapid SoH movement, the increased adjustment factor amplifies droop coefficient modulation, anticipating SoH excursions and applying preemptive corrections before emergency conditions materialize. This derivative control action is absent in all prior fixed or switched droop approaches.

To validate the strategy, Wang developed a comprehensive simulation model in MATLAB/Simulink R2021b featuring a 100 kW photovoltaic array, a 50 kWh lithium battery with 50 kW charge/discharge power, hydrogen equipment rated at 30 kW for both electrolyzer and fuel cell, and a hydrogen tank storing 5 kg at 350 bar, enabling roughly 4 to 6 hours of energy backup. All generation and storage units connect in parallel to a 480 V DC bus through bidirectional DC-DC converters. The simulation runs for 3,600 seconds with a 1 millisecond time step, capturing fast transient dynamics while maintaining computational efficiency. Realistic PV and load profiles incorporate deterministic diurnal variations and stochastic fluctuations representing cloud transients, with morning ramp-up, midday peak output of 95 kW, and afternoon decline, superimposed with 5 to 15 kW rapid fluctuations emulating solar intermittency.

The comparative results are striking. The proposed integrated strategy, combining fuzzy power allocation with variable-parameter droop control, achieves a 24.4 percent reduction in maximum voltage deviation compared to the best baseline, dropping from 19.3 V to 14.6 V. The RMS voltage deviation falls by 26.2 percent, from 8.4 V to 6.2 V, while the voltage violation rate plummets from 2.8 percent to 0.4 percent, meaning the system maintains stable voltage operation for 99.6 percent of the time. The hydrogen SoH excursion range narrows by 33.3 percent, from 42.7 percent to 28.5 percent, and the time spent in extreme operating zones drops dramatically by 83.6 percent. Most importantly, SoH violations are eliminated entirely under the proposed strategy. The electrolyzer and fuel cell start-stop counts are reduced by 57.1 percent and 50.0 percent respectively, and total power switching events decrease by 46.4 percent, resulting in smoother and more stable system operation. Overall system efficiency improves by 1.2 percentage points, reaching 92.9 percent.

The fuzzy controller’s specific contribution is equally impressive. Battery average power decreases by 18.3 percent, reducing stress and cycling, while hydrogen average power increases by 19.4 percent, enhancing utilization without exceeding ratings. The storage imbalance index falls to 63.6 percent of its baseline value, confirming equitable energy distribution between battery and hydrogen storage. The variable-parameter droop mechanism constrains maximum SoH below 90 percent while raising minimum SoH by 43.3 percent, eliminating overshoot and undershoot violations entirely. Average deviation from 50 percent SoH falls by 33.6 percent, and peak SoH rate-of-change drops by 42.9 percent, demonstrating tighter regulation and damping of rapid fluctuations. The adaptive droop ranges for electrolyzer and fuel cell enable proactive participation adjustments, with average adjustment of 34.2 percent and 8.4 events per hour, ensuring smooth modulation. During extreme high SoH conditions triggered when SoH crosses 90 percent, the electrolyzer shuts down while the fuel cell operates at maximum constant power, rapidly reducing SoH at 2.4 percent per minute and returning to normal within 5.2 minutes. Despite the stress, no emergency shutdowns or power violations occur, and equipment stress remains manageable, with the battery acting as the primary regulator to maintain voltage during hydrogen device inactivity. Wang notes that the strategy offers practical advantages including modular hierarchical design, model-free fuzzy computation, and autonomous droop operation without high-bandwidth communication, confirming its applicability to practical electrically-hydrogen coupled DC microgrid implementations.

Subject of Research: Engineering

Article Title: A power coordinated control strategy for an electrically–hydrogen coupled DC microgrid based on fuzzy control and variable-parameter droop

Article References: Wang, Y. (2026). A power coordinated control strategy for an electrically–hydrogen coupled DC microgrid based on fuzzy control and variable-parameter droop. Frontiers in Electronics, 7, Article 1773991. https://doi.org/10.3389/felec.2026.1773991

Image Credits: AI Generated

DOI: 10.3389/felec.2026.1773991

Keywords: DC microgrid, hydrogen energy storage, fuzzy control, droop control, photovoltaic, electrolyzer, fuel cell, power electronics, power, coordinated, control, strategy