https://orcid.org/0000-0002-0088-6628
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ABSTRACT
Owing to the current environmental concerns, the RESs have become popular as the microgrid structures for power generation. However, due to capricious weather and loading conditions, the generated power and thereby the microgrid frequency get adversely affected. This instant study puts forth the control strategy for the mitigation of frequency excursions, arising out of step load disturbance, in AC microgrid through adaptive network fuzzy inference system (ANFIS) scheduled fractional ordered proportional-integral-derivative (PID) control optimally tuned with a multiverse optimizer. The control strategy proposition is compared against multi-verse optimized PID and fractional order PI controls. Furthermore, the study investigates as to how EV affects in stabilizing the system frequency in the backdrop of a load disturbance. For a more realistic assessment, the proposition is assessed in the face of system nonlinearities and random load perturbations also to establish its robust and stable behavior. The results prove the efficacy of the multi-verse optimized ANFIS scheduled fractional ordered PID controller. Simulations are executed using MATLAB® software. The results are also validated by experimental studies employing a hardware-in-loop configuration on the OPAL-RT real-time simulator.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Nomenclature
| Abbreviation | = | |
| ANFIS | = | Adaptive Network Fuzzy Inference System |
| FOPID | = | Fractional-Ordered Proportional-Integral-Derivative |
| EV | = | Electric Vehicle |
| ICA | = | Imperialist Competition Algorithm |
| BESS | = | Battery Energy Storage System |
| MC | = | Microsource Controller |
| MVO | = | Multiverse Optimizer |
| RES | = | Renewable Energy Sources |
| V2G | = | Vehicle to Grid |
| DER | = | Distributed Energy Resources |
| AGC | = | Automatic Generation Control |
| FESS | = | Flywheel Energy Storage System |
| CES | = | Capacitive Energy Storage |
| PSO | = | Particle Swarm Optimization |
| LFC | = | Load Frequency Control |
| COA | = | Coyote Optimization Algorithm |
| ESS | = | Energy Storage Systems |
| GOA | = | Grasshopper Optimization Algorithm |
| DG | = | Distributed Generation |
| MPC | = | Model Predictive Control |
| PV | = | Photovoltaic |
| WTG | = | Wind Turbine Generator |
| MGCC | = | Microgrid Central Controller |
| LC | = | Load Controller |
| ITAE | = | Integral of Time multiplied Absolute Error |
| FC | = | Fuel Cell |
| PEV | = | Plug-in EV |
| LCC | = | Local Control Centre |
| ISE | = | Integral of Squared Error |
| MF | = | Membership Functions |
| LSE | = | Least Squares Error |
| DEG | = | Diesel Engine Generator |
| BP | = | BackPropagation |
| ALO | = | Ant Lion Optimization |
| FIS | = | Fuzzy Inference System |
| SMES | = | Superconducting Magnetic Energy Storage |
| PCC | = | Point of Common Coupling |
| DE | = | Differential Evolution |
| IAE | = | Integral of Absolute Error |
| TLBO | = | Teaching–Learning-Based Optimization |
| SSA | = | Salp Swarm Algorithm |
| Subscripts | = | |
| Tg | = | Generator Time Constant |
| TI/c | = | Interconnection Device Time Constant |
| TIN | = | Inverter Time Constant |
| Tt | = | Turbine Constant |
| KP | = | Proportional Gain |
| D | = | Damping Coefficient |
| KI | = | Integral Gain |
| R | = | Droop Constant |
| KD | = | Derivative Gain |
| H | = | Inertia Constant |
| Λ | = | Order of Integrator |
| TBESS | = | BESS Time Constant |
| µ | = | Order of Differentiator |
| TFESS | = | FESS Time Constant |