Abstract
This work is the earliest attempt to propose an integrated resource planning for distributed hybrid microgrids considering virtual-inertia support (VIS) and demand-response support (DRS) systems. Initially, three-distributed sustainable energy-based unequal hybrid microgrids are envisioned with the availability of solar/wind/bioenergy resources. In order to overcome the effects of intermittency in renewable resources and low inertia, each microgrid is incorporated with DRS and VIS units for demand- and supply-side management, respectively. The proposed system is simulated in MATLAB considering real-time recorded solar/wind data with realistic loading for 12 months. A novel quasi-oppositional chaotic selfish-herd optimization (QCSHO) algorithm is proposed by hybridizing quasi-opposition-based learning and chaotic linear search techniques into the selfish-herd optimization, for optimal regulation of voltage and frequency in microgrids. Then, the system responses are compared with 7 algorithms and 5 error functions to tune PID controllers’ gains, which confirmed the superiority of QCSHO over others. Then, the study proceeds to investigate the voltage, frequency, and tie-line power coordination in 5 extreme scenarios of source and load variations in the proposed system without retuning the controllers. Finally, the system responses are analyzed for 10 different possible allocation of VIS and DRS units in different microgrids to find the most suitable combinations, and the results are recorded.
Highlights
The mounting global demand of electric power and depleting fossil resources for conventional generation are hammering to hunt the alternative source of sustainable energy.the most promising sustainable resources such as solar and wind are intermittent in nature with weather dependency, and the harnessed power from single source with available technology is too small to meet the demand
Encouraged with all these recent literatures and their scopes, this work has proposed a combined supply-side management agement (SSM)-/demand-side management (DSM)-based IRP for 3 unequal interconnected microgrids considering the limitations on availability/accessibility of different resources to replicate dispersed generation (DG), where, each of the microgrids is encompassed with a distinct renewable energy systems (RES)-BGES unit pair to supply the tentative demand including Biodiesel engine generator (BDEG) unit as backup support and virtual-inertia support (VIS)/demand-response support (DRS)
The optimized step responses of the distributed microgrids are estimated at the same scenario using all 4 objective functions: integral absolute error (IAE), integral square error (ISE), integral time weighted absolute error (ITAE), and integral time weighted square error (ITSE), along with integral-square of weighted absolute error (ISWAE) and compared in Table 4, for quasi-oppositional chaotic selfish-herd optimization (QCSHO) tuned PID controllers, which confirmed the superiority of ISWAE over others
Summary
The mounting global demand of electric power and depleting fossil resources for conventional generation are hammering to hunt the alternative source of sustainable energy. This motivates to propose a demand reThe power quality of microgrids could be for improved by regulating voltage sponse support (DRS) system with suitable DR strategy. SHO [28] algorithm to replicate the chaotic behavior of the selfish herds Encouraged with all these recent literatures and their scopes, this work has proposed a combined SSM-/DSM-based IRP for 3 unequal interconnected microgrids considering the limitations on availability/accessibility of different resources to replicate dispersed generation (DG), where, each of the microgrids is encompassed with a distinct RES-BGES unit pair to supply the tentative demand including BDEG unit as backup support and VIS/DRS unit for system stability. Proposing IRP for simultaneous voltage–frequency regulation of multi-unit-based distributed microgrids by designing combined storage-based VIS system for SSM and HEV charging station-based DRS for DSM.
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