Optimal Voltage–Frequency Regulation in Distributed Sustainable Energy-Based Hybrid Microgrids with Integrated Resource Planning

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.

Isolated and Interconnected Multi-Area Hybrid Power Systems: A Review on Control Strategies

“…Most of the above methods are used for an isolated system. In [116], DRS is also implemented to control the frequency of the interconnected areas with non-conventional energy resources [128][129][130][131][132][133][134][135][136], where the application of DRS for concurrent frequency and voltage stabilization is established in [137].…”

“…Identifying an appropriate optimization tool for tuning of the controllers is no less important. To this end, many researchers [5,15,45,46,67,68,103,111,[137][138][139][140][141][142][143][144]148,[154][155][156][157][158][159][160] have obtained optimum performance of HPS using different intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic Control, Yellow Saddle Goatfish Algorithm (YSGA), Butterfly Optimization Technique (BOA) and so on. A number of papers have been published in the past few years that focus on the application of PSO as well as Cuckoo Search (CS).…”

Hybrid Wind/PV/Battery Energy Management-Based Intelligent Non-Integer Control for Smart DC-Microgrid of Smart University

“…In fact, in the standard design of the DC microgrid, the load converters and the energy sources are parallelly connected where the energy is consumed or supplied through the DC-link. Thus, the control of the DClink voltage is needed for an efficient and stable operation of the DC microgrid [11]- [12]. Several control strategies have appeared in the literature to address the issues of the DC-link voltage.…”

Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks

“…In this regard, scheduling problems need a smart controller for the VPP system using optimization algorithms. Thus, several optimization algorithms have been established by researchers recently, such as the genetic algorithm [7], gravitational search algorithm [9][10][11], butterfly algorithm [12], herd-related optimization approaches [13], whale optimization algorithm [14], cat swarm optimization [15], practical swarm optimization (PSO) [16], etc. The energy management duties are to ensure security; use a mixture of energy, generation, transmission, and distribution resources; and minimize losses and increase profit [17][18][19][20][21].…”