Abstract
The main aim of a power utility company is to supply quality and uninterrupted power to customers. This becomes a growing challenge as the continued increase in population calls for proportional increase in power supply to additional loads. If not well planned, this steady increase in power demand can lead to voltage collapse and eventual power blackouts. In instances where power demand exceeds generation within islanded microgrid or due to an occurrence of a contingency, optimum load shedding should be put in place so as to enhance system security and stability of the power system. Load shedding is traditionally done based on undervoltage measurements or underfrequency measurements of a given section of the grid. However, when compared with conventional methods, metaheuristic algorithms perform better in accurate determination of optimal amount of load to be shed during a contingency or undersupply situations. In this study, an islanded microgrid with high penetration of Renewable Energy Sources (RESs) is analyzed, and then Artificial Bee Colony (ABC) algorithm is applied for optimal load shedding. The results are then compared with those of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and GA-PSO hybrid. Both generation and overload contingencies are considered on a standard IEEE 30-bus system on a MATLAB platform. Different buses are assigned priority indices which forms the basis of the determination of which loads and what amount of load to shed at any particular time.
Highlights
Energy demand has been steadily increasing as a result of increase in population and industrial and economic growth in various sectors [1]
Undervoltage Load Shedding (UVLS) and Underfrequency Load Shedding (UFLS) are the criteria mainly used for load shedding [5]. rough the shedding of some loads, the perturbed system is forced to settle to a new state of equilibrium [6]
Loss of generation scenarios ranging from 0 to 75 MW at intervals of 5 MW were simulated while keeping the load demand constant at 328.4 MW. e line losses, convergence characteristics, and the amount of load to be shed by Artificial Bee Colony (ABC) algorithm at each instant were observed. is is as shown by Figures 3–7
Summary
Energy demand has been steadily increasing as a result of increase in population and industrial and economic growth in various sectors [1]. E sum of squares of the difference between the connected loads and the supplied power and ABC algorithm to arrive at the optimal amount of load to be shed during a contingency is proposed in [6]. In this case, the priority of important loads and buses was not considered in the study before shedding of the loads.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
