In developing countries like India there is a huge gap between power generation and demand where load shedding becomes necessary to sustain system stability. Conventional load shedding methods follow "round robin" technique wherein it is almost impossible to shed exact amount of load. And also shedding is carried out regardless of the type of loads connected to a feeder as a result of which available power is not delivered to the consumers in utmost need. This paper demonstrates Intelligent load shedding scheme to provide optimal solution for load relief based on time priority assigned to various loads. The genetic algorithm technique is employed for optimal load shedding solution to minimize the error between load to be shed and load being shed in a smart grid environment. Simulations are carried on a sample system based on a practical feeder data.

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For every country which is expecting a large growth in power demand in the near future or facing a power crisis, an effective load control and power distribution strategy is a necessity. Load shedding is done whenever power demand is more than power generation in order to sustain power system stability. The current load shedding strategies fails to shed exact amount of load as per the system requirement and does not prioritize loads which are being shed. Given the dimension of the problem, it would not be feasible computationally, to use regular optimization techniques to solve the problem. The problem is typically suited for application of meta-heuristic algorithms. This paper proposes a new scheme for optimizing load shedding using ant colony algorithm in a smart grid platform considering loads at utility level. The algorithm developed considers each electrical connection from Distribution Company as one lumped load and provides an effective methodology to control the load based on various constrains such as importance of load and time of load shedding.