Load-Shedding Management in a Smart Grid Architecture Through Smart Metering

This research focuses on load-forecasting using Cartesian Genetic Programming evolved Artificial Neural Networks (CGPANN) and load-balancing using Genetic Algorithm in an electrical system. An unbalanced load in a distribution feeder has adverse effects on the system. All the transformer units connected to the feeder have different operating loads, and the system's overall behaviour depends on them. Even if the transformers are not overloaded, any feeder phase can become overloaded due to excessive load contributed by individual transformers on that phase which results in a system-wide blackout. A custom-built monitoring device is installed on each transformer to monitor real-time electrical load data. A switching mechanism introduced at the transformer level can interchange the load between phases. This combination helps eliminate excessive load conditions and minimize unbalanced load conditions. Considering the involved parameters, i.e. transformers, phases, switching possibilities, and operating load, this constitutes a search problem within an available solution set. An optimum solution could be searched for and identified by interchanging the loads. This study aims to develop a feasible algorithm for such a search problem. The developed Genetic Algorithm can arrive at an optimum solution in minimum iterations. The load-forecasting model is used to predict load and identify system anomalies while the load-balancing model can re-adjust the system by shifting loads on individual transformers so as to balance the feeder's overall load with no excessive load condition.