Genetic Algorithm Based Peak Load Management for Low Voltage Consumers in Smart Grid – A Case Study

TLDR: In this paper, the concept of load shifting to low voltage consumers using several types of appliances and in large numbers is discussed. Load shifting with respect to day ahead forecast is formulated as a minimization problem and are solved using learning based evolutionary algorithm.

Abstract: To withstand the quick development with the demand for energy and the overall demand cost, enhanced effectiveness, dependability and adaptability, smart strategies should be carried forth in energy sector for our earth and vitality protection. In the electrical domain DSM can be a part of smart grid where the consumers can participate themselves to decrease the peak load and eventually the load profile can be reshaped. A portion of the DSM method is peak clipping, load shifting, valley filling and energy conservation. The paper involves the concept of load shifting to low voltage consumers using several types of appliances and in large numbers. Load shifting with respect to day ahead forecast is formulated as a minimization problem and are solved using learning based evolutionary algorithm. Simulations were carried out with a specific test case using Mat Lab and the results show a substantial peak reduction and cost savings for the future smart grid.