Abstract
Power generation and consumption is an instantaneous process and maintaining the
balance between demand and supply is crucial since the demand and supply mismatch leads to
various risks like over-investment, over-generation, under-generation, and the collapse of the power
system. Therefore, the reduction in demand and supply mismatch is critical to ensure the safety
and reliability of power system operation and economics. A typical and common approach, called
full load shedding (FLS), is practiced in cases where electric power demand exceeds the available
generation. FLS operation alleviates the power demand by cutting down the load for an entire area
or region, which results in several challenges and problems for the utilities and consumers. In this
study, a demand-side management (DSM) technique, called Soft-load shedding (SLS), is proposed,
which uses data analytics and software-based architecture, and utilizes the real-world time-series
energy consumption data available at one-minute granularity for a diversified group of residential
consumers. The procedure is based on pattern identification extracted from the dataset and allocates
a certain quota of power to be distributed on selected consumers such that the excessive demand is
reduced, thereby minimizing the demand and supply mismatch. The results show that the proposed
strategy obtains a significant reduction in the demand and supply mismatch such that the mismatch
remains in the range of 10–15%, especially during the period where demand exceeds generation,
operating within the utility constraints, and under the available generation, to avoid power system
failure without affecting any lifeline consumer, with a minimum impact on the consumer’s comfort