Brownout Energy Distribution Scheme for Mitigating Rolling Blackouts

Kelkar, Samrudha G.; Kothari, Nimish; Ramamritham, Krithi

doi:10.1145/2768510.2770937

Cited by 8 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…James et al proposed a method to select optimal customers to target for shifting of power from peak hour to off-peak time [25]. Similarly, Kelkar et al implemented such a strategy using the smart control unit (SCU) using the categorization of loads as essential (E) and non-essential (N) and giving the switching control to user [26].…”

Section: Introductionmentioning

confidence: 99%

Soft Load Shedding Based Demand Control of Residential Consumers

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%