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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.

The stored energy of charged batteries at a Battery Swapping Station (BSS) can be used as a source of electricity. This paper discusses, an optimal dispatch of the stored electrical energy at a BSS to power up the external load. The problem is solved using linear programming for a small farm cold storage with a pre-cooling effect. With the precooling effect, a 5% reduction in the price of electricity is obtained. The BSS can be used to provide cheaper electricity to the consumer and gain profits for the BSS owner by selling excess energy enhancing the potential of BSS technology adoption. CCS CONCEPTS• General and reference → Estimation; • Hardware → Reusable energy storage.