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.
Highlights
IntroductionModern power systems are subjected to variability, uncertainty, and location dependency
Introduction published maps and institutional affilModern power systems are subjected to variability, uncertainty, and location dependency
This study proposes a novel strategy based on Soft Load Shedding (SLS), which uses highly granular real-world time series energy consumption data and implements a strategy to minimize the mismatch between demand and supply
Summary
Modern power systems are subjected to variability, uncertainty, and location dependency. The generation reserve capacity of the power system determines the system flexibility. When the uncertainty pervades to the demand side to a larger scale than supply side, the types of required flexibility change simultaneously. Advances in technology for power system planning and operation have helped flexibility with new resources and services [1,2]. Power imbalances between generation and demand can occur, among others, due to the loss of power generators or increase in load. These imbalances cause frequency fluctuations, and subsequently the grid becomes unstable, even leading to black-outs [3]. Developing countries sometimes face a huge mismatch which leads to the two-fold energy crisis which is either unreliable iations
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