Fair Allocation Based Soft Load Shedding

Ali, Sarwan; Mansoor, Haris; Khan, Imdadullah; Arshad, Naveed; Faizullah, Safiullah; Khan, Muhammad Asad

doi:10.1007/978-3-030-55187-2_32

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…When there is an excessive generation, the mismatch is reduced by providing incentives to businesses and industry i.e., increasing the demand in a resourceful manner, while, in the case where the demand exceeds generation, the mismatch is reduced by shedding power from certain feeders or regions such that the overall demand remains under generation [6]. This shutdown of power is known as selective load shedding or forced load shedding (FLS) [7,8]. FLS keeps the demand under generation at all times, but there are certain challenges associated with this method which include the price to be paid by the utility for the value of the lost load, inefficient utilization of available generation, utility's credibility to provide energy, and hurting the consumer comfort.…”

Section: Introductionmentioning

confidence: 99%

Soft Load Shedding Based Demand Control of Residential Consumers

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

Soft Load Shedding Based Demand Control of Residential Consumers

et al. 2022

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“…Accu-rate fine-scale load forecasts thus result in optimal resource allocations and local energy generation, thereby reducing costs associated with transmission and distribution [3], [4]. Short-term load forecasting at household level is also pivotal for design of demand side management programs (DSM) such as demand response programs (DR) [5], peak shaving [6]- [8], dynamic pricing [9], [10] and soft load-shedding [11], [12]. Most of these DSM programs are designed to work at each hour.…”

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confidence: 99%

Past Vector Similarity for Short Term Electrical Load Forecasting at the Individual Household Level

et al. 2021

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Demand side management (DSM) programs are an integral part of the modern grid. Most of these DSM programs are designed to work at household and hour level. The optimization problems in these DSM programs are guided by the forecasted load. An error in the hour ahead load forecasting results in a suboptimal solution entailing economic cost to both the utility and the customers. Predicting loads at a fine granularity (e.g., households) is challenging due to a large number of (known or unknown) factors affecting power consumption. At larger scales (e.g., clusters of consumers), since the inherent stochasticity and fluctuations are averaged out, the problem becomes substantially easier. Many techniques have been proposed to predict loads of clusters of consumers in various localities with great accuracy. Also, these techniques generally utilize sociological and weather information and work better on data from a particular locality. In this paper, a new technique called Past Vector Similarity (PVS) has been proposed to predict electricity load one hour ahead at the level of an individual household. The proposed strategy is based on load information only and does not require calendar, weather or any other attributes. In fact, the idea is to extract the exact load patterns of individual households corresponding to their routine and socioeconomic values. Consequently, the technique makes use of the recent past vector and generate similar patterns for the prediction of future load profiles. Furthermore, the ensemble of these similar loads is an efficient prediction of electricity. PVS has just two parameters due to which it can be applied to the smaller dataset without overfitting issue. Moreover, due to the parallel nature of PVS, it can be scaled for a large number of customers without computation burden. The proposed PVS has been assessed empirically for two distinct datasets from Australia and Sweden. The simulation results demonstrate that the PVS significantly outperforms other state-of-the-art forecasting methods in terms of accuracy.

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