Delivering smart load-shedding for highly-stressed grids

Bashir, Noman; Sharani, Zohaib; Qayyum, Khushboo; Syed, Affan A.

doi:10.1109/smartgridcomm.2015.7436408

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Our method, however, only require load data of consumers. In [28] a direct load control method that is capable to enforce several user defined low power states is presented. It directly controls the appliances of the house to manage the load.…”

Section: Related Workmentioning

confidence: 99%

Fair Allocation Based Soft Load Shedding

Ali¹,

Mansoor²,

Khan³

et al. 2020

Preprint

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Renewable sources are taking center stage in electricity generation. Due to the intermittent nature of these renewable resources, the problem of the demand-supply gap arises. To solve this problem, several techniques have been proposed in the literature in terms of cost (adding peaker plants), availability of data (Demand Side Management "DSM"), hardware infrastructure (appliance controlling DSM) and safety (voltage reduction). However, these solutions are not fair in terms of electricity distribution. In many cases, although the available supply may not match the demand in peak hours, however, the total aggregated demand remains less than the total supply for the whole day. Load shedding (complete blackout) is a commonly used solution to deal with the demand-supply gap, which can cause substantial economic losses. To solve the demandsupply gap problem, we propose a solution called Soft Load Shedding (SLS), which assigns electricity quota to each household in a fair way. We measure the fairness of SLS by defining a function for household satisfaction level. We model the household utilities by parametric function and formulate the problem of SLS as a social welfare problem. We also consider revenue generated from the fair allocation as a performance measure. To evaluate our approach, extensive experiments have been performed on both synthetic and real-world datasets, and our model is compared with several baselines to show its effectiveness in terms of fair allocation and revenue generation.

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Section: Related Workmentioning

confidence: 99%

Fair Allocation Based Soft Load Shedding

Ali¹,

Mansoor²,

Khan³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In Reference [2], fine-grained load shedding policies for high-stressed grids were proposed. High-stressed grids are power grids with a very large and nearly continuous supply-demand gap.…”

Section: Related Workmentioning

confidence: 99%

“…An MG allows for fast installation of electricity supply without the need for expensive transmission infrastructure investments. It is composed of on-site distributed generators (DGs), energy storages (ESs), and loads [2]. DGs are power generators such as renewable energy sources (e.g., photovoltaic (PV), wind, or solar) and conventional generators such as microturbines or diesel generators.…”

Section: Introductionmentioning

confidence: 99%

Optimal Load Shedding for Maximizing Satisfaction in an Islanded Microgrid

2017

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Abstract:A microgrid (MG) is a discrete energy system that can operate either in parallel with or independently from a main power grid. It is designed to enhance reliability, carbon emission reduction, diversification of energy sources, and cost reduction. When a power fault occurs in a grid, an MG operates in an islanded manner from the grid and protects its power generations and loads from disturbance by means of intelligent load shedding. A load shedding is a control procedure that results in autonomous decrease of the power demands of loads in an MG. In this study, we propose a load shedding algorithm for the optimization problem to maximize the satisfaction of system components. The proposed algorithm preferentially assigns the power to the subdemand with a high preference to maximize the satisfaction of power consumers. In addition, the algorithm assigns the power to maximize the power sale and minimize the power surplus for satisfaction of power suppliers. To verify the performance of our algorithm, we implement a multi-agent system (MAS) on top of a conventional development framework and assess the algorithm's adaptability, satisfaction metric, and running time.

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“…Haroon et al demonstrate that measuring demand consumption helps understand consumers' energy consumption and suggests demand response programs [22]. Noman et al proposed a novel building DLC system that allows several user-defined voltage levels other than power or no power [23]. Madhur et al predict building consumption in real-time using historical data and propose several demand response models [24].…”

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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Delivering smart load-shedding for highly-stressed grids

Cited by 6 publications

References 12 publications

Fair Allocation Based Soft Load Shedding

Fair Allocation Based Soft Load Shedding

Optimal Load Shedding for Maximizing Satisfaction in an Islanded Microgrid

Soft Load Shedding Based Demand Control of Residential Consumers

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