Heuristic Scheduling Algorithm for Load Shift DSM Strategy in Smart Grids and IoT Scenarios

Bertineti, Daniel Pegoraro; Canha, Luciane Neves; Medeiros, A.; Azevedo, R. M. de; Silva, B. F. da

doi:10.1109/isgt-la.2019.8895488

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…e comparison of simulation results with results obtained from Particle Swarm Optimization (PSO) algorithm shows the effectiveness of BSA in terms of peak load reduction, cost effectiveness, and handling many different types of devices. In [22], the authors present the DSM strategies and propose an algorithm for multiobjective optimization (MOO) DSM problem for day-ahead scheduling of load. Discrete time varying model is used to determine price of energy for one day.…”

Section: Load Management Methodsmentioning

confidence: 99%

Recent Challenges and Methodologies in Smart Grid Demand Side Management: State-of-the-Art Literature Review

Nasir¹,

Bukhari

Raza³

et al. 2021

Mathematical Problems in Engineering

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The concept of smart grid was introduced a decade ago. Demand side management (DSM) is one of the crucial aspects of smart grid that provides users with the opportunity to optimize their load usage pattern to fill the gap between energy supply and demand and reduce the peak to average ratio (PAR), thus resulting in energy and economic efficiency ultimately. The application of DSM programs is lucrative for both utility and consumers. Utilities can implement DSM programs to improve the system power quality, power reliability, system efficiency, and energy efficiency, while consumers can experience energy savings, reduction in peak demand, and improvement of system load profile, and they can also maximize usage of renewable energy resources (RERs). In this paper, some of the strategies of DSM including peak shaving and load scheduling are highlighted. Furthermore, the implementation of numerous optimization techniques on DSM is reviewed.

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Section: Load Management Methodsmentioning

confidence: 99%

Recent Challenges and Methodologies in Smart Grid Demand Side Management: State-of-the-Art Literature Review

Nasir¹,

Bukhari

Raza³

et al. 2021

Mathematical Problems in Engineering

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show abstract

“…Lowering the PAR of the load profile aids the power system energy management by making the grid more stable, efficient, and reliable. This is achieved by increasing spare capacity in the event of a supply shortage (4) 37,38 𝑂𝐹 2 = 𝑚𝑖𝑛 (𝑃𝐴𝑅) = (…”

Section: Problem Formulationmentioning

confidence: 99%

Optimal Energy Management Applying Load Elasticity Integrating Renewable Resources

Ragab

Ibrahim

Desouki

et al. 2023

Preprint

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Urban growth aimed at developing smart cities confronts several obstacles, such as difficulties and costs in constructing stations and meeting consumer demands. These are possible to overcome by integrating Renewable Energy Resources (RESs) with the help of demand side management (DSM) for managing generation and loading profiles to minimize electricity bills while accounting for reduction in carbon emissions and the peak to average ratio (PAR) of the load. This study aims to achieve a multi-objective goal of optimizing energy management in smart cities which is accomplished by optimally allocating RESs combined with demand response (DR) for creating a flexible load profile under RESs and load uncertainty. A comprehensive study is applied to IEEE 69-bus with different scenarios using Sea-Horse Optimization (SHO) for optimal citing and sizing of the RESs while serving the objectives of minimizing total power losses and reducing PAR. SHO performance is evaluated and compared to other techniques such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO) and Whale Optimization (WO) algorithms. The results show that combining elastic load shifting with optimal sizing and allocation using SHO achieves a global optimum solution for the highest power loss reduction while using a significantly smaller sized RESs than the counterpart.

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“…There is a variety of algorithm approaches implemented into the load control systems to provide DSM operation. It has been admitted that the complexity of the DSM algorithms mainly depends on controllability properties of the electrical load components, electricity metering infrastructure and data communication [17][18][19][20][21][22]. While industrial consumers utilise comprehensive DSM methods to manage complicate structure load, the domestic DSM systems are relatively simple.…”

Section: Domestic Demand Side Managementmentioning

confidence: 99%

Domestic demand-side management: analysis of microgrid with renewable energy sources using historical load data

et al. 2021

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This paper provides a high accuracy assessment of domestic demand-side management (DSM) approach in the context of distributed renewable energy sources (RES). To determine the potential of domestic DSM for households, a microgrid model of a typical UK residential estate was developed to simulate the impact of RES. The microgrid model comprises of 15 UK households with appropriate allocation of washing machines (WM), tumble dryers (TD) and dishwashers (DW) in accordance to the statistical data. In order to obtain a high accuracy result, the power consumption of the microgrid model utilises real historical high-resolution data of household energy consumption and RES generation. Thereafter, 40% of distributed wind and solar energy are implemented in the model to produce two individual scenarios. The operation of the white appliances in the model is controlled using a domestic DSM based on a load shifting algorithm. The primary criterion of the DSM considered in this paper is the reduction of energy feedback to the grid in order to decrease the utilisation of the grid and to reduce the transmission losses. The results obtained from the model simulation are compared to the baseline model and discussed with respect to the possible benefits of implementation of domestic DSM under the impact of RES. It has been shown that the self-consumption ratio of the microgrid operating under the DSM is increased by 3% for both scenarios. The model analysis provides highly realistic results which can be used for efficiency assessment of various load shifting methods.

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Heuristic Scheduling Algorithm for Load Shift DSM Strategy in Smart Grids and IoT Scenarios

Cited by 9 publications

References 6 publications

Recent Challenges and Methodologies in Smart Grid Demand Side Management: State-of-the-Art Literature Review

Recent Challenges and Methodologies in Smart Grid Demand Side Management: State-of-the-Art Literature Review

Optimal Energy Management Applying Load Elasticity Integrating Renewable Resources

Domestic demand-side management: analysis of microgrid with renewable energy sources using historical load data

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