Residential Demand Side Management Using Artificial Intelligence

Vijayan, Ajith; Kurupath, Venugopalan; Das, Jani

doi:10.1109/icscc51209.2021.9528174

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…DSM is an important part of the smart grid and the concept of DSM is to control the demand side consumption and reduce peak loads [5]. In papers [6]- [7], some methods of application of DSM in microgrid are given. In paper [6], a load shifting procedure was presented in a simulated environment of a residential infrastructure.…”

Section: B Demand Side Managementmentioning

confidence: 99%

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A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization

Deng

Jiang

Hai-ya

et al. 2022

2022 6th International Conference on Smart Grid and Smart Cities (ICSGSC)

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Microgrid is playing an increasingly important role in making the utility grid more intelligent and efficient, since it can make better use of the renewable energy resources to simultaneously relieve the grid supply pressure and reduce carbon emissions. Innovations in electric technologies, information and communication technologies can facilitate better management of the power transmission and distribution in the microgrid. This paper proposes an optimization strategy, which considers distributed generations, photovoltaics and wind turbines, based on particle swarm optimization for the management of the microgrid. Simulation results demonstrate that with the optimal generation resources management and the effective use of demand side management in the microgrid, the proposed strategy can reduce electricity costs by 29.283% and 32.158% on weekdays and weekends, respectively.

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Section: B Demand Side Managementmentioning

confidence: 99%

“…In papers [6]- [7], some methods of application of DSM in microgrid are given. In paper [6], a load shifting procedure was presented in a simulated environment of a residential infrastructure. Through predicting the future load patterns, various types of controllable devices were managed to reduce the peak demand of load.…”

Section: B Demand Side Managementmentioning

confidence: 99%

A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization

Deng

Jiang

Hai-ya

et al. 2022

2022 6th International Conference on Smart Grid and Smart Cities (ICSGSC)

View full text Add to dashboard Cite

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“…Other authors have indeed researched the advantages of DSM in terms of economic benefits of DSM [6], the consequences of DSM on the industrial and residential sectors [7] [8], the interaction between DSM and other smart grid technologies [9], the important concepts of DSM [10], the influence of DSM on power systems [11], the optimal power flow procedures of DSM [12] [13], and the forecasts [14]. Furthermore, DSM has been implemented in several countries, including the United Kingdom [15], India [16], China [17], Italy [18], North America [19], India [20], Kuwait [21], Korea [22], Turkey [23] and Denmark [24] with promising results. With such a rapid growth rate, DSM activities perform a significant function in the electricity-powered energy system to manage power demand, which eventually influences the reliability and economical operation.…”

Section: Introductionmentioning

confidence: 99%

“…There are many ways to respond to demand, including peak clipping, valley filling, or load shifting [4]. Demand response is also known as "flexible load shape" because of its ability to respond to change [25]. In this scheme, utility companies encourage consumers to change their power consumption based on supply by giving them financial considerations [26], using schemes such as futures price, real time price and time of use rate [27].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

et al. 2023

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In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme's optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demandside management solutions is also illustrated congruently.

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“…Some DSM strategies have already been proposed in the present literature. Specifically, A. Vijayan et al [4] have proposed a residential DSM algorithm based on load shifting method, which aims to minimize the daily electricity cost for customers. Another load shifting algorithm proposed by N. D. Rahate [5] focus more on peak clipping and the peak to average ratio (PAR) achieves a reduction of 22.23%.…”

Section: Introductionmentioning

confidence: 99%

Demand Side Management Considering Household Appliances and EV

Dong

Jiang

Hai-ya

et al. 2022

2022 6th International Conference on Smart Grid and Smart Cities (ICSGSC)

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Combination of the information technology and the power engineering is the feature of next-generation grid. Depending on bidirectional communications, demand side management (DSM) aims at optimizing the electricity usage pattern of customers to improve energy efficiency and alleviate environmental impact. In this study, a DSM optimization algorithm is designed, which can perform load shifting on a household level based on the Time-of-Use strategy. Several flexible appliances, plug-in hybrid electric vehicle (EV) charging and rooftop photovoltaic (PV), are considered. Results show that the daily electricity cost has declined by 19% after the optimization. A 12% reduction of the domestic carbon emission is also achieved from the variation of grid carbon intensity and energy provided by rooftop PV. It is validated that with the growing penetration rate of EVs and renewable energy generation, smart scheduling of household load can greatly benefit grid stability and energy efficiency.

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Residential Demand Side Management Using Artificial Intelligence

Cited by 5 publications

References 9 publications

A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization

A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

Demand Side Management Considering Household Appliances and EV

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