Particle swarm optimization for demand side management in smart grid

Logenthiran, T.; Srinivasan, Dipti; Phyu, Ei Moe

doi:10.1109/isgt-asia.2015.7386973

Cited by 34 publications

(15 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A smart grid having three different areas (each with different types of customers, namely, residential, commercial and industrial) have been utilised for testing the potency of the SOS algorithm for solving the DSM problem. The details regarding different types of controllable devices, their amount and pattern of power consumption, the ideal time to operate particular devices, the duration for a run and start and end time intervals are provided in Tables 2–4, in order [14, 28].…”

Section: Simulation Resultsmentioning

confidence: 99%

Day‐ahead demand side management using symbiotic organisms search algorithm

Niharika

Mukherjee

2018

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Section: Simulation Resultsmentioning

confidence: 99%

Day‐ahead demand side management using symbiotic organisms search algorithm

Niharika

Mukherjee

2018

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

“…The PSO‐based techniques are reviewed in the recent study. Logenthiran et al modeled a day‐ahead load scheduling technique that incorporates the PSO algorithm based on the customers' inputs and forecasted hourly electricity rates 150 . In this study, the authors considered the shiftable and non‐shiftable loads controlled by a central controller of the SG.…”

Section: Progress Of Dsm Optimization Models and Applications Of Algomentioning

confidence: 99%

Progress on the demand side management in smart grid and optimization approaches

et al. 2020

View full text Add to dashboard Cite

The integration of demand side management (DSM) with smart grid (SG) can facilitate residents' transfer into smart homes and sustainable cities by reducing the carbon emission. This manuscript reviews the recent works related to the application of DSM in SG through discussing the techniques and algorithms and their associated challenges for effective implementation. This paper also critically discusses the operation mode of DSM, the profile of energy production, storage and consumption, and finally the benefit obtained by the DSM implementation. Previous literature suggested that DSM practice reduced peak-to-average ratio, energy cost and carbon emission by approximately 10% to 65%, 5% to 50%, and 14%, respectively. The implementation of DSM in SG deals with a number of challenges such as security and privacy, tariff regulation, energy transmission, distribution, and effective utilization of energy resources. A number of international organizations have taken various measures and solutions to guarantee the security and privacy of the DSM in SG discussed. So far, a number of algorithms have been used as optimization approach to solve the DSM optimization problems; however hybrid algorithms have showed better performance than single algorithms due to their faster convergence speed. At the end, the paper presents the research gaps and future research directions.

show abstract

“…This gives to the customer the possibility to program their demand, independently, by taking as reference the instantaneous operating cost delivered by the manager of the microgrid [23]. For this purpose several strategies have already been proven to be effective in load scheduling: the use of fuzzy logic for the optimal management and loads programming in a smart house [24], and many other metaheuristics have allowed moderate consumption planning such as Genetic Algorithm, as proposed in [25], and PSO presented in [26]. Also, Artificial Neural Network algorithm based forecasting model was developed in [27].…”

Section: Microgrid Descriptionmentioning

confidence: 99%

Smart Microgrid Management: A Hybrid Optimisation Approach

Amoura¹,

Pereira²,

Lima³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: The association of distributed generators, energy storage systems and controllable loads close to the energy consumers gave place to a small-scale electrical network called microgrid. The stochastic behavior of renewable energy sources, as well as the demand variation, can lead in some cases to problems related to the reliability of the microgrid system. On the other hand, the market price of electricity from mainly non-renewable sources becomes a concern for a simple consumer due to its high costs.Method: In this work, an energy management system was developed based on an innovative optimization method, combining linear programming, based on the simplex method, with particle swarm optimisation algorithm. Two scenarios have been proposed to characterise the relation price versus gas emissions for optimal energy management. The objective of this study is to nd the optimal setpoints of generators in a smart city supplied by a microgrid in order to ensure consumer comfort, minimising the emission of greenhouse gases and ensure an appropriate operating price for all smart city consumers. Results: The simulation results have demonstrated the reliability of the optimisation approach on the energy management system in the optimal scheduling of the microgrid generators power ows, having achieved a better energy price compared to a previous study with the same data. Conclusion: The energy management system based on the proposedoptimisation approach gave an inverse correlation between economic and environmental aspects, in fact, a multi-objective optimisation approach is performed as a continuation of the work proposed in this paper.

show abstract

Particle swarm optimization for demand side management in smart grid

Cited by 34 publications

References 15 publications

Day‐ahead demand side management using symbiotic organisms search algorithm

Day‐ahead demand side management using symbiotic organisms search algorithm

Progress on the demand side management in smart grid and optimization approaches

Smart Microgrid Management: A Hybrid Optimisation Approach

Contact Info

Product

Resources

About