Exploiting heuristic techniques for efficient energy management system in smart grid

Rahim, Muhammad Hassan; Javaid, Nadeem; Shafiq, Sundas; Iqbal, Muhammad; Khalid, Muhammad Shoaib; Memon, Ubed Ullah

doi:10.1109/iwcmc.2018.8450317

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, due to the uncertain nature of EV owners, it is difficult for them to follow the fixed TOU system. Besides, due to the fixed prices, the EVs herd toward the off-peak period, which results in overloading the grid [8]. Therefore, compared to the TOU system, the real-time prices (i.e., often update every 15 min) are more economical for grid operators and EV owners [9].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles

et al. 2022

View full text Add to dashboard Cite

The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV’s information, such as the battery size, current state-of-charge, and arrival & departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery’s desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems.

show abstract

Section: Introductionmentioning

confidence: 99%

A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This study is the extension of [8], a HEM scheme is presented which is a hybrid of: ACS and EHO algorithms and named, hybrid elephant adaptive cuckoo (HEAC). Moreover, we also calculate the consumer satisfaction (comfort) level.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Game Theoretic Based Coordination Among Appliances in Smart Homes for Efficient Energy Utilization

et al. 2018

Self Cite

View full text Add to dashboard Cite

In this paper, a demand side management (DSM) scheme is used to make energy utilization more efficient. The DSM scheme encourages the consumer to change energy utilization patterns which benefit the utility. In return, the consumer gets some incentives from the utility. The objectives of the proposed DSM system include: electricity bill reduction, reduced peak to average ratio (PAR), and maximization of consumer comfort. In the proposed system, the electrical devices are scheduled by using elephant herding optimization (EHO) and adaptive cuckoo search (ACS) algorithms. Moreover, a new algorithm called hybrid elephant adaptive cuckoo (HEAC) is proposed which uses the features of both former algorithms. A comparison of these algorithms is also presented in terms of three performance parameters. The HEAC shows better performance as compared to EHO and ACS which is evident from the simulation results. Different electricity tariffs are introduced by the utility to provide incentives to the consumers. A regional based time of use (ToU) tariff is used to make the system effective for different types of regions. Moreover, this enables the consumers to act according to the regional environment. The coordination can play a very important role in cost reduction as well as in consumer comfort maximization. The coordination is incorporated among the electrical devices by using cooperative game theory (GT) and dynamic programming (DP). Extensive simulations are performed to show the effectiveness of the proposed scheme in terms of electricity utilization cost, PAR reduction, and consumer comfort maximization.

show abstract

Electricity Load Forecasting in Smart Grids Using Support Vector Machine

Ayub

Javaid

Mujeeb

et al. 2019

Advanced Information Networking and Applications

View full text Add to dashboard Cite

Exploiting heuristic techniques for efficient energy management system in smart grid

Cited by 3 publications

References 10 publications

A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles

A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles

Exploiting Game Theoretic Based Coordination Among Appliances in Smart Homes for Efficient Energy Utilization

Electricity Load Forecasting in Smart Grids Using Support Vector Machine

Contact Info

Product

Resources

About