Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications

Papadimitrakis, Myron; Γιαμαρέλος, Νικόλαος; Stogiannos, Marios; Zois, Elias N.; Livanos, N.A.-I.; Alexandridis, Alex

doi:10.1016/j.rser.2021.111072

Cited by 85 publications

(43 citation statements)

References 153 publications

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“…The load model parameter estimation is performed in this step by using the particle swarm optimization technique (PSO), considering the adequate performance reported in other applications [36], [37]. Despite the previous, the approach presented in this paper is not constrained by any specific optimization algorithm; thus, other options can be considered.…”

Section: Online Stagementioning

confidence: 99%

Automatic Load Model Selection Based on Machine Learning Algorithms

2022

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Technology development and decentralized operations create changes in conventional electric systems, where load modeling has been a challenge in dynamic analysis. Consequently, accurate dynamic load models are required to ensure the quality of the studies in current systems. This paper presents an automatic strategy based on clustering, classification, and optimization algorithms, to obtain the load models in the case of several system operating conditions. The obtained load models can be helpful for the planning and operation of electric power systems. The proposed approach validation is performed using the IEEE 14-bus test system, where high performance is obtained. The average obtained cross-validation error for the load models assigned to the 13 clusters of disturbances is 5.36 ⇥ 10 3 . The cross-validation error is used as a tolerance value to determine when an online assigned load model is suitable to represent the measured disturbance. The proposed tests show the strategy's capabilities of defining the load model online, making this approach suitable for field applications.

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Section: Online Stagementioning

confidence: 99%

Automatic Load Model Selection Based on Machine Learning Algorithms

2022

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“…To address this problem, a reinforcement learning-based method was developed. Papadimitrakis et al [24] presented a structure for multi-inhabitant home energy consumption control based on mobility-aware resources. The proposed supportive game theory-based framework reduced total uncertainty for utility purposes.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

AlZaabi¹,

Shaalan²,

Ghazal³

et al. 2023

Computers, Materials &Amp; Continua

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Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer's energy requirements. In many regions, energy utilization in the housing area is 30%-40%. The growth of smart homes has raised the requirement for intelligence in applications such as asset management, energy-efficient automation, security, and healthcare monitoring to learn about residents' actions and forecast their future demands. To overcome the challenges of energy consumption optimization, in this study, we apply an energy management technique. Data fusion has recently attracted much energy efficiency in buildings, where numerous types of information are processed. The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate. The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%, which is higher than the previously published approaches.

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“…To reduce the computational complexity, they developed a modified convex relaxation method which can be used in real time. However, using such convex relaxation techniques can lead to large errors which can result in non-optimal solutions [24]. Other researchers used learning-based techniques to implement real-time smart charging scheduling algorithms.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Charging Scheduling and Optimization of Electric Buses in a Depot

Verbrugge

Rauf²,

Rasool

et al. 2022

Energies

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To improve the air quality in urban areas, diesel buses are getting replaced by battery electric buses (BEBs). This conversion introduces several challenges, such as the proper control of the charging process and a reduction in the operational costs, which can be addressed by introducing smart charging concepts for BEB fleets. Therefore, this paper proposes a real-time scheduling and optimization (RTSO) algorithm for the charging of multiple BEBs in a depot. The algorithm assigns a variable charging current to the different time slots the charging process of each BEB is divided to provide an optimal charging schedule that minimizes the charging cost, while satisfying the power limitations of the distribution network and maintaining the operation schedule of the BEBs. A genetic algorithm is used to solve the formulated cost function in real time. Several charging scenarios are tested in simulation, which show that a reduction in the charging cost up to 10% can be obtained under a dynamic electricity price scheme. Furthermore, the RTSO is implemented in a high-level charging management system, a new feature required to enable smart charging in practice, to test the developed algorithm with existing charging infrastructure. The experimental validation of the RTSO algorithm has proven the proper operation of the entire system.

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Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications

Cited by 85 publications

References 153 publications

Automatic Load Model Selection Based on Machine Learning Algorithms

Automatic Load Model Selection Based on Machine Learning Algorithms

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Real-Time Charging Scheduling and Optimization of Electric Buses in a Depot

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