Real-time energy optimization and scheduling of buildings integrated with renewable microgrid

Applied Energy, Journal Year: 2023, Volume and Issue: 335, P. 120640 - 120640

Published: Feb. 3, 2023

Language: Английский

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Research progress analysis of sustainable smart grid based on CiteSpace

Energy Strategy Reviews, Journal Year: 2023, Volume and Issue: 48, P. 101111 - 101111

Published: June 19, 2023

The smart grid (SG) research has continued unabated; however, some challenges, such as stability and security, cause us to focus on sustainable SG, which benefits society. Sustainable SG is complicated covers many topics that should be summarized systematically. This paper uses CiteSpace analyze SG's progress comprehensively understand this domain's current evolvement in recent years; then constructs a knowledge framework provides future characteristics. results show hot topic with increasing publications frequent regional collaboration; domain also integrates different disciplines various topics. Furthermore, integrating information technology social science may an emerging trend the future. study contributes because comprehensive demonstration of from perspectives dynamic view, makes scholars easily grasp essential information, potential characteristics provide more explicit technologies enablers for continue efficiently.

Language: Английский

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Forecasting energy consumption demand of customers in smart grid using Temporal Fusion Transformer (TFT)

Results in Engineering, Journal Year: 2023, Volume and Issue: 17, P. 100888 - 100888

Published: Jan. 20, 2023

Energy consumption prediction has always remained a concern for researchers because of the rapid growth human population and customers joining smart grids network home facilities. Recently, spread COVID-19 dramatically increased energy in residential sector. Hence, it is essential to produce per customers' requirements, improve economic efficiency, reduce production costs. The previously published papers literature have considered overall prediction, making difficult companies future demand. Using proposed study, can accurately their needs by forecasting demands. Scientists are trying minimize applying different optimization techniques; hence this study daily, weekly, monthly model using Temporal Fusion Transformer (TFT). This relies on TFT forecasting, which considers both primary valuable data sources batch training techniques. model's performance been related Long Short-Term Memory (LSTM), LSTM interpretable, Convolutional Network (TCN) models. better than other algorithms, with mean squared error (MSE), root (RMSE), absolute (MAE) 4.09, 2.02, 1.50. Further, symmetric percentage (sMAPE) LSTM, TCN, at 29.78%, 31.10%, 36.42%, 26.46%, respectively. sMAPE proved that performed deep learning

Language: Английский

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A Strategy for Multi-Objective Energy Optimization in Smart Grid Considering Renewable Energy and Batteries Energy Storage System

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 33872 - 33886

Published: Jan. 1, 2023

Multi-objective energy optimization is pivotal for reliable and secure power system operation. However, multi-objective challenging due to interdependent conflicting objectives. Thus, a model needed cater On this note, developed, where non-dominated genetic sorting algorithm employed optimize objectives pollution emission, operation cost, loss of load expectation (LOLE) considering renewable sources (RES). RES, like wind solar, are intermittent uncertain, which modelled using beta probability density function (PDF). The developed method's effectiveness applicability analyzed by implementing it on the 30-bus system, results compared two cases. Findings reveal that minimizes LOLE 59%, 7%, 2.67%, respectively, existing models.

Language: Английский

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Optimum sizing of stand-alone microgrids: Wind turbine, solar photovoltaic, and energy storage system

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 73, P. 108611 - 108611

Published: Sept. 18, 2023

Language: Английский

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Adaptive optimal energy management in multi-distributed energy resources by using improved slime mould algorithm with considering demand side management

e-Prime - Advances in Electrical Engineering Electronics and Energy, Journal Year: 2023, Volume and Issue: 3, P. 100108 - 100108

Published: March 1, 2023

In this article, a stochastic programming model is developed to solve multi-renewable sources-based energy management with the help of multi-objective improved slime mould algorithm (MOISMA). As result, microgrid's performance expected improve technique. However, such fast-acting methodology reduces operating generation costs and emissions by using multiple renewable sources. Firstly, Hong's (2 m + 1) point estimate method used generate as well control uncertainty wind speed solar irradiance patterns. Secondly, MOISMA-based-energy study outperforms better when compared genetic algorithms (MOGA) particle swarm optimization (MOPSO). All these performances are carried out at time use (ToU) based on demand response (DR) load management. Using techniques, which can reduce cost generating power increase resource use, demand-side be performed within 24 h data for analysis purposes. So, in research application management, we get minimum environmental emission 12.62% 7.43%, energy, similarly 31.53% 2.51% systems DR Thus, overall, it has been validated that novel optimization-based system cost, usage, peak shifting, emissions.

Language: Английский

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Two stage robust economic dispatching of microgrid considering uncertainty of wind, solar and electricity load along with carbon emission predicted by neural network model

Energy, Journal Year: 2024, Volume and Issue: 300, P. 131571 - 131571

Published: May 7, 2024

Language: Английский

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High gain differentiator based neuro-adaptive arbitrary order sliding mode control design for MPE of standalone wind power system

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(1), P. e0293878 - e0293878

Published: Jan. 18, 2024

In this paper, we introduce a novel Maximum Power Point Tracking (MPPT) controller for standalone Wind Energy Conversion Systems (WECS) with Permanent Magnet Synchronous Generators (PMSG). The primary novelty of our lies in its implementation an Arbitrary Order Sliding Mode Control (AOSMC) to effectively overcome the challenges caused by measurement noise system. considered model is transformed into control-convenient input-output form. Additionally, enhance control methodology simultaneously incorporating Feedforward Neural Networks (FFNN) and high-gain differentiator (HGO), further improving system performance. FFNN estimates critical nonlinear functions, such as drift term input channel, whereas HGO higher derivatives outputs, which are subsequently fed back inputs. reduces sensor sensitivity, rendering law more practical. To validate proposed technique, conduct comprehensive simulation experiments compared against established literature results MATLAB environment, confirming exceptional effectiveness maximizing power extraction wind energy applications.

Language: Английский

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Development of smart grid for the power sector in India

Cleaner Energy Systems, Journal Year: 2022, Volume and Issue: 2, P. 100011 - 100011

Published: June 18, 2022

Technological advancements in the energy industry have expedited growth of smart grid, necessitating multidisciplinary study power systems and management. India, world's third-largest producer consumer electricity, has various power-related issues, including significant transmission distribution losses, electricity theft, environmental concerns. As a result these is exploring new technologies to improve grid's efficiency, sustainability, security. This tries uncover elements that might be facilitators for India's grid development. To analyse components questionnaire surveys, interviews, workshops were conducted with eminent academicians, researchers, experts working sector. An integrated approach soft system methodology (SSM), thematic analysis fuzzy cognitive mapping been used this better comprehend intricate interactions between stakeholders. research findings reveal technical development along acceptance crucial factor efficacious implementation grid.

Language: Английский

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Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid

Energies, Journal Year: 2022, Volume and Issue: 15(19), P. 6900 - 6900

Published: Sept. 21, 2022

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental economic benefits. In this paper, a new DSM is proposed for the day-ahead scheduling problem SGs with high penetration of wind optimize tri-objective SGs: operating cost pollution emission minimization, minimization associated load curtailment, deviation between turbine (WT) output power demand. Due climatic conditions, nature source uncertain, its prediction challenging. Monte Carlo simulation (MCS) was used predict before integrating SG. The study consists real-time pricing incentives, which hybrid response program (H-DRP). To solve SG problem, an optimization technique, multi-objective genetic algorithm (MOGA), proposed, results non-dominated solutions feasible search area. Besides, decision-making mechanism (DMM) applied find optimal solution amongst model successfully optimizes objective functions. For simulation, MATLAB 2021a used. validation model, it tested on using multiple balancing constraints balance at consumer end.

Language: Английский

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