Flexible energy management of storage-based renewable energy hubs in the electricity and heating networks according to point estimate method

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 1627 - 1641

Published: Jan. 21, 2024

Hubs contain sources and storages that can transfer store energy. It is predicted the energy management of hubs enhances network's economic technical status. Therefore, paper presents flexible linked with electrical thermal grids. In hub, wind photovoltaic systems generate electricity, bio-waste units are utilized to concurrently produce electricity heat power. Compressed air storage control flexibility hubs. The objective function minimizes expected cost injected by upstream grid. Constraints network optimal power flow equations, operation model, limit hub. design has uncertain parameters caused load, price, unpredictable renewable point estimation technique helps model these variables overcome high volume problem accurately evaluate flexibility. Contributions include evaluating performance compressed-air equipped combined technology in considering both sectors due power, using method for modeling uncertainties hubs' reduce computation time provide accurate calculation scheme tested a standard case, where results show ability enhance promote status heating grids about 36.5% 36%− 43%, respectively, comparison method. Storages extract 100% conditions succeeded providing proper storage. Furthermore, presence equipment beside wind, solar, form have led more suitable economical operational state networks compared studies.

Language: Английский

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Simultaneous optimal site selection and sizing of a grid-independent hybrid wind/hydrogen system using a hybrid optimization method based on ELECTRE: A case study in Iran

International Journal of Hydrogen Energy, Journal Year: 2023, Volume and Issue: 55, P. 970 - 983

Published: Nov. 30, 2023

Language: Английский

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Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

Sustainability, Journal Year: 2023, Volume and Issue: 15(13), P. 9970 - 9970

Published: June 22, 2023

Multi-objective energy optimization is indispensable for balancing and reliable operation of smart power grid (SPG). Nonetheless, multi-objective challenging due to uncertainty multi-conflicting parameters at both the generation demand sides. Thus, opting a model that can solve load distributed source scheduling problems necessary. This work presents cost pollution emission with renewable in SPG. Solar photovoltaic wind are which have fluctuating uncertain nature. The proposed system uses probability density function (PDF) address generation. developed based on wind-driven (MOWDO) algorithm problem. To validate performance particle swarm (MOPSO) used as benchmark model. Findings reveal MOWDO minimizes operational by 11.91% 6.12%, respectively. findings demonstrate outperforms comparative models accomplishing desired goals.

Language: Английский

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Stochastic Economic Operation of Coupling Unit of Flexi-Renewable Virtual Power Plant and Electric Spring in the Smart Distribution Network

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 75979 - 75992

Published: Jan. 1, 2023

This paper outlines the operation of a Smart Distribution Network (SDN) that couples Virtual Power Plant and Electric Springs (CVEs). In fact, CVEs participate simultaneously in energy reactive service markets. The prime aim proposed scheme is to maximize predicted profits mentioned constraints problem formulation are AC optimal power flow equations, flexibility limits network, operating model CVEs. Further, design nonlinear formulation, which followed by linear approximation access unique response. Stochastic optimization used account for uncertainties price, load, renewable power, consumption mobile storage devices. addition, results from implementing on IEEE 69-bus SDN confirm potential enhance network's significant sources, devices, responsive load. Finally, achieved 100% through proper management CVEs, resulting an improvement indices between 15-97% compared studies. Moreover, profit modeling reduces approximately 19.6% deterministic under complete conditions.

Language: Английский

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An innovative machine learning based on feed-forward artificial neural network and equilibrium optimization for predicting solar irradiance

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 25, 2024

Abstract As is known, having a reliable analysis of energy sources an important task toward sustainable development. Solar one the most advantageous types renewable energy. Compared to fossil fuels, it cleaner, freely available, and can be directly exploited for electricity. Therefore, this study concerned with suggesting novel hybrid models improving forecast Irradiance (I S ). First, predictive model, namely Feed-Forward Artificial Neural Network (FFANN) forms non-linear contribution between I dominant meteorological temporal parameters (including humidity, temperature, pressure, cloud coverage, speed direction wind, month, day, hour). Then, framework optimized using several metaheuristic algorithms create predicting . According accuracy assessments, attained satisfying training FFANN by 80% data. Moreover, applying trained remaining 20% proved their high proficiency in forecasting unseen environmental circumstances. A comparison among optimizers revealed that Equilibrium Optimization (EO) could achieve higher than Wind-Driven (WDO), Optics Inspired (OIO), Social Spider Algorithm (SOSA). In another phase study, Principal Component Analysis (PCA) applied identify contributive factors. The PCA results used optimize problem dimension, as well suggest effective real-world measures solar production. Lastly, EO-based solution yielded form explicit formula more convenient estimation

Language: Английский

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