Research on Optimizing Operation of Low Carbon Economy in Comprehensive Energy System Considering Demand Response and Stepped Carbon Trading DOI
Huan Wang, Na Zheng, Jun Liu

et al.

Published: Nov. 24, 2023

The Integrated Energy System (IES) can utilize the complementary utilization of multiple energy sources to achieve industry energy-saving and emission reduction goals. development demand response carbon trading mechanisms has brought new opportunities challenges low-carbon operation IES. This paper introduces a mechanism constructs low economic optimization model study impact electricity gas on promoting IES emissions reduction, improving system efficiency. By comparing analyzing different application scenarios, it is verified that this method effectively reduce investment operating costs park, emissions. changed consumption plan original load, making load distribution more in line with power characteristics. As proportion (DR) increasing, cost shows gradually decreasing trend. When exceeds 25%, slows down.

Language: Английский

Emerging Trends and Approaches for Designing Net-Zero Low-Carbon Integrated Energy Networks: A Review of Current Practices DOI
Saddam Aziz, Ijaz Ahmed, K. A. Khan

et al.

Arabian Journal for Science and Engineering, Journal Year: 2023, Volume and Issue: 49(5), P. 6163 - 6185

Published: Oct. 11, 2023

Language: Английский

Citations

44
Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration DOI
Ahmad K. ALAhmad, Renuga Verayiah, Hussain Shareef

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 90, P. 111868 - 111868

Published: May 5, 2024

Language: Английский

Citations

17
Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables DOI
Ahmad K. ALAhmad, Renuga Verayiah, Agileswari K. Ramasamy

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 82, P. 110615 - 110615

Published: Jan. 25, 2024

Language: Английский

Citations

16
Transactive Energy Management for Efficient Scheduling and Storage Utilization in a Grid-connected Renewable Energy-based Microgrid DOI Creative Commons
Peter Anuoluwapo Gbadega, Olufunke Abolaji Balogun

e-Prime - Advances in Electrical Engineering Electronics and Energy, Journal Year: 2025, Volume and Issue: unknown, P. 100914 - 100914

Published: Jan. 1, 2025

Language: Английский

Citations

2
Economic-environmental dispatch of isolated microgrids based on dynamic classification sparrow search algorithm DOI
Guodong Xie, Mengjian Zhang, Deguang Wang

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 271, P. 126677 - 126677

Published: Jan. 31, 2025

Language: Английский

Citations

2
State-of-the-art review on energy sharing and trading of resilient multi microgrids DOI Creative Commons
Abhishek Kumar, Arvind R. Singh, L. Phani Raghav

et al.

iScience, Journal Year: 2024, Volume and Issue: 27(4), P. 109549 - 109549

Published: March 22, 2024

Independently run single microgrids (MGs) encounter difficulties with inadequate self-consumption of local renewable energy and frequent power exchange the grid. Combining numerous MGs to form a multi-microgrid (MMG) is viable approach enhance smart distribution networks' operational financial performance. However, correlation coordination intermittent generation within each MG network pose many techno-economic challenges for sharing trading. This review offers comprehensive analysis these framework MMG operations. It examines state-of-the-art methodologies optimizing multi-energy dispatch scrutinizes contemporary strategies markets that contribute resilience systems. The discourse extends burgeoning role blockchain technology in revolutionizing decentralized market frameworks intricacies reliable cost-effective distribution. Overall, this study provides ample inspiration theoretical practical research new entrants experts alike develop concepts markets, scheduling novel operating models future resilient networked systems/MMGs.

Language: Английский

Citations

12
Optimal sizing and placement of battery energy storage system for maximum variable renewable energy penetration considering demand response flexibility: A case in Lombok power system, Indonesia DOI Creative Commons
Chico Hermanu Brillianto Apribowo, Sasongko Pramono Hadi, F. Danang Wijaya

et al.

Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: 23, P. 100620 - 100620

Published: May 10, 2024

Indonesia is a tropical climate country with considerable renewable electrical energy source prospects, including photovoltaic (PV) and wind energies. Nevertheless, several variable sources (VREs) have exhibited uncertain attributes substantial reliance on natural conditions, leading to unstable load-related power supply risks. Hence, integrating battery storage systems (BESSs) VRE generators dependable approach bolster generator applications large-scale grid while providing load demand flexibility. This study determined adequate sizing placement of the BESS achieve maximum penetration considering response Key indicators, technical minimum system ramp capacity, were identified thermal generators. also combined unit commitment procedure direct current optimal flow (DC-OPF) as novel determine level. An optimization problem model concerning mixed integer linear programming (MILP) was subsequently employed in this using CPLEX solver general algebraic modeling (GAMS). The based IEEE RTS-24 modified real-life case Lombok Indonesia. Results from simulated highlighted that could lower costs by 37.66%, 33.63%, 22.26% compared conditions during weekday, weekend, lowest day scenarios, respectively. penetrations higher than 83%, 51%, 39%

Language: Английский

Citations

11
Smart EV Charging With Context-Awareness: Enhancing Resource Utilization via Deep Reinforcement Learning DOI Creative Commons
Muddsair Sharif, Hüseyin Şeker

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 7009 - 7027

Published: Jan. 1, 2024

The widespread adoption of electric vehicles (EVs) has introduced new challenges for stakeholders ranging from grid operators to EV owners. A critical challenge is develop an effective and economical strategy managing charging while considering the diverse objectives all involved parties. In this study, we propose a context-aware smart system that leverages deep reinforcement learning (DRL) accommodate unique requirements goals participants. Our DRL-based approach dynamically adapts changing contextual factors such as time day, location, weather optimize decisions in real time. By striking balance between cost, load reduction, fleet operator preferences, station energy efficiency, offers owners seamless cost-efficient experience. Through simulations, evaluate efficiency our proposed Deep Q-Network (DQN) by comparing it with other distinct DRL methods: Proximal Policy Optimization (PPO), synchronous Advantage Actor-Critic (A3C), Deterministic Gradient (DDPG). Notably, methodology, DQN, demonstrated superior computational performance compared others. results reveal achieves remarkable, approximately 18% enhancement traditional methods. Moreover, demonstrates about 12% increase cost-effectiveness owners, effectively reducing strain 20% curbing CO2 emissions 10% due utilization natural sources. system's success lies its ability facilitate sequential decision-making, decipher intricate data patterns, adapt dynamic contexts. Consequently, not only meets optimization maintainers but also exemplifies promising stride toward sustainable balanced management.

Language: Английский

Citations

8
Residential Prosumer Energy Management System with Renewable Integration Considering Multi-Energy Storage and Demand Response DOI Open Access
Asjad Ali, Abdullah Aftab, Muhammad Nadeem Akram

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(5), P. 2156 - 2156

Published: March 5, 2024

Rising energy demands, economic challenges, and the urgent need to address climate change have led emergence of a market wherein consumers can both purchase sell electricity grid. This leverages diverse sources storage systems achieve significant cost savings for while providing critical grid support utilities. In this study, an management system has been employed tackle optimization problem associated with various sources. approach relies on mixed-integer linear programming (MILP) optimize utilization adhering constraints, yielding feasible solution. model is applied real-world consumption data forecasts most cost-effective day-ahead plans different types loads engaged in demand response. Furthermore, time-based charging discharging strategies electric vehicles are considered, conducting comprehensive analysis costs across devices. Our findings demonstrate that implementing lead 18.26% reduction operational when using lithium batteries remarkable 14.88% lead–acid batteries, particularly integrating solar power EV into system, GHG reduced by 36,018 grams/day load 25 kW one particular scenario. However, reveals wind not economically viable due its comparatively higher costs.

Language: Английский

Citations

6
Optimizing Microgrid Planning for Renewable Integration in Power Systems: A Comprehensive Review DOI Open Access

Klever L. Quizhpe,

Paúl Arévalo, Danny Ochoa-Correa

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(18), P. 3620 - 3620

Published: Sept. 12, 2024

The increasing demand for reliable and sustainable electricity has driven the development of microgrids (MGs) as a solution decentralized energy distribution. This study reviews advancements in MG planning optimization renewable integration, using Preferred Reporting Items Systematic Reviews Meta-Analyses methodology to analyze peer-reviewed articles from 2013 2024. key findings highlight integration emerging technologies, like artificial intelligence, Internet Things, advanced storage systems, which enhance efficiency, reliability, resilience. Advanced modeling simulation techniques, such stochastic genetic algorithms, are crucial managing variability. Lithium-ion redox flow battery innovations improve density, safety, recyclability. Real-time simulations, hardware-in-the-loop testing, dynamic power electronic converters boost operational efficiency stability. AI machine learning optimize real-time operations, enhancing predictive analysis fault tolerance. Despite these advancements, challenges remain, including integrating new improving accuracy, sustainability, ensuring system resilience, conducting comprehensive economic assessments. Further research innovation needed realize MGs’ potential global sustainability fully.

Language: Английский

Citations

5