Optimization of BESS placement, technology selection, and operation in microgrids for minimizing energy losses and CO2 emissions: A hybrid approach

Cortés-Caicedo, Brandon; Grisales-Noreña, Luis Fernando; Montoya, Oscar Danilo; Bolaños, Rubén Iván

doi:10.1016/j.est.2023.108975

Cited by 12 publications

(3 citation statements)

References 39 publications

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“…The present scenario of the global energy landscape is immersed in a complex dilemma, where the need to mitigate the impacts of global warming and climate change, evidenced by the increasing effects observed in recent years, contrasts with the dependence on approximately 80% of fossil fuels for the world's primary energy and projected growth of this dependence by 2.3% annually from 2015 to 2040. [ 1,2 ] Faced with this situation, integrating renewable energy sources into the global energy matrix stands out as a potential solution to alleviate these adverse effects. Unlike fossil fuels, renewable sources do not produce emissions and can help reduce energy losses through efficiency, providing a more consistent energy source.…”

Section: Introductionmentioning

confidence: 99%

“…BESS allows excess energy generated during periods of high production from renewable sources to be stored and discharged during periods of high demand, mitigating energy losses, and enhancing reliability. [ 2 ] Additionally, proper management of BESS can assist in reducing dependence on fossil‐fuel‐based generation, thereby lowering CO 2 emissions. The system exhibits versatility in terms of storage, being used in various applications, both in portable systems and permanent applications, such as backup power and renewable energy storage in isolated areas.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Tool for Sizing and Technical–Financial Analysis of Energy‐Storage Systems Using Batteries

Monteiro,

Costa,

Vasconcelos

et al. 2024

Energy Tech

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In this article, an innovative approach is presented to the sizing and technical–economic analysis of battery energy‐storage systems (BESS) designed for customers in the free energy market in Brazil. The tool enables the integration of photovoltaic (PV) energy sources and includes a comparison between the BESS + PV system and diesel generators. Integrating the computational capabilities of Microsoft Excel in the backend and the intuitive interface of PowerApps in the front end, the sizing process is based on analyzing historical energy invoices spanning at least 12 months or loading data with a 1 h measurement interval. The data discretization over the 8760 h of the year, considering the consumer's load profile, is facilitated by the PowerApps interface, providing a comprehensive visualization of the technical‐economic sizing results for BESS. A case study is conducted for a commercial load with a specific tariff for free energy market customers, revealing viable solutions for BESS compared to diesel alternatives. In this approach, it is aimed to simplify the analysis and decision‐making process, offering a valuable tool for power system engineers to evaluate sustainable and economically viable solutions in the Brazilian free energy market.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a Tool for Sizing and Technical–Financial Analysis of Energy‐Storage Systems Using Batteries

Monteiro,

Costa,

Vasconcelos

et al. 2024

Energy Tech

View full text Add to dashboard Cite

show abstract

“…The success of each optimization approach will be thoroughly assessed by examining comparative metrics, such as total system costs, renewable penetration percentages, pollution reductions, and resilience indices, in order to derive relevant conclusions. [16][17][18][19][20] Anticipated Contributions: This study is expected to provide invaluable insights to both the academic and industrial sectors. The study endeavors to enlighten decision-makers, energy planners, and researchers on the most appropriate methodologies for various microgrid configurations and goals by conducting a comprehensive analysis of optimization strategies inside the realm of hybrid energy microgrids.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques

Nemova,

Rao,

Singh

et al. 2024

E3S Web Conf.

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This study examines the relative effectiveness of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Linear Programming (LP) in optimizing hybrid energy microgrids. Drawing upon empirical data derived from the study, the research explores many facets, including economic efficacy, environmental viability, and microgrid robustness. The use of GA showcases a significant 10% decrease in overall system expenses, highlighting its efficacy in augmenting economic feasibility. PSO diligently tracks, attaining an 8% decrease, while SA and LP make significant contributions but provide somewhat lesser cost reductions at 7% and 6%, correspondingly. Within the domain of renewable energy integration, GA and PSO have emerged as frontrunners, with remarkable advancements of 12% and 10%, respectively. SA and LP provide commendable contributions, demonstrating their effectiveness in optimizing the usage of renewable energy sources inside the microgrid, as seen by their respective increases of 8% and 7%. The environmental factor, as quantified by the decrease of carbon emissions, highlights the commendable efficacy of GA and PSO, resulting in significant reductions of 15% and 12% respectively. SA and LP provide praiseworthy environmental efforts, achieving reductions of 10% and 8% respectively. The resilience index highlights the strength of GA and PSO in assessing the resilience of the microgrid, with GA showing an increase of 0.05 and PSO showing an increase of 0.04. SA and LP make a significant contribution, with increments of 0.03 and 0.02, underscoring the potential of evolutionary and swarm-based methodologies to bolster the microgrid’s resilience against disturbances. Scenario analysis effectively brings unpredictability into the operational environment of the microgrid, continually showcasing the remarkable flexibility of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) over a wide range of situations. SA and LP demonstrate consistent efficacy but with somewhat reduced flexibility. Statistical evaluations provide compelling evidence confirming the exceptional efficacy of GA and PSO in improving microgrid metrics. Ultimately, this research provides valuable perspectives on the intricate trade-offs between various optimization techniques, empowering decision-makers to choose strategies that align with specific microgrid objectives. Moreover, it contributes to the wider discussion on resilient, sustainable, and economically feasible energy infrastructures.

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A multi-objective PSO for DC microgrids: Efficient battery management to minimize energy losses and operating costs

Cortés-Caicedo,

Ocampo-Toro,

Bolaños

et al. 2024

Journal of Energy Storage

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Optimization of BESS placement, technology selection, and operation in microgrids for minimizing energy losses and CO2 emissions: A hybrid approach

Cited by 12 publications

References 39 publications

Development of a Tool for Sizing and Technical–Financial Analysis of Energy‐Storage Systems Using Batteries

Development of a Tool for Sizing and Technical–Financial Analysis of Energy‐Storage Systems Using Batteries

Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques

A multi-objective PSO for DC microgrids: Efficient battery management to minimize energy losses and operating costs

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