An accelerated gradient-based optimization development for multi-reservoir hydropower systems optimization

Yin, Fang; Ahmadianfar, Iman; Samadi-Koucheksaraee, Arvin; Azarsa, Reza; Scholz, Miklas; Yaseen, Zaher Mundher

doi:10.1016/j.egyr.2021.11.010

Cited by 27 publications

(6 citation statements)

References 69 publications

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“…Renewable energy [80] GBO GBO Applying GBO to identify the optimal parameter of convolutional neural network COVID-19 [101] space containing various sets of vectors. In other words, MOGBO aims to decrease the distance to keep the convergence and increase the chance of selecting the best solution.…”

Section: Multi-objectivementioning

confidence: 99%

“…So, it can provide the resources allocation of the power system. Therefore, Fang et al [80] used accelerated version of GBO (AGBO) includes the sequential quadratic programming (SQP) technique to deal with complicated multi-reservoir hydropower system. AGBO was employed on a 10-reservoir hydropower system then on 23 benchmark functions to evaluate its performance.…”

Section: Energy and Power Flowmentioning

confidence: 99%

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Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Daoud

Shehab

Al-Mimi

et al. 2022

Arch Computat Methods Eng

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This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

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Section: Multi-objectivementioning

confidence: 99%

Section: Energy and Power Flowmentioning

confidence: 99%

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Daoud

Shehab

Al-Mimi

et al. 2022

Arch Computat Methods Eng

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“…Furthermore, the economic value added by the power generation of three reservoirs was about 401.7 billion VND. Fang et al 28 developed an accelerated version of gradient-based optimization (AGBO) to solve a complex multi-reservoir hydropower system. The optimal results showed that the method was superior to the other advanced optimization algorithms for maximizing the load demands in the hydropower system.…”

Section: Introductionmentioning

confidence: 99%

Applying the new multi-objective algorithms for the operation of a multi-reservoir system in hydropower plants

Samare Hashemi,

Robati,

Kazerooni

2024

Sci Rep

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The optimal operation of the multi-purpose reservoir system is a difficult, and, sometimes, non-linear problem in multi-objective optimization. By simulating biological behavior, meta-heuristic algorithms scan the decision space and can offer a set of points as a group of solutions to a problem. Because it is essential to simultaneously optimize several competing objectives and consider relevant constraints as the main problem in many optimization problems, researchers have improved their ability to solve multi-objective problems by developing complementary multi-objective algorithms. Because the AHA algorithm is new, its multi-objective version, MOAHA (multi-objective artificial hummingbird algorithm), was used in this study and compared with two novel multi-objective algorithms, MOMSA and MOMGA. Schaffer and MMF1 were used as two standard multi-objective benchmark functions to gauge the effectiveness of the proposed method. Then, for 180 months, the best way to operate the reservoir system of the Karun River basin, which includes Karun 4, Karun 3, Karun 1, Masjed-e-Soleyman, and Gotvand Olia dams to generate hydropower energy, supply downstream demands (drinking, agriculture, industry, environmental), and control flooding was examined from September 2000 to August 2015. Four performance appraisal criteria (GD, S, Δ, and MS) and four evaluation indices (reliability, resiliency, vulnerability, and sustainability) were used in Karun's multi-objective multi-reservoir problem to evaluate the performance of the multi-objective algorithm. All three algorithms demonstrated strong capability in criterion problems by using multi-objective algorithms’ criteria and performance indicators. The large-scale (1800 dimensions) of the multi-objective operation of the Karun Basin reservoir system was another problem. With a minimum of 1441.71 objectives and an average annual hydropower energy manufacturing of 17,166.47 GW, the MOAHA algorithm demonstrated considerable ability compared to the other two. The final results demonstrated the MOAHA algorithm’s excellent performance, particularly in difficult and significant problems such as multi-reservoir systems' optimal operation under various objectives.

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“…Several works have been conducted for reservoir and hydropower operation in Vietnam [18,19] and in the RRB [20] based on streamflow synthesis and reservoir regulation programs to approximate the river flow, and based on general purpose software, the HEC-ResSim, to simulate the reservoir operation. Among the suggested methods were modeling the optimal operation of hydropower systems to maximize the entire energy production of reservoir systems by using dynamic programming algorithms [21] or using a genetic algorithm [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Operating Multi-Purpose Reservoirs in the Red River Basin: Hydropower Benefit Optimization in Conditions Ensuring Enough Water for Downstream Irrigation

Nguyen

2023

Sustainability

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Operational management of multiple reservoirs and hydropower plants in the Red River Basin (RRB) in Vietnam was investigated for optimal benefit of hydropower generation and to ensure the water supply for agricultural and social–economic development downstream during the dry season. This research will investigate the operation of three hydropower reservoirs, including Hoa Binh, Thac Ba, and Tuyen Quang reservoirs. Those reservoirs are managed under the operating Decision No. 740 of the Prime Minister in 2019, which stipulates the dry season and water - enhanced discharge period to supply water for agriculture and ensures that the minimum water level in Hanoi is above 2.2 m, which may lead to lack of water for hydropower plants. To do this, I used the optimization approach to determine the optimal water discharge scenario in these three reservoirs during the enhanced discharge period (irrigation water supply). Based on the optimal scenario, I calculated the amount of saved water which is then compared with the standard discharge scenario under Decision No. 740. This study also found that there is an increasing economic benefit from saved water and hydropower generation during peak hours (after the winter–spring crop). Addtionally, the results demonstrated that the economic value added by the power generation of three reservoirs is about 401.7 billion VND. If compared with using thermal power plants, it saves 858.0 billion VND.

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An accelerated gradient-based optimization development for multi-reservoir hydropower systems optimization

Cited by 27 publications

References 69 publications

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Applying the new multi-objective algorithms for the operation of a multi-reservoir system in hydropower plants

Operating Multi-Purpose Reservoirs in the Red River Basin: Hydropower Benefit Optimization in Conditions Ensuring Enough Water for Downstream Irrigation

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