Optimal Design of Water Tank Size for Power System Flexibility and Water Quality

Yao, Yiming; Li, Chunyan; Xie, Kaigui; Tai, Heng–Ming; Hu, Bo; Niu, Tao

doi:10.1109/tpwrs.2021.3081724

Cited by 12 publications

(3 citation statements)

References 28 publications

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“…Both systems act as a fluid flow to complete the entire interaction of the sorting section. The input system is technically influenced by a constant water flow rate, signal generator, and the maximum inflow of the tank [11] [4]. The water flow is channeled using a pump from the storage tank.…”

Section: Desigh Researchmentioning

confidence: 99%

Stability of Water Flow in Tanks Using Particle Swarm Optimization (PSO) Method

Siswanto,

Ali,

Agil Haikal

et al. 2024

E3S Web Conf.

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The stability of the speed and pressure of the water flow is determined by the height and volume of the water. The speed of water flow in the actuator is determined by the use of this flow sensor system. A good tank-based water flow control model must be developed. At a certain point, the actuator stabilizes the water production rate per minute. Therefore, it is necessary to develop automatic and precise control techniques. Many Artificial Intelligence (AI) methods are used in system optimization. Among them are the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). In this research, conventional methods, Auto tuning methods, and PSO methods are used. The PSO method produces better optimization compared to the previous method. The water flow stability indicator in this simulation is shown by the size of the overshot and undershot values for each method. The best water level control simulation results are the PSO method with the smallest overshot value of 0.0333 pu, the smallest undershot value of 0.0347 pu, and the output flow results have the smallest overshot value of 0.0013 pu, the smallest undershot value of 0.0011 pu.

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Section: Desigh Researchmentioning

confidence: 99%

Stability of Water Flow in Tanks Using Particle Swarm Optimization (PSO) Method

Siswanto,

Ali,

Agil Haikal

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Recently, various flexibility resources in the power systems have been studied, such as combined heat-and-electricity supply system, electricity storage devices, heat storage devices, electric boilers [12], water distribution system [13], hybrid AC/DC system [14], unified power flow controllers [15], industrial steam system [16], and multi-energy system [17,18].…”

Section: Flexibility Resource Modellingmentioning

confidence: 99%

“…The capacity of the electrolyzer is given by 0 ≥ P ELZ n,t ≥ −P ELZ max n (13) where P ELZ n,t is the charging capacity of the electrolyzer, and P ELZ max n is the maximum charging capacity.…”

Section: Hydrogen Refuelling Station Modelmentioning

confidence: 99%

Day‐ahead flexibility enhancement via joint optimization for new energy vehicle fleets and electric vehicle charging/hydrogen refuelling stations

Qin

Guo

et al. 2022

IET Renewable Power Gen

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Power systems are striving for the decarbonization goal for long-term sustainability. As a result, the renewable energy sources (RES) have been playing an increasingly important role in the generation portfolio. Power system flexibility maintaining the power balance under uncertain and variable RES generation has become a major concern during the energy transition. This paper proposes to apply new energy vehicles (NEV) including electric vehicles (EVs) and fuel cell vehicles (FCVs) as day-ahead flexibility resources to make revenue by providing comprehensive capacity/energy flexibility in the ancillary service market. First, the detailed joint optimized operational strategies of the NEV fleet and the EV charging/hydrogen refuelling stations are formulated. Second, the design and service clearing methodology of the ancillary service market for flexibility enhancement is introduced. A game-theoretic model is applied to determine the equilibrium price for both the demand and suppliers (namely NEV fleets) of operational flexibility. Third, case studies using IEEE benchmark systems are provided to validate the proposed methods. The results demonstrate that the joint optimization of NEV fleets, charging stations, and hydrogen refuelling stations can improve the power system operational flexibility, reduce the charging expense of NEV owners, and reduce the carbon emission by utilizing the green hydrogen.

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Optimal pump scheduling in multi-phase distribution networks using Benders decomposition

Ayyagari

Gatsis

2022

Electric Power Systems Research

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Optimal Design of Water Tank Size for Power System Flexibility and Water Quality

Cited by 12 publications

References 28 publications

Stability of Water Flow in Tanks Using Particle Swarm Optimization (PSO) Method

Stability of Water Flow in Tanks Using Particle Swarm Optimization (PSO) Method

Day‐ahead flexibility enhancement via joint optimization for new energy vehicle fleets and electric vehicle charging/hydrogen refuelling stations

Optimal pump scheduling in multi-phase distribution networks using Benders decomposition

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