Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer

Hao, Yongsheng; Chen, Zhuo; Sun, Li; Liang, Junyu; Zhu, Hongxia

doi:10.3390/su12198235

Cited by 6 publications

(4 citation statements)

References 35 publications

(49 reference statements)

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“…In this paper, the artificial bee colony algorithm (IGABC) [16,17] is used to solve the optimal load distribution problem. For the day-ahead optimization, the optimization variables selected are the power output values of the units and energy storage.…”

Section: Optimization Algorithm and Resultsmentioning

confidence: 99%

Day-ahead load optimal distribution of thermal power coupled electric energy storage frequency regulation system considering the effect of overtemperature

Zhou

Wang²,

Li³

et al. 2023

E3S Web Conf.

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Coupling energy storage devices on the generation side can significantly improve the AGC frequency regulation performance of thermal power units and bring frequency regulation benefits. In the traditional joint frequency regulation mode, energy storage is generally used to compensate the deviation between thermal power output and dispatching command, without considering the deep coordination between thermal power units and energy storage system. Thermal power units are still in fast variable load operation state, and the potential of coupled energy storage in improving the safety and other comprehensive performance of the units has not been explored deeply. Therefore, in this paper, the energy storage system is used to actively cooperate with the unit for secondary frequency regulation, and the load command is optimally distributed by minimizing the total cost function, including the variable load cost of the unit. The maintenance cost caused by overtemperature is considered in the cost model, and is analyzed through a simulation example. The results show that the total cost, considering the influence of over temperature on the optimal load distribution, is lower than that when the unit responds at a constant high load changing rate. The active load bearing of energy storage alleviates the cost increase caused by rapid load changing of the unit, making the unit benefit from energy storage and the total cost tends to be optimal.

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Section: Optimization Algorithm and Resultsmentioning

confidence: 99%

Day-ahead load optimal distribution of thermal power coupled electric energy storage frequency regulation system considering the effect of overtemperature

Zhou

Wang²,

Li³

et al. 2023

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…In this paper, an artificial bee colony intelligent optimization algorithm (ABC) is adopted to find out the optimal configuration of the IES [18,19] . In the search process of the algorithm, the employed bee and the onlooker bee are responsible for finding the optimal solution, and the scout bee is responsible for avoiding falling into a local optimum during the search process, and once it falls into a local optimum, a new honey source is randomly generated, and its search process is mainly as follows:…”

Section: Optimization Algorithmmentioning

confidence: 99%

Optimal Design of Biomass-centered Integrated Energy System under Uncertainty

Zhao,

Zhu,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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To achieve the goal of carbon peak and carbon neutrality, China is actively promoting the production and consumption of clean and sustainable energy such as biomass. The optimization of integrated energy system (IES) configuration including biomass has gradually become a research hotspot. In this paper, aiming at the configuration optimization problem of biomass-centered IES under uncertainty, considering the uncertainty of user load and energy price, the maximum annual profit is selected as the objective function, and the configuration planning model of IES under uncertainty is established. Then, the artificial bee colony (ABC) algorithm is used to solve the optimization model, and the influence of uncertainty factors on the configuration optimization of IES is explored. The results of the example show that with the two-stage stochastic programming model used to deal with the uncertainty factors in the IES, the equipment configuration capacity and operating parameters in the system are changed compared with the results of the deterministic conditions. It can overcome the problem of over-optimism in the deterministic condition and reduce the system operation risk due to the random variation of uncertainty factors.

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“…Since the vehicle scheduling problem belongs to NP hard problem, the amount of calculation will increase exponentially with the increase of the problem scale. Therefore, for the solution of large-scale vehicle scheduling problem, scholars mainly use approximate algorithms, such as genetic algorithm (GA) [23] and artificial bee colony algorithm [24], tabu search algorithm, etc. [25].…”

Section: The Solutions Of Vehicle Scheduling Problemsmentioning

confidence: 99%

Research on Optimization Method and Algorithm Design of Green Simultaneous Pick-up and Delivery Vehicle Scheduling under Uncertain Demand

et al. 2022

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In order to solve the problem that the existing low-carbon vehicle scheduling model ignores the economic benefits of enterprises and cannot fully reflect the fuzzy needs of customers, the green simultaneous pick-up and delivery vehicle scheduling problem is studied here. With the goal of minimizing the total cost composed of service cost, fuel consumption cost, and carbon emission cost, a multi-objective comprehensive model of green simultaneous pick-up and delivery under fuzzy demand is established. In order to fully consider the objective uncertainty of customer demand and customer service time, triangular fuzzy numbers are introduced and simultaneous delivery demand is considered. An improved genetic tabu search algorithm is proposed to solve this problem. In the improved GA-TS algorithm, the penalty factor is introduced into the fitness function, the selection operator combined with elite strategy is adopted, and a mutation operator combined with tabu search algorithm is proposed. The Taguchi analysis method is used to obtain reasonable parameter settings of the GA-TS algorithm. Finally, a case study is used to verify the effectiveness of the model and hybrid algorithm. The experimental results show that the proposed comprehensive model can effectively optimize the scheduling of low-carbon simultaneous pick-up and delivery vehicles under fuzzy demand, and the effectiveness and feasibility of genetic tabu search algorithm are verified by comparing the experimental results of different algorithms and different case sizes.

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Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer

Cited by 6 publications

References 35 publications

Day-ahead load optimal distribution of thermal power coupled electric energy storage frequency regulation system considering the effect of overtemperature

Day-ahead load optimal distribution of thermal power coupled electric energy storage frequency regulation system considering the effect of overtemperature

Optimal Design of Biomass-centered Integrated Energy System under Uncertainty

Research on Optimization Method and Algorithm Design of Green Simultaneous Pick-up and Delivery Vehicle Scheduling under Uncertain Demand

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