A flexibility-based multi-objective model for contingency-constrained transmission expansion planning incorporating large-scale hydrogen/compressed-air energy storage systems and wind/solar farms

Artis, Reza; Shivaie, Mojtaba; Weinsier, Philip D.

doi:10.1016/j.est.2023.108086

Cited by 10 publications

(2 citation statements)

References 43 publications

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“…In this respect, it is more important to stress medium-and long-term load forecasting. Time series models [2], the multiple regression analysis model [3], and the cumulative average temperature model [4] are too simplistic inability to accurately simulate the phased characteristics of medium-and long-term power loads. In [5], a back propagation neural network (BPNN) model is proposed that combines residual and correction.…”

Section: Introductionmentioning

confidence: 99%

Energy storage peak-shaving demand prediction based on an improved neural network with hidden layers

Deng

2024

J. Phys.: Conf. Ser.

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The result shows that the BP neural network has a very low convergent speed for load prediction, which leads to local optimization. This paper presents a modified GA for the optimization of the threshold value, the initial weight, and the addition of the smoothing coefficient. In addition, it improves the BP neural network’s adaptive learning speed and further improves the efficiency of the search. The proposed model is characterized by high convergent speed and global space retrieval capability. To validate the validity of the modified GA-BP model, the prediction of power load in some regions is made. Compared with the BP neural network forecast algorithm, this method has better performance in terms of prediction accuracy and takes less time in terms of prediction time than the BP neural network. In parallel, the realization of multiple sources of electricity from wind, solar, and heat storage is an important way to deal with large-scale new grid connections. Starting from an improvement in the efficiency of wind and PV generation, it is also possible to build a joint optimization scheduling model for wind, solar, and heat storage. The purpose of taking the minimum net load fluctuation is for optimization. Taking the typical output day of user scenarios as the optimization scenario, the scheduling strategies of wind power, PV, thermal units, and energy storage systems are introduced and simulated on the platform. It is proved that the proposed model can decrease the reduction ratio and the peak and valley load.

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Section: Introductionmentioning

confidence: 99%

Energy storage peak-shaving demand prediction based on an improved neural network with hidden layers

Deng

2024

J. Phys.: Conf. Ser.

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“…Te technique for order preference by similarity to an ideal solution (TOPSIS) method is then employed to evaluate the quality of nondominated solutions in the Pareto solution set, ultimately obtaining the recommended solution. Te authors of [29] employ a potent symphony orchestra search algorithm (SOSA) to solve nonconvex mixed-integer nonlinear master-slave optimization problems. Ten, a conservative fuzzy satisfying method is utilized to select the best compromise solution that meets the economic and fexibility requirements of energy storage systems.…”

Section: Introductionmentioning

confidence: 99%

The Multiobjective Control Based on Tolerance Optimization in a Multienergy System

Ma,

Li,

Jiang

et al. 2024

International Transactions on Electrical Energy Systems

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To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems.

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Techno-economic analysis of energy storage integration combined with SCUC and STATCOM to improve power system stability

Kumbhar,

Jadhav

2024

Electr Eng

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A flexibility-based multi-objective model for contingency-constrained transmission expansion planning incorporating large-scale hydrogen/compressed-air energy storage systems and wind/solar farms

Cited by 10 publications

References 43 publications

Energy storage peak-shaving demand prediction based on an improved neural network with hidden layers

Energy storage peak-shaving demand prediction based on an improved neural network with hidden layers

The Multiobjective Control Based on Tolerance Optimization in a Multienergy System

Techno-economic analysis of energy storage integration combined with SCUC and STATCOM to improve power system stability

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