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2. Provincial Grid Investment Scale Forecasting Based on MLR and RBF Neural Network

Author

Jinchao Li, Dong Peng, Zhao Lang, Pan Ersheng, Xue Yawei, and Long Wangcheng

Subjects
Mathematical optimization, education.field_of_study, Article Subject, Maximum power principle, Artificial neural network, Scale (ratio), Computer science, business.industry, lcsh:Mathematics, 020209 energy, General Mathematics, Population, General Engineering, 02 engineering and technology, lcsh:QA1-939, Investment (macroeconomics), Grid, lcsh:TA1-2040, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Power grid, Electricity, lcsh:Engineering (General). Civil engineering (General), education, business
Abstract

Accurate calculation of power grid investment scale is an important work of power grid management. It is very important to power grid efficient development. Due to the characteristics of short data time series, lots of influencing factors, and large change of power grid investment, it is very difficult to calculate grid investment accurately. Firstly, this paper uses hierarchical clustering analysis method to divide the 23 provinces into four classes with considering fifteen power grid influencing factors, then uses spearman’s rank-order correlation to find out five key influencing factors, and then establishes the regression relationship between the growth rate of investment scale and GDP, permanent population, total social electricity consumption, installed power capacity of operation area, maximum power load, and other growth rates by using the multiple linear regression method (MLR), and the estimation error is corrected by using RBF neural network. Finally, the validity of the model is verified by using data related to power grid investment. The calculation error indicates that the model is feasible and effective.

Published
2019

8. Operation optimization of integrated energy systems based on heat storage characteristics of heating network.

Author

Pan, Ersheng, Li, Hui, Wang, Zhidong, Peng, Dong, Zhao, Lang, Fan, Lisha, Wang, Yongli, Huang, Feifei, Ma, Yang, and Liu, Lin

Subjects
*ELECTRIC heating, *HEAT, *ENERGY storage, *PARTICLE swarm optimization, *HEAT storage
Abstract

With the development of multi‐energy technology, the electric‐heat integrated energy system has become an important research direction for multi‐energy joint supply. The dynamic characteristics and energy storage capacity of heat supply network provide potential for joint dispatching of electric heating energy system. Aiming at the problem of electric‐heat joint dispatching, this paper presents an operation optimization model of electric‐heat integrated energy system considering the virtual energy storage characteristics of heat supply network. Firstly, according to the characteristics of transmission delay and user temperature fuzzy, the virtual energy storage characteristics of heat supply network are studied, and a model of the dynamic transfer of energy in the heat system was built. Then, the operation optimization model of the electric‐heat integrated energy system is established to minimize the operation cost. In order to improve the robustness of scheduling optimization results, the Monte Carlo Simulation embedded Quantum Particle Swarm Optimization algorithm is proposed to solve the model. In order to prove the validity of the proposed model, this paper selects a park (a 36 node thermal system) in the northwest region of China as a simulation case. The results show that the operation optimization method considering the virtual energy storage of heat supply network will greatly enhance the complementary potential of the electric‐heat integrated energy system and reduce the operation cost of the system. [ABSTRACT FROM AUTHOR]

Published
2021
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