Decentralized computation method for robust operation of multi-area joint regional-district integrated energy systems with uncertain wind power

Wang, Gan; Yan, Mingyu; Yao, Wei; Guo, Jianbo; Ai, Xiaomeng; Wen, Jinyu

doi:10.1016/j.apenergy.2021.117280

Cited by 47 publications

(11 citation statements)

References 37 publications

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“…DC power flow and heating flow constraints are also considered according to the topology of system, which employ the form as in [19], [32] and [33].…”

Section: ) Other Constraintsmentioning

confidence: 99%

Improved Ramping and Reserve Modeling of Combined Heat and Power in Integrated Energy Systems for Better Renewable Integration

Jin

Chen²,

Wen

et al. 2022

IEEE Trans. Sustain. Energy

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“…DC power flow and heating flow constraints are also considered according to the topology of system, which employ the form as in [19], [32] and [33].…”

Section: ) Other Constraintsmentioning

confidence: 99%

Improved Ramping and Reserve Modeling of Combined Heat and Power in Integrated Energy Systems for Better Renewable Integration

Jin

Chen²,

Wen

et al. 2022

IEEE Trans. Sustain. Energy

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“…When HE and HS cannot meet the heating power balance, GB, EB converts electrical energy into heat to meet it. HS, like EVs, stores heating energy when the heating energy cost is low and performs heating energy release when the heating energy cost is high [24]. PV and wind power carry out the power supply, and if there is still a shortage of electrical load, it can be supplemented by purchasing power from the upper grid.…”

Section: Ehg-ies Structural Analysismentioning

confidence: 99%

Robust Optimal Scheduling of EHG-IES Based on Uncertainty of Wind Power and PV Output

Yang

Sun

et al. 2022

International Transactions on Electrical Energy Systems

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The Electricity-Heat-Gas Integrated Energy System (EHG-IES) is important for achieving carbon neutrality. However, the uncertainty of wind power and photovoltaic (PV) output has a large impact on EHG-IES scheduling optimization. In order to solve this problem, a robust scheduling optimization method considering the uncertainty of wind power and PV output and the coupling relationship of each device in EHG-IES is proposed. In the proposed optimization method, the uncertainty of wind power and PV output is described by an additive maximum uncertainty set. Then, the day-ahead scheduling optimization model is proposed based on the coupling relationship of each equipment and electric vehicle (EV) traveling demand. Also, the proposed model is introduced in detail. The simulation is verified by a park calculation example. The economy and accuracy of the proposed method in this paper are verified by comparing the robust optimization results under different uncertainties with stochastic optimization. For robust parameters of 20% and 10%, the economic cost is 1.016% and 1.041% lower than that of the stochastic optimization. Meanwhile, the proposed method can improve the economy of the EHG-IES and realize the economic and safe operation of the EHG-IES under the consideration of EVs and heating storage equipment.

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“…IES also faces uncertainty in the operation of multi-energy loads and renewable energy output. The different methods of handling random variable prediction errors, it is mainly divided into scenario method (Li et al, 2022b), chance-constrained programming (Huo et al, 2021), robust optimization (Gan et al, 2021), etc. A scenariogeneration based approach for integrated regional energy expansion planning was proposed in (Lei et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Planning model for rural distribution networks with integrated energy stations via robust optimization method

Yang,

Min,

Yang

et al. 2024

Front. Energy Res.

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With the modernization and intelligent development of agriculture, the energy demand in rural areas continues to increase, which leads to an increased operational burden on the existing rural distribution network. Integrated energy stations (IESs), in rural areas, use renewable energy sources such as biogas, wind power, and photovoltaic as energy inputs, which can fully improve energy efficiency and help reduce the operating load and peak valley difference of rural distribution networks. In this paper, a multistage planning model is proposed for rural distribution networks with IESs based on the robust optimization method. Firstly, a rural distribution network operation framework with IESs is proposed, and a mathematical model of rural IESs is built based on the energy hub (EH). Then, the multistage robust planning model of rural distribution net-works with IESs is developed and typical scenarios of stochastic source and load are generated based on improved k-means. An iterative solution method for a two-stage robust optimization method is proposed based on the nested column constraint generation (NC&CG) algorithm. Finally, the effectiveness of the presented model and solution approach is assessed through case studies on a modified IEEE 33-node distribution network and a real 152-node distribution network.

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Decentralized computation method for robust operation of multi-area joint regional-district integrated energy systems with uncertain wind power

Cited by 47 publications

References 37 publications

Improved Ramping and Reserve Modeling of Combined Heat and Power in Integrated Energy Systems for Better Renewable Integration

Improved Ramping and Reserve Modeling of Combined Heat and Power in Integrated Energy Systems for Better Renewable Integration

Robust Optimal Scheduling of EHG-IES Based on Uncertainty of Wind Power and PV Output

Planning model for rural distribution networks with integrated energy stations via robust optimization method

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