Low‐carbon coordinated scheduling of integrated electricity‐gas distribution system with hybrid AC/DC network

Qing, Yuliang; Liu, Tianqi; He, Chuan; Lu, Nan; Dong, Guicen; Gao, Weikai; Yu, Yinqian

doi:10.1049/rpg2.12439

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…The penalty cost C II of the CHP‐VPP system involves the cost of polluting gas emissions, the cost of WT curtailment and PV curtailment, and the cost of exceeding the constraints [74]:

\begin{equation} \def\eqcellsep{&}\begin{array}{l} {C}^{{\rm{II}}} = \displaystyle\sum_{t = 1}^T {\left( {{s}^{c{o}_2}{\varepsilon }^{gt,c{o}_2}P_t^{gt} + {s}^{c{o}_2}{\varepsilon }^{gb,c{o}_2}Q_t^{gb}} \right)} \\[12pt] \qquad + \displaystyle\sum_{t = 1}^T {{\psi }^{wt}\left( {\hat{P}_t^{wt} - P_t^{wt}} \right)} + \sum_{t = 1}^T {{\psi }^{pv}\left( {\hat{P}_t^{pv} - P_t^{pv}} \right)} \\[12pt] \qquad + \chi \displaystyle\sum_{t = 1}^T {\sum_{{a}^x \in A} {\left[ {\varphi \max \left( {\left| {{a}^{x,t + 1} - {a}^{x,t}} \right| - \Delta {K}_a^{x,\max },0} \right)} \right]} } \end{array} \end{equation}

where

{s}^{c{o}_2}

is the penalty cost of treating the unit quantity of CO 2 generated by GTs and GBs;

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach

Tan

Shen

et al. 2022

IET Renewable Power Gen

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To realize the national strategies for carbon emission reduction, electric power industries should undergo reforms to cope with the multiple challenges of decarbonization, marketization, and energy transition. How to design a dispatch strategy that considers both low‐carbon demand and economic cost has become a major concern in integrated energy systems. To realize multi‐energy complementation and low‐carbonization, a scheduling framework of combined heat and power‐virtual power plants (CHP‐VPPs) considering carbon capture and power‐to‐gas is proposed. The stochastic dynamic optimization problem is modelled as a Markov decision process. An improved deep reinforcement learning‐based algorithm named multi‐level backtracking prioritized experience replay‐twin delayed deep deterministic policy gradient (MBEPR‐TD3) is employed to solve the low‐carbon economic dispatch problem. The results show that: (1) The profits have been increased by 85.8% and carbon emissions have been reduced by 30.3% because of the addition of power‐to‐gas in CHP‐VPP; (2) the revenues have been improved by 22.24% and carbon emissions have been decreased by 37.04% owing to the introduction of carbon capture and trading; (3) compared with results using DQN, DDPG, and TD3, the optimal dispatch strategy obtained by MBEPR‐TD3 has higher reward returns and more stable convergence.

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“…The penalty cost C II of the CHP‐VPP system involves the cost of polluting gas emissions, the cost of WT curtailment and PV curtailment, and the cost of exceeding the constraints [74]:

\begin{equation} \def\eqcellsep{&}\begin{array}{l} {C}^{{\rm{II}}} = \displaystyle\sum_{t = 1}^T {\left( {{s}^{c{o}_2}{\varepsilon }^{gt,c{o}_2}P_t^{gt} + {s}^{c{o}_2}{\varepsilon }^{gb,c{o}_2}Q_t^{gb}} \right)} \\[12pt] \qquad + \displaystyle\sum_{t = 1}^T {{\psi }^{wt}\left( {\hat{P}_t^{wt} - P_t^{wt}} \right)} + \sum_{t = 1}^T {{\psi }^{pv}\left( {\hat{P}_t^{pv} - P_t^{pv}} \right)} \\[12pt] \qquad + \chi \displaystyle\sum_{t = 1}^T {\sum_{{a}^x \in A} {\left[ {\varphi \max \left( {\left| {{a}^{x,t + 1} - {a}^{x,t}} \right| - \Delta {K}_a^{x,\max },0} \right)} \right]} } \end{array} \end{equation}

where

{s}^{c{o}_2}

is the penalty cost of treating the unit quantity of CO 2 generated by GTs and GBs;

…”

Section: Methodsmentioning

confidence: 99%

“…The penalty cost C II of the CHP-VPP system involves the cost of polluting gas emissions, the cost of WT curtailment and PV curtailment, and the cost of exceeding the constraints [74]:…”

Section: 32mentioning

confidence: 99%

Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach

Tan

Shen

et al. 2022

IET Renewable Power Gen

View full text Add to dashboard Cite

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“…In the context of global warming, low-carbon operation is one of the trends in distribution system. At present, the distribution network can achieve the purpose of low-carbon operation (Qing et al, 2021) by means of operation optimization such as reconfiguration (Zhan et al, 2020) and multi-energy coupling comprehensive energy system (Zhang et al, 2022;Li Y et al, 2021). Distribution network reconfiguration (DNR) is an important part of distribution management system (DMS) which can improve the operating state of distribution network (DN) (Zhan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Fast reconfiguration method of low-carbon distribution network based on convolutional neural network

Yang²,

Ye³

et al. 2023

Front. Energy Res.

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The existing meta-heuristic distribution network reconfiguration (DNR) algorithm has excellent optimization ability through iteration. However, it is difficult to realize the large-scale fast calculation and online real-time response of DNR solution. In order to improve the security and low-carbon economy of distribution network, this paper proposes a fast reconfiguration method of distribution network based on convolutional neural network (CNN). Taking IEEE 33 system and 185 node system as examples, the effectiveness of the proposed method is verified. The reasons why the proposed method can achieve better results are as follows: By mining the historical data of distribution network, the corresponding relationship between load mode (LM) and its optimal topology is established. For a load mode in actual operation, the reconfiguration scheme can be quickly obtained according to the established corresponding relationship. Thus, iterative calculation is avoided and computational efficiency is improved. A multi-branch CNN model is established based on the distribution network structure, and an inception module is introduced into CNN to improve the ability of CNN to extract data features. This model can reduce the dependence on the specific distribution network structure and is easy to expand.

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Low-carbon optimal dispatch of integrated energy system considering the operation of oxy-fuel combustion coupled with power-to-gas and hydrogen-doped gas equipment

Yun,

Zhang,

Yang

et al. 2023

Energy

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Low‐carbon coordinated scheduling of integrated electricity‐gas distribution system with hybrid AC/DC network

Cited by 7 publications

References 35 publications

Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach

Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach

Fast reconfiguration method of low-carbon distribution network based on convolutional neural network

Low-carbon optimal dispatch of integrated energy system considering the operation of oxy-fuel combustion coupled with power-to-gas and hydrogen-doped gas equipment

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