Modeling Integrated Power and Transportation Systems: Impacts of Power-to-Gas on the Deep Decarbonization

Li, Bo; Chen, Minyou; Ma, Ziming; He, Gang; Dai, Wei; Liu, Dongran; Zhang, Chi; Zhong, Haiwang

doi:10.1109/tia.2021.3116916

Cited by 29 publications

(9 citation statements)

References 30 publications

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“…Both EES units and HES units need to satisfy the charging/ discharging power limits and storage capacity limits [71]:

\begin{equation}\left\{ \def\eqcellsep{&}\begin{array}{l} S_{\min }^{{n}^{es}} \le S_t^{{n}^{es}} \le S_{\max }^{{n}^{es}}\\[6pt] - P_{\max }^{d,{n}^{es}} \le P_t^{{n}^{es}} \le P_{\max }^{c,{n}^{es}}\\[6pt] S_{\min }^{{n}^{hs}} \le S_t^{{n}^{hs}} \le S_{\max }^{{n}^{hs}}\\[6pt] - Q_{\max }^{d,{n}^{hs}} \le Q_t^{{n}^{es}} \le Q_{\max }^{c,{n}^{hs}} \end{array} \right.\end{equation}

where

S_{\min }^{{n}^{es}}

S_{\max }^{{n}^{es}}

P_{\max }^{c,{n}^{es}}

, and

P_{\max }^{d,{n}^{es}}

are the minimum electric power storage, the maximum electric power storage, the maximum electric charging power, and the maximum electric discharging power of the n es th EES unit;

S_{\min }^{{n}^{hs}}

…”

Section: Methodsmentioning

confidence: 99%

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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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“…Both EES units and HES units need to satisfy the charging/ discharging power limits and storage capacity limits [71]:

\begin{equation}\left\{ \def\eqcellsep{&}\begin{array}{l} S_{\min }^{{n}^{es}} \le S_t^{{n}^{es}} \le S_{\max }^{{n}^{es}}\\[6pt] - P_{\max }^{d,{n}^{es}} \le P_t^{{n}^{es}} \le P_{\max }^{c,{n}^{es}}\\[6pt] S_{\min }^{{n}^{hs}} \le S_t^{{n}^{hs}} \le S_{\max }^{{n}^{hs}}\\[6pt] - Q_{\max }^{d,{n}^{hs}} \le Q_t^{{n}^{es}} \le Q_{\max }^{c,{n}^{hs}} \end{array} \right.\end{equation}

where

S_{\min }^{{n}^{es}}

S_{\max }^{{n}^{es}}

P_{\max }^{c,{n}^{es}}

, and

P_{\max }^{d,{n}^{es}}

are the minimum electric power storage, the maximum electric power storage, the maximum electric charging power, and the maximum electric discharging power of the n es th EES unit;

S_{\min }^{{n}^{hs}}

…”

Section: Methodsmentioning

confidence: 99%

“…Both EES units and HES units need to satisfy the charging/ discharging power limits and storage capacity limits [71]:…”

Section: Energy Storage System Constraintsmentioning

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

show abstract

“…Electric vehicles (EVs) have proven to be a sustainable and efficient solution to the rising global environmental issues related to gas emissions [1]. Generally, batteries are used as the main energy source in EVs because of their high energy density property and ability to store most of the onboard electrical energy.…”

Section: Introductionmentioning

confidence: 99%

Improved Deep Learning-Based Energy Management Strategy for Battery-Supercapacitor Hybrid Electric Vehicle With Adaptive Velocity Prediction

Udeogu

Lim

2022

IEEE Access

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The uncertainties and disturbances in the actual driving conditions of hybrid electric vehicles (HEVs) complicate the design of energy management strategy (EMS). To achieve better EMS performance for a battery-supercapacitor HEV, this paper proposes an improved and adaptive deep learning-based velocity prediction control EMS that can prolong the battery lifetime through efficient utilization of both the battery and supercapacitor. First, feature engineering techniques are used to extract and increase the key features from the historical driving cycle data of known driving conditions. With the extracted features, an improved long short-term memory (LSTM) velocity predictor was developed to predict future driving cycles for a realtime EMS under an unknown driving condition. Second, a real-time EMS based on the rule-based framework optimized with a neural network is proposed to optimize the power allocation online. Simulation results show that the proposed strategy smoothens battery peak power (i.e. prolongs battery life span) by approximately 26.85% on average and increases supercapacitor participation in the EMS, as evidenced by its increased energy throughput. Furthermore, compared with other EMS approaches, the proposed strategy improved the efficiency by significantly reducing total energy losses by approximately 22.25%. These results validate the reliability and robustness of the proposed strategy.

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“…As the emphasis on environmental concerns grows, electricity and transportation systems are likely to play a greater role in decarbonising the energy system [1]. Electric vehicles (EVs) have been identified as the most promising alternative to internal combustion engines (ICEs) [2].…”

Section: Introductionmentioning

confidence: 99%

Optimal semi‐dynamic traffic and power flow assignment of coupled transportation and power distribution systems for electric vehicles

Zhao

Wei

Liu

et al. 2022

IET Electrical Syst in Trans

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As the most promising alternative to internal combustion engines (ICEs), electric vehicles (EVs) have an excellent development outlook. The charging route scheduling of EVs can simultaneously affect traffic congestion in the transportation network (TN) and power flow distribution in the power distribution network (PDN). The research on TN and PDN coupling networks based on the static traffic flow model is relatively mature; however, it ignores that the traffic flow will spread across periods in a short scheduling period. In this paper, a semi‐dynamic traffic flow model is proposed to represent the dynamic propagation characteristics of EVs and ICEs flow. Furthermore, the cost of carbon emission and system operation are combined as the overall goal of system optimisation. Since the model has become a more complex non‐linear model, this paper proposes to combine the heuristic sequential boundary tightening and binary expansion method to linearise the model. The study compared four cases and found that a 20% penetration rate of EVs can reduce carbon emissions by 4.2% while reducing the system's total cost by 10%. Moreover, the impact of network congestion on the spatiotemporal distribution of traffic flow and power flow in the coupled network is alleviated.

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Modeling Integrated Power and Transportation Systems: Impacts of Power-to-Gas on the Deep Decarbonization

Cited by 29 publications

References 30 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

Improved Deep Learning-Based Energy Management Strategy for Battery-Supercapacitor Hybrid Electric Vehicle With Adaptive Velocity Prediction

Optimal semi‐dynamic traffic and power flow assignment of coupled transportation and power distribution systems for electric vehicles

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