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学者姓名：邵振国
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Abstract ：
Nowadays, integrated energy systems (IESs) have become an influential approach in the backdrop of energy interconnection and low-carbon energy concepts. This paper proposes a multi-energy trading strategy for IESs that simultaneously considers carbon emissions transaction (CET) and tradable green certificate (TGC) to promote low-carbon energy development further. Firstly, a multi-stage robust optimization method addresses uncertainties in renewable energy, loads, and electricity prices to ensure stable operation of the IES. Secondly, a trading mechanism is proposed by integrating CET with TGC to establish a coupled electricity-heat-carbon- green certificate market. Accordingly, a cooperative game framework among multiple IESs is modeled, which considers different contribution allocations while promoting the economic and low-carbon operation of IES. Finally, the model is solved using the alternating direction method of multipliers (ADMM) algorithm. The proposed strategy's effectiveness in improving the low-carbon economic operation of IESs has been proved through simulation studies.
Keyword ：
Carbon emissions transaction (CET) Carbon emissions transaction (CET) Contribution allocations Contribution allocations Integrated energy systems (IESs) Integrated energy systems (IESs) Multi-stage robust optimization Multi-stage robust optimization Tradable green certificate (TGC) Tradable green certificate (TGC)
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| GB/T 7714 | Gao, Jin , Shao, Zhenguo , Chen, Feixiong et al. Multi-energy trading strategies for integrated energy systems based on low-carbon and green certificate [J]. | ELECTRIC POWER SYSTEMS RESEARCH , 2025 , 238 . |
| MLA | Gao, Jin et al. "Multi-energy trading strategies for integrated energy systems based on low-carbon and green certificate" . | ELECTRIC POWER SYSTEMS RESEARCH 238 (2025) . |
| APA | Gao, Jin , Shao, Zhenguo , Chen, Feixiong , Lak, Mohammadreza . Multi-energy trading strategies for integrated energy systems based on low-carbon and green certificate . | ELECTRIC POWER SYSTEMS RESEARCH , 2025 , 238 . |
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With the rapid development of dual-carbon targets, many distributed power sources, represented by wind power and photovoltaics, are being connected to distribution networks. This will further exacerbate the intermittency and volatility of power output. Dynamic reconfiguration of active distribution networks constitutes a complex, high-dimensional, mixed-integer, nonlinear, and stochastic optimization problem. Traditional algorithms exhibit numerous shortcomings in addressing this issue. By integrating the advantages of both deep learning and reinforcement learning, the deep reinforcement learning algorithm is highly suitable for formulating dynamically reconfigurable strategies for active distribution networks, which are currently of great concern. This paper first summarizes the characteristics of the active distribution network of the new generation power system, and analyzes the progress and challenges of the current research on the dynamic reconfiguration of the active distribution network in mathematical models. Secondly, the coding method of the distribution network dynamic reconfiguration is discussed, and the deep reinforcement learning algorithm is systematically reviewed. Furthermore, the shortcomings of the existing algorithms in dealing with the dynamic reconfiguration of the active distribution network are analyzed, and the research status and advantages of the deep reinforcement learning algorithm in the dynamic reconfiguration of the active distribution network are summarized. Finally, the future research directions for the dynamic reconfiguration of active distribution networks are presented. © 2025 Science Press. All rights reserved.
Keyword ：
Active learning Active learning DC distribution systems DC distribution systems Deep learning Deep learning Deep reinforcement learning Deep reinforcement learning Learning algorithms Learning algorithms Network coding Network coding Power distribution networks Power distribution networks Reinforcement learning Reinforcement learning
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| GB/T 7714 | Jiang, Changxu , Guo, Chen , Liu, Chenxi et al. Review of Active Distribution Network Dynamic Reconfiguration Based on Deep Reinforcement Learning [J]. | High Voltage Engineering , 2025 , 51 (4) : 1801-1816 . |
| MLA | Jiang, Changxu et al. "Review of Active Distribution Network Dynamic Reconfiguration Based on Deep Reinforcement Learning" . | High Voltage Engineering 51 . 4 (2025) : 1801-1816 . |
| APA | Jiang, Changxu , Guo, Chen , Liu, Chenxi , Lin, Junjie , Shao, Zhenguo . Review of Active Distribution Network Dynamic Reconfiguration Based on Deep Reinforcement Learning . | High Voltage Engineering , 2025 , 51 (4) , 1801-1816 . |
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The energy sector contributes significantly to carbon dioxide emissions, and speeding up the shift to a low-carbon power system is essential for tackling global warming. This paper introduces a method for low-carbon economic scheduling that considers the marginal carbon emission factor to improve carbon reduction. First, the marginal carbon emission factor encourages users to adjust their energy plans. A demand response model for pre-scheduling and rescheduling within the grid is developed using price-based strategies. Second, a two-stage model is created to optimize low-carbon scheduling and minimize overall system costs. The method's effectiveness is demonstrated using the IEEE39-node grid.
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| GB/T 7714 | Su, Weiqi , Chen, Feixiong , Shao, Zhenguo et al. A low-carbon economic scheduling of power systems considering marginal carbon emission factor [J]. | 2024 3RD INTERNATIONAL CONFERENCE ON ENERGY INTERNET AND POWER SYSTEMS, ICEIPS 2024 , 2025 , 2935 . |
| MLA | Su, Weiqi et al. "A low-carbon economic scheduling of power systems considering marginal carbon emission factor" . | 2024 3RD INTERNATIONAL CONFERENCE ON ENERGY INTERNET AND POWER SYSTEMS, ICEIPS 2024 2935 (2025) . |
| APA | Su, Weiqi , Chen, Feixiong , Shao, Zhenguo , Huang, Tianhao , Wu, Hongbin . A low-carbon economic scheduling of power systems considering marginal carbon emission factor . | 2024 3RD INTERNATIONAL CONFERENCE ON ENERGY INTERNET AND POWER SYSTEMS, ICEIPS 2024 , 2025 , 2935 . |
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"风电场+储能"配套建设的新模式逐渐成为主流,面对高比例风储系统接入的新型电力系统,提出一种计及需求响应的风储集群电力系统多时间尺度优化调度策略.首先,日前长时间尺度阶段通过价格型需求响应调整用户负荷功率,以净负荷功率波动和运行成本最小为目标,求得实施价格型需求响应后的负荷曲线;其次,在日内短时间尺度阶段实施激励型需求响应并调度风储集群系统,以电力系统总运行成本最低为目标,考虑风储集群系统的有功功率变化最大限值约束,制定电力系统日内调度计划;最后,以改进的IEEE39节点系统作为算例,验证所提策略能够减少风储集群系统的弃风电量,降低其并网功率波动.
Keyword ：
多时间尺度调度 多时间尺度调度 电池储能系统 电池储能系统 需求响应 需求响应 风电功率平抑 风电功率平抑 风电并网 风电并网
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| GB/T 7714 | 彭伟鹏 , 邵振国 , 陈飞雄 et al. 计及需求响应的风储集群电力系统多时间尺度优化调度 [J]. | 太阳能学报 , 2025 , 46 (2) : 282-292 . |
| MLA | 彭伟鹏 et al. "计及需求响应的风储集群电力系统多时间尺度优化调度" . | 太阳能学报 46 . 2 (2025) : 282-292 . |
| APA | 彭伟鹏 , 邵振国 , 陈飞雄 , 张抒凌 , 高统彤 , 陈大玮 . 计及需求响应的风储集群电力系统多时间尺度优化调度 . | 太阳能学报 , 2025 , 46 (2) , 282-292 . |
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电-气互联系统是提高能源利用效率、实现能源清洁化利用的重要载体.在不确定性愈发复杂交错、影响日益增强的背景下,如何量化分析电-气动态传输过程中的不确定性传递成为亟待解决的问题.为此,提出基于时域嵌入的电-气互联系统仿射动态能流算法,揭示不确定因素对电-气互联系统动态过程的影响机理.首先,基于仿射算术表征不确定因素,构建电-气互联系统仿射动态能流模型;在此基础上,通过时域嵌入重构仿射微分方程组,将仿射微分方程组求解问题转换为能流状态量泰勒幂级数系数递归计算问题,进而递归求解得到仿射能流关于时间的显式表达式;接着,为提升计算效率并降低保守性,提出基于噪声元动态校正的多时段计算方法,获取连续时域的仿射动态能流分布.仿真结果验证所提算法能够从时间维度量化分析源荷不确定性的传递影响,具有精度高、保守度低与计算效率高等优势.
Keyword ：
仿射算术 仿射算术 全纯嵌入法 全纯嵌入法 动态能流计算 动态能流计算 电-气互联系统 电-气互联系统
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| GB/T 7714 | 陈飞雄 , 张河 , 邵振国 et al. 基于时域嵌入的电-气互联系统仿射动态能流算法 [J]. | 中国电机工程学报 , 2025 , 45 (7) : 2594-2604,中插12 . |
| MLA | 陈飞雄 et al. "基于时域嵌入的电-气互联系统仿射动态能流算法" . | 中国电机工程学报 45 . 7 (2025) : 2594-2604,中插12 . |
| APA | 陈飞雄 , 张河 , 邵振国 , 吴鸿斌 , 胡昆熹 . 基于时域嵌入的电-气互联系统仿射动态能流算法 . | 中国电机工程学报 , 2025 , 45 (7) , 2594-2604,中插12 . |
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The power flow solution calculated by the existing affine power flow method can only reflect the power flow distribution of the system in a certain range of the source load power, which is difficult to meet the online calculation requirements of multi-scenario uncertainty analysis. To solve these problems, an affine power flow multi-scenario uncertainty analysis method is proposed in this paper. Various holomorphic embedding variables with different physical meanings are used to scale the central values and partial deviation coefficients of the affine injection power. In this way, the interval variation of uncertain injection power is tracked and described, and the analytical dimension of the affine power flow is expanded from one dimension to multiple dimensions. The calculation of affine power flow is divided into two parts: analytic expression solving and online calculation. The multivariate power series coefficients of affine status variables are solved recursively, and the analytical expression for affine power flow is obtained. The online calculation of affine power flow in specific scenarios is realized by substituting the target value of embedded variables. The simulation results verify the advantages of the proposed method in convergence, conservatism and multi-scenario analysis efficiency. © 2025 Power System Technology Press. All rights reserved.
Keyword ：
Embeddings Embeddings Load flow control Load flow control Load flow optimization Load flow optimization
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| GB/T 7714 | Chen, Feixiong , Wu, Hongbin , Shao, Zhenguo et al. Affine Power Flow Multi-scenario Uncertainty Analysis Method for Power System [J]. | Power System Technology , 2025 , 49 (1) : 252-262 . |
| MLA | Chen, Feixiong et al. "Affine Power Flow Multi-scenario Uncertainty Analysis Method for Power System" . | Power System Technology 49 . 1 (2025) : 252-262 . |
| APA | Chen, Feixiong , Wu, Hongbin , Shao, Zhenguo , Li, Yimin , Zhang, He , Su, Weiqi . Affine Power Flow Multi-scenario Uncertainty Analysis Method for Power System . | Power System Technology , 2025 , 49 (1) , 252-262 . |
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An enormous challenge for the harmonic state estimation of distribution networks is how to perceive the complex and varied dynamic harmonics in a higher resolution method. To solve this problem, this article proposes an interval dynamic harmonic high-resolution state estimation method for distribution networks based on multisource measurement data fusion. First, to obtain the typical high-resolution harmonic measurement information of distribution networks under the limited measurement devices, a selection method for the measurement sites of high-resolution power quality monitoring devices (PQMDs) is proposed based on the harmonic electrical distance. On this basis, a multisource data fusion method based on the time period inclusion index is proposed to establish hybrid interval measurement datasets. Second, to improve the efficiency of interval dynamic harmonic state estimation, the interval intermediate variables are introduced to construct the three-stage hybrid interval harmonic measurement equations. Finally, an interval dynamic harmonic high-resolution state estimation method is proposed based on the predictor-corrector method, the IGG-III robust interval Kalman filter (IGGIII-RIKF) is used as the predictor stage, and the forward-backward interval constraint propagation (FBICP) algorithm is used as the corrector stage to realize interval dynamic harmonic high-resolution state estimation. The effectiveness and feasibility of the proposed method have been demonstrated on the IEEE 33-bus system and the IEEE 118-bus system.
Keyword ：
Current measurement Current measurement Dynamic harmonic state estimation Dynamic harmonic state estimation Electric variables measurement Electric variables measurement Harmonic analysis Harmonic analysis high-resolution high-resolution interval approach interval approach Measurement uncertainty Measurement uncertainty multisource measurement data fusion multisource measurement data fusion Phasor measurement units Phasor measurement units Power measurement Power measurement power quality power quality Power system dynamics Power system dynamics Power system harmonics Power system harmonics State estimation State estimation Time measurement Time measurement
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| GB/T 7714 | Zhu, Tiechao , Shao, Zhenguo , Lin, Junjie et al. Interval Dynamic Harmonic High-Resolution State Estimation for Distribution Networks Based on Multisource Measurement Data Fusion [J]. | IEEE SENSORS JOURNAL , 2025 , 25 (4) : 6682-6697 . |
| MLA | Zhu, Tiechao et al. "Interval Dynamic Harmonic High-Resolution State Estimation for Distribution Networks Based on Multisource Measurement Data Fusion" . | IEEE SENSORS JOURNAL 25 . 4 (2025) : 6682-6697 . |
| APA | Zhu, Tiechao , Shao, Zhenguo , Lin, Junjie , Zhang, Yan , Chen, Feixiong . Interval Dynamic Harmonic High-Resolution State Estimation for Distribution Networks Based on Multisource Measurement Data Fusion . | IEEE SENSORS JOURNAL , 2025 , 25 (4) , 6682-6697 . |
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随着双碳目标的快速发展,大量以风电、光伏为代表的分布式电源接入配电网,这将进一步加剧电源出力的间歇性与波动性.主动配电网动态重构属于一个复杂的高维混合整数非线性随机优化问题,传统算法在解决该问题的过程中存在着诸多不足之处.而深度强化学习算法结合了深度学习与强化学习的优势,非常适用于制定当前备受关注的主动配电网动态重构策略.该文首先对新型电力系统主动配电网特征进行总结,并对当前主动配电网动态重构研究在构建数学模型方面所取得的进展以及所面临的挑战进行了深入分析.其次,对配电网动态重构编码方式进行了探讨,并对深度强化学习算法进行了系统性地综述.进而,重点分析了现有算法在处理主动配电网动态重构时的不足之处,并对深度强化学习算法在主动配电网动态重构方面的研究现状与优势进行了总结与概括.最后,对主动配电网动态重构的未来研究方向进行了展望.
Keyword ：
主动配电网 主动配电网 人工智能 人工智能 动态重构 动态重构 机器学习 机器学习 深度强化学习 深度强化学习 编码方式 编码方式
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| GB/T 7714 | 江昌旭 , 郭辰 , 刘晨曦 et al. 基于深度强化学习的主动配电网动态重构综述 [J]. | 高电压技术 , 2025 , 51 (4) : 1801-1816,中插16-中插20 . |
| MLA | 江昌旭 et al. "基于深度强化学习的主动配电网动态重构综述" . | 高电压技术 51 . 4 (2025) : 1801-1816,中插16-中插20 . |
| APA | 江昌旭 , 郭辰 , 刘晨曦 , 林俊杰 , 邵振国 . 基于深度强化学习的主动配电网动态重构综述 . | 高电压技术 , 2025 , 51 (4) , 1801-1816,中插16-中插20 . |
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现有仿射潮流方法计算所得潮流解仅能反映某一源荷功率区间范围下的系统潮流分布,难以满足多场景不确定性分析的在线计算需求.针对上述问题,提出一种电力系统仿射潮流多场景不确定性分析方法.该方法采用多个具有不同物理意义的全纯嵌入变量,缩放仿射注入功率的中心值和噪声元系数,以此追踪和描述不确定性注入功率的区间变化,将仿射潮流的解析维度由一维拓展至高维.仿射潮流计算可划分为仿射型潮流解析表达式求解和在线计算2部分,以递归的方式求解仿射状态量的各阶多元幂级数系数,获取仿射型潮流解析表达式,进而通过代入嵌入变量目标值实现具体场景下的仿射潮流在线计算.仿真结果验证了所提方法在收敛性、保守性和多场景分析效率等方面的优势.
Keyword ：
不确定性 不确定性 仿射潮流 仿射潮流 多场景分析 多场景分析 多维全纯嵌入 多维全纯嵌入
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| GB/T 7714 | 陈飞雄 , 吴鸿斌 , 邵振国 et al. 电力系统仿射潮流多场景不确定性分析方法 [J]. | 电网技术 , 2025 , 49 (1) : 252-262,中插87-中插91 . |
| MLA | 陈飞雄 et al. "电力系统仿射潮流多场景不确定性分析方法" . | 电网技术 49 . 1 (2025) : 252-262,中插87-中插91 . |
| APA | 陈飞雄 , 吴鸿斌 , 邵振国 , 李壹民 , 张河 , 苏炜琦 . 电力系统仿射潮流多场景不确定性分析方法 . | 电网技术 , 2025 , 49 (1) , 252-262,中插87-中插91 . |
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Electric-hydrogen refueling stations (EHRSs), capable of simultaneously serving hydrogen-powered and electric vehicles, present a promising avenue for efficient and cost-effective implementation leveraging existing charging infrastructure. An EHRS operating model prioritizing both economic viability and operational safety is developed. A heat transfer model is established to accurately characterize the temperature fluctuations within the Hydrogen Storage Tank (HST) during filling and emptying operations. Further, we introduce a vehicle operation model accommodating uncertainties in arrival times and energy consumption, utilizing a distributionally robust optimization (DRO) approach based on the Wasserstein distance to capture the distinct energy consumption distributions during peak and valley periods. This infinite-dimensional DRO model is transformed into a tractable finite-dimensional linear model. A tailored relaxation-iteration algorithm addresses the complex temperature model constraints. Case studies demonstrate the robustness of our model, effectively mitigating the impact of uncertainties in bus arrival time and energy consumption. For instance, Our approach address temperature violations in the HST while maintaining profitability. The DRO approach yields a 66% improvement in operating profits compared to the robust approach ($-996.45 vs. $-335.89), demonstrating its ability to balance robustness and economic performance while ensuring HST safety.
Keyword ：
A relaxation-iteration algorithm A relaxation-iteration algorithm Distributionally robust optimization (DRO) Distributionally robust optimization (DRO) Electric-hydrogen refueling stations (EHRS) Electric-hydrogen refueling stations (EHRS) Heat transfer model Heat transfer model Numerical model Numerical model Vehicle uncertainty Vehicle uncertainty
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| GB/T 7714 | Zheng, Wendi , Chen, Rui , Shao, Zhenguo et al. Safe and economical operation of electric-hydrogen refueling stations: A temperature-aware scheduling approach under vehicle uncertainties [J]. | INTERNATIONAL JOURNAL OF HYDROGEN ENERGY , 2025 , 113 : 249-260 . |
| MLA | Zheng, Wendi et al. "Safe and economical operation of electric-hydrogen refueling stations: A temperature-aware scheduling approach under vehicle uncertainties" . | INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 113 (2025) : 249-260 . |
| APA | Zheng, Wendi , Chen, Rui , Shao, Zhenguo , Pan, Jianing , Zhu, Youzhe . Safe and economical operation of electric-hydrogen refueling stations: A temperature-aware scheduling approach under vehicle uncertainties . | INTERNATIONAL JOURNAL OF HYDROGEN ENERGY , 2025 , 113 , 249-260 . |
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