为加快故障发生后配电网供电恢复方法的求解效率,文中提出了一种基于改进交替方向乘子法(alternating direction method of multipliers,ADMM)的配电网多时段分布式供电恢复方法。以故障后最大化负荷恢复量和系统损耗最低为目标,建立了...为加快故障发生后配电网供电恢复方法的求解效率,文中提出了一种基于改进交替方向乘子法(alternating direction method of multipliers,ADMM)的配电网多时段分布式供电恢复方法。以故障后最大化负荷恢复量和系统损耗最低为目标,建立了配电网多时段供电恢复模型。引入超松弛技术和惩罚参数动态调整技术对ADMM进行改进,提出了基于自适应松弛惩罚参数ADMM的配电网多时段分布式供电恢复方法。最后在改进的IEEE-33节点系统上进行算例分析,结果表明文中所提基于自适应松弛惩罚参数ADMM具有较好的分布式计算性能。展开更多
With the rapid development of renewable energy,the Microgrid Coalition(MGC)has become an important approach to improving energy utilization efficiency and economic performance.To address the operational optimization p...With the rapid development of renewable energy,the Microgrid Coalition(MGC)has become an important approach to improving energy utilization efficiency and economic performance.To address the operational optimization problem inmulti-microgrid cooperation,a cooperative game strategy based on the Nash bargainingmodel is proposed,aiming to enable collaboration among microgrids to maximize overall benefits while considering energy trading and cost optimization.First,each microgrid is regarded as a game participant,and a multi-microgrid cooperative game model based on Nash bargaining theory is constructed,targeting the minimization of total operational cost under constraints such as power balance and energy storage limits.Second,the Nash bargaining solution is introduced as the benefit allocation scheme to ensure individual rationality and coalition stability.Finally,theAlternating Direction Method of Multipliers(ADMM)is employed to decompose the centralized optimization problem into distributed subproblems for iterative solution,thereby reducing communication burden and protecting privacy.Case studies reveal that the operational costs of the threemicrogrids are reduced by 26.28%,19.00%,and 17.19%,respectively,and the overall renewable energy consumption rate is improved by approximately 66.11%.展开更多
Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing o...Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.展开更多
稀疏重建是当前CT(computed tomography)领域的研究热点,其实质是用稀疏视角下的投影来重建图像,以减少扫描过程中对病患的辐射剂量。随着压缩感知理论的提出,稀疏重建算法已经被广泛应用到了医学CT中。TV(total variation)算法是可以...稀疏重建是当前CT(computed tomography)领域的研究热点,其实质是用稀疏视角下的投影来重建图像,以减少扫描过程中对病患的辐射剂量。随着压缩感知理论的提出,稀疏重建算法已经被广泛应用到了医学CT中。TV(total variation)算法是可以实现稀疏重建的一种有效方法。本文设计了一种基于ADMM(alternating direction method of multipliers)的TV算法,先将非约束的优化问题转换为约束形式,然后引入乘子,最后通过交替方向法实现迭代过程。该方法将复杂的优化问题分解为了若干个具有闭合形式的子优化问题,故迭代速度较快。仿真实验表明,与传统的滤波反投影算法相比,该算法可以实现稀疏角度下的高精度图像重建。同时还初步探讨了平衡因子在不同噪声情形下对重建精度的影响。展开更多
单一区域综合能源系统(Regional integrated energy system,RIES)在面对大规模、高比例可再生能源接入和日益增长的负荷多样性时难以实现多能协同和灵活调度。针对上述问题,首先构建了计及电、热、天然气及氢气的多能耦合多区域综合能...单一区域综合能源系统(Regional integrated energy system,RIES)在面对大规模、高比例可再生能源接入和日益增长的负荷多样性时难以实现多能协同和灵活调度。针对上述问题,首先构建了计及电、热、天然气及氢气的多能耦合多区域综合能源系统。然后利用拉丁超立方体抽样及K-means算法来捕捉可再生能源和各类负荷的不确定性。随后,针对多子系统参与、多能耦合、多不确定性问题构建了多RIES非合作与基于纳什谈判的P2P(Peer-to-peer)合作模型。对后者利用交替方向乘子法求解出最优策略并通过Shapley值法保证联盟收益的公平分配。仿真结果验证了用所构建的P2P合作模型能够实现多RIES协同和灵活资源互补、提高可再生能源本地消纳率、减少系统碳排放、降低各RIES运行成本和系统对能源网的依赖。展开更多
针对图像的传输中可能会产生噪声的影响和传输时间开销过大,导致图像的恢复效果较差的问题,基于数学中熵最大的原理,提出了一种基于熵函数的去噪重构算法。将该算法运用交替方向乘子法(alternating direction method of multipliers,AD...针对图像的传输中可能会产生噪声的影响和传输时间开销过大,导致图像的恢复效果较差的问题,基于数学中熵最大的原理,提出了一种基于熵函数的去噪重构算法。将该算法运用交替方向乘子法(alternating direction method of multipliers,ADMM)分而治之的思想提出了一种新的快速去噪算法。通过归一化均方误差(normalized mean square error,NMSE)和峰值信噪比(peak signal to noise ratio,PSNR)等评价标准进行实验仿真,验证所提算法的优越性。实验结果表明:根据上面思路提出的方法具有很好的效果,在去噪方面具有一定的用途。展开更多
随着电动汽车数量不断增加,大量电动汽车的无序充电行为会导致电网过载和电池寿命损耗。虽然当前已有很多研究关注电动汽车的有序充电行为,但如何在大规模有序充电过程中实现最大化车主便捷性同时减少电池寿命损耗尚未被研究。研究关注...随着电动汽车数量不断增加,大量电动汽车的无序充电行为会导致电网过载和电池寿命损耗。虽然当前已有很多研究关注电动汽车的有序充电行为,但如何在大规模有序充电过程中实现最大化车主便捷性同时减少电池寿命损耗尚未被研究。研究关注充电便捷性和减少电池损坏的充电服务调度优化对充电站充电服务质量和用户满意度提升具有重要意义。笔者提出一个实时充电服务调度策略来协调大量电动汽车的充电行为,以实现最大化车主便捷性同时降低电池损耗。为减少充电过程中信息直接交换造成隐私泄露,同时降低算法计算复杂度,基于交替方向多乘子(ADMM,alternating direction method of multipliers)的分布式算法被提出。大量实验表明所提算法比已有算法有显著提升,能减少33.0%的电池寿命损耗和18.3%的电费支出。展开更多
文摘为加快故障发生后配电网供电恢复方法的求解效率,文中提出了一种基于改进交替方向乘子法(alternating direction method of multipliers,ADMM)的配电网多时段分布式供电恢复方法。以故障后最大化负荷恢复量和系统损耗最低为目标,建立了配电网多时段供电恢复模型。引入超松弛技术和惩罚参数动态调整技术对ADMM进行改进,提出了基于自适应松弛惩罚参数ADMM的配电网多时段分布式供电恢复方法。最后在改进的IEEE-33节点系统上进行算例分析,结果表明文中所提基于自适应松弛惩罚参数ADMM具有较好的分布式计算性能。
基金funded by StateGrid Beijing Electric PowerCompany Technology Project,grant number 520210230004.
文摘With the rapid development of renewable energy,the Microgrid Coalition(MGC)has become an important approach to improving energy utilization efficiency and economic performance.To address the operational optimization problem inmulti-microgrid cooperation,a cooperative game strategy based on the Nash bargainingmodel is proposed,aiming to enable collaboration among microgrids to maximize overall benefits while considering energy trading and cost optimization.First,each microgrid is regarded as a game participant,and a multi-microgrid cooperative game model based on Nash bargaining theory is constructed,targeting the minimization of total operational cost under constraints such as power balance and energy storage limits.Second,the Nash bargaining solution is introduced as the benefit allocation scheme to ensure individual rationality and coalition stability.Finally,theAlternating Direction Method of Multipliers(ADMM)is employed to decompose the centralized optimization problem into distributed subproblems for iterative solution,thereby reducing communication burden and protecting privacy.Case studies reveal that the operational costs of the threemicrogrids are reduced by 26.28%,19.00%,and 17.19%,respectively,and the overall renewable energy consumption rate is improved by approximately 66.11%.
基金supported in part by the National Natural Science Foundation of China under Grant 52307134the Fundamental Research Funds for the Central Universities(xzy012025022)。
文摘Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.
文摘稀疏重建是当前CT(computed tomography)领域的研究热点,其实质是用稀疏视角下的投影来重建图像,以减少扫描过程中对病患的辐射剂量。随着压缩感知理论的提出,稀疏重建算法已经被广泛应用到了医学CT中。TV(total variation)算法是可以实现稀疏重建的一种有效方法。本文设计了一种基于ADMM(alternating direction method of multipliers)的TV算法,先将非约束的优化问题转换为约束形式,然后引入乘子,最后通过交替方向法实现迭代过程。该方法将复杂的优化问题分解为了若干个具有闭合形式的子优化问题,故迭代速度较快。仿真实验表明,与传统的滤波反投影算法相比,该算法可以实现稀疏角度下的高精度图像重建。同时还初步探讨了平衡因子在不同噪声情形下对重建精度的影响。
文摘单一区域综合能源系统(Regional integrated energy system,RIES)在面对大规模、高比例可再生能源接入和日益增长的负荷多样性时难以实现多能协同和灵活调度。针对上述问题,首先构建了计及电、热、天然气及氢气的多能耦合多区域综合能源系统。然后利用拉丁超立方体抽样及K-means算法来捕捉可再生能源和各类负荷的不确定性。随后,针对多子系统参与、多能耦合、多不确定性问题构建了多RIES非合作与基于纳什谈判的P2P(Peer-to-peer)合作模型。对后者利用交替方向乘子法求解出最优策略并通过Shapley值法保证联盟收益的公平分配。仿真结果验证了用所构建的P2P合作模型能够实现多RIES协同和灵活资源互补、提高可再生能源本地消纳率、减少系统碳排放、降低各RIES运行成本和系统对能源网的依赖。
文摘针对图像的传输中可能会产生噪声的影响和传输时间开销过大,导致图像的恢复效果较差的问题,基于数学中熵最大的原理,提出了一种基于熵函数的去噪重构算法。将该算法运用交替方向乘子法(alternating direction method of multipliers,ADMM)分而治之的思想提出了一种新的快速去噪算法。通过归一化均方误差(normalized mean square error,NMSE)和峰值信噪比(peak signal to noise ratio,PSNR)等评价标准进行实验仿真,验证所提算法的优越性。实验结果表明:根据上面思路提出的方法具有很好的效果,在去噪方面具有一定的用途。
文摘随着电动汽车数量不断增加,大量电动汽车的无序充电行为会导致电网过载和电池寿命损耗。虽然当前已有很多研究关注电动汽车的有序充电行为,但如何在大规模有序充电过程中实现最大化车主便捷性同时减少电池寿命损耗尚未被研究。研究关注充电便捷性和减少电池损坏的充电服务调度优化对充电站充电服务质量和用户满意度提升具有重要意义。笔者提出一个实时充电服务调度策略来协调大量电动汽车的充电行为,以实现最大化车主便捷性同时降低电池损耗。为减少充电过程中信息直接交换造成隐私泄露,同时降低算法计算复杂度,基于交替方向多乘子(ADMM,alternating direction method of multipliers)的分布式算法被提出。大量实验表明所提算法比已有算法有显著提升,能减少33.0%的电池寿命损耗和18.3%的电费支出。
