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Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming 通过随机动态编程实现定价者社区储能的能源管理
IF 7.1 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.02720
Lirong Deng;Xuan Zhang;Tianshu Yang;Hongbin Sun;Yang Fu;Qinglai Guo;Shmuel S. Oren
In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arbitrage value. However, this price-smoothing effect can result in significant external welfare changes by reducing consumer costs and producer revenues, which is not negligible for the community with energy storage systems. As such, we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare. To incorporate market interaction into the SDP format, we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices. Then we present an analytical SDP algorithm that does not require state discretization. Apart from computational efficiency, another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value. Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage. The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.
在本文中,我们提出了一种分析性随机动态编程(SDP)算法,以解决社区储能价格制定者的优化管理问题。作为价格制定者,储能可以平滑价格差异,从而降低能源套利价值。然而,这种价格平滑效应会降低消费者成本和生产者收入,从而带来显著的外部福利变化,这对于拥有储能系统的社区来说是不可忽视的。因此,我们将社区储能管理表述为一个 SDP,旨在实现能源套利和社区福利的最大化。为了将市场互动纳入 SDP 格式,我们提出了一个框架,该框架可获得部分但充分的市场信息,以近似估计储能操作对市场价格的影响。然后,我们提出了一种无需状态离散化的 SDP 分析算法。除计算效率外,分析算法的另一个优势是通过直接比较储能当前边际值和预期未来边际值来指导储能充放电。案例研究表明,同时实现套利和福利价值最大化的社区储能比只实现套利最大化的储能能获得更多收益。所提出的算法确保了最优性,并大大降低了标准 SDP 的计算复杂度。
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引用次数: 0
Cyber-Physical Resilience Enhancement for Power Transmission Systems with Energy Storage Systems 利用储能系统增强输电系统的网络物理复原力
IF 7.1 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2022.07570
Wenhao Zhang;Dongyang Rui;Weihong Wang;Yang Guo;Zhaoxia Jing;Wenhu Tang
In a power system, when extreme events occur, such as ice storm, large scale blackouts may be unavoidable. Such small probability but high risk events have huge impact on power systems. Most resilience research in power systems only considers faults on the physical side, which would lead to overly idealistic results. This paper proposes a two-stage cyber-physical resilience enhancement method considering energy storage (ES) systems. The first stage calculates optimal planning of ES systems, and the second stage assesses resilience and enhancement of ES systems during the disaster. In the proposed model, cyber faults indirectly damage the system by disabling monitoring and control function of control center. As a result, when detection and response process of physical faults are blocked by cyber failures, serious load shedding occurs. Such a cyber-physical coupling mechanism of fault, response, restoration process is demonstrated in the modified IEEE Reliable Test System-79 (RTS-79). Simulation results show compared with the physical-only system, the cyber-physical system has a more accurate but degraded resilient performance. Besides, ES systems setting at proper place effectively enhance resilience of the cyber-physical transmission system with less load Shedding.
在电力系统中,当发生冰风暴等极端事件时,大规模停电可能不可避免。这种小概率但高风险的事件会对电力系统产生巨大影响。大多数电力系统复原力研究只考虑物理方面的故障,这将导致过于理想化的结果。本文提出了一种考虑到储能(ES)系统的两阶段网络物理弹性增强方法。第一阶段计算 ES 系统的最优规划,第二阶段评估 ES 系统在灾难期间的恢复能力和增强能力。在所提出的模型中,网络故障会使控制中心的监控功能失效,从而间接损害系统。因此,当物理故障的检测和响应过程被网络故障阻断时,就会出现严重的甩负荷现象。这种故障、响应和恢复过程的网络-物理耦合机制在修改后的 IEEE 可靠性测试系统-79(RTS-79)中得到了验证。仿真结果表明,与纯物理系统相比,网络物理系统具有更高的准确性,但弹性性能有所下降。此外,在适当位置设置 ES 系统可有效提高网络物理输电系统的恢复能力,减少甩负荷。
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引用次数: 0
Generic Modeling and Control Framework for Power Systems Dominated by Power Converters Connected Through a Passive Transmission and Distribution Grid 通过无源输配电网连接的变流器主导电力系统的通用建模和控制框架
IF 7.1 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.06400
Qing-Chang Zhong;Marcio Stefanello
In this paper, a compact mathematical model having an elegant structure, together with a generic control framework, are proposed for generic power systems dominated by power converters that are interconnected through a passive transmission and distribution (T&D) grid, by adopting the port-Hamiltonian (pH) systems theory and the fundamental circuit theory. The models of generic T&D lines are developed and then the model of a generic T&D grid is established. With the proposed control framework, the controlled converters are proven to be passive and Input-to-State Stable (ISS). The compact mathematical model is scalable and can be applied to power systems with multiple power electronic converters with generic passive controllers, passive local loads, and different types of passive T&D lines connected in a meshed configuration without self-loops, so it is very generic. Moreover, the resulting power system is proven to be ISS as well. The analysis is carried out without assumptions on constant frequency/voltage, constant loads, and/or lossless networks, except the need of passivity for all parts involved, and without using the Clarke/Park transformations or the graph theory. To simplify the presentation, three-phase balanced systems are adopted but the results can be easily adapted for single-phase or unbalanced three-phase systems.
本文采用端口-哈密顿(pH)系统理论和基本电路理论,针对以电力转换器为主、通过无源输配电网互联的通用电力系统,提出了结构优雅的紧凑型数学模型和通用控制框架。首先建立了一般输配电线路的模型,然后建立了一般输配电网的模型。利用所提出的控制框架,受控变流器被证明是无源和输入到状态稳定(ISS)的。该紧凑型数学模型具有可扩展性,可应用于带有通用无源控制器的多个电力电子变流器、无源本地负载以及以无自环网状配置连接的不同类型无源输配电线路的电力系统,因此具有很强的通用性。此外,由此产生的电力系统也被证明是 ISS。除了所有相关部分都需要无源之外,分析中没有假设恒定频率/电压、恒定负载和/或无损网络,也没有使用克拉克/帕克变换或图形理论。为简化表述,本文采用了三相平衡系统,但其结果可轻松适用于单相或不平衡的三相系统。
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引用次数: 0
Joint Planning of Active Distribution Network and EV Charging Stations Considering Vehicle-to-Grid Functionality and Reactive Power Support 考虑车辆到电网功能和无功功率支持的有功配电网与电动汽车充电站联合规划
IF 6.9 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.03930
Yongheng Wang;Xinwei Shen;Yan Xu
This paper proposes a collaborative planning model for active distribution network (ADN) and electric vehicle (EV) charging stations that fully considers vehicle-to-grid (V2G) function and reactive power support of EVs in different regions. This paper employs a sequential decomposition method based on physical characteristics of the problem, breaking down the holistic problem into two sub-problems for solution. Subproblem I optimizes the charging and discharging behavior of autopilot electric vehicles (AEVs) using a mixed-integer linear programming (MILP) model. Subproblem II uses a mixed-integer second-order cone programming (MISOCP) model to plan ADN and retrofit or construct V2G charging stations (V2GCS), as well as multiple distributed generation resources (DGRs). The paper also analyzes the impact of bi-directional active-reactive power interaction of V2GCS on ADN planning. The presented model is tested in the 47-node ADN in Longgang District, Shenzhen, China, and the IEEE 33-node ADN, demonstrating that decomposition can significantly improve the speed of solving large-scale problems while maintaining accuracy with low AEV penetration.
本文提出了一种主动配电网(ADN)和电动汽车(EV)充电站的协同规划模型,充分考虑了不同地区电动汽车的车对网(V2G)功能和无功功率支持。本文根据问题的物理特征,采用顺序分解法,将整体问题分解为两个子问题进行求解。子问题 I 采用混合整数线性规划(MILP)模型优化自动驾驶电动汽车(AEV)的充电和放电行为。子问题 II 采用混合整数二阶圆锥编程 (MISOCP) 模型来规划 ADN 和改造或建造 V2G 充电站 (V2GCS),以及多个分布式发电资源 (DGR)。本文还分析了 V2GCS 的双向有功-无功功率相互作用对 ADN 规划的影响。本文提出的模型在中国深圳龙岗区 47 个节点的 ADN 和 IEEE 33 个节点的 ADN 中进行了测试,结果表明,在 AEV 渗透率较低的情况下,分解可以显著提高解决大规模问题的速度,同时保持准确性。
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引用次数: 0
Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework 发电机跳闸方案的智能预测:基于知识融合的深度强化学习框架
IF 7.1 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2022.08970
Lingkang Zeng;Wei Yao;Ze Hu;Hang Shuai;Zhouping Li;Jinyu Wen;Shijie Cheng
Generator tripping scheme (GTS) is the most commonly used scheme to prevent power systems from losing safety and stability. Usually, GTS is composed of offline predetermination and real-time scenario match. However, it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power system. To improve efficiency of predetermination, this paper proposes a framework of knowledge fusion-based deep reinforcement learning (KF-DRL) for intelligent predetermination of GTS. First, the Markov Decision Process (MDP) for GTS problem is formulated based on transient instability events. Then, linear action space is developed to reduce dimensionality of action space for multiple controllable generators. Especially, KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making process. This can enhance the efficiency and learning process. Moreover, the graph convolutional network (GCN) is introduced to the policy network for enhanced learning ability. Numerical simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.
发电机跳闸方案(GTS)是防止电力系统失去安全性和稳定性的最常用方案。通常,GTS 由离线预判和实时情景匹配两部分组成。然而,对于大型现代电力系统来说,人工预判非常耗时耗力。为提高预判效率,本文提出了一种基于知识融合的深度强化学习(KF-DRL)框架,用于 GTS 的智能预判。首先,基于暂态不稳定事件,建立了 GTS 问题的马尔可夫决策过程(Markov Decision Process,MDP)。然后,开发了线性行动空间,以降低多个可控发电机的行动空间维度。特别是,KF-DRL 利用有关 GTS 的领域知识来掩盖决策过程中的无效行动。这可以提高效率,改善学习过程。此外,还在策略网络中引入了图卷积网络(GCN),以增强学习能力。在新英格兰电力系统上获得的数值模拟结果表明,针对 GTS 提出的 KF-DRL 框架优于纯数据驱动的 DRL 方法。
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引用次数: 0
Convexification of Hybrid AC-DC Optimal Power Flow with Line-Commutated Converters 带线路换流器的交直流混合优化功率流的凸化
IF 7.1 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2022.05250
Hongyuan Liang;Zhigang Li;J. H. Zheng;Q. H. Wu
Line-commutated converter (LCC)-based high-voltage DC (HVDC) systems have been integrated with bulk AC power grids for interregional transmission of renewable power. The nonlinear LCC model brings additional nonconvexity to optimal power flow (OPF) of hybrid AC-DC power grids. A convexification method for the LCC station model could address such nonconvexity but has rarely been discussed. We devise an equivalent reformulation for classical LCC station models that facilitates second-order cone convex relaxation for the OPF of LCC-based AC-DC power grids. We also propose sufficient conditions for exactness of convex relaxation with its proof. Equivalence of the proposed LCC station models and properties, exactness, and effectiveness of convex relaxation are verified using four numerical simulations. Simulation results demonstrate a globally optimal solution of the original OPF can be efficiently obtained from relaxed model.
基于线路换流器(LCC)的高压直流(HVDC)系统已与大容量交流电网集成,用于可再生能源电力的跨区域传输。非线性 LCC 模型给交直流混合电网的最优功率流 (OPF) 带来了额外的非凸性。LCC 电站模型的凸化方法可以解决这种非凸性,但很少有人讨论。我们为经典的 LCC 电站模型设计了一种等效的重述方法,有助于对基于 LCC 的交直流电网 OPF 进行二阶锥凸松弛。我们还提出了凸松弛精确性的充分条件及其证明。我们通过四次数值模拟验证了所提出的 LCC 电站模型和凸松弛的等价性、精确性和有效性。仿真结果表明,从松弛模型中可以有效地获得原始 OPF 的全局最优解。
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引用次数: 0
System Strength Assessment Based on Multi-task Learning 基于多任务学习的系统强度评估
IF 7.1 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.00440
Baoluo Li;Shiyun Xu;Huadong Sun;Zonghan Li;Lin Yu
Increase in permeability of renewable energy sources (RESs) leads to the prominent problem of voltage stability in power system, so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable range. Therefore, a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method. First, calculation of critical short circuit ratio (CSCR) is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator. Then, the construction process of CSCR dataset is proposed, and a batch simulation program of samples is developed accordingly, which provides a data basis for subsequent research. Finally, a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point, which significantly reduces evaluation error caused by weak links. Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system, which provides strong technical support for real-time monitoring of system strength.
可再生能源(RES)渗透率的增加导致电力系统电压稳定性问题突出,因此迫切需要一种既准确又实用的系统强度评估方法,将其接入规模控制在合理范围内。因此,结合机制法和数据驱动法的优点,提出了一种混合智能增强法。首先,以多可再生能源电站短路率为量化指标,将临界短路率(CSCR)的计算作为智能提升的方向。然后,提出了 CSCR 数据集的构建过程,并据此开发了样本批量仿真程序,为后续研究提供了数据基础。最后,利用基于渐进分层提取的多任务学习模型,同时预测各 RESs 连接点的 CSCR,大大降低了因薄弱环节造成的评估误差。所提方法的预测性能和抗噪声性能在 CEPRI-FS-102 总线系统上得到了验证,为实时监测系统强度提供了有力的技术支持。
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引用次数: 0
Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control 基于并行系统的有功功率校正控制人工智能量化评估与自我进化
IF 7.1 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.00190
Tianyun Zhang;Jun Zhang;Feiyue Wang;Peidong Xu;Tianlu Gao;Haoran Zhang;Ruiqi Si
In artificial intelligence (AI) based-complex power system management and control technology, one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution. However, there is, currently, nearly no standard technical framework for objective and quantitative intelligence evaluation. In this article, based on a parallel system framework, a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems, by resorting to human intelligence evaluation theories. On this basis, this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning (AutoRL) systems. A parallel system based quantitative assessment and self-evolution (PLASE) system for power grid corrective control AI is thereby constructed, taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results. Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent, and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results, effectively, as well as intuitively improving its intelligence level through self-evolution.
在基于人工智能(AI)的复杂电力系统管理和控制技术中,当务之急之一是评估人工智能的智能,并发明一种自主智能进化的方法。然而,目前几乎没有一个客观、定量的智能评估标准技术框架。本文基于并行系统框架,借鉴人类智能评价理论,建立了一种客观定量评价现代电力系统主动功率纠偏控制人工智能代理智能水平的方法。在此基础上,本文通过将定量智能评估方法嵌入自动强化学习(AutoRL)系统,提出了一种基于智能评估的人工智能自我进化方法。以贝叶斯优化(Bayesian Optimization)作为人工智能进化的衡量标准,构建了基于并行系统的电网纠偏控制人工智能量化评估与自进化(PLASE)系统,实现了人工智能在智能评估结果指导下的自主进化。以电网纠偏控制人工智能代理为例的实验结果表明,PLASE 系统能够可靠、定量地评估电网纠偏控制代理的智能水平,并能在智能评估结果的指导下有效地促进电网纠偏控制代理的进化,同时通过自我进化直观地提高电网纠偏控制代理的智能水平。
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引用次数: 0
Design and Experimental Testing of a Moving Coil Actuator with Compensation Coils 带补偿线圈的动圈致动器的设计与实验测试
IF 7.1 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.05240
Boyuan Yin;Xianwu Zeng;John Frederick Eastham;Emelie Nilsson;Jean-francois Rouquette;Jean Rivenc;Ludovic Ybanez;Xiaoze Pei
Hydrogen-powered electric aircraft have attracted significant interests aiming to achieve decarbonization targets. Onboard DC electric networks are facing great challenges in DC fault protection requirements. Vacuum interrupters are widely used in low voltage and medium voltage power systems due to being environmentally friendly with low maintenance. In this paper a moving coil actuator with compensation coils for a vacuum interrupter, as part of a hybrid direct current circuit breaker, is designed and experimentally tested. Compensation coils are used to improve operating speed compared with original moving coil actuator. Comparisons between four possible connections of compensation coils and original moving coil actuator are carried out. Experimental results show comparisons between different connections of actuator coils in terms of opening time and coil current with a range of pre-charged capacitor voltages. Dynamic performance of each actuator connection is also compared. The actuator with compensation coils is shown to have a higher current rising rate and achieve faster opening speed, which is a critical requirement for electric aircraft network protection. The parallel connection actuator achieves the highest opening speed within 3.5 ms with capacitor voltage of 50 V.
氢动力电动飞机在实现脱碳目标方面备受关注。机载直流电网在直流故障保护要求方面面临巨大挑战。真空灭弧室因其环保和低维护成本的特点,被广泛应用于低压和中压电力系统。本文设计了一种带补偿线圈的动圈传动装置,用于真空灭弧室,作为混合直流断路器的一部分,并进行了实验测试。与原来的动圈传动器相比,补偿线圈用于提高运行速度。对补偿线圈的四种可能连接方式和原始动圈推杆进行了比较。实验结果表明,在预充电容电压范围内,不同连接方式的致动器线圈在打开时间和线圈电流方面都有可比性。此外,还比较了每种致动器连接的动态性能。结果表明,带补偿线圈的致动器具有更高的电流上升率和更快的打开速度,而这正是飞机电网保护的关键要求。并联致动器在电容器电压为 50 V 时,可在 3.5 ms 内达到最高打开速度。
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引用次数: 0
Adaptive Emergency Control of Power Systems Based on Deep Belief Network 基于深度信念网络的电力系统自适应应急控制
IF 6.9 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2022.00070
Junyong Wu;Baoqin Li;Liangliang Hao;Fashun Shi;Pengjie Zhao
Emergency control is an essential means to help system maintain synchronism after fault clearance. Traditional “offline calculation, online matching” scheme faces significant challenges on adaptiveness and robustness problems. To address these challenges, this paper proposes a novel closed-loop framework of transient stability prediction (TSP) and emergency control based on Deep Belief Network (DBN). First, a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted, which takes into account accuracy and rapidity at the same time. Next, a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity. When impending instability of the system is foreseen, optimal emergency control strategy can be determined in time. Lastly, responses after emergency control are fed back to the TSP model. If prediction result is still unstable, an additional control strategy will be implemented. Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council (NPCC) 140-bus system. Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.
应急控制是帮助系统在故障排除后保持同步的重要手段。传统的 "离线计算、在线匹配 "方案在适应性和鲁棒性问题上面临巨大挑战。针对这些挑战,本文提出了一种基于深度信念网络(DBN)的新型瞬态稳定性预测(TSP)和紧急控制闭环框架。首先,采用分层实时抗抖动 TSP 方法,利用滑动时间窗,同时兼顾准确性和快速性。其次,建立灵敏度回归模型,挖掘功率角与灵敏度之间的隐含关系。当预见到系统即将出现不稳定时,可以及时确定最佳紧急控制策略。最后,将紧急控制后的响应反馈给 TSP 模型。如果预测结果仍不稳定,则将实施额外的控制策略。对新英格兰 IEEE 39 总线系统和东北电力协调委员会(NPCC)140 总线系统进行了综合数值案例研究。结果表明,所提出的方法能尽快检测出系统的不稳定性,并协助维持可靠的系统同步性。
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引用次数: 0
期刊
CSEE Journal of Power and Energy Systems
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