Sustainable Energy Grids & Networks最新文献_第5页

Robust optimization of electric bus charging-operation scheduling considering charging discrepancy 考虑充电差异的电动客车充电调度鲁棒优化

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-06 DOI: 10.1016/j.segan.2025.102084

Zhouzuo Wang , Xinghua Hu , Jiahao Zhao , Fang Liu , Lanping Si

Optimizing electric bus (EB) scheduling is crucial for advancing urban bus systems and reducing carbon emissions. In this study, we establish an EB scheduling model using a robust optimization paradigm to address the challenges associated with charging demand uncertainty during the operation period. To model the charging process of electric buses (EBs), we adopted a piecewise linear function to handle the nonlinear charging function. This approach improves the practicality of the model while ensuring basic realism. This study introduced a mixed-integer programming model to maximize the profit of the EB system, including the weighted delay time. The main constraints include the departure time window and the charging process. To account for the impact of multiple vehicle types on the scheduling of EBs, a distributed robust optimization model is established for the uncertainty of the EB operation. An instantiated analysis is conducted to schedule an EB line in a Chinese city. The results demonstrate that the distributed robust optimization model enhances the expected profit by approximately 27.27 %-54.24 % compared with the deterministic model. Additionally, the robust optimization model exhibits a steeper increase in expected profit as the uncertainty level increases. Furthermore, the mixed scheduling strategies with multiple vehicle types in the robust optimization model enhance the profit compared to the model relying solely on a single vehicle type. The results demonstrate the applicability and effectiveness of the proposed model for EB scheduling.

优化电动公交调度对于推进城市公交系统建设和减少碳排放至关重要。在本研究中，我们使用鲁棒优化范式建立了一个EB调度模型，以解决与运营期间充电需求不确定性相关的挑战。为了模拟电动公交车的充电过程，我们采用分段线性函数来处理非线性充电函数。这种方法在保证基本真实感的同时提高了模型的实用性。本文引入了一个混合整数规划模型，使EB系统的利润最大化，并考虑了加权延迟时间。主要的约束条件包括出发时间窗口和收费过程。为了考虑多种车辆类型对电动汽车调度的影响，针对电动汽车运行的不确定性，建立了分布式鲁棒优化模型。以中国某城市的EB线调度为例进行了实例分析。结果表明，与确定性优化模型相比，分布式鲁棒优化模型的预期利润提高了27.27 % ~ 54.24 %。此外，鲁棒优化模型显示，随着不确定性水平的增加，期望利润的增加幅度更大。此外，在鲁棒优化模型中，多车型混合调度策略比单一车型混合调度策略的收益更高。结果表明，该模型在电子商务调度中的适用性和有效性。

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引用次数: 0

Multi-objective reinforcement learning for electric vehicle charging 电动汽车充电的多目标强化学习

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-06 DOI: 10.1016/j.segan.2025.102083

Maximiliano Trimboli, Luis Avila

The transportation sector is a significant contributor to global greenhouse gas emissions, and Electric Vehicles (EVs) have emerged as a promising solution to mitigate this impact by reducing emissions and integrating renewable energy sources. However, battery charging remains a major obstacle to widespread EV adoption, as charging speed is constrained by battery specifications, C-rate limits, and the need to prevent degradation due to thermal and electrochemical stress. To address these challenges, this work proposes a Multi-Objective Reinforcement Learning (MORL) approach for optimal EV battery charging. Unlike traditional methods that rely on hand-crafted scalar rewards, MORL enables the agent to learn control policies that dynamically balance multiple, often conflicting, objectives—such as fast charging and thermal safety—based on user-defined preferences. Leveraging the architecture of a Deep RL agent, the proposed method adapts its charging strategy in real-time, applying high currents when thermal conditions are favorable and reducing them near critical thresholds. Experimental results show the policy’s adaptability: faster charging is achieved when temperature constraints are relaxed, while more conservative profiles emerge when battery longevity is prioritized. This highlights the potential of MORL to enhance both the safety and efficiency of EV charging.

交通运输部门是全球温室气体排放的重要贡献者，电动汽车（ev）已经成为一种有希望的解决方案，通过减少排放和整合可再生能源来减轻这种影响。然而，电池充电仍然是电动汽车广泛采用的主要障碍，因为充电速度受到电池规格、c -速率限制以及防止热应力和电化学应力导致的退化的需要的限制。为了解决这些挑战，本研究提出了一种多目标强化学习（MORL）方法来优化电动汽车电池充电。与依赖手工制作的标量奖励的传统方法不同，MORL使智能体能够根据用户定义的偏好学习动态平衡多个经常相互冲突的目标（例如快速充电和热安全）的控制策略。利用Deep RL代理的架构，所提出的方法可以实时调整其充电策略，在热条件有利时施加大电流，并在临界阈值附近降低电流。实验结果表明，该策略具有较强的适应性：当温度约束较宽松时，充电速度较快；而当电池寿命优先考虑时，充电曲线较为保守。这凸显了MORL在提高电动汽车充电安全性和效率方面的潜力。

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引用次数: 0

Applying two-stage risk-based market structures for energy hub-based plug-in electric vehicles using information decision gap theory and a hybrid recurrent convolutional network 基于信息决策缺口理论和混合递归卷积网络的两阶段风险型插电式电动汽车市场结构研究

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-06 DOI: 10.1016/j.segan.2025.102085

A. Heidari , R.C. Bansal , R. Bo

This paper investigates the optimal operation of an energy hub engaged in both day-ahead and real-time trading. A two-stage optimization framework Information Gap Decision Theory (IGDT) for day-ahead bidding and stochastic programming with Monte Carlo scenarios for real-time recourse is applied. Risk-neutral, risk-averse, and risk-taking strategies are considered to capture different risk preferences. The hub integrates combined heat and power, renewable energy, plug-in electric vehicles, and vehicle-to-grid and grid-to-vehicle technologies. Price and load forecasts are generated using a hybrid recurrent convolutional network (HRCN). Results highlight the trade-off between risk management and economic performance: costs are 16.5 % higher in the risk-averse mode than in the risk-neutral mode, and 55.6 % higher than in the risk-taking mode. Natural gas accounts for the most in the risk-taking case, at ∼33 % of the total cost. Under the tested conditions, the proposed IGDT–stochastic–HRCN framework improves expected costs relative to baselines, though outcomes may vary under different market rules, fuel prices, or volatility regimes.

本文研究了一个能源枢纽同时进行日前交易和实时交易的最优运行问题。将信息缺口决策理论（IGDT）应用于蒙特卡罗情景下的日前竞价和随机规划的两阶段优化框架。风险中性、风险厌恶和风险承担策略被认为可以捕获不同的风险偏好。该中心集成了热电联产、可再生能源、插电式电动汽车、车对网和网对车技术。价格和负荷预测使用混合循环卷积网络（HRCN）生成。结果强调了风险管理和经济绩效之间的权衡：风险厌恶模式的成本比风险中性模式高16.5% %，比风险承担模式高55.6% %。在风险承担情况下，天然气占最大，约占总成本的33% %。在测试条件下，拟议的igdt - random - hrcn框架提高了相对于基线的预期成本，尽管结果可能因不同的市场规则、燃料价格或波动机制而异。

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引用次数: 0

Nonlinear integrated energy market optimization based on smoothing approaches 基于平滑方法的非线性综合能源市场优化

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-06 DOI: 10.1016/j.segan.2025.102089

Jian Jia, Weifeng Chen

To address the computational complexity of the mixed-integer programming (MIP) model in integrated energy system (IES) optimization, a smooth nonlinear programming (NLP) method based on a bi-level optimization model is proposed. In this approach, the upper-level model maximizes the profit of the energy hub (EH) by coordinating supply and demand decisions with the lower-level system. Integer variables are replaced with continuous variables through a smoothing method, which reduces computational complexity while preserving operational equivalence. Relaxed complementarity constraints are incorporated into the KKT conditions to ensure that the smoothed nonlinear model can be effectively solved. Furthermore, incorporating the full nonlinear power flow (NLPF) model in the optimization allows a more accurate representation of the system’s intrinsic characteristics. This approach also helps prevent potential safety risks associated with constraint violations in linear power flow (LPF) models. The case study results demonstrate that the smooth NLP model produces results comparable to the mixed-integer linear programming (MILP) model, and demonstrate its good applicability in handling nonlinear problems.

针对综合能源系统优化中混合整数规划（MIP）模型的计算复杂性，提出了一种基于双层优化模型的光滑非线性规划（NLP）方法。在这种方法中，上层模型通过与下层系统协调供需决策，使能源枢纽（EH）的利润最大化。通过平滑方法将整型变量替换为连续型变量，在保持运算等价的同时降低了计算复杂度。在KKT条件中加入了松弛互补约束，保证了光滑非线性模型的有效求解。此外，在优化中加入全非线性潮流（NLPF）模型可以更准确地表示系统的内在特性。这种方法还有助于防止线性潮流（LPF）模型中与约束违规相关的潜在安全风险。算例研究结果表明，光滑NLP模型的计算结果与混合整数线性规划（MILP）模型相当，在处理非线性问题时具有良好的适用性。

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引用次数: 0

Dynamic pricing strategies for electric vehicle charging: Enhancing cost-reflectivity and revenue stability 电动汽车充电动态定价策略：提高成本反射性和收益稳定性

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-05 DOI: 10.1016/j.segan.2025.102082

Toni Simolin , Tim Unterluggauer , Mattia Secchi , Francesco Pastorelli , Mattia Marinelli , Pertti Järventausta

Public charging infrastructure is essential for accelerating electric vehicle (EV) adoption. Currently, in Europe, customers are often offered fixed charging prices, while the costs incurred by charging site owners (CSOs) vary significantly due to factors such as electricity prices and power grid tariffs. This paper proposes alternative pricing solutions to improve cost-reflectivity based on an analysis of the current pricing landscape and related scientific literature. Simulations are carried out, using Danish and Finnish charging session data of multiple locations and electricity price databases, to assess the impact of the proposed pricing solutions on CSO revenues and their potential implications for the charging service business model. The findings indicate that dynamic cost-reflective pricing enhances the stability of CSO revenues and allows users to optimise their charging decisions by providing transparency through precise hourly charging costs. Furthermore, the results show that the proposed dynamic pricing schemes provide a competitive economic advantage for the CSO over the competitors using the present pricing schemes. Additionally, the proposed pricing schemes lead to lower charging costs for 53–64 % of the users even if they do not alter their charging behaviour.

公共充电基础设施对于加速电动汽车的普及至关重要。目前，在欧洲，客户通常获得固定的充电价格，而充电站点所有者（cso）所产生的成本由于电价和电网关税等因素而差异很大。本文在分析当前定价格局和相关科学文献的基础上，提出了提高成本反射率的替代定价方案。利用丹麦和芬兰多个地点的充电时段数据和电价数据库进行了模拟，以评估拟议的定价解决方案对CSO收入的影响及其对充电服务商业模式的潜在影响。研究结果表明，动态成本反射定价提高了CSO收入的稳定性，并允许用户通过精确的小时收费成本提供透明度来优化他们的收费决策。此外，结果表明，所提出的动态定价方案为CSO提供了比使用现有定价方案的竞争对手更具竞争力的经济优势。此外，拟议的定价方案导致53-64 %的用户的收费成本降低，即使他们不改变他们的收费行为。

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引用次数: 0

Optimal management of green hydrogen production in renewable energy systems using deep reinforcement learning methods 利用深度强化学习方法优化可再生能源系统中绿色制氢的管理

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-05 DOI: 10.1016/j.segan.2025.102075

Donguk Yang , Junki Shim , Jinkun Lee , Seongim Choi

This research focuses on developing a deep reinforcement learning (DRL) framework to optimize green hydrogen production within renewable energy systems. By integrating a DRL-based model, the study aims to enhance real-time management of energy supply, storage, and distribution, involving an electrolyzer and balancing energy flows from photovoltaic (PV) sources, an energy storage system (ESS) and grid power. Utilizing real-world data, the DRL model adapts dynamically to fluctuations in renewable energy output and market prices, thereby optimizing operational efficiency. The study compares various DRL algorithms, including proximal policy optimization (PPO), soft actor-critic (SAC), and advantage actor-critic (A2C), assessing their performance in maximizing predefined reward functions. The findings demonstrate the robustness of the PPO algorithm, demonstrating significant reward accumulation and adaptability in managing dynamic environments. This validation is supported by empirical data and learning curves, confirming the DRL model’s proficiency in optimizing energy use and enhancing operational performance in green hydrogen systems. The integration of DRL with the framework for green hydrogen and renewable energy suggests a comprehensive solution that improves energy efficiency, operational costs, and sustainability initiatives. The research highlights the potential of advanced machine learning techniques for enhanced operational efficiency of renewable energy systems.

本研究的重点是开发一个深度强化学习（DRL）框架，以优化可再生能源系统中的绿色氢气生产。通过集成基于drl的模型，该研究旨在增强能源供应、存储和分配的实时管理，包括电解槽和平衡来自光伏（PV）源、储能系统（ESS）和电网的能量流。DRL模型利用实际数据，动态适应可再生能源产量和市场价格的波动，从而优化运行效率。该研究比较了各种DRL算法，包括近端策略优化（PPO）、软行为者批评（SAC）和优势行为者批评（A2C），评估了它们在最大化预定义奖励函数方面的表现。研究结果证明了PPO算法的鲁棒性，在管理动态环境中展示了显著的奖励积累和适应性。这一验证得到了经验数据和学习曲线的支持，证实了DRL模型在优化能源使用和提高绿色氢系统运行性能方面的熟练程度。DRL与绿色氢和可再生能源框架的整合提出了一个全面的解决方案，可以提高能源效率、运营成本和可持续性举措。该研究强调了先进的机器学习技术在提高可再生能源系统运行效率方面的潜力。

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引用次数: 0

Unified peer-to-peer energy and frequency response reserve trading in isolated multi-microgrid systems 孤立多微电网系统中统一点对点能量和频率响应储备交易

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-05 DOI: 10.1016/j.segan.2025.102080

Chao Sun , Yun Liu , Ziyu Chen , Jizhong Zhu

Peer-to-peer (P2P) energy trading in a multi-microgrid (MMG) system can incentivize energy sharing and reduce the overall operational cost. However, the MMG system operating in isolated mode may face a reduction in system frequency response reserves, especially the inertia and primary frequency response (IPFR) reserve due to the growing integration of renewable energy resources (RESs) via power electronic inverters. Therefore, the current P2P trading framework ignoring the component of IPFR reserve could lead to frequency insecurity. To overcome these limitations, this paper proposes a two-stage P2P energy and IPFR reserve trading mechanism while considering the participation of synchronous generators (SGs) and inverter-based RES (IBRs) in a MMG system. In the first stage, a frequency-constrained unit commitment (UC) problem is formulated, where the unified transfer function structure is implemented in SGs and IBRs to analyze the frequency dynamic processes. In the second stage, each microgrid autonomously negotiates optimal energy and IPFR reserve trading based on the determined UC results through a fully decentralized ADMM based iterative algorithm, clearly reflecting the costs and prices involved. Case studies on 4-MG and 10-MG systems demonstrate that the proposed scheme ensures frequency-secure operation with good scalability. Results show that an additional cost of 995 CNY per day can avoid an economic loss of 1599 CNY per minute during frequency collapse events, confirming the economic efficiency and frequency-security benefits of the proposed approach.

多微电网（MMG）系统中的点对点（P2P）能源交易可以激励能源共享，降低整体运营成本。然而，在隔离模式下运行的MMG系统可能会面临系统频率响应储备的减少，特别是由于可再生能源（RESs）通过电力电子逆变器的日益整合，惯性和一次频率响应（IPFR）储备的减少。因此，目前的P2P交易框架忽略了IPFR储备的组成部分，可能导致频率不安全。为了克服这些限制，本文提出了一种考虑同步发电机（SGs）和基于逆变器的RES （IBRs）在MMG系统中的参与的两阶段P2P能源和IPFR储备交易机制。首先，提出频率约束单元承诺（UC）问题，在SGs和IBRs中实现统一的传递函数结构，分析频率动态过程；在第二阶段，每个微电网通过完全分散的基于ADMM的迭代算法，根据确定的UC结果自主协商最优能源和IPFR储备交易，清楚地反映所涉及的成本和价格。对4-MG和10-MG系统的实例研究表明，该方案保证了频率安全运行，具有良好的可扩展性。结果表明，每天995元的额外费用可以避免频率崩溃事件中每分钟1599元的经济损失，证实了该方法的经济效率和频率安全效益。

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引用次数: 0

Optimal dispatch and impact analysis of power–heat–gas integrated energy systems considering carbon pricing schemes 考虑碳定价方案的电-热-气综合能源系统优化调度及影响分析

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-03 DOI: 10.1016/j.segan.2025.102079

Seoeun Rho , Hee Seung Moon , Won Young Park , Dongjun Won

With the global shift toward carbon neutrality and the implementation of emissions trading systems, the interaction between emission allowance allocation scheme and the operation of sector-coupled integrated energy systems becomes increasingly important. This paper develops a linearized optimal dispatch strategy for an power–heat–gas integrated energy systems with renewable energy that considers allocation schemes to evaluate the impact of carbon pricing on economic outcomes and emission decrease. The model analyzes how system marginal prices are determined under different allocation schemes and quantifies trade-offs between carbon and cost under various policy and market conditions. Sensitivity analyses are conducted considering advances in cross-sectoral technologies and various energy and carbon prices. The results show that, unless the dispatch approach is properly aligned with the allocation scheme, improvements in the efficiency of combined heat and power units beyond a certain point can unintentionally increase both indirect emissions and operating costs. Through case studies under diverse scenarios, the study provides practical recommendations for system operators, investors, and policymakers to support affordable and low-carbon energy transitions. The findings underscore the importance of well-designed emission allocation policies and cost-effective investment strategies in achieving climate and energy transition goals.

随着全球向碳中和的转变和排放权交易制度的实施，排放配额分配方案与行业耦合综合能源系统运行之间的相互作用变得越来越重要。本文提出了一种考虑分配方案的可再生能源电-热-气集成能源系统的线性最优调度策略，以评估碳定价对经济效益和减排的影响。该模型分析了在不同分配方案下系统边际价格是如何确定的，并量化了在不同政策和市场条件下碳和成本之间的权衡。考虑到跨部门技术的进步以及各种能源和碳价格，进行了敏感性分析。结果表明，除非调度方式与分配方案相匹配，否则热电联产机组效率提高到一定程度后，会在无意中增加间接排放和运行成本。通过不同情景下的案例研究，本研究为系统运营商、投资者和政策制定者提供了切实可行的建议，以支持可负担的低碳能源转型。研究结果强调了精心设计的排放分配政策和具有成本效益的投资战略对实现气候和能源转型目标的重要性。

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引用次数: 0

Uncertainty quantification in load profiles with rising EV and PV adoption: The case of residential, industrial, and office buildings 随着电动汽车和光伏采用的增加，负载剖面的不确定性量化：以住宅、工业和办公楼为例

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-03 DOI: 10.1016/j.segan.2025.102078

Aiko Fias, Md Umar Hashmi , Geert Deconinck

The integration of photovoltaic (PV) generation and electric vehicle (EV) charging introduces significant uncertainty in electricity consumption patterns, particularly at the distribution level. This paper presents a comparative study for selecting metrics for uncertainty quantification (UQ) for net load profiles of residential, industrial, and office buildings under increased DER penetration. A variety of statistical metrics is evaluated for their usefulness in quantifying uncertainty, including, but not limited to, standard deviation, entropy, ramps, and distance metrics. The proposed metrics are classified into baseline-free, with baseline and error-based. These UQ metrics are evaluated for increased penetration of EV and PV. The results highlight suitable metrics to quantify uncertainty per consumer type and demonstrate how net load uncertainty is affected by EV and PV adoption. Additionally, it is observed that joint consideration of EV and PV can reduce overall uncertainty due to compensatory effects of EV charging and PV generation resulting from temporal alignment during the day. Uncertainty reduction is observed across all datasets and is most pronounced for the office building dataset.

光伏（PV）发电和电动汽车（EV）充电的整合在电力消费模式中引入了重大的不确定性，特别是在配电层面。本文提出了一项比较研究，以选择不确定性量化指标（UQ）的住宅，工业和办公建筑的净负荷剖面在增加的渗透。评估各种统计度量在量化不确定性方面的有用性，包括但不限于标准偏差、熵、斜坡和距离度量。建议的度量被分为无基线、有基线和基于错误。对这些UQ指标进行评估，以增加EV和PV的渗透率。结果强调了量化每种消费者类型不确定性的合适指标，并展示了电动汽车和光伏采用对净负荷不确定性的影响。此外，研究还发现，由于电动汽车充电和光伏发电在白天的时间排列造成的补偿效应，联合考虑电动汽车和光伏发电可以降低总体不确定性。在所有数据集中都观察到不确定性的降低，其中办公楼数据集的不确定性降低最为明显。

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引用次数: 0

Changing the paradigm of distribution networks planning and operation: A systematic review of the distributed energy resources impact 改变配电网规划和运行模式：分布式能源影响的系统回顾

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-02 DOI: 10.1016/j.segan.2025.102070

Lovro Lukač, Tomislav Antić, Tomislav Capuder

The connection of new distributed energy resources (DER) is recently being delayed by the system operators primarily due to the approach where each connection request is separately assessed. The lack of coordination between connection requests is making the process time-consuming and also creating virtual congestion in the network, hindering further investments. With the development of advanced analytical tools and increased observability, Distribution System Operators (DSOs) are starting to adopt new planning and operational approaches. Calculating a network’s hosting capacity (HC) is one of the most investigated planning concepts in modern power systems. However, HC is a conservative approach that considers worst-case scenarios, thereby limiting new connections to the network. This has created the need to develop the dynamic operating envelopes (DOE) concept. DOEs are envisioned as the concept bridging the gap between planning and operational phases, as well as an approach to test the relaxation of conservative fixed connection rules defined in national grid codes. A step further is the near real-time upgrade of DOE defined as P-Q flexibility regions, improving the previous concepts by estimating system-level service provision capabilities. The concept is based on controlling active and reactive power and, consequently, increasing system’s flexibility. The paper contributes in the form of an extensive review of modeling techniques and algorithms, defining the necessary dataset for each of the concepts and models. Furthermore, it discusses the importance of including various technical constraints. Additionally, the paper identifies necessary improvements in data collection to properly assess the value and constraints of DER providing services to the distribution system.

新分布式能源（DER）的连接最近被系统运营商延迟，主要原因是每个连接请求都被单独评估的方法。连接请求之间缺乏协调使得这一过程非常耗时，同时也在网络中造成了虚拟拥塞，阻碍了进一步的投资。随着先进分析工具的发展和可观察性的提高，配电系统运营商（dso）开始采用新的规划和操作方法。网络承载能力的计算是现代电力系统规划中研究最多的概念之一。然而，HC是一种考虑最坏情况的保守方法，因此限制了网络的新连接。这就产生了开发动态操作包络（DOE）概念的需求。did被设想为弥合规划和运营阶段之间差距的概念，以及测试国家电网规范中定义的保守固定连接规则放松的方法。更进一步的是将DOE定义为P-Q灵活性区域的近实时升级，通过估计系统级服务提供能力来改进先前的概念。这个概念是基于控制有功和无功功率，从而增加系统的灵活性。本文对建模技术和算法进行了广泛的回顾，为每个概念和模型定义了必要的数据集。此外，还讨论了包括各种技术限制的重要性。此外，本文还确定了数据收集方面的必要改进，以正确评估为配电系统提供服务的DER的价值和限制。

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引用次数: 0