Sustainable Energy Grids & Networks最新文献_第7页

A game theory-based edge device for renewable energy communities optimal management 基于博弈论的可再生能源社区优化管理边缘装置

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-31 DOI: 10.1016/j.segan.2025.102034

Giuseppe Sciumè, Francesco Montana, Eleonora Riva Sanseverino, Gaetano Zizzo

In most running implementations, Renewable Energy Communities aggregate consumers who share the use of renewable energy with the goal of reducing environmental impact. Each member, connected to the power grid via a single Point of Delivery, should optimize their consumption in order to achieve the community’s goal. However a comprehensive knowledge of the production capacities and consumption profiles of all members is required. To address this challenge, this paper proposes a distributed load scheduling method based on Game-Theory that achieves an optimal balance between individual and collective goals while preserving privacy and enabling energy services provision. The method distributes the computational workload among all users, making it feasible to implement on low-cost hardware devices. In addition, the method allows users to choose their own preferences regarding overall community goals. The proposed approach was evaluated in two case studies, showing that in a few iterations of the game, users reach an optimal equilibrium that not only maximizes individual profits but also satisfies community goals, without the need to share sensitive data.

在大多数正在运行的实现中，可再生能源社区将共享可再生能源使用的消费者聚集在一起，以减少对环境的影响。每个成员，通过一个单一的交付点连接到电网，应该优化他们的消费，以实现社区的目标。但是，需要全面了解所有成员的生产能力和消费概况。为了解决这一挑战，本文提出了一种基于博弈论的分布式负载调度方法，该方法在保护隐私和实现能源服务提供的同时，实现了个人和集体目标之间的最佳平衡。该方法将计算工作量分配给所有用户，使其能够在低成本硬件设备上实现。此外，该方法允许用户根据整体社区目标选择自己的偏好。在两个案例研究中对所提出的方法进行了评估，结果表明，在游戏的几次迭代中，用户达到了最优平衡，不仅使个人利润最大化，而且满足了社区目标，而不需要共享敏感数据。

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引用次数: 0

Impact of communication protocols on cost-optimization using home energy management systems 通信协议对家庭能源管理系统成本优化的影响

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-30 DOI: 10.1016/j.segan.2025.102038

Fabian Haslbeck , Nico Fuchs , Dirk Müller

Renewable electricity generation is volatile and requires flexibility in energy conversion and storage. In the residential sector, this flexibility can be incentivized by time-varying electricity prices. Recent literature has already investigated how a home energy management system (HEMS) can adapt residential electricity consumption to time-varying prices by controlling heat pumps or electric vehicles. Although the communication protocols of these devices can constrain their flexibility, none of the research has considered these influences yet. Thus, the novelty of the current study lies in quantifying the impact of real-world communication protocols on the performance of a cost-optimizing HEMS. For this, a HEMS system with a heat pump, electric vehicle, photovoltaic (PV) plant, and battery energy storage system is modeled. The HEMS uses a two-layer architecture to separate the communication protocol and the control strategy. The device communication layer abstracts the communication to the devices. The control layer uses a model predictive controller to minimize the total electricity costs of the system. To compare different communication protocols, a HEMS is simulated over one year using various consumption price offsets, system configurations, and packet loss probabilities. Results show that heat pumps can achieve the highest savings via Modbus direct load control with up to 6.3 % of the baseline costs. For electric vehicles, ISO 15118–20 shows the highest savings with up to 38.8 %, including battery degradation. However, the savings decrease as the consumption price offset increases.

可再生能源发电不稳定，在能源转换和储存方面需要灵活性。在住宅领域，这种灵活性可以通过时变电价来激励。最近的文献已经研究了家庭能源管理系统（HEMS）如何通过控制热泵或电动汽车来适应住宅用电量随时间变化的价格。虽然这些设备的通信协议限制了它们的灵活性，但目前还没有研究考虑到这些影响。因此，当前研究的新颖之处在于量化现实世界通信协议对成本优化HEMS性能的影响。为此，对一个包含热泵、电动汽车、光伏电站和电池储能系统的HEMS系统进行了建模。HEMS采用两层结构将通信协议和控制策略分离。设备通信层抽象了与设备之间的通信。控制层采用模型预测控制器使系统总电力成本最小。为了比较不同的通信协议，使用各种消耗价格补偿、系统配置和包丢失概率对HEMS进行了一年的模拟。结果表明，热泵可以通过Modbus直接负荷控制实现最高的节省，最多可节省基准成本的6.3%。对于电动汽车，ISO 15118-20显示最高的节省高达38.8%，包括电池退化。然而，储蓄随着消费价格抵消的增加而减少。

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引用次数: 0

Bounded rational bidding strategy of GenCo in electricity spot market based on prospect theory and distributional reinforcement learning 基于前景理论和分布式强化学习的电力现货市场有限理性竞价策略研究

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-28 DOI: 10.1016/j.segan.2025.102036

Qiyuan Liu , Donghan Feng , Yun Zhou , Yuanhao Feng , Quan Zhou

With the increasing penetration of renewable energy (RE) in power systems, the electricity spot market has become increasingly uncertain, presenting significant challenges for generation companies (GenCos) in formulating effective bidding strategies. Most existing studies assume that GenCos act as perfectly rational decision makers, overlooking the impact of irrational bidding behaviors in uncertain market environments. To address this, we model GenCo decision-making with prospect theory (PT) and formulate a bilevel stochastic model for strategic bidding in the spot market. We further propose a distributional reinforcement learning (DistRL) framework to learn risk-aware bidding policies for bounded rational GenCo. The framework is validated on a 27-bus system from eastern China. Across 16 uncertainty scenarios with RE penetration ranging from 5 % to 80 %, our DistRL agent consistently achieves higher average returns and lower volatility than Deep Q-Network (DQN), Double DQN (DDQN), and prioritized experience replay DQN (PER-DQN) in every scenario. When integrating a gated recurrent unit (GRU) network, performance improves further, accompanied by a limited increase in training time. These results indicate that aligning DistRL with bounded rational preferences yields more robust bidding under market uncertainty.

随着可再生能源在电力系统中的日益普及，电力现货市场的不确定性越来越大，这给发电公司制定有效的竞价策略带来了重大挑战。现有研究大多假设发电公司是完全理性的决策者，忽略了不确定市场环境下非理性竞价行为的影响。为了解决这一问题，我们利用前景理论（PT）对发电公司决策进行建模，并建立了现货市场战略竞价的双层随机模型。我们进一步提出了一个分布式强化学习（DistRL）框架来学习有界理性发电公司的风险感知投标策略。该框架在中国东部的27辆公交系统上得到了验证。在16个不确定性场景中，RE渗透率从5%到80%不等，我们的DistRL代理在每个场景中都比Deep Q-Network （DQN）、Double DQN （DDQN）和优先体验重播DQN （PER-DQN）始终实现更高的平均回报和更低的波动性。当集成门控循环单元（GRU）网络时，性能进一步提高，伴随着有限的训练时间增加。这些结果表明，在市场不确定性下，将DistRL与有限理性偏好相结合可以产生更强的稳健竞价。

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引用次数: 0

A python toolbox for flexibility aggregation and disaggregation: PyFlexAD 一个用于灵活性聚合和分解的python工具箱：PyFlexAD

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-27 DOI: 10.1016/j.segan.2025.102033

Emrah Öztürk , Kevin Kaspar , Timm Faulwasser , Karl Worthmann , Peter Kepplinger , Klaus Rheinberger

The increasing penetration of volatile renewables and growing electricity demand pose several challenges for power systems. Simultaneously, flexible devices – so called distributed energy resources (DER) – are becoming more widespread, making them attractive for providing ancillary services. The flexibility of a single device can be represented by a set of reference power profiles, and the flexibility of multiple devices by the summation of individual flexibility sets. However, set addition, also known as the Minkowski sum, is usually computationally intractable. This has led to the development of various approximation methods in the literature. The current study improves upon our previously published vertex-based inner approximation, by extending it to more general storage devices and hierarchical aggregation settings. We validate the efficacy and accuracy of the proposed method through case studies using real data and provide the source code of the algorithm as a Python package that enables the (dis-)aggregation of various flexible devices in real-world scenarios.

不稳定的可再生能源的不断渗透和不断增长的电力需求给电力系统带来了一些挑战。同时，灵活的设备——即所谓的分布式能源（DER）——正变得越来越普遍，这使得它们在提供辅助服务方面具有吸引力。单个设备的灵活性可以用一组参考功率曲线来表示，而多个设备的灵活性可以用单个灵活性集的总和来表示。然而，集合加法，也被称为闵可夫斯基和，通常是难以计算的。这导致了文献中各种近似方法的发展。当前的研究改进了我们之前发表的基于顶点的内部近似，将其扩展到更通用的存储设备和分层聚合设置。我们通过使用真实数据的案例研究验证了所提出方法的有效性和准确性，并提供了算法的源代码作为Python包，该包可以在真实场景中实现各种灵活设备的（非）聚合。

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引用次数: 0

Delay-structured noise robust dynamic mode decomposition for power system modal estimation with faulty PMU data 基于延迟结构噪声鲁棒动态模态分解的PMU故障模态估计

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-27 DOI: 10.1016/j.segan.2025.102029

Rajarshi Roychowdhury , Xuan Wu , Mahesh S. Illindala

Power System Wide-area monitoring systems (WAMS) rely on high-resolution phasor measurement unit (PMU) data to enable real-time situational awareness and oscillation mode analyses. However, the integrity and availability of PMU data are increasingly threatened by communication failures, sensor malfunctions, and sophisticated adversarial attacks, leading to missing entries and corrupted measurements that can undermine conventional modal estimation techniques. This paper presents a robust algorithm for electromechanical mode identification that leverages advanced low-rank and delay-structured recovery methods to accurately extract system modes even when PMU datasets are severely degraded by both random missing data and adversarial corruption. The proposed approach is systematically evaluated under extreme scenarios with up to 70 % missing data and 25 % bad data, far exceeding the stress conditions considered in most prior studies. The proposed method was benchmarked against the three most widely used techniques in power system analysis: Prony, Matrix Pencil (MP), and Eigensystem Realization Algorithm (ERA). Results on real-world WAMS datasets demonstrate that the proposed method substantially outperforms these established algorithms in the presence of adversarial data, ensuring reliable mode estimation.

电力系统广域监测系统（WAMS）依靠高分辨率相量测量单元（PMU）数据来实现实时态势感知和振荡模式分析。然而，PMU数据的完整性和可用性日益受到通信故障、传感器故障和复杂的对抗性攻击的威胁，导致丢失条目和损坏的测量，从而破坏传统的模态估计技术。本文提出了一种鲁棒的机电模式识别算法，该算法利用先进的低秩和延迟结构恢复方法，即使PMU数据集因随机丢失数据和对抗性损坏而严重退化，也能准确提取系统模式。所提出的方法在极端情况下进行了系统评估，其中多达70%的数据缺失和25%的数据不良，远远超过了大多数先前研究中考虑的应力条件。提出的方法与电力系统分析中最常用的三种技术：proony、矩阵铅笔（MP）和特征系统实现算法（ERA）进行了基准测试。在实际WAMS数据集上的结果表明，在存在对抗性数据的情况下，所提出的方法实质上优于这些已建立的算法，确保了可靠的模式估计。

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引用次数: 0

Research on the multi-scenario potential analysis of long-duration energy storage in off-grid microgrids 离网微电网长时程储能多场景潜力分析研究

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-27 DOI: 10.1016/j.segan.2025.102030

Jialin Li , Shuxia Yang , Yu Hu , Xufeng Zhang , Min Yu , Mengyu Wang

Long-duration energy storage (LDES) plays a crucial role in ensuring the stability of high-penetration renewable energy systems. However, its application in off-grid microgrids has not been comprehensively examined, particularly in multi-scenario analyses. This study develops a linear programming model for various scenarios to investigate the application prospects of LDES in off-grid microgrids powered solely by photovoltaic (PV), wind turbine (WT), or a hybrid of both. The results show that in all scenarios, microgrid systems using LDES achieve lower costs than those with short-duration energy storage (SDES), demonstrating its advantages. Among different configurations, the photovoltaic-wind-hydrogen (PV-WT-HYD) system has the lowest cost, reducing expenses by 46.61 % compared to the most expensive lithium-ion storage. In terms of resource attributes, wind power better leverages the economic advantages of LDES. Specifically, during the transition from SDES to LDES, WT-LDES microgrids achieve cost reductions 46.94 % faster than PV-LDES microgrids. Furthermore, from a policy perspective, the northern China, southeast coastal region, and central China offer favorable conditions for developing PV-WT-HYD off-grid microgrids due to abundant wind and photovoltaic resources, lower hydrogen production costs, and suitable hydrogen storage conditions. Thus, targeted policies could be introduced to facilitate the adoption and development of LDES.

长时间储能（LDES）对于保证高渗透可再生能源系统的稳定性起着至关重要的作用。然而，其在离网微电网中的应用尚未得到全面研究，特别是在多场景分析中。本研究针对不同场景建立了线性规划模型，以探讨LDES在仅由光伏（PV）、风力涡轮机（WT）或两者混合供电的离网微电网中的应用前景。结果表明，在所有情况下，使用LDES的微电网系统比使用短时间储能（SDES）的微电网系统实现更低的成本，显示了其优势。在不同的配置中，光伏-风-氢（PV-WT-HYD）系统的成本最低，与最昂贵的锂离子储能相比，成本降低了46.61 %。在资源属性上，风电更好地发挥了LDES的经济优势。具体来说，在从SDES到LDES的过渡过程中，WT-LDES微电网的成本降低速度比PV-LDES微电网快46.94 %。此外，从政策角度看，中国北方、东南沿海和中部地区风能和光伏资源丰富，制氢成本较低，储氢条件适宜，为发展PV-WT-HYD离网微电网提供了有利条件。因此，可以采取有针对性的政策来促进采用和发展低成本经济系统。

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引用次数: 0

Evaluation of electric power losses in wind farms considering temperature-dependent power flow 考虑温度相关潮流的风电场电力损耗评估

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-25 DOI: 10.1016/j.segan.2025.102003

Theofilos A. Papadopoulos , Kalliopi D. Pippi , Evangelos E. Pompodakis , Georgios C. Kryonidis , Andreas I. Chrysochos

In this paper an investigation on energy loss calculations in underground cable systems of onshore wind farms is performed by taking into consideration the cable resistance temperature-dependency. Two temperature-dependent power flow models have been developed on the basis of the IEC 60287 and the IEC 60853 electrothermal modelling approaches. Quasi-static simulations are conducted over an one year horizon of operation of two real-world onshore wind farms by using the two temperature-dependent models, as well as conventional power flow analysis, where the cable resistances are assumed constant; significant differences are obtained under specific cases. The potential reduction in annual losses is also quantified in monetary terms. The impact of several important parameters on the estimation of the wind farm power losses is investigated; these include the soil thermal resistivity and diffusivity as well as the ambient temperature. The findings of this study serve as a valuable resource for wind farm owners, helping to evaluate the efficiency and profitability of their investment.

本文研究了考虑电缆电阻温度依赖的陆上风电场地下电缆系统的能量损失计算。在IEC 60287和IEC 60853电热建模方法的基础上，开发了两个温度相关的功率流模型。通过使用两种温度相关模型，以及传统的潮流分析（假设电缆电阻恒定），对两个现实世界陆上风电场进行了一年的准静态模拟；在具体的情况下，有显著的差异。每年损失的潜在减少也用货币来量化。研究了几个重要参数对风电场损失估计的影响；这些包括土壤热电阻率和扩散系数以及环境温度。这项研究的结果为风电场所有者提供了宝贵的资源，帮助他们评估投资的效率和盈利能力。

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引用次数: 0

Holistic framework for real-world management of hosting capacity in low-voltage distribution networks 低压配电网主机容量实际管理的整体框架

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-25 DOI: 10.1016/j.segan.2025.102026

Mohammed G. Mahairi , Bassam Mohamed , Xavier Domínguez , Adrian Miranda , Pablo Arboleya

The increasing deployment of photovoltaic systems, electric vehicles, and other distributed energy resources introduces significant operational challenges in low-voltage (LV) unbalanced distribution networks. This paper presents a holistic and scalable framework for hosting capacity (HC) assessment and management, combining time-series Nodal Hosting Capacity (NHC) analysis with Monte Carlo Simulation (MCS) to evaluate generation and consumption limits under realistic, time-varying conditions. The methodology accounts for unbalanced network conditions. These include negative- and zero-sequence voltage unbalance and neutral current constraints. The framework is implemented as a performance-optimized, user-centered software tool for Distribution System Operators (DSOs). It offers high computational efficiency, dynamic visualizations, and an interactive interface to support decision-making. The impact of flexibility mechanisms, such as coordinated electric vehicle charging, is also demonstrated, showing their potential to reduce violations and enhance HC. The framework is validated on a real European LV network. It enables DSOs to manage HC as a dynamic operational resource, improving interconnection processes, network utilization, and planning transparency.

光伏系统、电动汽车和其他分布式能源的日益普及给低压（LV）不平衡配电网带来了重大的运营挑战。本文提出了一个整体和可扩展的托管容量（HC）评估和管理框架，将时间序列节点托管容量（NHC）分析与蒙特卡罗模拟（MCS）相结合，以评估现实时变条件下的发电和消费限制。该方法考虑了不平衡的网络条件。这些包括负序和零序电压不平衡和中性电流约束。该框架是为配电系统运营商（dso）实现的性能优化、以用户为中心的软件工具。它提供了高计算效率、动态可视化和支持决策的交互式界面。柔性机制（如协调电动汽车充电）的影响也得到了证明，显示了它们在减少违规和增强HC方面的潜力。该框架在一个真实的欧洲LV网络中得到了验证。它使dso能够将HC作为动态运营资源进行管理，从而改善互连流程、网络利用率和规划透明度。

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引用次数: 0

A scalable decentralized framework for transactive energy management in low-voltage residential community 一种可扩展的分布式低压住宅社区能源管理框架

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-25 DOI: 10.1016/j.segan.2025.102023

Vishnu Dharssini A.C. , Charles Raja S. , Dinesh Kanna M. , Hemanth G.R.

India’s power grid continues to face high distribution losses, outdated infrastructure, and uneven supply reliability, underscoring the need for decentralized energy solutions. While existing peer-to-peer (P2P) trading models demonstrate potential, they often rely on simplified pricing schemes, lack robust forecasting, and provide limited community engagement. To overcome these limitations, this paper proposes a User-Interfaced Peer-to-Peer Energy Trading System (UIETS) for low-voltage residential communities, validated in Malligai Homes, Madurai, Tamil Nadu. Unlike prior frameworks, the proposed system uniquely integrates (i) a Smart Residential Energy Management System (SREMS) employing Long Short-Term Memory (LSTM) models for simultaneous load and solar forecasting, (ii) a community-specific Online Energy Trading Scheme (OETS) portal that actively engages prosumers and consumers, and (iii) Local Community Market Operators (LCMOs) who execute a Periodic Double Auction Pool (PDAP) to determine a fair and computationally efficient Transactive Clearing Price (TCP). Real-time validation shows 30–35 % lower purchase costs for households and 25–35 % higher revenues for prosumers compared to grid-only operation, outperforming existing auction- and optimization-based models. Beyond cost savings, the UIETS enhances renewable adoption, transparency, and scalability, offering a novel, community-driven framework for decentralized transactive energy management aligned with global energy transition goals.

印度的电网仍然面临着配电损耗大、基础设施陈旧、供应可靠性不稳定等问题，这凸显了分散能源解决方案的必要性。虽然现有的点对点（P2P）交易模式显示出潜力，但它们往往依赖于简化的定价方案，缺乏可靠的预测，并且提供有限的社区参与。为了克服这些限制，本文提出了一种针对低压住宅社区的用户界面点对点能源交易系统（UIETS），并在泰米尔纳德邦马杜赖的malikai Homes进行了验证。与之前的框架不同，拟议的系统独特地集成了(i)采用长短期记忆（LSTM）模型的智能住宅能源管理系统（SREMS），用于同时进行负荷和太阳能预测；（ii）社区特定的在线能源交易计划（OETS）门户网站，积极吸引生产消费者和消费者；（iii）执行定期双重拍卖池（PDAP）以确定公平且计算效率高的交易结算价格（TCP）的本地社区市场运营商（lcmo）。实时验证显示，与纯电网运营相比，家庭购买成本降低30 - 35% %，消费者收入提高25 - 35% %，优于现有的拍卖和优化模式。除了节约成本，UIETS还提高了可再生能源的采用、透明度和可扩展性，为分散式交易能源管理提供了一个新颖的、社区驱动的框架，与全球能源转型目标保持一致。

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引用次数: 0

Predictive risk-aware control for microgrids: Operation of a revenue-generating energy management system 微电网的预测性风险感知控制：创收能源管理系统的运行

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-24 DOI: 10.1016/j.segan.2025.102028

Tereza Ábelová, Marek Wadinger, Michal Kvasnica

Economically sustainable operation of microgrid systems helps with transition of energy sector towards renewable energy sources. This paper presents a comprehensive energy management framework for microgrids, integrating advanced control methodologies with practical deployment considerations. The proposed framework employs a layered control structure, focusing on the energy management layer that utilizes a scenario-based model predictive control (MPC) approach to address uncertainties using a risk measure in microgrid systems. A novel two-stage MPC method is introduced to enhance computational efficiency, combining stochastic and deterministic MPC. The software architecture is designed for scalability, consciously managing cloud-based resources for computationally demanding tasks while maintaining local control for real-time operations. The framework supports modular microgrid composition, revenue stacking, and delivers profit generation by optimizing multiple revenue streams, including energy arbitrage, peak-shaving, and imbalance settlement. The framework is validated through a case study of a commercial microgrid with photovoltaic power plant accompanied by a large-scale battery energy storage, demonstrating significant economic benefits and operational reliability. The results highlight the framework’s competence in making microgrid operation profitable.

微电网系统的经济可持续运行有助于能源部门向可再生能源过渡。本文提出了一个综合的微电网能源管理框架，将先进的控制方法与实际部署考虑相结合。提出的框架采用分层控制结构，重点关注能源管理层，利用基于场景的模型预测控制（MPC）方法，利用微电网系统中的风险度量来解决不确定性问题。为了提高计算效率，提出了一种新的两阶段MPC方法，将随机和确定性MPC相结合。软件架构是为可扩展性而设计的，有意识地管理基于云的资源，用于计算要求高的任务，同时保持对实时操作的本地控制。该框架支持模块化微电网组成、收益叠加，并通过优化多种收益流（包括能源套利、调峰和不平衡结算）来产生利润。该框架通过一个商业微电网的案例研究进行了验证，该微电网具有光伏发电厂和大型电池储能，展示了显著的经济效益和运行可靠性。结果突出了该框架在使微电网运营盈利方面的能力。

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引用次数: 0