Sustainable Energy Grids & Networks最新文献

Distribution system state estimation for system identification and network model validation: An experience on a real low voltage network

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-04-22 DOI: 10.1016/j.segan.2025.101710

Marta Vanin , Reinhilde D’hulst , Dirk Van Hertem

Distribution network data in utility databases are known to present multiple issues that may lead to problematic results when used in physics-based engines, e.g., leading to constraint violations in (optimal) power flow. This paper discusses the application of state and parameter estimation methods to a real low voltage network, where power and voltage time series from digital meters are used to improve the utility’s network data. Good input data are crucial for the advanced decision support tools that are needed to manage networks with increased shares of low carbon technology.

Conventional state and parameter estimation methods leverage measurements from a single (or few) time stamp(s) to detect sparse, local data errors or sudden changes in the system (e.g., a line being de-energized). The methods in this paper differ in that their goal is to estimate “historical” states and reconstruct system parameters from scratch for all users and branches. This is possible through the augmentation of conventional state vectors (i.e., voltage phasors) to include asset properties (e.g., phase connectivity), and binding the asset states as time-independent throughout the time series.

Discussions of real-life experiences are uncommon, but valuable to highlight the differences between working with synthetic or field data. For example, the main contribution of this work rests in exploring the use of state estimation for the statistical validation of data-driven models for real networks, for which the ground-truth is not available (contrary to the case of synthetic data).

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引用次数: 0

Expansion planning via decomposition to achieve fully renewable power and freshwater systems

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-04-19 DOI: 10.1016/j.segan.2025.101713

Mubarak J. Al-Mubarak , Antonio J. Conejo

As the reliance on electricity for producing freshwater continues to grow, the development of an expansion planning model that captures the interdependency between power and freshwater systems becomes increasingly important. This paper proposes a two-stage stochastic expansion planning model that represents the interdependence of these systems and accounts for the uncertainties involved. The first stage represents investments for achieving fully renewable power and freshwater systems, while the subsequent stage represents the operation of both systems. The model accounts for both long-term uncertainties, which pertain to growth in power and freshwater demands, and short-term uncertainties, which pertain to the daily fluctuations in freshwater and power demands as well as in renewable production. Due to the complexity of representing the operation of both systems under numerous operating conditions, expansion planning models often become computationally burdensome. To reduce the computational burden, we propose an effective partitioning technique that relies on Benders’ decomposition, dividing the expansion planning problem into a sufficiently small master problem and numerous subproblems. To enhance convergence, we incorporate the operation constraints pertaining to the worst operating condition into the master problem. Numerical experiments underscore the efficacy of utilizing the proposed technique to solve the expansion planning of large-scale systems.

随着对电力生产淡水的依赖程度不断增加，开发一种能够反映电力和淡水系统之间相互依存关系的扩建规划模型变得越来越重要。本文提出了一个两阶段随机扩展规划模型，该模型体现了这些系统之间的相互依存关系，并考虑了其中的不确定性。第一阶段表示为实现完全可再生的电力和淡水系统而进行的投资，而随后的阶段则表示这两个系统的运行。该模型既考虑了与电力和淡水需求增长有关的长期不确定性，也考虑了与淡水和电力需求以及可再生能源产量的日常波动有关的短期不确定性。由于这两个系统在多种运行条件下的运行情况非常复杂，扩建规划模型的计算负担往往很重。为了减轻计算负担，我们提出了一种有效的分区技术，该技术依赖于本德斯分解，将扩展规划问题划分为一个足够小的主问题和众多子问题。为了提高收敛性，我们在主问题中加入了与最差运行条件相关的操作约束。数值实验证明了利用所提技术解决大规模系统扩展规划问题的有效性。

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引用次数: 0

Day-ahead joint market operation strategy of grid-connected wind farms with flexible allowable generation deviation rates

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-04-17 DOI: 10.1016/j.segan.2025.101714

Tianhui Meng, Jilai Yu, Yufeng Guo

The uncertainty of wind power output affects the efficient operation of the electricity spot market and has become a key factor restricting the participation of wind farms in the market. To this end, this paper proposes a day-ahead joint market operation strategy that considers allowable deviation rates of wind power output. Unlike traditional electricity markets which impose uniform deviation requirements on all wind farms, the main grid side provides a more diverse range of selectable deviation rates. The bidding strategy for wind farms in the joint day-ahead and balancing markets is explored, allowing them to independently select deviation rates and submit schedule curves and offer prices. A joint clearing model for the day-ahead energy-reserve and balancing market is established, incorporating the carbon emission trading costs of thermal power units, with the aim of minimizing the system operating cost. Numerical results indicate that compared with the traditional market participation method, the proposed strategy not only encourages wind farms to improve output accuracy, but also reflects the market economic principle of high quality and high price. Meanwhile, integrating carbon emission trading costs into the model helps to reduce carbon emissions while ensuring the economic operation of the system.

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引用次数: 0

Probabilistic Load Forecasting of distribution power systems based on empirical copulas

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-04-16 DOI: 10.1016/j.segan.2025.101708

Pål Forr Austnes , Celia García-Pareja , Fabio Nobile , Mario Paolone

Accurate and reliable electricity load forecasts are becoming increasingly important as the share of intermittent resources in the system increases. Distribution System Operators (DSOs) are called to accurately forecast their production and consumption to place optimal bids in the day-ahead market. Violations of their dispatch-plan requires activation of reserve-power which has a direct cost for the DSO, and also necessitates available reserve-capacity. Forecasts must account for the volatility of weather-parameters that impacts both the production and consumption of electricity. If DSO-loads are small or lower-granularity forecasts are needed, parametric statistical methods may fail to provide reliable performance since they rely on a priori statistical distributions of the variables to forecast. In this paper, we introduce a Probabilistic Load Forecast (PLF) method based on Empirical Copulas (ECs). The model is data-driven, does not need a priori assumption on parametric distribution for variables, nor the dependence structure (copula). It employs a kernel density estimate of the underlying distribution using beta kernels that have bounded support on the unit hypercube. The method naturally supports variables with widely different distributions, such as weather data (including forecasted ones) and historic electricity consumption, and produces a conditional probability distribution for every time step in the forecast, which allows inferring the quantiles of interest. The proposed non-parametric approach differs significantly from previous forecasting methods based on copulas, which typically uses copulas to model hierarchical dependence. Our approach is highly flexible and can produce meaningful forecasts even at very low aggregated levels (e.g. neighborhoods). The bandwidth of the beta kernel density estimators is optimized using Integrated Square Error (ISE) and such optimization can be performed online (i.e. without knowing the realization). We also investigate rule-of-thumb and Quantile Loss (QL) as objectives for the bandwidth-optimization. We present results from an open dataset and showcase the strength of the model with respect to Quantile Regression (QR) using standard probabilistic evaluation metrics.

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引用次数: 0

Ensuring secure connectivity in smart vehicular to grid technology: An elliptic curve-based authentication key agreement framework

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-04-12 DOI: 10.1016/j.segan.2025.101696

Samiulla Itoo , Faheem Syeed Masoodi , Musheer Ahmad

The seamless and secure operation of Vehicle-to-Grid (V2G) networks is paramount for the future of smart grid technology, where Electric Vehicles (EVs) not only draw power but also supply it back to the grid. However, the integration of EVs into the grid introduces significant security and privacy challenges, particularly in the exchange of sensitive information between EV owners and charging station aggregators. This paper presents a novel authentication key agreement protocol specifically designed to address these challenges within V2G networks. The proposed protocol leverages Elliptic Curve Cryptography (ECC) and a robust hash function to establish a secure communication channel, ensuring that personal data remains protected from a wide array of security threats, including eavesdropping, impersonation, and replay attacks. The protocol’s effectiveness and security are rigorously validated through simulation analysis using the Scyther tool, which confirms its resilience against potential vulnerabilities. Moreover, the protocol is designed for compatibility with various encryption techniques, ensuring its adaptability across different V2G network configurations. Our analysis also highlights the protocol’s efficiency, demonstrating minimal processing and communication overhead, making it suitable for real-time applications in resource-constrained environments. The findings of this study suggest that the proposed protocol not only enhances the security and privacy of V2G networks but also contributes to the broader goal of creating a more secure, reliable, and user-friendly smart grid ecosystem. By safeguarding the exchange of sensitive information, this protocol ensures that V2G networks can operate safely, fostering greater confidence among EV owners and facilitating the wider adoption of this innovative technology.

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引用次数: 0

DNkS: A distance-based neighborhood k-search algorithm for determining meter–transformer connectivity in low-voltage grids

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-04-11 DOI: 10.1016/j.segan.2025.101707

Iker Garcia , Roberto Santana , Jennifer Gonzalez

The Distance-based Neighborhood k-Search (DN

k

S) algorithm, introduced in this article, offers a novel approach to enhancing meter–transformer connectivity modeling in low-voltage grids. By employing a local

k

-neighborhood search strategy, DN

k

S effectively subdivides the grid into manageable sections, ensuring robust connectivity assessments. Utilizing metrics such as Adjusted Mutual Information and Accuracy, DN

k

S demonstrated superior performance in trials, achieving up to 100% accuracy in certain cases, significantly outperforming existing state-of-the-art methods such as deep convolutional time-series clustering and spectral embedding-based meter–transformer mapping. Although DN

k

S is effective, its performance critically depends on the accuracy of meter and transformer coordinates. In comparative analyses across various network configurations, DN

k

S consistently outperformed other methods, affirming its utility and effectiveness. The versatile nature of the algorithm would allow its integration into existing systems in various ways, for example, through an API or a web interface. Implementing DN

k

S promises substantial improvements in the reliability and accuracy of utility network models, directly contributing to enhanced grid management practices.

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引用次数: 0

Regression based anomaly detection in electric vehicle state of charge fluctuations through analysis of electric vehicle charging infrastructure Data

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-04-11 DOI: 10.1016/j.segan.2025.101704

Sagar Babu Mitikiri , Yash Tiwari , Vedantham Lakshmi Srinivas , Mayukha Pal

With the increase in the number of electric vehicles (EV), there is a need for the development of the EV charging infrastructure (EVCI) to facilitate fast charging, thereby mitigating the EV congestion at charging stations. The role of the public charging station depot is to charge the vehicle, prioritizing the achievement of the desired state of charge (SoC) value for the EV battery or charging till the departure of the EV, whichever occurs first. The integration of cyber and physical components within EVCI defines it as a cyber physical power system (CPPS), increasing its vulnerability to diverse cyber attacks. When an EV interfaces with the EVCI, mutual exchange of data takes place via various communication protocols like the Open Charge Point Protocol (OCPP), and IEC 61850. Unauthorized access to this data by intruders leads to cyber attacks, potentially resulting in consequences like energy theft, and revenue loss. These scenarios may cause the EVCI to incur higher charges than the actual energy consumed or the EV owners to remit payments that do not correspond adequately to the amount of energy they have consumed. This article proposes an EVCI architecture connected to the utility grid and uses the EVCI data to identify the anomalies or outliers present in the EV transmitted data, particularly focusing on SoC irregularities. The proposed methodology involves utilizing a ridge regression based machine learning (ML) model for predicting changes in the SoC. The adversaries have the capability of spoofing these change in SoC values, consequently making the EVCI incapable of achieving the desired task. Three distinct spoofing techniques namely, decimal shifting, incremental array spoofing, and random spoofing are implemented on the data and subsequently tested with the proposed methodology. The results show that the proposed methodology detects the anomaly accurately and also classifies the type of spoofing that causes the anomaly.

随着电动汽车（EV）数量的增加，有必要发展电动汽车充电基础设施（EVCI）以促进快速充电，从而缓解充电站的电动汽车拥堵问题。公共充电站的作用是为车辆充电，优先实现电动汽车电池所需的充电状态（SoC）值或充电至电动汽车离开，以先发生者为准。EVCI 内网络和物理组件的集成将其定义为网络物理电力系统 (CPPS)，从而增加了其面对各种网络攻击的脆弱性。电动汽车与 EVCI 连接时，会通过各种通信协议（如开放充电点协议 (OCPP) 和 IEC 61850）相互交换数据。入侵者未经授权访问这些数据会导致网络攻击，可能造成能源盗窃和收入损失等后果。这些情况可能会导致 EVCI 产生高于实际能源消耗的费用，或导致电动汽车车主汇出的付款与其消耗的能源量不符。本文提出了一种连接到公用电网的 EVCI 架构，并使用 EVCI 数据来识别电动汽车传输数据中存在的异常或离群值，尤其侧重于 SoC 不规则性。建议的方法包括利用基于脊回归的机器学习（ML）模型来预测 SoC 的变化。对手有能力欺骗这些 SoC 值的变化，从而使 EVCI 无法完成预期任务。我们在数据上实施了三种不同的欺骗技术，即十进制移位、增量阵列欺骗和随机欺骗，随后用所提出的方法进行了测试。结果表明，所提出的方法能准确检测到异常，并能对导致异常的欺骗类型进行分类。

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引用次数: 0

Analytical introduction of uncertainty into long term distribution systems decision-making

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-04-11 DOI: 10.1016/j.segan.2025.101711

J.-P. Dib , M.-C. Alvarez-Herault , O. Ionescu Riffaud , B. Raison

Distribution system planning consists in imagining the evolution of the design and operation of distribution systems over a horizon going from several years to decades. There is a lack of standard methodologies that integrate the growing number of uncertainties. In this article, our aim is to provide a framework for integrating uncertainty, from the diagnosis of network constraints and the setup of solutions to their economic evaluations. To do so, we start by modeling the network under load uncertainty. This allows us to use probabilistic power flow calculations for constraint estimations. We use these to determine the best strategy, between line reinforcement and demand response. Finally, we use a compound option model to assess the economic validity of undertaking a unique action, or series of actions, when uncertainty is taken into account. This framework was successfully applied to the IEEE 70 bus network with a discussion on DSO’s options: ”waiting for more information”, ”investing” or ”activating demand response”. Results show that demand response is not optimal on the lower part of the network but should be used on the upper part since only some nodes would see under-voltage constraints during 0.2% of the year. Also, a sensitivity analysis on the cost of demand response enables drawing the DSO’s willingness to pay considering two scenarios (expected load evolution and worst case).

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引用次数: 0

An efficient coordinated energy trading mechanism for economic sustainability of EVCSs and EVs in competitive electricity market 竞争性电力市场中促进电动汽车和低碳能源系统经济可持续性的高效协调能源交易机制

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-04-10 DOI: 10.1016/j.segan.2025.101712

Naresh Boda, Prashant Kumar Tiwari

The integration of electric vehicle charging stations (EVCS) into the power grid significantly impacts the electricity market due to the uncoordinated charging behavior of electric vehicles (EVs). This causes changes in load demand, which is responsible for grid imbalances, price changes, higher grid maintenance costs, and a higher chance of power outages, especially in low-voltage grids. This work focuses on enhancing the profit for EVCS while concurrently minimizing the charging cost for EVs owners by using coordinated charging of EVs at EVCS. The better coordinated charging of EVs increases the profit of each EVCS and lowers the cost of charging for EVs owners. This paper proposes a Monte Carlo simulation-based bi-level coordinated transactive energy trading mechanism. In the first part, the Monte Carlo simulation coordinates the EVs charging process based on their respective arrival time at the EVCS, where as in the second part, a cooperative game theory-based energy trading mechanism has been modeled to trade energy in the transactive energy market through the Distribution System Operators(DSO) with different EVCSs. The proposed coordinated charging approach enhances the profitability of all EVCSs as compared to the other existing method. The proposed technique has been implemented and validated on a modified IEEE-33 bus system.

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引用次数: 0

Effect of electric vehicles, heat pumps, and solar panels on low-voltage feeders: Evidence from smart meter profiles

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-04-08 DOI: 10.1016/j.segan.2025.101705

Thijs Becker , Raf Smet , Bruno Macharis , Koen Vanthournout

Electric Vehicles (EVs), Heat Pumps (HPs) and solar panels are Low-Carbon Technologies (LCTs) that are being connected to the Low-Voltage Grid (LVG) at a rapid pace. One of the main hurdles to understand their impact on the LVG is the lack of recent, large electricity consumption datasets, measured in real-world conditions. We investigated the contribution of LCTs to the size and timing of peaks on LV feeders by using a large dataset of 42,089 smart meter profiles of residential LVG customers. These profiles were measured in 2022 by Fluvius, the Distribution System Operator (DSO) of Flanders, Belgium. The dataset contains customers that proactively requested higher-resolution smart metering data, and hence is biased towards energy-interested people. LV feeders of different sizes were statistically modeled with a profile sampling approach. For feeders with 40 connections, we found a contribution to the feeder peak of 1.2 kW for a HP, 1.4 kW for an EV and 2.0 kW for an EV charging faster than 6.5 kW. A visual analysis of the feeder-level loads shows that the classical duck curve is replaced by a night-camel curve for feeders with only HPs and a night-dromedary curve for feeders with only EVs charging faster than 6.5 kW. Consumption patterns will continue to change as the energy transition is carried out, because of e.g. dynamic electricity tariffs or increased battery capacities. Our introduced methods are simple to implement, making it a useful tool for DSOs that have access to smart meter data to monitor changing consumption patterns.

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引用次数: 0