IET Smart Grid最新文献

{"title":"An information theoretic metric for measurement vulnerability to data integrity attacks on smart grids","authors":"Xiuzhen Ye,&nbsp;Iñaki Esnaola,&nbsp;Samir M. Perlaza,&nbsp;Robert F. Harrison","doi":"10.1049/stg2.12163","DOIUrl":"10.1049/stg2.12163","url":null,"abstract":"<p>A novel metric that describes the vulnerability of the measurements in power systems to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), leverages information theoretic measures to assess the attack effect in terms of the fundamental limits of the disruption and detection tradeoff. The result of computing the VuIx of the measurements in the system yields an ordering of their vulnerability based on the degree of exposure to data integrity attacks. This new framework is used to assess the measurement vulnerability of IEEE 9-bus and 30-bus test systems and it is observed that power injection measurements are significantly more vulnerable to data integrity attacks than power flow measurements. A detailed numerical evaluation of the VuIx values for IEEE test systems is provided.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-stage stochastic-robust planning of distributed energy storage systems with Archimedes optimisation algorithm","authors":"Tianmeng Yuan,&nbsp;Zhuoxu Chen,&nbsp;Zechun Hu","doi":"10.1049/stg2.12171","DOIUrl":"10.1049/stg2.12171","url":null,"abstract":"<p>With the advancement of energy storage technologies, energy storage systems (ESSs) have emerged as a promising solution for distribution networks to mitigate the impact of intermittent and violate renewable energy sources. The optimal planning of distributed ESS is studied to minimise the investment and operational costs for the distribution system operator. To address the various uncertainties associated with load demand and distributed generation, the authors formulate the problem as a two-stage stochastic-robust optimisation problem. The proposed formulation implements various representative scenarios of actual operating conditions and constructs the robust uncertainty set to ensure feasibility under worst-case scenarios. In view of the computational complexity of the proposed model, a solution approach combining the Archimedes optimisation algorithm and the global optimisation method is presented. By decomposing the investment and operation stages, the subproblems are relaxed into mixed integer second-order cone programming models, which can be solved in parallel based on scenarios. Numerical studies are carried out on a 17-node test system to demonstrate the validity of the proposed model and algorithm. In addition, a comparison between the proposed method and the genetic algorithm is performed, to illustrate its superiority in solving speed and solution optimality.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart grid public datasets: Characteristics and associated applications","authors":"Emran Altamimi,&nbsp;Abdulaziz Al-Ali,&nbsp;Qutaibah M. Malluhi,&nbsp;Abdulla K. Al-Ali","doi":"10.1049/stg2.12161","DOIUrl":"10.1049/stg2.12161","url":null,"abstract":"<p>The development of smart grids, traditional power grids, and the integration of internet of things devices have resulted in a wealth of data crucial to advancing energy management and efficiency. Nevertheless, public datasets remain limited due to grid operators' and companies' reluctance to disclose proprietary information. The authors present a comprehensive analysis of more than 50 publicly available datasets, organised into three main categories: micro- and macro-consumption data, detailed in-home consumption data (often referred to as non-intrusive load monitoring datasets or building data) and grid data. Furthermore, the study underscores future research priorities, such as advancing synthetic data generation, improving data quality and standardisation, and enhancing big data management in smart grids. The aim of the authors is to enable researchers in the smart and power grid a comprehensive reference point to pick suitable and relevant public datasets to evaluate their proposed methods. The provided analysis highlights the importance of following a systematic and standardised approach in evaluating future methods and directs readers to future potential venues of research in the area of smart grid analytics.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141019638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaborative strategy for electric vehicle charging scheduling and route planning","authors":"Jingyi Zhang,&nbsp;Wenpeng Jing,&nbsp;Zhaoming Lu,&nbsp;Haotian Wu,&nbsp;Xiangming Wen","doi":"10.1049/stg2.12170","DOIUrl":"https://doi.org/10.1049/stg2.12170","url":null,"abstract":"<p>Due to varying energy demands and supply levels in different regions, the distribution of power load exhibits an imbalanced state. It contributes to increased power loss and poses a threat to the security constraints of the electrical grid. Simultaneously, the global energy transition has led to a continuous increase in the proportion of renewable energy integrated into the grid. Electric vehicles (EVs), serving as representative of renewable energy, further magnify this load imbalance with their charging requirements, which poses a significant challenge to the stable operation of the grid. Therefore, to ensure the smooth operation of the grid under the context of renewable energy integration, the authors investigate the coordinated strategies of EV charging scheduling and route planning. The authors first model the coupling of the transportation network with the smart grid as a cyber-physical system. Subsequently, the authors simulate and analyse the daily charging load curve of the network, capturing the travel characteristics of EVs. Based on this, the authors research the EV charging scheduling in both individual and collective travel scenarios during peak and off-peak hours. For the off-peak travel period of EVs, a charging schedule strategy based on travel plans is proposed, which reduces the time cost of EV owners' travel. Furthermore, for the collective travel of a large number of EVs within the system, a multi-EV charging scheduling strategy based on charging station load balancing is presented. This strategy effectively balances the load levels of various charging stations while reducing the overall system travel time. Ultimately, through experimental results, the authors demonstrate that by deploying appropriate charging scheduling strategies, EVs cease to be a burden on the grid and can be transformed into tools for balancing the loads across different regions.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using conditional Invertible Neural Networks to perform mid-term peak load forecasting","authors":"Benedikt Heidrich,&nbsp;Matthias Hertel,&nbsp;Oliver Neumann,&nbsp;Veit Hagenmeyer,&nbsp;Ralf Mikut","doi":"10.1049/stg2.12169","DOIUrl":"https://doi.org/10.1049/stg2.12169","url":null,"abstract":"<p>Measures for balancing the electrical grid, such as peak shaving, require accurate peak forecasts for lower aggregation levels of electrical loads. Thus, the Big Data Energy Analytics Laboratory (BigDEAL) challenge—organised by the BigDEAL—focused on forecasting three different daily peak characteristics in low aggregated load time series. In particular, participants of the challenge were asked to provide long-term forecasts with horizons of up to 1 year in the qualification. The authors present the approach of the KIT-IAI team from the Institute for Automation and Applied Informatics at the Karlsruhe Institute of Technology. The approach to the challenge is based on a hybrid generative model. In particular, the authors use a conditional Invertible Neural Network (cINN). The cINN gets the forecast of a sliding mean as representative of the trend, different weather features, and calendar information as conditioning input. By this, the proposed hybrid method achieved second place overall and won two out of three tracks of the BigDEAL challenge.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive framework for predicting electric vehicle's participation in ancillary service markets","authors":"Saeed Naghdizadegan Jahromi,&nbsp;Amir Abdollahi,&nbsp;Ehsan Heydarian-Forushani,&nbsp;Mehdi Shafiee","doi":"10.1049/stg2.12167","DOIUrl":"https://doi.org/10.1049/stg2.12167","url":null,"abstract":"<p>Electric vehicles (EVs) have significant potential to offer unused capacity in ancillary service markets, providing unique opportunities for market operators to utilise these resources. EVs have a rapid response and high availability, making them a good fit for the frequency containment reserve (FCR) market. However, EV aggregators (EVAGs) must aggregate capacity blocks due to the limited capacity of individual EVs. An application of a supervised machine learning method named XGBoost is suggested to help EVAGs predict the amount of EV participation in the FCR market. The objective is to forecast yearly involvement using data from only a single week, using the game theory method SHapley Additive exPlanations (SHAP) to minimise extra data. The proposed strategy helps aggregators and uses feature engineering to select EVs with high potential to boost revenue. The proposed framework is effective in predicting EV performance in the DK-2 market, as shown by multiple analyses.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Local privacy-friendly verification of customer participation in frequency regulation services using smart meter data","authors":"Emilio J. Palacios-Garcia,&nbsp;Vladimir Vrabel,&nbsp;Geert Deconinck","doi":"10.1049/stg2.12165","DOIUrl":"10.1049/stg2.12165","url":null,"abstract":"<p>The delivery of flexibility from distributed assets guarantees the stable operation of the power system as increasing volumes of renewable energy are deployed. Nevertheless, verifying the adequate provision is challenging when considering behind-the-meter resources. A cost-effective alternative to dedicated metring is using measurements from smart meters. However, flexibility activations must be discerned from the rest of the loads in the household. Furthermore, privacy issues arise since electricity consumption contains personal data. The authors tackle both issues by developing a data-driven privacy-friendly verification algorithm for participation in frequency containment reserves (FCRs). Our methodology evaluated three machine learning (ML) classification models, deployed locally, and fed with total consumption measurements and activation set points to verify users' participation. The amount of information that leaves the premises was reduced from low-granularity power measurements to simple compliance indicators. The models were trained and evaluated using a real dataset of households, where FCR was delivered by behind-the-meter batteries, resulting in an accuracy close to 0.90. A proof-of-concept setup was employed to test the algorithms under real circumstances. Even with several background loads, an accuracy of up to 0.83 was observed, promising results considering the privacy-friendly features, use of simple ML models, and embedded deployment.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140381157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BigDEAL Challenge 2022: Forecasting peak timing of electricity demand","authors":"Shreyashi Shukla,&nbsp;Tao Hong","doi":"10.1049/stg2.12162","DOIUrl":"10.1049/stg2.12162","url":null,"abstract":"<p>Peak load forecasting is crucial to power system planning and operations. While the literature has reported many studies on forecasting the magnitude of peak load, few have focused on the timing aspect. In the fall of 2022, the Big Data Energy Analytics Laboratory (BigDEAL) organised the BigDEAL Challenge 2022, which was devoted to short-term ex-ante peak timing forecasting. The competition attracted 78 teams formed by 121 contestants from 27 countries. The authors introduce the competition in detail, including its precursor competitions held in the 2010s, the framework and setup, and a summary of the methods used by the participants. The authors also publish the data of the BigDEAL Challenge 2022 along with this paper. Lastly, the authors present their perspective on the research challenges of peak timing forecasting and future load forecasting competitions.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140210828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced energy balancing and optimal load curtailment strategy for DC microgrid integration in hybrid AC/DC distribution networks","authors":"Sohail Sarwar,&nbsp;Haroon Zafar,&nbsp;Michael Merlin,&nbsp;Ali Arsalan,&nbsp;Behnaz Papari,&nbsp;Aristides Kiprakis","doi":"10.1049/stg2.12164","DOIUrl":"10.1049/stg2.12164","url":null,"abstract":"<p>Unleashing the potential of distributed renewable energy sources (RESs), intelligent and autonomous microgrids are becoming pivotal in attaining net-zero carbon emission goals. Hybrid AC/DC microgrids rise as cutting-edge microgrid topologies, capitalising on the best of both AC and DC systems. However, the integration of intermittent renewables and uncertainties in loading poses stability challenges. Advanced bidirectional converter controls provide efficient power exchange, but in extreme contingencies, a resilient supervisory control framework (load management/load curtailment approach) is inevitable to withstand/avoid unplanned renewable disruptions/blackouts. Moreover, the operational paradigm shift towards achieving net-zero emissions, isolated operation of RESs, and conventional load shedding methods are anticipated to encounter substantial challenges, necessitating the development of alternative strategies. In order to improve the stability of hybrid microgrid systems in islanding scenarios, this research presents an energy balancing and load curtailment strategy. The proposed method aims at optimising resource utilisation, prioritising essential loads, and executing an optimal load curtailment strategy (if required), thereby augmenting the stability of systems. Unlike a meta-heuristic or exhaustive search, which depends on 2^<i>n</i> − 1 possible combinations and become unworkable as load numbers increase, the suggested methodology is based on a mathematically modelled load restriction method. By including load criticality, this strategy effectively prevents blackouts even with an increasing number of loads, providing a significantly more useful and practical solution. Additionally, the proposed charging algorithm ensures that the energy storage system imports energy from the grid during off-peak hours and maximises power generation from the DC subgrid. The efficacy of the proposed strategy is validated using a modified IEEE-33 bus system as a test case for a hybrid AC/DC microgrid. Simulation results demonstrate the effectiveness of the MILP-based load curtailment approach in maintaining system stability and preventing blackouts during unforeseen events.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140216776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven identification method for impedance stability analysis of inverter-based resources","authors":"Hongyi Wang,&nbsp;Pingyang Sun,&nbsp;Jalal Sahebkar Farkhani,&nbsp;Zhe Chen","doi":"10.1049/stg2.12160","DOIUrl":"10.1049/stg2.12160","url":null,"abstract":"<p>Obtaining inverter controller information may be a premise for seeking its dynamic behaviour. But accurate knowledge of such information would be unrealistic for real functioning inverter-interfaced generators (IIGs), which hinders the stability analysis of the IIG. A new data-driven impedance identification method is proposed for stability analysis, which involves an improved sparse identification algorithm as an ancillary function within the system identification framework. It contains mainly two design stages. First, the transform basis matrix (TBM) is devised systematically as a prior knowledge library to contain the possibly existing control structures. In the second stage, a sparse identification algorithm is reformulated in order to extract the relevant structures in TBM while obtaining controller parameters. The authors demonstrate that the sparse vector between the TBM and output signal is closely related to the controller structure. The effectiveness of the proposed method is verified on grid-connected inverters based on droop control and virtual synchronous machine control.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12160","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}