IET Smart Grid最新文献

{"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":"7 5","pages":"610-627"},"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":"7 5","pages":"593-609"},"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":"7 4","pages":"442-459"},"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":"7 4","pages":"400-411"},"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":"7 5","pages":"572-582"},"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}

{"title":"Multi-microgrid bi-layer economic scheduling strategy considering evolutionary-stackelberg hybrid game of electric vehicles","authors":"Qian Zhang,&nbsp;Jiaqi Wu,&nbsp;Tao Sun,&nbsp;Yaoyu Huang,&nbsp;Chunyan Li","doi":"10.1049/stg2.12159","DOIUrl":"10.1049/stg2.12159","url":null,"abstract":"<p>Aiming at the problem that the mobility characteristics of electric vehicles (EVs) lead to the complexity of optimal scheduling among multiple decision-making subjects, the authors propose a multi-microgrid bi-layer economic scheduling strategy considering evolutionary-stackelberg hybrid game of EVs. Firstly, in order to accurately analyse the influence of interaction among EVs, an electric vehicle aggregator (EVA) selection strategy for EV users based on evolutionary game among microgrids and a reconciliation strategy of EVA service fee are established. Secondly, a two-layer economic scheduling strategy for microgrids is proposed based on the Stackelberg game. The microgrid operator, as a leader, sets the internal price of microgrid based on the supply-demand balance; aggregators, as followers, adjust their electricity consumption and EVA choices based on the internal price and the evolutionary game model. Then, a multi-microgrid electricity-sharing trading strategy is constructed using the supply-demand ratio to encourage sub-microgrids to participate in internal transactions. Finally, the case shows that the proposed strategy can optimise the distribution of EVs among microgrids. Combining the across-time-and-space energy transmission potential of EVs and the flexible complementary capability of multi-microgrid, it can improve the operating economy of each microgrid.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"7 5","pages":"554-571"},"PeriodicalIF":2.4,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139842216","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":"Impact of EV charging on electrical distribution network and mitigating solutions – A review","authors":"Inam Nutkani,&nbsp;Hamish Toole,&nbsp;Nuwantha Fernando,&nbsp;Loh Poh Chiang Andrew","doi":"10.1049/stg2.12156","DOIUrl":"10.1049/stg2.12156","url":null,"abstract":"<p>Rapidly increasing uptake of Electric Vehicles (EVs) is expected to have a significant impact on electrical power distribution networks. Considerable work has been carried out to understand this impact and quantify the distribution networks hosting capacity, with and without network management solutions. However, the current body of knowledge does not have a comprehensive review of the research done to-date on this topic which is vital to understand the scope of the existing studies, the data used in analysing the impact, and, most importantly, the findings. A comprehensive yet focused review of impact of EV charging on distribution networks is presented by delving into the main factors restricting EV hosting capacity and the strategies used to maximise EV hosting capacity by managing the aforementioned impacts. The authors comprehensively summarise the approaches used to quantify the impact, network and data types, and the proposed solutions to increase network hosting capacity. Moreover, the shortcomings in the existing work are identified and recommendations for future research are provided to help stakeholders understand the current state-of-the-art, make informed decisions, and to be considered by future researchers.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"7 5","pages":"485-502"},"PeriodicalIF":2.4,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857900","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":"Coordinated control and energy management combined with cyberattack identification in multi-microgrid integrated with hybrid renewable-storage","authors":"Hossien Faraji,&nbsp;Reza Hemmati","doi":"10.1049/stg2.12158","DOIUrl":"10.1049/stg2.12158","url":null,"abstract":"<p>A comprehensive model is developed for coordinated control of voltage-frequency-inertia and identifying multiple cyberattacks simultaneously in two microgrids (MGs). The MGs are integrated with solar units, Wind turbine (WT), hybrid supercapacitor-battery, and fuelcell. The MGs are modelled and controlled for operation under both an island and connected states. In the proposed method, a data centre is designed in which all the electrical and control signals related to the solar, wind, hybrid supercapacitor-battery, and Fuel cell (FC) are collected, evaluated, and matched. The data centre comprises the following blocks: voltage-frequency control, inertia control of WT, and identification of false data injection (FDI) cyberattacks on frequency, power, power/frequency, and voltage. The technique used in this article to identify FDI attacks is based on the real-time method coupled with logical comparisons conducted in the time domain. This methodology provides prompt and precise detection, allowing for timely preventive measures and strategic responses. After FDI attacks occur, the implemented control system effectively manages and regulates the voltage and frequency at the desired levels, efficiently differentiating between ordinary functioning, faulty states, and potential cyber-attacks. The unhealthy MG can transfer its load to the healthy MG for safety reasons. The healthy MG is then connected to the external grid and the synchronisation conditions are checked by the proposed control system. The results of the non-linear simulation performed in MATLAB-Simulink software confirm that the proposed model successfully operates and controls all resources (i.e. solar/wind/battery/FC), regulates the voltage/frequency under various loading conditions, and identifies FDI cyberattacks.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"7 5","pages":"531-553"},"PeriodicalIF":2.4,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139803035","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":"Voltage regulation in low voltage distribution networks with unbalanced penetrations of photovoltaics and battery storage systems","authors":"Alexander Micallef,&nbsp;Cyril Spiteri-Staines,&nbsp;John Licari","doi":"10.1049/stg2.12155","DOIUrl":"10.1049/stg2.12155","url":null,"abstract":"<p>Grid integration constraints are limiting the deployment potential of renewable energy sources in Malta. Large penetrations of photovoltaics in the low voltage (LV) distribution network pose a significant risk to grid stability due to their inherent intermittency and are known to cause overvoltages and reverse power flows. The authors evaluate how self-consumption strategies with distributed battery energy storage systems can contribute to the voltage regulation in LV networks and the reduction of reverse power flows. The batteries are controlled to absorb the reverse power flow at the dwellings' point of common coupling, before this is injected into the LV network. Simulations show that uncoordinated strategies are not suitable to address the distribution network challenges during reverse power flows and evening peak demands. On the other hand, self-consumption coordinated by a time-varying feed-in tariff (FiT) can provide higher profitability to the prosumers while providing added benefits to the utility. The net-billing profitability for the prosumers in a self-consumption scenario with time-varying FiT is transformed from the downward trend of the uncoordinated scenario to an upward trend against the increasing values of storage capacity.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"7 3","pages":"264-276"},"PeriodicalIF":2.3,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139815574","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":"Total supply capability of electricity distribution networks considering flexible interconnection of low-voltage service transformers","authors":"Guoqiang Zu,&nbsp;Ying Wang,&nbsp;Xun Jiang,&nbsp;Ziyuan Hao,&nbsp;Xin Zhang","doi":"10.1049/stg2.12157","DOIUrl":"10.1049/stg2.12157","url":null,"abstract":"<p>Under the target of ‘emission peak and carbon neutrality’, electricity distribution networks will massively access low-carbon technologies, which will result in problems such as insufficient hosting capacity, unbalanced electricity loads, degraded power quality etc. The low-voltage flexible distribution network (LVFDN), which interconnects its low-voltage service transformers using flexible power electronic devices (flexible interconnected devices [FIDs]) is considered an effective means to deal with the challenges above. The total supply capability (TSC) of LVFDN is proposed. Firstly, the typical structures of LVFDN and their operation modes are proposed. Then, the TSC model of LVFDN, which formulates flexible power flow control and multi-level (medium-voltage feeder and low-voltage flexible interconnection) load transfer is proposed. Due to the non-linear non-convex characteristics of the proposed TSC model, a new algorithm based on the ‘branch and bound algorithm’ is also provided. In the case study, the TSC of an actual electricity distribution network is calculated and tested by the N-1 verification method. Finally, the variations of TSC with different capacities of the low-voltage FID are analysed. Suggestions for the planning and operation of LVFDN are also given. A theoretical basis for the application of flexible interconnection technology in low-voltage electricity distribution networks is provided.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"7 4","pages":"386-399"},"PeriodicalIF":2.4,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12157","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140474897","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}