IET Smart Grid最新文献

{"title":"Incentivising peers in local transactive energy markets: A case study for consumers, prosumers and prosumagers","authors":"Yaa S. A. Kwateng,&nbsp;Dawei Qiu,&nbsp;Goran Strbac","doi":"10.1049/stg2.12166","DOIUrl":"10.1049/stg2.12166","url":null,"abstract":"<p>A decarbonised future grid should couple technological novelty with innovative market models to efficiently capture the value of grid-edge decarbonised assets. The transactive energy (TE) concept inverts the centralised grid model by leveraging the evolution of consumers to prosumers to prosumagers. The principal TE market design challenge is transactive control—using market and pricing mechanisms to coordinate autonomous peer interactions, to optimally allocate power and incentivise peers. Peer attraction, incentivisation and retention are all critical for practical TE implementation along three adoption stages, starting from independent peer transactions with the centralised market; to decentralised peer coordination; towards distributed peer-to-peer trading. Addressing gaps in related scholarship, the authors investigate the economic positions of distinct peer roles in each adoption stage and two local pricing strategies. Using a real market dataset, trading decisions are simulated over a 1-year horizon at hourly granularity. Coordinated action achieves better transactive control for the community, with economic superiority over centralised and distributed mechanisms. Distinct peer incentives should equitably align with their contribution to market functionality, such as the value ascribed to prosumagers' flexibility in local pricing and the constrained bargaining power of prosumers in distributed bilateral negotiations.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"7 5","pages":"672-694"},"PeriodicalIF":2.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141341812","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":"Comprehensive review on dynamic state estimation techniques with cybersecurity applications","authors":"Zhuoran Zhou,&nbsp;Xin Zhang,&nbsp;Jinning Zhang,&nbsp;Gareth Taylor","doi":"10.1049/stg2.12168","DOIUrl":"https://doi.org/10.1049/stg2.12168","url":null,"abstract":"<p>The role of cybersecurity in cyber-physical power systems (CPPS) is reviewed, focusing on the applications of dynamic state estimation (DSE) techniques. These DSE techniques are particularly relevant with the integration of phasor measurement units (PMUs) and advanced communication infrastructure. A comprehensive review on DSE techniques and applications to efficiently protect CPPS against cyberattacks is classified into three cyber resilience phases including prevention, detection, and mitigation. The DSE techniques in the prevention phase are surveyed to improve the observability of the CPPS by the robust design of the Kalman filter and optimal protection of PMUs. The DSE techniques in the detection phase are surveyed to improve the adaptability of CPPS in various attack detection scenerios and optimise the detection accuracy. The DSE techniques in the mitigation phase are surveyed to enhance the flexibility of CPPS resource utilisation with compensation-based, isolation-based, and scheduling-based strategies. Finally, the benefits and limitations of each DSE technique are summarised with potential suggestions on research directions for enhancing the cyber resilience of CPPS.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"7 4","pages":"370-385"},"PeriodicalIF":2.4,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994330","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":"Guest Editorial: Energy storage for green transition of electrical grids","authors":"Navid Bayati,&nbsp;Thomas Ebel,&nbsp;Mehdi Savaghebi,&nbsp;Zhengyu Lin,&nbsp;Haoran Zhao,&nbsp;Mousa Marzband,&nbsp;Payman Dehghanian","doi":"10.1049/stg2.12174","DOIUrl":"https://doi.org/10.1049/stg2.12174","url":null,"abstract":"&lt;p&gt;Energy storage systems (ESSs) are needed in the smart grids both at the generation, transmission, and distribution levels, and different types of ESSs have widely different characteristics and are suitable for different tasks and situations. In recent years, the smart grid concept has been dramatically developed in different applications, such as islands, shipboards, aircraft, microgrids and grid integration of renewables. With the wide application of ESS in smart grids, significant technical challenges remain along with the enhancement of smart grid operations and services. These challenges can be categorised as control of ESS, optimal operation, and energy management systems, optimal design of hybrid ESS, protection of ESS, and power electronics for ESS connections. This necessitates suitable design and control of the interfaces between ESSs in smart grids, as well as consideration of different applications of smart grid systems. This special issue of IET smart grid is focused on research ideas, articles, and experimental studies related to ‘Energy Storage for Green Transition of Electrical Grids’ from contributors in universities, industries, and research laboratories to develop and propose novel solutions on applications of ESS in smart grids.&lt;/p&gt;&lt;p&gt;This special issue presents six papers providing some methodologies within the field of ongoing research and development, all of which were selected after undergoing a thorough peer-review process. Below, we expand the publications on this special issue. Some common themes within the papers on this special issue include control of hybrid storage systems, energy management of electric vehicles (EVs), photovoltaics (PV), and ESS, and voltage regulation of storage systems. All of these attributes are vitally important to the integration of energy storage for the green transition of power grids. Moreover, the overall submissions have high quality, which marks the success of this special issue.&lt;/p&gt;&lt;p&gt;In the paper ‘Moth-Flame-Optimization Based Parameter Estimation for Model-Predictive-Controlled SMES-Battery Hybrid Energy Storage System’ by Liu et al., the authors propose an improved model-predictive-control (MPC) approach for superconducting magnetic energy storage (SMES)-battery hybrid energy storage system (HESS) by using the moth-flame-optimisation (MFO) algorithm to determine the circuit parameters in real-time. The actual parameters are updated by MFO and then sent to the MPC to minimise the model mismatches. The advantages of the proposed method, in terms of accuracy and convergence speed, are verified by comparison with Grey Wolf optimization (GWO) and particle swarm optimization (PSO). The simulation results prove that by taking the proposed strategy, DC bus voltage is more stable and the SMES can maintain more than 95% of capacity utilisation and avoid over-discharge even if the model parameters are inconsistent with the actual ones under circumstances of AC grid fault and fluctuation of n","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"7 3","pages":"187-190"},"PeriodicalIF":2.3,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141292580","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 deep learning based communication traffic prediction approach for smart monitoring of distributed energy resources in virtual power plants","authors":"Meng Hou,&nbsp;Shidong Liu,&nbsp;Qingrong Zheng,&nbsp;Chuan Liu,&nbsp;Xi Zhang,&nbsp;Chongqing Kang","doi":"10.1049/stg2.12173","DOIUrl":"10.1049/stg2.12173","url":null,"abstract":"<p>Virtual power plants (VPPs) have been widely recognized as a key enabler for energy system neutrality. The communication traffic of a VPP fundamentally indicates its activeness in interacting with the power system, thus providing a new dimension in depicting the behaviour characteristics of distributed energy resources in VPPs. Therefore, the prediction of communication traffic is significant in improving the control efficiency of VPPs. However, due to the involvement of numerous interactive agents characterised by both individual randomness and coordinative characteristics, traditional prediction models are no longer capable of fitting VPP communication traffic effectively. Therefore, a  novel prediction model is introduced that enhances the prediction accuracy by integrating long short-term memory (LSTM) and variational mode decomposition (VMD). This model employs VMD as the initial step for extracting the intrinsic modes from the traffic sequence, thereby mitigating the impact of incidental noise. Then, LSTM is applied to fit each intrinsic mode individually. Additionally, considering the outer influencing factors, the attention mechanism is incorporated. Finally, all sub-prediction algorithms are neatly integrated as a whole prediction model. The proposed model is evaluated through simulation prediction using realistic VPP communication traffic data, and the results demonstrate its effectiveness.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"7 5","pages":"653-671"},"PeriodicalIF":2.4,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127080","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":"Guest Editorial: Planning and operation of integrated energy systems for decarbonisation","authors":"Yue Xiang,&nbsp;Hongcai Zhang,&nbsp;Chenghong Gu,&nbsp;Xin Zhang,&nbsp;Can Wan,&nbsp;Shenxi Zhang,&nbsp;Wei Sun,&nbsp;Jia Liu,&nbsp;Zhukui Tan","doi":"10.1049/stg2.12172","DOIUrl":"https://doi.org/10.1049/stg2.12172","url":null,"abstract":"&lt;p&gt;Integrated energy systems’ decarbonization is vital to deal with the global warming problem. An integrated energy system, which is interconnected with various energy resources and highly aggregated with groups of residential, commercial, and/or industrial buildings, is becoming the primary target for low-carbon transition due to its large energy consumption and high carbon emission density. The decarbonisation of integrated energy systems is of great significance in achieving the goal of carbon neutrality. The integrated energy system generally provides ideas for integrating multiple elements of the urban energy system, such as electricity, natural gas, heat networks, as well as residential, commercial, industrial or associated service systems and transportation, which makes the mechanism and method of decarbonisation more difficult and complicated. Meanwhile, digital technologies such as IoT, ICT, machine learning, and big data are drawing much attention as they can aid the decarbonisation process. With these novel technologies' promotion, the improvement will be made in terms of economy, energy efficiency and environmental benefit when developing an integrated energy system towards low-carbon/zero-carbon.&lt;/p&gt;&lt;p&gt;This IET Smart Grid special issue on Planning and Operation of Integrated Energy Systems for Decarbonisation invites a broad spectrum of contributors from universities, industry, research laboratories, and policymakers to develop and present novel solutions and technologies that will facilitate and advance the agenda of deep decarbonisation of integrated energy systems.&lt;/p&gt;&lt;p&gt;This special issue solicits original research papers that target at, but are not restricted to, the following aspects. It is worth noting that this special issue places an emphasis on addressing the mutual research interests of academics and industry.&lt;/p&gt;&lt;p&gt;In this special issue, we have received several papers, all of which underwent the peer-review process. Of the submitted papers, only six have been accepted. Thus, the overall submissions were of high quality, which marks the success of this special issue.&lt;/p&gt;&lt;p&gt;The six accepted papers can be clustered into four main categories, including energy market, energy development, energy operation in distribution networks and state estimation. Among these categories, the first one offers a framework of energy market, such as peer-to-peer (P2P) transaction for third-party prosumers, operation and planning techniques for multi-stakeholders planning in distribution networks, whose authors are Xiang et al. The second one exhibits novelties in the energy management, such as power generation with flexible energy resource pathways evolution from the view of CO&lt;sub&gt;2&lt;/sub&gt; emission, and renewable energies development in extreme scenario generation, whose authors are Li et al. and Peng et al. The third one proposes some methods for energy operation in distribution networks, such as the total supply capability (TSC) and operati","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"7 4","pages":"367-369"},"PeriodicalIF":2.4,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.12172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994044","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":"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":"7 5","pages":"583-592"},"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":"7 5","pages":"643-652"},"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":"7 5","pages":"503-530"},"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":"7 5","pages":"628-642"},"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":"7 4","pages":"460-472"},"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}