Pub Date : 2018-05-01DOI: 10.1109/ICSGCE.2018.8556775
Y. Wadhawan, C. Neuman
The security of critical infrastructure such as Smart Grid is of significant concern because cyber-physical attacks are becoming a frequent occurrence. Cybercriminals compromise cyberinfrastructure to control physical processes maliciously. It is the system administrator's goal to find vulnerabilities in the smart grid functions and patch them before they are compromised. Unfortunately, limited resources and a large attack surface make it difficult to decide which function to protect in a particular system state. In this research paper, we tackle the problem of resource allocation in the smart grid system by proposing a tool, Reinforcement Learning-Bayesian Attack Graph for Smart Grid System (RLBAGS), which provides functionality to the system engineers to compute optimal policies on regular intervals about whether to SCAN or PATCH a particular function of the smart grid system. RL-BAGS considers functions and network architecture of the system to generate a Bayesian Network, which represents the state of the system. RL-BAGS implements two reinforcement learning algorithms, Q-Learning and SARSA learning, on the generated Bayesian Network to learn optimal policies. RL-BAGS assists system administrators performing in-depth studies of one of the functions of the smart grid system advising effective actions to scan or patch a system component.
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Pub Date : 2018-05-01DOI: 10.1109/ICSGCE.2018.8556723
C. Su, Yu-Chi Pu, Hai-Ming Ching, Jheng-He Kuo
Distribution transformer monitoring has been essential and important for smart grids. In a new generation of energy provision paradigm, i.e., Energy Internet (EI), the Web service technology is integrated into the field of smart metering for advanced meter infrastructure (AMI) devices. Distribution transformer loading obtained from the AMI data by using an accurate low voltage operation model can be used to develop the dynamic thermal model of distribution transformers based on IEEE C57.91 standards and weather information from the Web. The dynamic thermal models are then used to estimate the top oil temperature and hottest temperature and equivalent aging factor for the transformer. The results are used for evaluating the transformer loss-of-life (LoL) accordingly. To facilitate the analysis, a Web based friendly human machine interface (HMI) is designed. Test results of a practical single-phase 100 kVA transformer widely installed in Taiwan Power Company (Taipower) distribution system are reported.
{"title":"Energy Internet Based Distribution Transformer Loss-of-Life Evaluation","authors":"C. Su, Yu-Chi Pu, Hai-Ming Ching, Jheng-He Kuo","doi":"10.1109/ICSGCE.2018.8556723","DOIUrl":"https://doi.org/10.1109/ICSGCE.2018.8556723","url":null,"abstract":"Distribution transformer monitoring has been essential and important for smart grids. In a new generation of energy provision paradigm, i.e., Energy Internet (EI), the Web service technology is integrated into the field of smart metering for advanced meter infrastructure (AMI) devices. Distribution transformer loading obtained from the AMI data by using an accurate low voltage operation model can be used to develop the dynamic thermal model of distribution transformers based on IEEE C57.91 standards and weather information from the Web. The dynamic thermal models are then used to estimate the top oil temperature and hottest temperature and equivalent aging factor for the transformer. The results are used for evaluating the transformer loss-of-life (LoL) accordingly. To facilitate the analysis, a Web based friendly human machine interface (HMI) is designed. Test results of a practical single-phase 100 kVA transformer widely installed in Taiwan Power Company (Taipower) distribution system are reported.","PeriodicalId":366392,"journal":{"name":"2018 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116766108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/icsgce.2018.8556787
Robyr Jean-Luc, Frederick Gonon, Ludovic Favre, E. Niederhäuser
Better energy management systems for buildings could play a significant role in achieving nowadays greenhouse gas emission reduction targets. In this context, a regulation algorithm to manage the interaction between local renewable energy production, local energy storage devices and an external power source (power grid) was developed. The innovative aspect of this project compared to existing solution is the simultaneous optimization following three criteria: the external energy consumption, the cost and ecological impacts. The new optimization algorithm is based on the genetic algorithm method due to the large solutions space and the non-linearity of the optimization function. This method is coupled to a physical model of the building under study (a typical dwelling house) and its energetic network (production and storage). In addition, weather forecast data as well as data on the user habits are integrated. This paper shows the results of the optimization algorithm applied to a set of realistic values. The genetic algorithm is compared to a pure random optimization approach and their optimization efficiencies are analyzed. Finally, the best strategy obtained by the genetic algorithm for a realistic computation time of several minutes is presented and investigated in detailed. This results shows that the genetic algorithm can perform a 48 hours simulation with no outcome costs, a global production of 4.3 kWh of energy and a greenhouse gas production of −1.4 kg of CO2 equivalent, whereas the consumption of the building costs +1.3 CHF, consumes 7.0 kWh of energy and generates +1.3 kg of CO2 equivalent.
{"title":"Convergence of Multi-Criteria Optimization of a Building Energetic Resources by Genetic Algorithm","authors":"Robyr Jean-Luc, Frederick Gonon, Ludovic Favre, E. Niederhäuser","doi":"10.1109/icsgce.2018.8556787","DOIUrl":"https://doi.org/10.1109/icsgce.2018.8556787","url":null,"abstract":"Better energy management systems for buildings could play a significant role in achieving nowadays greenhouse gas emission reduction targets. In this context, a regulation algorithm to manage the interaction between local renewable energy production, local energy storage devices and an external power source (power grid) was developed. The innovative aspect of this project compared to existing solution is the simultaneous optimization following three criteria: the external energy consumption, the cost and ecological impacts. The new optimization algorithm is based on the genetic algorithm method due to the large solutions space and the non-linearity of the optimization function. This method is coupled to a physical model of the building under study (a typical dwelling house) and its energetic network (production and storage). In addition, weather forecast data as well as data on the user habits are integrated. This paper shows the results of the optimization algorithm applied to a set of realistic values. The genetic algorithm is compared to a pure random optimization approach and their optimization efficiencies are analyzed. Finally, the best strategy obtained by the genetic algorithm for a realistic computation time of several minutes is presented and investigated in detailed. This results shows that the genetic algorithm can perform a 48 hours simulation with no outcome costs, a global production of 4.3 kWh of energy and a greenhouse gas production of −1.4 kg of CO2 equivalent, whereas the consumption of the building costs +1.3 CHF, consumes 7.0 kWh of energy and generates +1.3 kg of CO2 equivalent.","PeriodicalId":366392,"journal":{"name":"2018 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123255299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ICSGCE.2018.8556741
M. Ahmadi, N. Mithulananthan
The dynamics of EV chargers combined with that of induction motors and unique characteristics of distribution system could prove dynamic voltage stability a threat to secure operation of distribution systems. In previous studies, the dynamics of EV chargers and single-phase induction motors haven't been considered. EV chargers are new loads added to distribution grids which have unique properties, e.g., higher R/X ratio. In this paper, to capture and study the interactions between complex systems of a distribution network, detailed models of chargers and motors are used, and dynamic voltage stability effects of EV charging is studied in two different cases. First, with individual PFC quick chargers spread over the grid. Second, with multi-slot Fast Charging Stations able to dynamically maintain the PCC voltage. The results show that while uncoordinated EV charging might cause dynamic voltage instability, Fast Charging Stations can alleviate this problem by implementing an LVRT scheme, injecting reactive power into the grid and regulating the rate of EV charging.
{"title":"Dynamic Voltage Stability of Distribution Grids with Fast Charging Stations for EV Units","authors":"M. Ahmadi, N. Mithulananthan","doi":"10.1109/ICSGCE.2018.8556741","DOIUrl":"https://doi.org/10.1109/ICSGCE.2018.8556741","url":null,"abstract":"The dynamics of EV chargers combined with that of induction motors and unique characteristics of distribution system could prove dynamic voltage stability a threat to secure operation of distribution systems. In previous studies, the dynamics of EV chargers and single-phase induction motors haven't been considered. EV chargers are new loads added to distribution grids which have unique properties, e.g., higher R/X ratio. In this paper, to capture and study the interactions between complex systems of a distribution network, detailed models of chargers and motors are used, and dynamic voltage stability effects of EV charging is studied in two different cases. First, with individual PFC quick chargers spread over the grid. Second, with multi-slot Fast Charging Stations able to dynamically maintain the PCC voltage. The results show that while uncoordinated EV charging might cause dynamic voltage instability, Fast Charging Stations can alleviate this problem by implementing an LVRT scheme, injecting reactive power into the grid and regulating the rate of EV charging.","PeriodicalId":366392,"journal":{"name":"2018 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132811298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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