Pub Date : 2018-10-01DOI: 10.1109/ISGTEurope.2018.8571874
W. Infante, Jin Ma
Although electric vehicles (EV) are expected to increase in number, the EV ecosystem supporting this growth is still in the early stages. To manage the risks involved, ecosystem infrastructure investments such as battery charging stations need practical EV station visit predictions. In this research, a forecasting technique is proposed that employs an adapted K-means clustering approach and depends on previous visits. Using aggregated traffic, the practical cluster number is chosen based on a variance explained threshold. Representative probabilities from the clusters are then linked to individual travel behaviors. In contrast to conventional EV station forecasts, the proposed technique is dependent on previous visits creating a realistic case where the visit of EV owners will likely depend on their distance travelled and their previous station visit. The EV station visit forecasting technique has been recently performed in charging stations meant for city and inter-state use in Australia leveraging its potential for practical use in supporting the EV ecosystem.
{"title":"Clustering and Previous Visit Dependency Technique for Electric Vehicle Station Visits","authors":"W. Infante, Jin Ma","doi":"10.1109/ISGTEurope.2018.8571874","DOIUrl":"https://doi.org/10.1109/ISGTEurope.2018.8571874","url":null,"abstract":"Although electric vehicles (EV) are expected to increase in number, the EV ecosystem supporting this growth is still in the early stages. To manage the risks involved, ecosystem infrastructure investments such as battery charging stations need practical EV station visit predictions. In this research, a forecasting technique is proposed that employs an adapted K-means clustering approach and depends on previous visits. Using aggregated traffic, the practical cluster number is chosen based on a variance explained threshold. Representative probabilities from the clusters are then linked to individual travel behaviors. In contrast to conventional EV station forecasts, the proposed technique is dependent on previous visits creating a realistic case where the visit of EV owners will likely depend on their distance travelled and their previous station visit. The EV station visit forecasting technique has been recently performed in charging stations meant for city and inter-state use in Australia leveraging its potential for practical use in supporting the EV ecosystem.","PeriodicalId":302863,"journal":{"name":"2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125615022","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 : 2018-10-01DOI: 10.1109/ISGTEurope.2018.8571424
Martina Bucciarelli, S. Paoletti, A. Vicino
—Active demand is a type of demand response mechanism based on the idea that small commercial and household consumers may become active participants in electricity systems by changing their consumption patterns in return of a monetary reward. This participation is mediated by an aggregator, which gathers the consumers' flexibilities to build up active demand products. These products are then offered in suitable electricity markets. After market closure, the distribution system operator validates the cleared products by checking their compatibility with network constraints and operation. This process is called technical validation. The aim of this paper is to support the above described decision making process by taking uncertainty due to baseline load and distributed generation into account. A scenario-based approach is proposed, which makes it possible to provide a statistical characterization of the validation results. The methodology proposed in the paper is tested by simulating the validation process on real data of demand and generation from an Italian low voltage network.
{"title":"A Scenario-Based Framework for Technical Validation of Demand Response","authors":"Martina Bucciarelli, S. Paoletti, A. Vicino","doi":"10.1109/ISGTEurope.2018.8571424","DOIUrl":"https://doi.org/10.1109/ISGTEurope.2018.8571424","url":null,"abstract":"—Active demand is a type of demand response mechanism based on the idea that small commercial and household consumers may become active participants in electricity systems by changing their consumption patterns in return of a monetary reward. This participation is mediated by an aggregator, which gathers the consumers' flexibilities to build up active demand products. These products are then offered in suitable electricity markets. After market closure, the distribution system operator validates the cleared products by checking their compatibility with network constraints and operation. This process is called technical validation. The aim of this paper is to support the above described decision making process by taking uncertainty due to baseline load and distributed generation into account. A scenario-based approach is proposed, which makes it possible to provide a statistical characterization of the validation results. The methodology proposed in the paper is tested by simulating the validation process on real data of demand and generation from an Italian low voltage network.","PeriodicalId":302863,"journal":{"name":"2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)","volume":"9 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125988058","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 : 2018-10-01DOI: 10.1109/ISGTEurope.2018.8571857
V. Katić, Aleksandar M. Stanisavljević, R. Turovic, B. Dumnic, B. Popadic
Paper addresses performances of recursive technique, extended Kalman filter, for voltage dips and interruption detection. Performance of this method is tested in microgrid with high level of distributed generation, and compared with RMS, FFT and Kalman filter. Kalman filter and extended Kalman filter are applied with basically different approach. Modeling and testing are carried out in the MATLAB/SimPowerSystems environment. Modified IEEE13 bus system with high level of distributed generation is used. Methods are also applied in real-time laboratory system, as part of grid-tie inverter control, and based on measured computation complexity and ease-of-use conclusions about cost of implementation are derived.
{"title":"Extended Kalman filter for voltage dips detection in grid with distributed energy resources","authors":"V. Katić, Aleksandar M. Stanisavljević, R. Turovic, B. Dumnic, B. Popadic","doi":"10.1109/ISGTEurope.2018.8571857","DOIUrl":"https://doi.org/10.1109/ISGTEurope.2018.8571857","url":null,"abstract":"Paper addresses performances of recursive technique, extended Kalman filter, for voltage dips and interruption detection. Performance of this method is tested in microgrid with high level of distributed generation, and compared with RMS, FFT and Kalman filter. Kalman filter and extended Kalman filter are applied with basically different approach. Modeling and testing are carried out in the MATLAB/SimPowerSystems environment. Modified IEEE13 bus system with high level of distributed generation is used. Methods are also applied in real-time laboratory system, as part of grid-tie inverter control, and based on measured computation complexity and ease-of-use conclusions about cost of implementation are derived.","PeriodicalId":302863,"journal":{"name":"2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131961994","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 : 2018-10-01DOI: 10.1109/ISGTEurope.2018.8571848
Tobias Heß, P. Schegner
In order to achieve the objectives of the energy transition, flexibility of demand and generation has to be utilized with the coupling of different energy sectors. To avoid congestion in the distribution network a pure market-based integration of flexible units is not permitted. Rather, a comprehensive demand side management (DSM) similar to the management in the transmission grid is necessary. In contrast to transmission grid the method has to be automated and has to provide a simple parameter which describes the grid capacity. This paper defines therefore the power limit value (PLV) of the distribution grid. The PLV is thereby determined on the basis of a forecast of all non-controllable loads and generation units in the network. The calculated values define limits within the planing period were energy trade can take place without restrictions. The paper presents a method for calculating the PLV. The usage of the method is illustrated in a low voltage distribution grid.
{"title":"Definition and Calculation of Power Limit Values for Distribution Grids - Basics of Comprehensive Demand Side Managemen","authors":"Tobias Heß, P. Schegner","doi":"10.1109/ISGTEurope.2018.8571848","DOIUrl":"https://doi.org/10.1109/ISGTEurope.2018.8571848","url":null,"abstract":"In order to achieve the objectives of the energy transition, flexibility of demand and generation has to be utilized with the coupling of different energy sectors. To avoid congestion in the distribution network a pure market-based integration of flexible units is not permitted. Rather, a comprehensive demand side management (DSM) similar to the management in the transmission grid is necessary. In contrast to transmission grid the method has to be automated and has to provide a simple parameter which describes the grid capacity. This paper defines therefore the power limit value (PLV) of the distribution grid. The PLV is thereby determined on the basis of a forecast of all non-controllable loads and generation units in the network. The calculated values define limits within the planing period were energy trade can take place without restrictions. The paper presents a method for calculating the PLV. The usage of the method is illustrated in a low voltage distribution grid.","PeriodicalId":302863,"journal":{"name":"2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132512882","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 : 2018-10-01DOI: 10.1109/ISGTEurope.2018.8571689
M. Mansouri, D. Westwick, A. Knight
A blind identification method based on Subspace State Space System IDentification (N4SID) is proposed to identify the parameters of disturbances, such as electromechanical modes and harmonics, in a power system. The challenge, however, is how to identify the disturbances without the knowledge of systems' inputs. The disturbances are only measured by Phasor Measurement Units (PMUs) in the power system as signals representing the system's outputs; the system's parameters and inputs have to be estimated only from the measured outputs. This leads to a blind identification problem; therefore, N4SID, categorized as blind identification, is employed to address the problem. To solve the problem, the output data is formulated in the N4SID terminology, system matrices are calculated in 6 steps, and finally, the forward stochastic model of the system is derived. The proposed method has some advantages such as robustness and no need to know the parameters of the power system for design. To evaluate the performance of the proposed method, simulation studies are carried out on two practical power systems. The simulations show that the proposed method has a desirable performance.
{"title":"A N4SID-Based Strategy to Estimate the Parameters of Disturbances in Power Systems","authors":"M. Mansouri, D. Westwick, A. Knight","doi":"10.1109/ISGTEurope.2018.8571689","DOIUrl":"https://doi.org/10.1109/ISGTEurope.2018.8571689","url":null,"abstract":"A blind identification method based on Subspace State Space System IDentification (N4SID) is proposed to identify the parameters of disturbances, such as electromechanical modes and harmonics, in a power system. The challenge, however, is how to identify the disturbances without the knowledge of systems' inputs. The disturbances are only measured by Phasor Measurement Units (PMUs) in the power system as signals representing the system's outputs; the system's parameters and inputs have to be estimated only from the measured outputs. This leads to a blind identification problem; therefore, N4SID, categorized as blind identification, is employed to address the problem. To solve the problem, the output data is formulated in the N4SID terminology, system matrices are calculated in 6 steps, and finally, the forward stochastic model of the system is derived. The proposed method has some advantages such as robustness and no need to know the parameters of the power system for design. To evaluate the performance of the proposed method, simulation studies are carried out on two practical power systems. The simulations show that the proposed method has a desirable performance.","PeriodicalId":302863,"journal":{"name":"2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132638505","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 : 2018-10-01DOI: 10.1109/ISGTEurope.2018.8571550
Eleni Tsioumpri, B. Stephen, Neil Dunn-Birch, S. Mcarthur
The operation of distribution networks has become more challenging in recent years with increasing levels of embedded generation and other low carbon technologies pushing these towards their design limits. To identify the nature and extent of these challenges, network operators are deploying monitoring equipment on low voltage feeders, leading to new insights into fault behaviour and usage characterisation. With this heightened level of observability comes the additional challenge of finding models that translate raw data streams into outputs on which operational decisions can be based or supported. In this paper, operational low voltage substation and feeder monitoring data from a UK distribution network is used to identify fault occurrence relations to localised meteorological data, characterise the localised network sensitivities of demand dynamics and infer the effects of embedded generation not visible to the network operator. These case studies are then used to show how additional operational context can be provided to the network operator through the application of analytics.
{"title":"Data Analytics to Support Operational Distribution Network Monitoring","authors":"Eleni Tsioumpri, B. Stephen, Neil Dunn-Birch, S. Mcarthur","doi":"10.1109/ISGTEurope.2018.8571550","DOIUrl":"https://doi.org/10.1109/ISGTEurope.2018.8571550","url":null,"abstract":"The operation of distribution networks has become more challenging in recent years with increasing levels of embedded generation and other low carbon technologies pushing these towards their design limits. To identify the nature and extent of these challenges, network operators are deploying monitoring equipment on low voltage feeders, leading to new insights into fault behaviour and usage characterisation. With this heightened level of observability comes the additional challenge of finding models that translate raw data streams into outputs on which operational decisions can be based or supported. In this paper, operational low voltage substation and feeder monitoring data from a UK distribution network is used to identify fault occurrence relations to localised meteorological data, characterise the localised network sensitivities of demand dynamics and infer the effects of embedded generation not visible to the network operator. These case studies are then used to show how additional operational context can be provided to the network operator through the application of analytics.","PeriodicalId":302863,"journal":{"name":"2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134605215","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 : 2018-10-01DOI: 10.1109/ISGTEurope.2018.8571658
C. Kittl, Dzanan Sarajlic, C. Rehtanz
Representative and sophisticated network models are often needed for research. Models of real grids are specific and confidential, artificial ones deficient in lifelikeness. This paper presents research contributing to enhanced artificial grid models. As German low voltage grid models cannot be analysed in total, this paper proposes a substitute methodology to group similar supply tasks to be fulfilled by low voltage grids.
{"title":"$k$-means based identification of common supply tasks for low voltage grids","authors":"C. Kittl, Dzanan Sarajlic, C. Rehtanz","doi":"10.1109/ISGTEurope.2018.8571658","DOIUrl":"https://doi.org/10.1109/ISGTEurope.2018.8571658","url":null,"abstract":"Representative and sophisticated network models are often needed for research. Models of real grids are specific and confidential, artificial ones deficient in lifelikeness. This paper presents research contributing to enhanced artificial grid models. As German low voltage grid models cannot be analysed in total, this paper proposes a substitute methodology to group similar supply tasks to be fulfilled by low voltage grids.","PeriodicalId":302863,"journal":{"name":"2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)","volume":"54 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131269121","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 : 2018-10-01DOI: 10.1109/ISGTEurope.2018.8571799
Yuta Takasawa, Satoru Akagi, S. Yoshizawa, H. Ishii, Y. Hayashi
In this study, we evaluate the Volt- VArand Volt-V AR-Watt functions of smart inverters based on the PV penetration level (PVPL) and the photovoltaic (PV) curtailment. The Volt- VArand Volt- V Ar- Watt functions are compared with the optimal control parameters that maintain the distributed network voltage within an admissible range and minimize PV curtailment. The study results at each PVPL (0-100%) with a typical distribution network model highlight the following points within our conditions. (1) Optimal control parameters vary with the PVPL and two weather conditions; (2) The maximum PVPL does not reach 100% with the Volt-V Arfunction, whereas the Volt-VAR-Watt function enables the maximum PVPL to reach 100%; and (3) the Volt-VAR-Watt function curtails PV generation, and the average ratio of PV curtailment on cloudy and sunny days is about 12.1 % and 0.16%, respectively.
在本研究中，我们基于光伏渗透水平(PVPL)和光伏(PV)削减来评估智能逆变器的Volt- var和Volt- v AR-Watt功能。将Volt- var和Volt- V - Ar- Watt函数与使分布式电网电压保持在允许范围内并使光伏减容最小化的最优控制参数进行了比较。在我们的条件下，典型配电网模型在每个PVPL(0-100%)下的研究结果突出了以下几点。(1)最优控制参数随PVPL和两种天气条件的变化而变化;(2)使用伏特-伏特-瓦特功能时，最大PVPL不能达到100%，而伏特-伏特-瓦特功能可使最大PVPL达到100%;(3)伏-变-瓦函数限制光伏发电，阴天和晴天平均削减比例分别约为12.1%和0.16%。
{"title":"Evaluation of Voltage Regulation Functions of Smart Inverters Based on Penetration Level and Curtailment in Photovoltaic Systems","authors":"Yuta Takasawa, Satoru Akagi, S. Yoshizawa, H. Ishii, Y. Hayashi","doi":"10.1109/ISGTEurope.2018.8571799","DOIUrl":"https://doi.org/10.1109/ISGTEurope.2018.8571799","url":null,"abstract":"In this study, we evaluate the Volt- VArand Volt-V AR-Watt functions of smart inverters based on the PV penetration level (PVPL) and the photovoltaic (PV) curtailment. The Volt- VArand Volt- V Ar- Watt functions are compared with the optimal control parameters that maintain the distributed network voltage within an admissible range and minimize PV curtailment. The study results at each PVPL (0-100%) with a typical distribution network model highlight the following points within our conditions. (1) Optimal control parameters vary with the PVPL and two weather conditions; (2) The maximum PVPL does not reach 100% with the Volt-V Arfunction, whereas the Volt-VAR-Watt function enables the maximum PVPL to reach 100%; and (3) the Volt-VAR-Watt function curtails PV generation, and the average ratio of PV curtailment on cloudy and sunny days is about 12.1 % and 0.16%, respectively.","PeriodicalId":302863,"journal":{"name":"2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115355848","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 : 2018-10-01DOI: 10.1109/ISGTEurope.2018.8571523
Wen-jun Tang, Sin-Yi Shih, Hong-Tzer Yang
Renewable energy (RE) is commonly used nowadays not only to fulfill the increasing power demand but also to reduce global warming and environmental pollution. However, the uncertain characteristics of renewable energy heavily affect the capacity planning of operating reserve and thus reduce the reliability and security of power system. Appropriate planning of reserve capacity is, therefore, needed to solve these problems while maintaining cost minimization and power system stability. The proposed planning is performed based on a day-ahead market with the reserve providers including external grid, automatic generation control, demand response (DR) program and RE curtailment. Stochastic models including independent uncertainty-related factors of RE generation and load are constructed in Monte Carlo simulations. To keep the dynamic reserve adequate and solve the aforementioned risk and cost balance problem, a chance-constrained optimal power flow is employed as a probabilistic constraint to enforce operating reserve to offer a certain extent backup capacity and risk tolerance. Moreover, the effectiveness of DR is also imitated with cross-elasticity and self-elasticity for the amount and price a consumer will bid in DR market. To verify the proposed approach for reserve capacity planning, the proposed method is tested in a modified IEEE 30-bus system with high RE penetration. The result shows a day-ahead arrangement of operating reserve with good efficiency and economy.
{"title":"Day-Ahead Optimal Reserve Capacity Planning Based on Stochastic RE and DR Models","authors":"Wen-jun Tang, Sin-Yi Shih, Hong-Tzer Yang","doi":"10.1109/ISGTEurope.2018.8571523","DOIUrl":"https://doi.org/10.1109/ISGTEurope.2018.8571523","url":null,"abstract":"Renewable energy (RE) is commonly used nowadays not only to fulfill the increasing power demand but also to reduce global warming and environmental pollution. However, the uncertain characteristics of renewable energy heavily affect the capacity planning of operating reserve and thus reduce the reliability and security of power system. Appropriate planning of reserve capacity is, therefore, needed to solve these problems while maintaining cost minimization and power system stability. The proposed planning is performed based on a day-ahead market with the reserve providers including external grid, automatic generation control, demand response (DR) program and RE curtailment. Stochastic models including independent uncertainty-related factors of RE generation and load are constructed in Monte Carlo simulations. To keep the dynamic reserve adequate and solve the aforementioned risk and cost balance problem, a chance-constrained optimal power flow is employed as a probabilistic constraint to enforce operating reserve to offer a certain extent backup capacity and risk tolerance. Moreover, the effectiveness of DR is also imitated with cross-elasticity and self-elasticity for the amount and price a consumer will bid in DR market. To verify the proposed approach for reserve capacity planning, the proposed method is tested in a modified IEEE 30-bus system with high RE penetration. The result shows a day-ahead arrangement of operating reserve with good efficiency and economy.","PeriodicalId":302863,"journal":{"name":"2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114758626","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 : 2018-10-01DOI: 10.1109/ISGTEurope.2018.8571787
Emil Namor, F. Sossan, E. Scolari, R. Cherkaoui, M. Paolone
The paper discusses the model identification, validation and experimental testing of current-to-voltage dynamic circuit models for a grid-connected MW-class battery. The model refers to an utility-scale 720 kVA/560 kWh battery energy storage system (BESS) and is used in a model predictive control framework to forecast the evolution of the battery DC voltage as a function of the current trajectory. The model is identified using measurements from a dedicated experimental session where the BESS is controlled with a pseudo random binary signal (PRBS) to excite the system on a broad spectrum. The identified model relies on the assumption that the battery is a single cell. To test this assumption and assess the quality of predictions, we test the model performance by using a second data set coming from a real-life power system application, where the BESS is used to dispatch the operation of a group of stochastic prosumers (demand and PV generation). Experimental results show that the root mean square voltage prediction error of the best performing model (i.e. two time constant model, TTC) is less than 0.55 % for look-ahead times in the range 10 seconds-l0 minutes and better than persistence for all considered forecasting horizons.
{"title":"Experimental Assessment of the Prediction Performance of Dynamic Equivalent Circuit Models of Grid-Connected Battery Energy Storage Systems","authors":"Emil Namor, F. Sossan, E. Scolari, R. Cherkaoui, M. Paolone","doi":"10.1109/ISGTEurope.2018.8571787","DOIUrl":"https://doi.org/10.1109/ISGTEurope.2018.8571787","url":null,"abstract":"The paper discusses the model identification, validation and experimental testing of current-to-voltage dynamic circuit models for a grid-connected MW-class battery. The model refers to an utility-scale 720 kVA/560 kWh battery energy storage system (BESS) and is used in a model predictive control framework to forecast the evolution of the battery DC voltage as a function of the current trajectory. The model is identified using measurements from a dedicated experimental session where the BESS is controlled with a pseudo random binary signal (PRBS) to excite the system on a broad spectrum. The identified model relies on the assumption that the battery is a single cell. To test this assumption and assess the quality of predictions, we test the model performance by using a second data set coming from a real-life power system application, where the BESS is used to dispatch the operation of a group of stochastic prosumers (demand and PV generation). Experimental results show that the root mean square voltage prediction error of the best performing model (i.e. two time constant model, TTC) is less than 0.55 % for look-ahead times in the range 10 seconds-l0 minutes and better than persistence for all considered forecasting horizons.","PeriodicalId":302863,"journal":{"name":"2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114969540","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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