Pub Date : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281829
Yashar Kor, M. Reformat, P. Musílek
Improvements in monitoring and data collection practices provide opportunities for more comprehensive modelling and managing grid operations. At the same time, advanced data analysis methods should be able to address service quality degradation due to outages, weather patterns and asset-related performance.In this paper, we apply Machine Learning and Computational Intelligence methods for the analysis of power distribution system data and constructing a system for predicting power outages. Weather and outage data are utilized by the proposed system for predicting purposes. We evaluate the prediction performance of different types of prediction models. We also propose and validate three different architectures of a system for predicting types of weather-related outages. We focus on outages caused by wind, snow and ice. An analysis of the prediction results is provided.
{"title":"Predicting Weather-related Power Outages in Distribution Grid","authors":"Yashar Kor, M. Reformat, P. Musílek","doi":"10.1109/PESGM41954.2020.9281829","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281829","url":null,"abstract":"Improvements in monitoring and data collection practices provide opportunities for more comprehensive modelling and managing grid operations. At the same time, advanced data analysis methods should be able to address service quality degradation due to outages, weather patterns and asset-related performance.In this paper, we apply Machine Learning and Computational Intelligence methods for the analysis of power distribution system data and constructing a system for predicting power outages. Weather and outage data are utilized by the proposed system for predicting purposes. We evaluate the prediction performance of different types of prediction models. We also propose and validate three different architectures of a system for predicting types of weather-related outages. We focus on outages caused by wind, snow and ice. An analysis of the prediction results is provided.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132034704","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281786
A. Lorestani, J. Chebeir, R. Ahmed, J. Cotton
This study deals with parameter identification of behavioral model of the electric vehicle’s (EV) battery, which can be cast as a difficult optimization problem. This necessitates the employment of a powerful and global optimization algorithm to ensure the reliability of the results. In this study, a newly developed optimization technique referred to as evolutionary-particle swarm optimization (E-PSO) is implemented. A statistical analysis is conducted, and the proposed algorithm is compared with other widespread metaheuristic algorithms in terms of convergence and simulation time. To do so, first, the current of the battery is determined using a typical EV model and a standard driving cycle. Then, experimental tests are conducted on Lithium Polymer off the shelf cell to calculate the actual terminal voltage. Finally, this actual data is used in an optimization frame to calculate the parameters of the model by which the behavioral model and the real battery are in the closest agreement. The results show that the E-PSO algorithm outperforms other metaheuristic optimization algorithms in terms of finding better solution in a lower convergence time. It is also demonstrated that the solution obtained by E-PSO provides a more accurate estimation of the actual battery.
{"title":"A New Optimization Algorithm for Parameters Identification of Electric Vehicles’ Battery","authors":"A. Lorestani, J. Chebeir, R. Ahmed, J. Cotton","doi":"10.1109/PESGM41954.2020.9281786","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281786","url":null,"abstract":"This study deals with parameter identification of behavioral model of the electric vehicle’s (EV) battery, which can be cast as a difficult optimization problem. This necessitates the employment of a powerful and global optimization algorithm to ensure the reliability of the results. In this study, a newly developed optimization technique referred to as evolutionary-particle swarm optimization (E-PSO) is implemented. A statistical analysis is conducted, and the proposed algorithm is compared with other widespread metaheuristic algorithms in terms of convergence and simulation time. To do so, first, the current of the battery is determined using a typical EV model and a standard driving cycle. Then, experimental tests are conducted on Lithium Polymer off the shelf cell to calculate the actual terminal voltage. Finally, this actual data is used in an optimization frame to calculate the parameters of the model by which the behavioral model and the real battery are in the closest agreement. The results show that the E-PSO algorithm outperforms other metaheuristic optimization algorithms in terms of finding better solution in a lower convergence time. It is also demonstrated that the solution obtained by E-PSO provides a more accurate estimation of the actual battery.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130950592","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9282052
Long Chen, Almir Ekic, Di Wu
The electric power grid is undergoing significant changes in the mix of generation source types. The increasing penetration of renewable energy resources (RERs), such as wind and solar, is improving energy efficiency, but meanwhile it is also challenging grid planners and operators to maintain reliable electricity services. The high penetration of RERs may drive the power grid toward weak grid conditions, which may cause grid stability and reliability issues. One of ways to address these issues is to select appropriate points of interconnection for integrating RERs into power grids. In previous works, grid stability analysis and grid reliability assessment are evaluated separately for selecting the points of interconnection. This paper presents an integrated method for selecting such points by coordinating grid strength assessment and grid reliability evaluation as well as investment cost. The efficacy of the proposed method is demonstrated on the IEEE reliability test system.
{"title":"Multi-Objective Grid Planning for Renewable Energy Integration","authors":"Long Chen, Almir Ekic, Di Wu","doi":"10.1109/PESGM41954.2020.9282052","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9282052","url":null,"abstract":"The electric power grid is undergoing significant changes in the mix of generation source types. The increasing penetration of renewable energy resources (RERs), such as wind and solar, is improving energy efficiency, but meanwhile it is also challenging grid planners and operators to maintain reliable electricity services. The high penetration of RERs may drive the power grid toward weak grid conditions, which may cause grid stability and reliability issues. One of ways to address these issues is to select appropriate points of interconnection for integrating RERs into power grids. In previous works, grid stability analysis and grid reliability assessment are evaluated separately for selecting the points of interconnection. This paper presents an integrated method for selecting such points by coordinating grid strength assessment and grid reliability evaluation as well as investment cost. The efficacy of the proposed method is demonstrated on the IEEE reliability test system.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130959181","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281527
A. Kirakosyan, E. El-Saadany, M. E. El Moursi, A. Yazdavar, M. Salama
Hybrid configurations of the ac/dc microgrids are considered in this paper. It is shown that the asynchronous ac microgrids connected through dc microgrid might not achieve the desired sharing of the loading in each subgrid. Usage of a global loading index in dc microgrid is suggested to overcome differences of the dc loading seen at different points of the dc subgrid, which indirectly assists in reaching the required loading between the ac subgrids. The simulation study conducted in the Matlab/ Simulink environment illustrates the existing problem and verifies the effectiveness of the implemented solution.
{"title":"Sharing of the loading of asynchronous ac microgrids connected through dc microgrids","authors":"A. Kirakosyan, E. El-Saadany, M. E. El Moursi, A. Yazdavar, M. Salama","doi":"10.1109/PESGM41954.2020.9281527","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281527","url":null,"abstract":"Hybrid configurations of the ac/dc microgrids are considered in this paper. It is shown that the asynchronous ac microgrids connected through dc microgrid might not achieve the desired sharing of the loading in each subgrid. Usage of a global loading index in dc microgrid is suggested to overcome differences of the dc loading seen at different points of the dc subgrid, which indirectly assists in reaching the required loading between the ac subgrids. The simulation study conducted in the Matlab/ Simulink environment illustrates the existing problem and verifies the effectiveness of the implemented solution.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130220332","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9282085
Michela Moschella, M. Murad, E. Crisostomi, F. Milano
The number of Plug-in Electric Vehicles (PEVs) is increasing worldwide, as well as are the rates with which vehicles can be charged. This poses the question regarding how many PEVs may ultimately be connected simultaneously for charging, and how quickly the load of PEVs can increase, before the existing power grids show stability issues. In particular, we denote the first as a static limit, and has mainly an impact in terms of node voltages, while the second is a dynamic limit, which mainly affects the frequency of the system. In this paper, we shall use a transient stability model of power systems to assess both limits for a realistic power transmission system, and conclude that the static limit is actually the most critical one.
{"title":"On the Impact of PEV Charging on Transmission System: Static and Dynamic Limits","authors":"Michela Moschella, M. Murad, E. Crisostomi, F. Milano","doi":"10.1109/PESGM41954.2020.9282085","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9282085","url":null,"abstract":"The number of Plug-in Electric Vehicles (PEVs) is increasing worldwide, as well as are the rates with which vehicles can be charged. This poses the question regarding how many PEVs may ultimately be connected simultaneously for charging, and how quickly the load of PEVs can increase, before the existing power grids show stability issues. In particular, we denote the first as a static limit, and has mainly an impact in terms of node voltages, while the second is a dynamic limit, which mainly affects the frequency of the system. In this paper, we shall use a transient stability model of power systems to assess both limits for a realistic power transmission system, and conclude that the static limit is actually the most critical one.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130484762","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281740
Wenjing Huang, Z. Li, J. H. Zheng, Q. Wu
The growing tight interconnections between regional power systems brings about the critical need to coordinate multi-area economic dispatch. Because of the limitations on private data exchange and model management, it is suitable to address the multi-area economic dispatch problem in a distributed manner. In this paper, the linearized AC power flow model is used to consider the reactive power to make the dispatch more accurate. The dispatch model is decomposed into regional sub-problems and the coordinator’s problem by using the boundary phase angles and voltage amplitudes as coupling variables. In order to handle the multi-parametric programming, a critical region projection method is used to obtain high-quality solutions with finite convergence. Numerical tests on multi-area interconnected systems are conducted to show the excellent computational performance and the more accurate solution of the proposed method.
{"title":"Distributed Multi-Area Economic Dispatch Considering Reactive Power Using Critical Region Projection","authors":"Wenjing Huang, Z. Li, J. H. Zheng, Q. Wu","doi":"10.1109/PESGM41954.2020.9281740","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281740","url":null,"abstract":"The growing tight interconnections between regional power systems brings about the critical need to coordinate multi-area economic dispatch. Because of the limitations on private data exchange and model management, it is suitable to address the multi-area economic dispatch problem in a distributed manner. In this paper, the linearized AC power flow model is used to consider the reactive power to make the dispatch more accurate. The dispatch model is decomposed into regional sub-problems and the coordinator’s problem by using the boundary phase angles and voltage amplitudes as coupling variables. In order to handle the multi-parametric programming, a critical region projection method is used to obtain high-quality solutions with finite convergence. Numerical tests on multi-area interconnected systems are conducted to show the excellent computational performance and the more accurate solution of the proposed method.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127851495","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 : 2020-08-02DOI: 10.1109/pesgm41954.2020.9281970
W. Du, R. Lasseter, A. Khalsa
Grid-forming sources are voltage sources that draw necessary currents to meet any load changes. A load step can cause part or all of these sources to become overloaded in a microgrid. This paper presents an overload mitigation controller that addresses the two overload issues in a microgrid by actively controlling the sources’ frequency. When part of the sources in a microgrid is overloaded, the controller autonomously transfers the extra load to other sources by rapidly reducing its frequency. The frequency difference between sources during transient results in a change of phase angle, which redistributes the power flow. When all sources in a microgrid are overloaded, each source keeps dropping the frequency. Therefore, under frequency load shedding can be used to trip the non-critical loads resulting in the survival of microgrid. The advantages of these concepts are that communications between sources are not needed during transient, and the robust voltage control is maintained. Simulation and field tests from CERTS/AEP microgrid test site verify that the control strategy is effective in both purely inverter-based microgrids and inverter & generator mixed microgrids.
{"title":"Survivability of Autonomous Microgrid during Overload Events","authors":"W. Du, R. Lasseter, A. Khalsa","doi":"10.1109/pesgm41954.2020.9281970","DOIUrl":"https://doi.org/10.1109/pesgm41954.2020.9281970","url":null,"abstract":"Grid-forming sources are voltage sources that draw necessary currents to meet any load changes. A load step can cause part or all of these sources to become overloaded in a microgrid. This paper presents an overload mitigation controller that addresses the two overload issues in a microgrid by actively controlling the sources’ frequency. When part of the sources in a microgrid is overloaded, the controller autonomously transfers the extra load to other sources by rapidly reducing its frequency. The frequency difference between sources during transient results in a change of phase angle, which redistributes the power flow. When all sources in a microgrid are overloaded, each source keeps dropping the frequency. Therefore, under frequency load shedding can be used to trip the non-critical loads resulting in the survival of microgrid. The advantages of these concepts are that communications between sources are not needed during transient, and the robust voltage control is maintained. Simulation and field tests from CERTS/AEP microgrid test site verify that the control strategy is effective in both purely inverter-based microgrids and inverter & generator mixed microgrids.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131718126","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9282102
Lei Yan, Jiayu Han, Hong Wang, Zhiyi Li, Zuyi Li
This paper presents an online transient-based electrical appliance state tracking method for nonintrusive load monitoring (NILM). The proposed Factorial Particle based Hidden Markov Model (FPHMM) method takes advantage of transient features (TF) in high-resolution data to infer states in the transient process and conducts steady state verification (SSV) to rectify falsely identified appliances. The FPHMM method can overcome the common feature similarity problem in NILM by combining particle filter method and Markov Chain Monte Carlo (MCMC) sampling method, and by mining the intra-relationship of states inside a single appliance and the inter-relationship of states among multiple appliances. The FPHMM method is tested on the LIFTED dataset with appliance-level details and high sampling rates. Testing results demonstrate that the FPHMM method can resolve the feature similarity problem thus achieving high accuracy.
提出了一种基于暂态的非侵入式负荷在线跟踪方法。本文提出的基于析因粒子的隐马尔可夫模型(FPHMM)方法利用高分辨率数据中的瞬态特征(TF)来推断瞬态过程中的状态，并进行稳态验证(SSV)来纠正错误识别的器具。FPHMM方法将粒子滤波方法与马尔可夫链蒙特卡罗(Markov Chain Monte Carlo, MCMC)采样方法相结合，通过挖掘单个设备内部的状态关系和多个设备之间的状态关系，克服了NILM中常见的特征相似问题。在具有设备级细节和高采样率的lift数据集上对FPHMM方法进行了测试。测试结果表明，FPHMM方法可以很好地解决特征相似度问题，从而达到较高的准确率。
{"title":"An Online Transient-Based Electrical Appliance State Tracking Method Via Markov Chain Monte Carlo Sampling","authors":"Lei Yan, Jiayu Han, Hong Wang, Zhiyi Li, Zuyi Li","doi":"10.1109/PESGM41954.2020.9282102","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9282102","url":null,"abstract":"This paper presents an online transient-based electrical appliance state tracking method for nonintrusive load monitoring (NILM). The proposed Factorial Particle based Hidden Markov Model (FPHMM) method takes advantage of transient features (TF) in high-resolution data to infer states in the transient process and conducts steady state verification (SSV) to rectify falsely identified appliances. The FPHMM method can overcome the common feature similarity problem in NILM by combining particle filter method and Markov Chain Monte Carlo (MCMC) sampling method, and by mining the intra-relationship of states inside a single appliance and the inter-relationship of states among multiple appliances. The FPHMM method is tested on the LIFTED dataset with appliance-level details and high sampling rates. Testing results demonstrate that the FPHMM method can resolve the feature similarity problem thus achieving high accuracy.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128799507","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281509
A. Yazdavar, A. Eajal, E. El-Saadany, M. Salama
Isolated microgrids are operated based on droop characteristics in terms of frequency-active power and voltage-reactive power to evenly share active and reactive powers among their dispatchable DGs. Although active powers are accurately distributed among the different DGs, there is an inherent error in the sharing of reactive powers. This error is a function of the DGs’ locations, and accordingly, should be addressed in the planning. This paper presents a planning platform to simultaneously site and size DGs and capacitor banks (CBs) for isolated microgrids that takes into account the operational characteristics at the planning stage. For this aim, a suitable power flow that considers the specific features of isolated microgrids is employed along with an additional constraint that is introduced to the planning. Loads’ and DGs’ intermittent behaviors are modelled through a probabilistic model. To validate the effectiveness of the proposed planning approach, the PG& E 69-bus system has been used.
{"title":"A Planning Platform for Droop-based Isolated Microgrids","authors":"A. Yazdavar, A. Eajal, E. El-Saadany, M. Salama","doi":"10.1109/PESGM41954.2020.9281509","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281509","url":null,"abstract":"Isolated microgrids are operated based on droop characteristics in terms of frequency-active power and voltage-reactive power to evenly share active and reactive powers among their dispatchable DGs. Although active powers are accurately distributed among the different DGs, there is an inherent error in the sharing of reactive powers. This error is a function of the DGs’ locations, and accordingly, should be addressed in the planning. This paper presents a planning platform to simultaneously site and size DGs and capacitor banks (CBs) for isolated microgrids that takes into account the operational characteristics at the planning stage. For this aim, a suitable power flow that considers the specific features of isolated microgrids is employed along with an additional constraint that is introduced to the planning. Loads’ and DGs’ intermittent behaviors are modelled through a probabilistic model. To validate the effectiveness of the proposed planning approach, the PG& E 69-bus system has been used.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128809727","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281651
Kishan Prudhvi Guddanti, Y. Ye, Panitarn Chongfuangprinya, Bo Yang, Yang Weng
Due to the high penetration of distributed energy resources (DERs) in the distribution system, there is an increasing need for advanced tools to thoroughly study the impacts of DERs on distribution networks under various DER control/modeling scenarios. This type of tools not only requires a powerful network simulation engine in distribution grids, but also a flexible and interactive environment for easy development of advanced analysis/control algorithms, e.g., cutting-edge machine learning packages. If the software can be open-sourced, the power industry can further enjoy transparency and faster-time-to-market transition to expedite renewable integration. Past work does not give a fully independent data structure to separate the simulation layer and the application layer. Therefore, this work aims at providing full independence while integrating the two most powerful open-source tools in distribution grid simulation and an extremely popular programming language: GridLAB-D and Python. Specifically, we carefully create (1) an open and flexible design, (2) easy-to-develop analytical application scenarios, and (3) compatibility with a variety of third-party tools. We demonstrate features (1) and (2) of this co-simulation framework with a use case study on integration capacity analysis (ICA) and we demonstrate feature (3) as an example to conduct graphical analysis in Python for distribution system analysis with a near-zero effort. A highly accurate and fast system-wide ICA result demonstrates the supreme data structure and easy-to-extend architecture for speeding renewable integration. The code is available for download.
{"title":"Better Data Structures for Co-simulation of Distribution System with GridLAB-D and Python","authors":"Kishan Prudhvi Guddanti, Y. Ye, Panitarn Chongfuangprinya, Bo Yang, Yang Weng","doi":"10.1109/PESGM41954.2020.9281651","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281651","url":null,"abstract":"Due to the high penetration of distributed energy resources (DERs) in the distribution system, there is an increasing need for advanced tools to thoroughly study the impacts of DERs on distribution networks under various DER control/modeling scenarios. This type of tools not only requires a powerful network simulation engine in distribution grids, but also a flexible and interactive environment for easy development of advanced analysis/control algorithms, e.g., cutting-edge machine learning packages. If the software can be open-sourced, the power industry can further enjoy transparency and faster-time-to-market transition to expedite renewable integration. Past work does not give a fully independent data structure to separate the simulation layer and the application layer. Therefore, this work aims at providing full independence while integrating the two most powerful open-source tools in distribution grid simulation and an extremely popular programming language: GridLAB-D and Python. Specifically, we carefully create (1) an open and flexible design, (2) easy-to-develop analytical application scenarios, and (3) compatibility with a variety of third-party tools. We demonstrate features (1) and (2) of this co-simulation framework with a use case study on integration capacity analysis (ICA) and we demonstrate feature (3) as an example to conduct graphical analysis in Python for distribution system analysis with a near-zero effort. A highly accurate and fast system-wide ICA result demonstrates the supreme data structure and easy-to-extend architecture for speeding renewable integration. The code is available for download.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123327837","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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