Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183554
Inês M. Alves, Vladimiro Miranda, L. Carvalho
The Sequential Monte Carlo Simulation (SMCS) is a powerful and flexible method commonly used for generating system adequacy assessment. By sampling outage events in sequence and their respective duration, this method can easily incorporate time-dependent issues such as renewable power production, the capacity of hydro units, scheduled maintenance, complex correlated load models, etc, and is the only method that provides probability distributions for the reliability indexes. Despite these advantages, the SMCS method requires considerably more simulation time than the Non-sequential Monte Carlo Simulation approach to provide accurate estimates for the reliability indexes. In an attempt to reduce the simulation time, the SMCS method has been implemented in parallel using a Graphics Processing Unit (GPU) to take advantage of the fast calculations provided by these computing platforms. Two parallelization strategies are proposed: Strategy A, which creates and evaluates yearly samples in a completely parallel approach and while the estimates of the reliability indexes are computed in the CPU; and Strategy B, which consists on concurrently sampling the outage events for the generating units while the state evaluation and the index estimation stages are executed in serial. Simulation results for the IEEE RTS 79, IEEE RTS 96, and the new IEEE RTS GMLC test systems, show that both implementations lead to a significant acceleration of the SMCS method while keeping all its advantages. In addition, it was observed that Strategy B results in less simulation time than Strategy A for generation system adequacy assessment.
{"title":"Parallel GPU Implementation for Fast Generating System Adequacy Assessment via Sequential Monte Carlo Simulation","authors":"Inês M. Alves, Vladimiro Miranda, L. Carvalho","doi":"10.1109/PMAPS47429.2020.9183554","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183554","url":null,"abstract":"The Sequential Monte Carlo Simulation (SMCS) is a powerful and flexible method commonly used for generating system adequacy assessment. By sampling outage events in sequence and their respective duration, this method can easily incorporate time-dependent issues such as renewable power production, the capacity of hydro units, scheduled maintenance, complex correlated load models, etc, and is the only method that provides probability distributions for the reliability indexes. Despite these advantages, the SMCS method requires considerably more simulation time than the Non-sequential Monte Carlo Simulation approach to provide accurate estimates for the reliability indexes. In an attempt to reduce the simulation time, the SMCS method has been implemented in parallel using a Graphics Processing Unit (GPU) to take advantage of the fast calculations provided by these computing platforms. Two parallelization strategies are proposed: Strategy A, which creates and evaluates yearly samples in a completely parallel approach and while the estimates of the reliability indexes are computed in the CPU; and Strategy B, which consists on concurrently sampling the outage events for the generating units while the state evaluation and the index estimation stages are executed in serial. Simulation results for the IEEE RTS 79, IEEE RTS 96, and the new IEEE RTS GMLC test systems, show that both implementations lead to a significant acceleration of the SMCS method while keeping all its advantages. In addition, it was observed that Strategy B results in less simulation time than Strategy A for generation system adequacy assessment.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114278141","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183446
V. Di Giorgio, R. Langella, A. Testa, S. Djokic, M. Zou
This paper presents a non-homogeneous Markov Chain (MC) model for generation of wind speed (WS) and wind direction (WD) synthetic time series taking into account their daily, monthly and seasonal characteristics. The bivariate nature of the wind process, represented by WS and WD, is modelled by means of an equivalent univariate random variable W, capable of taking into account the statistical dependency existing between WS and WD. A statistical characterization of the wind energy resource at the specific considered site demonstrates the time non-stationarity of the wind process over the year and over the seasons, so twelve monthly transition probability matrices of the variable W are developed. One thousand synthetic time series, each of three years length, are generated in a Monte Carlo framework, demonstrating the excellent performances and overall robustness of the presented model, also using new non-conventional metrics based on Markov transition matrices.
{"title":"First Order Non-homogeneous Markov Chain Model for Generation of Wind Speed and Direction Synthetic Time Series","authors":"V. Di Giorgio, R. Langella, A. Testa, S. Djokic, M. Zou","doi":"10.1109/PMAPS47429.2020.9183446","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183446","url":null,"abstract":"This paper presents a non-homogeneous Markov Chain (MC) model for generation of wind speed (WS) and wind direction (WD) synthetic time series taking into account their daily, monthly and seasonal characteristics. The bivariate nature of the wind process, represented by WS and WD, is modelled by means of an equivalent univariate random variable W, capable of taking into account the statistical dependency existing between WS and WD. A statistical characterization of the wind energy resource at the specific considered site demonstrates the time non-stationarity of the wind process over the year and over the seasons, so twelve monthly transition probability matrices of the variable W are developed. One thousand synthetic time series, each of three years length, are generated in a Monte Carlo framework, demonstrating the excellent performances and overall robustness of the presented model, also using new non-conventional metrics based on Markov transition matrices.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129537596","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183655
S. Perkin, Arnbjörg Arnardóttir, K. Sigurjonsson, Þorvaldur Jacobsen
An extreme weather event affected the Icelandic power system on the 10th and 11th of December 2019, causing dozens of disturbances and multiple instances of unserved energy. Landsnet, the Icelandic Transmission System Operator, has been developing disturbance probability forecast models as one means of improving situational awareness. This paper provides an ex-post analysis of these models during the extreme weather event. The disturbance forecasts provided useful information at a regional scale, and showed sensitivity to exogenous data. Opportunities to improve disturbance probability models are identified and regulatory drivers are highlighted.
{"title":"Performance of probabilistic disturbance forecasts in extreme weather on the Icelandic power system","authors":"S. Perkin, Arnbjörg Arnardóttir, K. Sigurjonsson, Þorvaldur Jacobsen","doi":"10.1109/PMAPS47429.2020.9183655","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183655","url":null,"abstract":"An extreme weather event affected the Icelandic power system on the 10th and 11th of December 2019, causing dozens of disturbances and multiple instances of unserved energy. Landsnet, the Icelandic Transmission System Operator, has been developing disturbance probability forecast models as one means of improving situational awareness. This paper provides an ex-post analysis of these models during the extreme weather event. The disturbance forecasts provided useful information at a regional scale, and showed sensitivity to exogenous data. Opportunities to improve disturbance probability models are identified and regulatory drivers are highlighted.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128539613","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 : 2020-08-01DOI: 10.1109/pmaps47429.2020.9183677
{"title":"PMAPS 2020 Cover Page","authors":"","doi":"10.1109/pmaps47429.2020.9183677","DOIUrl":"https://doi.org/10.1109/pmaps47429.2020.9183677","url":null,"abstract":"","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125963583","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183441
J. Browell, C. Gilbert
Probabilistic forecasts quantify the uncertainty associated with predictions about the future. They are useful in decision-making, and essential when the user’s objective is risk management, or optimisation with asymmetric cost functions. Probabilistic forecasts are widely utilised in finance and weather services, and increasingly by the energy industry, to name a few applications. The R package ProbCast provides a framework for producing probabilistic forecasts using a range of leading predictive models, plus visualisation, and evaluation of the resulting forecasts. It supports both parametric and non-parametric density forecasting, and high-dimensional dependency modelling based on Gaussian Copulas. ProbCast enables a simple workflow for common tasks associated with probabilistic forecasting, making leading methodologies more accessible then ever before. These features are described and then illustrated using an example from energy forecasting, and the first public release of the package itself accompanies this paper.
{"title":"ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts","authors":"J. Browell, C. Gilbert","doi":"10.1109/PMAPS47429.2020.9183441","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183441","url":null,"abstract":"Probabilistic forecasts quantify the uncertainty associated with predictions about the future. They are useful in decision-making, and essential when the user’s objective is risk management, or optimisation with asymmetric cost functions. Probabilistic forecasts are widely utilised in finance and weather services, and increasingly by the energy industry, to name a few applications. The R package ProbCast provides a framework for producing probabilistic forecasts using a range of leading predictive models, plus visualisation, and evaluation of the resulting forecasts. It supports both parametric and non-parametric density forecasting, and high-dimensional dependency modelling based on Gaussian Copulas. ProbCast enables a simple workflow for common tasks associated with probabilistic forecasting, making leading methodologies more accessible then ever before. These features are described and then illustrated using an example from energy forecasting, and the first public release of the package itself accompanies this paper.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123980698","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183453
Arpan Koirala, T. Acker, D. Van Hertem, Juliano Camargo, R. D’hulst
Recent evolutions in low voltage distribution system (LVDS), e.g., distributed generation and electric vehicles, have introduced a higher level of uncertainty. To determine the probability of violating grid constraints, e.g., undervoltage, such system must be assessed using a probabilistic power flow, which considers these uncertainties. Several approaches exist, including simulation-based and analytical methods. A well-known example of the simulation-based methods is the crude Monte Carlo (MC) approach which is very common in scientific computation due to its simplicity. Recently, analytical methods such as the general polynomial chaos (gPC) approach have gained increasing interest. This paper illustrates the effectiveness of the gPC approach compared to the MC method in determining the uncertainty of certain grid measures. Both methods are compared with respect to computational time and accuracy using a small test case with stochastic input which coheres to a univariate continuous distribution.
{"title":"General Polynomial Chaos vs Crude Monte Carlo for Probabilistic Evaluation of Distribution Systems","authors":"Arpan Koirala, T. Acker, D. Van Hertem, Juliano Camargo, R. D’hulst","doi":"10.1109/PMAPS47429.2020.9183453","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183453","url":null,"abstract":"Recent evolutions in low voltage distribution system (LVDS), e.g., distributed generation and electric vehicles, have introduced a higher level of uncertainty. To determine the probability of violating grid constraints, e.g., undervoltage, such system must be assessed using a probabilistic power flow, which considers these uncertainties. Several approaches exist, including simulation-based and analytical methods. A well-known example of the simulation-based methods is the crude Monte Carlo (MC) approach which is very common in scientific computation due to its simplicity. Recently, analytical methods such as the general polynomial chaos (gPC) approach have gained increasing interest. This paper illustrates the effectiveness of the gPC approach compared to the MC method in determining the uncertainty of certain grid measures. Both methods are compared with respect to computational time and accuracy using a small test case with stochastic input which coheres to a univariate continuous distribution.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125051267","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183408
Raphael Wu, G. Sansavini
Strengthening distribution grids reliability and resilience against technical and natural hazards is a costly endeavor including equipment upgrades and distributed energy resources. Therefore, using accurate data when assessing grid reliability is key to identify effective solutions. As literature parameters can be inaccurate for specific locations, tuning and validating reliability models against real-world data is key for accurate assessments. In this paper, distribution grid reliability is modelled by considering three failure mechanisms in a Monte Carlo simulation: bus and line failures within the distribution grid, blackouts of the surrounding grid, and dependent failures due to extreme events. Ten parameters governing the frequency and duration distributions of the three failure mechanisms are tuned using metaheuristic optimization. A subsequent global sensitivity analysis quantifies the importance of the estimated parameters.
{"title":"Parameter Estimation for Distribution Grid Reliability Assessment","authors":"Raphael Wu, G. Sansavini","doi":"10.1109/PMAPS47429.2020.9183408","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183408","url":null,"abstract":"Strengthening distribution grids reliability and resilience against technical and natural hazards is a costly endeavor including equipment upgrades and distributed energy resources. Therefore, using accurate data when assessing grid reliability is key to identify effective solutions. As literature parameters can be inaccurate for specific locations, tuning and validating reliability models against real-world data is key for accurate assessments. In this paper, distribution grid reliability is modelled by considering three failure mechanisms in a Monte Carlo simulation: bus and line failures within the distribution grid, blackouts of the surrounding grid, and dependent failures due to extreme events. Ten parameters governing the frequency and duration distributions of the three failure mechanisms are tuned using metaheuristic optimization. A subsequent global sensitivity analysis quantifies the importance of the estimated parameters.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124633424","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183581
Wei Lin, Juan Yu, Zhifang Yang, Xuebin Wang
With the rapid increase of renewables and power demands, probabilistic optimal power flow (POPF) has become an important tool to investigate the stochastic characteristics of power systems. However, the POPF calculation requires repeatedly solving a tremendous number of optimization problems. The computational burden has been the main bottleneck for its practical applications. To overcome this problem, this paper adopts a linear OPF model with reactive power and voltage magnitude to construct the optimization model for samples. Then, a modified multi-parametric programming process is introduced to fast calculate the optimal solutions of samples by avoiding the iterative optimization process. Compared with the traditional multi-programming process, the reduced affine maps between the sample optimization solutions and the stochastic variables are explicitly formulated while keeping the desired accuracy. The IEEE 30-bus and 118-bus systems are used to demonstrate the effectiveness of the proposed method.
{"title":"Fast Probabilistic Optimal Power Flow Based on Modified Multi-Parametric Programming","authors":"Wei Lin, Juan Yu, Zhifang Yang, Xuebin Wang","doi":"10.1109/PMAPS47429.2020.9183581","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183581","url":null,"abstract":"With the rapid increase of renewables and power demands, probabilistic optimal power flow (POPF) has become an important tool to investigate the stochastic characteristics of power systems. However, the POPF calculation requires repeatedly solving a tremendous number of optimization problems. The computational burden has been the main bottleneck for its practical applications. To overcome this problem, this paper adopts a linear OPF model with reactive power and voltage magnitude to construct the optimization model for samples. Then, a modified multi-parametric programming process is introduced to fast calculate the optimal solutions of samples by avoiding the iterative optimization process. Compared with the traditional multi-programming process, the reduced affine maps between the sample optimization solutions and the stochastic variables are explicitly formulated while keeping the desired accuracy. The IEEE 30-bus and 118-bus systems are used to demonstrate the effectiveness of the proposed method.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132920047","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183659
A. Dalton, B. Bekker, M. Koivisto
The variability of the wind resource is the primary challenge associated with the introduction of large scale wind power onto electricity networks. In addressing uncertainties associated with wind power, probabilistic power flow analysis (PPF) is often used in resolving current or future system states. These simulations are informed by input scenarios - i.e. time series that represent conditions being simulated. Thereby defining appropriate input scenarios is a critically important part of the process. This study proposes a novel methodology for clustering wind power time series based on the dominant concurrent atmospheric circulation patterns. These patterns are classified into generalized architypes using Self Organizing Maps. Thereby the probabilistic properties and data dependency structures of wind farms are approximated at the hand of the causative weather phenomena. It was found that this methodology resulted in significant variations in the probabilistic properties of wind power time series and the correlations between wind generators. It is anticipated that this methodology could be effectively applied in defining the input characteristics into operational scenarios within a PPF analysis, and inform correlations between wind farms in spatiotemporal probabilistic forecasts.
{"title":"Atmospheric circulation archetypes as clustering criteria for wind power inputs into probabilistic power flow analysis","authors":"A. Dalton, B. Bekker, M. Koivisto","doi":"10.1109/PMAPS47429.2020.9183659","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183659","url":null,"abstract":"The variability of the wind resource is the primary challenge associated with the introduction of large scale wind power onto electricity networks. In addressing uncertainties associated with wind power, probabilistic power flow analysis (PPF) is often used in resolving current or future system states. These simulations are informed by input scenarios - i.e. time series that represent conditions being simulated. Thereby defining appropriate input scenarios is a critically important part of the process. This study proposes a novel methodology for clustering wind power time series based on the dominant concurrent atmospheric circulation patterns. These patterns are classified into generalized architypes using Self Organizing Maps. Thereby the probabilistic properties and data dependency structures of wind farms are approximated at the hand of the causative weather phenomena. It was found that this methodology resulted in significant variations in the probabilistic properties of wind power time series and the correlations between wind generators. It is anticipated that this methodology could be effectively applied in defining the input characteristics into operational scenarios within a PPF analysis, and inform correlations between wind farms in spatiotemporal probabilistic forecasts.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125716947","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 : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183699
Shahrzad Mahdavi, Hossein Panamtash, A. Dimitrovski, Qun Zhou
This paper proposes predictive cooperative voltage control method in a power system with high penetration of photovoltaic (PV) units. Cooperative distributed control of the reactive power output of PV inverters is coordinated with operation of voltage regulators (VRs) to maintain system voltages within an appropriate bandwidth. Probabilistic forecasting of the solar power generation and the loads is applied to estimate voltage changes which, in turn, are used to set the VR tap positions for preventing large voltage fluctuations with the lowest risk considering the voltage distribution estimation. The fine tuning of voltage adjustment is achieved by cooperative control of PV inverters to maintain a uniform voltage profile across the system. The proposed method is tested on the modified IEEE 123-node test feeder with high PV penetration using real insolation data and with constant loads replaced by several different load profiles. Simulation results demonstrate the effectiveness of the coordinated approach for voltage control with cooperative PV and predictive VR controls taking into account probabilistic load and solar power forecasts.
{"title":"Predictive and Cooperative Voltage Control with Probabilistic Load and Solar Generation Forecasting","authors":"Shahrzad Mahdavi, Hossein Panamtash, A. Dimitrovski, Qun Zhou","doi":"10.1109/PMAPS47429.2020.9183699","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183699","url":null,"abstract":"This paper proposes predictive cooperative voltage control method in a power system with high penetration of photovoltaic (PV) units. Cooperative distributed control of the reactive power output of PV inverters is coordinated with operation of voltage regulators (VRs) to maintain system voltages within an appropriate bandwidth. Probabilistic forecasting of the solar power generation and the loads is applied to estimate voltage changes which, in turn, are used to set the VR tap positions for preventing large voltage fluctuations with the lowest risk considering the voltage distribution estimation. The fine tuning of voltage adjustment is achieved by cooperative control of PV inverters to maintain a uniform voltage profile across the system. The proposed method is tested on the modified IEEE 123-node test feeder with high PV penetration using real insolation data and with constant loads replaced by several different load profiles. Simulation results demonstrate the effectiveness of the coordinated approach for voltage control with cooperative PV and predictive VR controls taking into account probabilistic load and solar power forecasts.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131593364","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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