Pub Date : 2016-10-01DOI: 10.1109/PMAPS.2016.7764070
M. Papic, I. Dobson
The paper presents an initial comparison of a transmission planning study of cascading outages with a statistical analysis of historical outages. The planning study identifies the most vulnerable places in the Idaho system and outages that lead to cascading and interruption of load. This analysis is based on a number of case scenarios (short-term and long-term) that cover different seasonal and operating conditions. The historical analysis processes Idaho outage data and estimates statistics, using the number of transmission line outages as a measure of the extent of cascading. An initial number of lines outaged can lead to a cascading propagation of further outages. How much line outages propagate is estimated from Idaho Power outage data. Also, the paper discusses some similarities in the results and highlights the different assumptions of the two approaches to cascading failure analysis.
{"title":"Comparing a transmission planning study of cascading with historical line outage data","authors":"M. Papic, I. Dobson","doi":"10.1109/PMAPS.2016.7764070","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764070","url":null,"abstract":"The paper presents an initial comparison of a transmission planning study of cascading outages with a statistical analysis of historical outages. The planning study identifies the most vulnerable places in the Idaho system and outages that lead to cascading and interruption of load. This analysis is based on a number of case scenarios (short-term and long-term) that cover different seasonal and operating conditions. The historical analysis processes Idaho outage data and estimates statistics, using the number of transmission line outages as a measure of the extent of cascading. An initial number of lines outaged can lead to a cascading propagation of further outages. How much line outages propagate is estimated from Idaho Power outage data. Also, the paper discusses some similarities in the results and highlights the different assumptions of the two approaches to cascading failure analysis.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115231965","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764081
Jingrui Xie, Tao Hong
Model selection is an important step for both point and probabilistic load forecasting. In the point load forecasting literature and practices, point error measures, such as mean absolute percentage error (MAPE), are often used for model selection. On the other hand, many probabilistic load forecasting methodologies rely on the model selection mechanism developed for point load forecasting. In other words, the models for probabilistic load forecasting are selected to minimize point error measures rather than probabilistic ones, such as quantile score. Intuitively, selecting models for probabilistic forecasting based on a point error measure is less computationally intensive and less accurate than its counterpart. The practical question is whether we can gain significant accuracy by taking the more computationally intensive route. This paper presents a comparative study on model selection for probabilistic load forecasting, using point and probabilistic error measures respectively. The data for the case study is from the load forecasting track of the Global Energy Forecasting Competition 2014. We find that the two model selection mechanisms indeed return different underlying models. While on average, the models from quantile score based model selection method can lead to more accurate probabilistic forecasts, the improvement over the MAPE based model selection method is marginal.
{"title":"Comparing two model selection frameworks for probabilistic load forecasting","authors":"Jingrui Xie, Tao Hong","doi":"10.1109/PMAPS.2016.7764081","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764081","url":null,"abstract":"Model selection is an important step for both point and probabilistic load forecasting. In the point load forecasting literature and practices, point error measures, such as mean absolute percentage error (MAPE), are often used for model selection. On the other hand, many probabilistic load forecasting methodologies rely on the model selection mechanism developed for point load forecasting. In other words, the models for probabilistic load forecasting are selected to minimize point error measures rather than probabilistic ones, such as quantile score. Intuitively, selecting models for probabilistic forecasting based on a point error measure is less computationally intensive and less accurate than its counterpart. The practical question is whether we can gain significant accuracy by taking the more computationally intensive route. This paper presents a comparative study on model selection for probabilistic load forecasting, using point and probabilistic error measures respectively. The data for the case study is from the load forecasting track of the Global Energy Forecasting Competition 2014. We find that the two model selection mechanisms indeed return different underlying models. While on average, the models from quantile score based model selection method can lead to more accurate probabilistic forecasts, the improvement over the MAPE based model selection method is marginal.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115756996","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764064
E. Scolari, D. Torregrossa, J.-Y. Le Boudec, M. Paolone
The paper describes a heuristic method for the ultra-short-term computation of prediction intervals (PIs) for photovoltaic (PV) power generation. The method allows for directly forecasting the AC active power output of a PV system by simply extracting information from past time series. Two main approaches are investigated. The former relies on experimentally observed correlations between the time derivative of the PV AC active power output and the errors caused by a generic point forecast technique. The latter approach represents an improvement of the first one, where the mentioned correlations are clustered as a function of the value of the AC active power. The work is framed in the context of microgrids and inertialess power systems control, where accounting for the fastest dynamics of the solar irradiance can become extremely valuable. We validate the proposed model using one month of AC active power measurements and for sub-second time horizons: 100, 250 and 500 ms.
{"title":"Ultra-short-term prediction intervals of photovoltaic AC active power","authors":"E. Scolari, D. Torregrossa, J.-Y. Le Boudec, M. Paolone","doi":"10.1109/PMAPS.2016.7764064","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764064","url":null,"abstract":"The paper describes a heuristic method for the ultra-short-term computation of prediction intervals (PIs) for photovoltaic (PV) power generation. The method allows for directly forecasting the AC active power output of a PV system by simply extracting information from past time series. Two main approaches are investigated. The former relies on experimentally observed correlations between the time derivative of the PV AC active power output and the errors caused by a generic point forecast technique. The latter approach represents an improvement of the first one, where the mentioned correlations are clustered as a function of the value of the AC active power. The work is framed in the context of microgrids and inertialess power systems control, where accounting for the fastest dynamics of the solar irradiance can become extremely valuable. We validate the proposed model using one month of AC active power measurements and for sub-second time horizons: 100, 250 and 500 ms.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115490666","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764159
Markus Loschenbrand, M. Korpås
This paper introduces an agent based model for Frequency Activated Reserve Markets. Generation Units (GenUns) bid both prices and quantity in interconnected and dynamically congested Market Areas in order to reach their optimal production point. The units are limited by their spare capacity after their actions on the spot market. Generation Companies (GenCos) manage the strategy portfolios of their subordinate agents with the goal of coordinating the bidding behavior and subsequently increasing profits. A case study of Monte Carlo simulated units will show the dominance of Marginal Cost bidding over different periods and pricing modes (System Price and Pay-as-Bid) as well as the quality of the chosen modeling approach.
{"title":"An agent based model of a frequency activated electricity reserve market","authors":"Markus Loschenbrand, M. Korpås","doi":"10.1109/PMAPS.2016.7764159","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764159","url":null,"abstract":"This paper introduces an agent based model for Frequency Activated Reserve Markets. Generation Units (GenUns) bid both prices and quantity in interconnected and dynamically congested Market Areas in order to reach their optimal production point. The units are limited by their spare capacity after their actions on the spot market. Generation Companies (GenCos) manage the strategy portfolios of their subordinate agents with the goal of coordinating the bidding behavior and subsequently increasing profits. A case study of Monte Carlo simulated units will show the dominance of Marginal Cost bidding over different periods and pricing modes (System Price and Pay-as-Bid) as well as the quality of the chosen modeling approach.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123183901","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764168
Edgar Nuño, N. Cutululis
Experience has shown the limitations of deterministic criteria when accommodating the intrinsic uncertainties associated to modern power systems. Hereof, probabilistic risk assessment represent a powerful enhancement in order to ensure the overall power system reliability rather than a worst-case scenario analysis. This paper presents a general-purpose methodology intended to generate plausible operating states. The main focus lies on the generation of correlated random samples using a heuristic of the NORmal-to-Anything (NORTA) method. The proposed methodology was applied to model wind generation in the Danish Western power system, analyzing the effect of the marginal distributions and errors in the correlation matrix definition.
{"title":"A heuristic for the synthesis of credible operating states in the presence of renewable energy sources","authors":"Edgar Nuño, N. Cutululis","doi":"10.1109/PMAPS.2016.7764168","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764168","url":null,"abstract":"Experience has shown the limitations of deterministic criteria when accommodating the intrinsic uncertainties associated to modern power systems. Hereof, probabilistic risk assessment represent a powerful enhancement in order to ensure the overall power system reliability rather than a worst-case scenario analysis. This paper presents a general-purpose methodology intended to generate plausible operating states. The main focus lies on the generation of correlated random samples using a heuristic of the NORmal-to-Anything (NORTA) method. The proposed methodology was applied to model wind generation in the Danish Western power system, analyzing the effect of the marginal distributions and errors in the correlation matrix definition.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124120374","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7763924
Wan Lingyun, Zhang Ying, Wei Tingting, Liao Yixi, Zhou Qing, Xia Lei, Wang Zhuding, Tang Fengying
For the reliability evaluation of overhead medium voltage distribution networks, the required data of traditional methods are too big to be collected and inputted. Moreover, some data of a distribution power grid, especially the planning distribution networks, cannot be completely provided. As a result, it is necessary to put forward simplified reliability evaluation formulae. In this paper, the simplified evaluation formulae of system average interruption duration and frequency indexes for failure outage and scheduled interruption are deduced respectively, considering the influences of not only the main lines, distribution transformers and switches of a single type, but also big lateral lines and various types of switches, thus making the formulae more practical. Moreover, according to the interruption times based on the line length or the line segment number, two sets of evaluation formulae of scheduled interruption are deduced. The reliability evaluation of IEEE RBTS-Bus2 is performed by using the deduced formulae, and the results of better precision are obtained with little increased input data.
{"title":"Simplified reliability evaluation formulae for overhead medium voltage distribution networks","authors":"Wan Lingyun, Zhang Ying, Wei Tingting, Liao Yixi, Zhou Qing, Xia Lei, Wang Zhuding, Tang Fengying","doi":"10.1109/PMAPS.2016.7763924","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7763924","url":null,"abstract":"For the reliability evaluation of overhead medium voltage distribution networks, the required data of traditional methods are too big to be collected and inputted. Moreover, some data of a distribution power grid, especially the planning distribution networks, cannot be completely provided. As a result, it is necessary to put forward simplified reliability evaluation formulae. In this paper, the simplified evaluation formulae of system average interruption duration and frequency indexes for failure outage and scheduled interruption are deduced respectively, considering the influences of not only the main lines, distribution transformers and switches of a single type, but also big lateral lines and various types of switches, thus making the formulae more practical. Moreover, according to the interruption times based on the line length or the line segment number, two sets of evaluation formulae of scheduled interruption are deduced. The reliability evaluation of IEEE RBTS-Bus2 is performed by using the deduced formulae, and the results of better precision are obtained with little increased input data.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124254427","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764188
Lesia Mitridati, P. Pinson
The large penetration of renewables in the power system increases the need for flexibility. Flexibility gains and wind curtailment reduction can be achieved through a better coordination with other energy systems, in particular with district heating. Loose interactions between these two systems already exist due to the participation of CHPs in both markets. New market structures must be developed in order to exploit these synergies. Recognizing the above-mentioned challenges this paper proposes a stochastic hierarchical formulation of the heat economic dispatch problem in a system with high penetration of CHPs and wind. The objective of this optimization problem is to minimize the heat production cost, subject to constraints describing day-ahead electricity market clearing scenarios. Uncertainties concerning wind power production, electricity demand and rival participants offers are efficiently modelled using a finite set of scenarios. This model takes advantage of existing market structures and provides a decision-making tool for heat system operators. The proposed model is implemented in a case study and results are discussed to show the benefits and applicability of this approach.
{"title":"Optimal coupling of heat and electricity systems: A stochastic hierarchical approach","authors":"Lesia Mitridati, P. Pinson","doi":"10.1109/PMAPS.2016.7764188","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764188","url":null,"abstract":"The large penetration of renewables in the power system increases the need for flexibility. Flexibility gains and wind curtailment reduction can be achieved through a better coordination with other energy systems, in particular with district heating. Loose interactions between these two systems already exist due to the participation of CHPs in both markets. New market structures must be developed in order to exploit these synergies. Recognizing the above-mentioned challenges this paper proposes a stochastic hierarchical formulation of the heat economic dispatch problem in a system with high penetration of CHPs and wind. The objective of this optimization problem is to minimize the heat production cost, subject to constraints describing day-ahead electricity market clearing scenarios. Uncertainties concerning wind power production, electricity demand and rival participants offers are efficiently modelled using a finite set of scenarios. This model takes advantage of existing market structures and provides a decision-making tool for heat system operators. The proposed model is implemented in a case study and results are discussed to show the benefits and applicability of this approach.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128046903","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764179
Haodi Li, Lingfeng Wang, Yingmeng Xiang, Jun Tan, Ruosong Xiao, K. Xie, Yun Xia
Due to the increasing integration of renewable resources and the deployment of energy storage units at the power distribution level, conventional deterministic approaches may not be suitable or effective for evaluating the reliability of active distribution networks anymore. This paper proposes a new method to evaluate the active distribution system reliability including microgrid and energy storage. The power output of distributed generator (DG) within the microgrid is first calculated based on the approach of generalized capacity outage tables (GCOTs). Then Monte Carlo Simulation (MCS) is utilized for performing power system reliability evaluation. The results obtained considering different energy storage capacities are compared. Furthermore, real-time pricing (RTP) strategy is considered in optimizing the control strategy of the energy storage device and the corresponding reliability indices are recalculated.
{"title":"Reliability evaluation of active distribution systems considering energy storage and real-time electricity pricing","authors":"Haodi Li, Lingfeng Wang, Yingmeng Xiang, Jun Tan, Ruosong Xiao, K. Xie, Yun Xia","doi":"10.1109/PMAPS.2016.7764179","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764179","url":null,"abstract":"Due to the increasing integration of renewable resources and the deployment of energy storage units at the power distribution level, conventional deterministic approaches may not be suitable or effective for evaluating the reliability of active distribution networks anymore. This paper proposes a new method to evaluate the active distribution system reliability including microgrid and energy storage. The power output of distributed generator (DG) within the microgrid is first calculated based on the approach of generalized capacity outage tables (GCOTs). Then Monte Carlo Simulation (MCS) is utilized for performing power system reliability evaluation. The results obtained considering different energy storage capacities are compared. Furthermore, real-time pricing (RTP) strategy is considered in optimizing the control strategy of the energy storage device and the corresponding reliability indices are recalculated.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128433057","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764157
A. Mutule, Ervin Grebesh, I. Oleinikova, A. Obushevs
In this work, a concept for overhead power line weak point analysis based on the calculation of minimal clearance to ground is presented. Line temperature should be known before mechanical calculations are performed. For that purpose IEEE 738 Std. was taken. Calculation accuracy was previously verified by authors with real line parameters and described in the paper. To calculate thermal behavior of conductor, several parameters should be known, such as wind speed, wind direction and ambient weather temperature. These data were artificially generated from three weather stations ten years' time series located near to the line. To acquire the data on line, the interpolation geostatistical toolbox was used. Several line weak points were revealed. Line weak point position can be used as an area where monitoring equipment for dynamic line rating should be installed when the transmission system operator has an economical restriction, and it is impossible to have multiple areas for DLR equipment installation.
{"title":"Overhead line weak point mechanical analysis based on Markov chain method","authors":"A. Mutule, Ervin Grebesh, I. Oleinikova, A. Obushevs","doi":"10.1109/PMAPS.2016.7764157","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764157","url":null,"abstract":"In this work, a concept for overhead power line weak point analysis based on the calculation of minimal clearance to ground is presented. Line temperature should be known before mechanical calculations are performed. For that purpose IEEE 738 Std. was taken. Calculation accuracy was previously verified by authors with real line parameters and described in the paper. To calculate thermal behavior of conductor, several parameters should be known, such as wind speed, wind direction and ambient weather temperature. These data were artificially generated from three weather stations ten years' time series located near to the line. To acquire the data on line, the interpolation geostatistical toolbox was used. Several line weak points were revealed. Line weak point position can be used as an area where monitoring equipment for dynamic line rating should be installed when the transmission system operator has an economical restriction, and it is impossible to have multiple areas for DLR equipment installation.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128573432","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 : 2016-10-01DOI: 10.1109/PMAPS.2016.7764109
Liu Keyan, Jia Dongli, He Kaiyuan, Zhao Tingting, Zhao Fengzhan
With the rapid development of intelligent distribution network, the uncertainty of the load and the randomness of distributed generation have brought new challenges to distribution network control operation especial in reactive power optimization. This paper uses probabilistic power flow algorithm based on three-point estimate method to solve the uncertainty caused by power flow calculation in the stochastic models of load and wind power so as to propose a method of information entropy principle to measure the voltage fluctuation. On the basis of this method, a model of probabilistic reactive power optimization considering minimum network loss and voltage fluctuation is built. Taking the IEEE 33 nodes system which contains wind power generation as an example and we draw a conclusion that if we add the minimum voltage entropy to multi-objective reactive power optimization objective function, the probability distribution of node voltage is more centralized than that of single objective reactive power optimization. Thus, to optimize reactive power by means of this model could improve the voltage stability of the system and make the voltage distribution near a certain value that within the scope of control in large probability. The proposed multi-objective probabilistic reactive power optimization model is suitable for the actual distribution network reactive voltage control with random properties.
{"title":"Research on probabilistic reactive power optimization considering the randomness of distribution network","authors":"Liu Keyan, Jia Dongli, He Kaiyuan, Zhao Tingting, Zhao Fengzhan","doi":"10.1109/PMAPS.2016.7764109","DOIUrl":"https://doi.org/10.1109/PMAPS.2016.7764109","url":null,"abstract":"With the rapid development of intelligent distribution network, the uncertainty of the load and the randomness of distributed generation have brought new challenges to distribution network control operation especial in reactive power optimization. This paper uses probabilistic power flow algorithm based on three-point estimate method to solve the uncertainty caused by power flow calculation in the stochastic models of load and wind power so as to propose a method of information entropy principle to measure the voltage fluctuation. On the basis of this method, a model of probabilistic reactive power optimization considering minimum network loss and voltage fluctuation is built. Taking the IEEE 33 nodes system which contains wind power generation as an example and we draw a conclusion that if we add the minimum voltage entropy to multi-objective reactive power optimization objective function, the probability distribution of node voltage is more centralized than that of single objective reactive power optimization. Thus, to optimize reactive power by means of this model could improve the voltage stability of the system and make the voltage distribution near a certain value that within the scope of control in large probability. The proposed multi-objective probabilistic reactive power optimization model is suitable for the actual distribution network reactive voltage control with random properties.","PeriodicalId":265474,"journal":{"name":"2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130072046","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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