Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183429
Kai Zhou, J. Cruise, Chris J. I kill, I. Dobson, L. Wehenkel, Zhaoyu Wang, Amy L. Wilson
Despite the important role transmission line outages play in power system reliability analysis, it remains a challenge to estimate individual line outage rates accurately enough from limited data. Recent work using a Bayesian hierarchical model shows how to combine together line outage data by exploiting how the lines partially share some common features in order to obtain more accurate estimates of line outage rates. Lower variance estimates from fewer years of data can be obtained. In this paper, we explore what can be achieved with this new Bayesian hierarchical approach using real utility data. In particular, we assess the capability to detect increases in line outage rates over time, quantify the influence of bad weather on outage rates, and discuss the effect of outage rate uncertainty on a simple availability calculation.
{"title":"Applying Bayesian estimates of individual transmission line outage rates","authors":"Kai Zhou, J. Cruise, Chris J. I kill, I. Dobson, L. Wehenkel, Zhaoyu Wang, Amy L. Wilson","doi":"10.1109/PMAPS47429.2020.9183429","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183429","url":null,"abstract":"Despite the important role transmission line outages play in power system reliability analysis, it remains a challenge to estimate individual line outage rates accurately enough from limited data. Recent work using a Bayesian hierarchical model shows how to combine together line outage data by exploiting how the lines partially share some common features in order to obtain more accurate estimates of line outage rates. Lower variance estimates from fewer years of data can be obtained. In this paper, we explore what can be achieved with this new Bayesian hierarchical approach using real utility data. In particular, we assess the capability to detect increases in line outage rates over time, quantify the influence of bad weather on outage rates, and discuss the effect of outage rate uncertainty on a simple availability calculation.","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":"124426298","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/PMAPS47429.2020.9183543
Victor F. Zwetkoff, J. G. C. Costa, A. M. Leite da Silva
This work presents a new probabilistic methodology for cost allocation of transmission systems, considering the intermittency of the wind power source. The proposed algorithm inserts a nodal transmission pricing scheme in a chronological simulation environment, which allows analyzing the behavior of transmission charges against the variable power output of a wind power plant. The aim is to calculate an equivalent tariff for each market participant taking into account the systems operational reality. The proposed method is applied to the IEEE RTS considering a modified configuration with insertion of a wind power plant.
{"title":"Probabilistic Method for Transmission System Pricing Considering Intermittence of Wind Power Sources","authors":"Victor F. Zwetkoff, J. G. C. Costa, A. M. Leite da Silva","doi":"10.1109/PMAPS47429.2020.9183543","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183543","url":null,"abstract":"This work presents a new probabilistic methodology for cost allocation of transmission systems, considering the intermittency of the wind power source. The proposed algorithm inserts a nodal transmission pricing scheme in a chronological simulation environment, which allows analyzing the behavior of transmission charges against the variable power output of a wind power plant. The aim is to calculate an equivalent tariff for each market participant taking into account the systems operational reality. The proposed method is applied to the IEEE RTS considering a modified configuration with insertion of a wind power plant.","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":"130546895","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/PMAPS47429.2020.9183500
C. J. Wallnerström, M. Dalheim, Mihai Seratelius, T. Johansson
This paper presents statistics based on over 15 years of power outage related data in Sweden collected by the national regulatory authority (NRA). In the early 2000s, Sweden introduced its first economic incentive scheme regarding continuity of supply (CoS) for power distribution system operators (DSO). For this purpose, the NRA began to collect power outage data from each DSO on an aggregated level. A few years later, in 2005, a severe hurricane struck Sweden that highlighted the vulnerability of the Swedish power system, resulting in a new regulatory framework related to power outages. To be able to effectively monitor the CoS in Sweden, the NRA began in 2010 to collect data on power outages on a customer level. Since 2012 a new revenue cap regulation with economic CoS incentives was implemented with major revisions from 2016 and 2020 respectively.The amount of detailed data available enables the NRA to closely monitor the CoS in the Swedish power grid. As a result of the stricter rules on power outages, there have been major investments in more reliable power distribution systems over the past decade. A positive tendency can be seen even if the CoS fluctuates from year to year due to e.g. weather events. The CoS is slightly better for years with mild weather and the impact on the CoS is less negative for years with severe storms, even if it is still far from good enough. The aim of this paper is to publish statistics with some concluding remarks from the NRA. We believe that sharing our experiences from Sweden may be of value for others, e.g. when developing new laws and regulations. The paper also contributes by informing about available data related to Swedish power outages for others to use when comparing countries or developing probabilistic models.
{"title":"Power outage related statistics in Sweden since the early 2000s and evaluation of reliability trends","authors":"C. J. Wallnerström, M. Dalheim, Mihai Seratelius, T. Johansson","doi":"10.1109/PMAPS47429.2020.9183500","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183500","url":null,"abstract":"This paper presents statistics based on over 15 years of power outage related data in Sweden collected by the national regulatory authority (NRA). In the early 2000s, Sweden introduced its first economic incentive scheme regarding continuity of supply (CoS) for power distribution system operators (DSO). For this purpose, the NRA began to collect power outage data from each DSO on an aggregated level. A few years later, in 2005, a severe hurricane struck Sweden that highlighted the vulnerability of the Swedish power system, resulting in a new regulatory framework related to power outages. To be able to effectively monitor the CoS in Sweden, the NRA began in 2010 to collect data on power outages on a customer level. Since 2012 a new revenue cap regulation with economic CoS incentives was implemented with major revisions from 2016 and 2020 respectively.The amount of detailed data available enables the NRA to closely monitor the CoS in the Swedish power grid. As a result of the stricter rules on power outages, there have been major investments in more reliable power distribution systems over the past decade. A positive tendency can be seen even if the CoS fluctuates from year to year due to e.g. weather events. The CoS is slightly better for years with mild weather and the impact on the CoS is less negative for years with severe storms, even if it is still far from good enough. The aim of this paper is to publish statistics with some concluding remarks from the NRA. We believe that sharing our experiences from Sweden may be of value for others, e.g. when developing new laws and regulations. The paper also contributes by informing about available data related to Swedish power outages for others to use when comparing countries or developing probabilistic models.","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":"116211267","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/PMAPS47429.2020.9183579
Upama Nakarmi, M. Rahnamay-Naeini
Cascading failures in power grids are high impact societal and economical phenomena. Local interactions among the components of the system and interactions at-distance, based on the physics of electricity, as well as various stochastic and interdependent parameters and factors (from within and outside of the power systems) contribute to the complexity of these phenomena. As such, predicting the size and path of cascading failures, when triggered, are challenging and interesting research problems. In recent years, interaction graphs, which help in capturing the underlying interactions and influences among the components during cascading failures, are proposed towards simplifying the modeling and analysis of cascades. In this paper, a Markov chain model is designed based on the community structures embedded in the data-driven graphs of interactions for power grids. This model exploits the properties of community structures in interactions to enable the probabilistic analysis of cascade sizes in power grids.
{"title":"A Markov Chain Approach for Cascade Size Analysis in Power Grids based on Community Structures in Interaction Graphs","authors":"Upama Nakarmi, M. Rahnamay-Naeini","doi":"10.1109/PMAPS47429.2020.9183579","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183579","url":null,"abstract":"Cascading failures in power grids are high impact societal and economical phenomena. Local interactions among the components of the system and interactions at-distance, based on the physics of electricity, as well as various stochastic and interdependent parameters and factors (from within and outside of the power systems) contribute to the complexity of these phenomena. As such, predicting the size and path of cascading failures, when triggered, are challenging and interesting research problems. In recent years, interaction graphs, which help in capturing the underlying interactions and influences among the components during cascading failures, are proposed towards simplifying the modeling and analysis of cascades. In this paper, a Markov chain model is designed based on the community structures embedded in the data-driven graphs of interactions for power grids. This model exploits the properties of community structures in interactions to enable the probabilistic analysis of cascade sizes in power grids.","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":"115766764","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/PMAPS47429.2020.9183486
J. F. C. Castro, P. Rosas, L. H. A. Medeiros, A. M. Leite da Silva
This paper evaluates the use of energy storage systems integrated to wind generation to increase the operating reserve of an electrical power network, in order to improve the short-term operation and reduce the risk of load interruption. The spinning reserve levels, which are required to ensure the system reliability, are assessed through risk indices evaluated using Monte Carlo simulation and cross-entropy method. Electrical vehicles insertion in the power network is represented as uncertainties in the short-term load model. The proposed method is applied to the IEEE-RTS-Wind system.
{"title":"Operating Reserve Assessment in Systems with Energy Storage and Electric Vehicles","authors":"J. F. C. Castro, P. Rosas, L. H. A. Medeiros, A. M. Leite da Silva","doi":"10.1109/PMAPS47429.2020.9183486","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183486","url":null,"abstract":"This paper evaluates the use of energy storage systems integrated to wind generation to increase the operating reserve of an electrical power network, in order to improve the short-term operation and reduce the risk of load interruption. The spinning reserve levels, which are required to ensure the system reliability, are assessed through risk indices evaluated using Monte Carlo simulation and cross-entropy method. Electrical vehicles insertion in the power network is represented as uncertainties in the short-term load model. The proposed method is applied to the IEEE-RTS-Wind system.","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":"114018820","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/PMAPS47429.2020.9183638
Wenyu Wang, N. Yu
Accurate estimates of network parameters are essential for advanced control and monitoring in power distribution systems. The existing methods for parameter estimation either assume a simple single-phase network model or require widespread installation of micro-phasor measurement units (micro-PMUs), which are cost prohibitive. In this paper, we propose a parameter estimation approach, which considers three-phase series impedance and only leverages readily available smart meter measurements. We first build a physical model based on the linearized three-phase power flow manifold, which links the network parameters with the smart meter measurements. The parameter estimation problem is then formulated as a maximum likelihood estimation (MLE) problem. We prove that the correct network parameters yield the highest likelihood value. A stochastic gradient descent (SGD) method with early stopping is then adopted to solve the MLE problem. Comprehensive numerical tests show that the proposed algorithm improves the accuracy of the network parameters.
{"title":"Parameter Estimation in Three-Phase Power Distribution Networks Using Smart Meter Data","authors":"Wenyu Wang, N. Yu","doi":"10.1109/PMAPS47429.2020.9183638","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183638","url":null,"abstract":"Accurate estimates of network parameters are essential for advanced control and monitoring in power distribution systems. The existing methods for parameter estimation either assume a simple single-phase network model or require widespread installation of micro-phasor measurement units (micro-PMUs), which are cost prohibitive. In this paper, we propose a parameter estimation approach, which considers three-phase series impedance and only leverages readily available smart meter measurements. We first build a physical model based on the linearized three-phase power flow manifold, which links the network parameters with the smart meter measurements. The parameter estimation problem is then formulated as a maximum likelihood estimation (MLE) problem. We prove that the correct network parameters yield the highest likelihood value. A stochastic gradient descent (SGD) method with early stopping is then adopted to solve the MLE problem. Comprehensive numerical tests show that the proposed algorithm improves the accuracy of the network 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":"123489193","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/PMAPS47429.2020.9183519
M. Kamruzzaman, Xiaohu Zhang, Michael Abdelmalak, M. Benidris, Di Shi
This paper proposes a smart charging/discharging-based method to evaluate the expected maximum hosting capacity (EMHC) of power systems to electric vehicles (EVs). The rapid growth in the use of EVs increases the challenges to satisfy their charging demand using existing power system resources. Therefore, a method to quantify the EMHC of power systems to EVs is required to plan for system improvements and ensure maximum utilization of resources. In this work, a method to calculate the EMHC of power systems to EVs is developed based on variable charging/discharging rates. The EMHC is calculated for charging stations at both homes and workplaces. The charging/discharging rates are varied based on daily energy demand and parking durations of EVs and network constraints. The parking duration is calculated based on probability distribution functions (PDFs) of arrival and departure times. The energy required to travel each mile and PDF of daily travel distances are used to calculate the daily energy demand of EVs. The optimization problem to maximize the hosting capacity is formulated using a linearized AC power flow model. The Monte Carlo simulation is used to calculate the EMHC. The proposed method is demonstrated on the modified IEEE 33-bus system. The results show that the daily EMHC of the modified IEEE 33-bus system varies between 20-41 cars for selected nodes.
{"title":"A Method to Evaluate the Maximum Hosting Capacity of Power Systems to Electric Vehicles","authors":"M. Kamruzzaman, Xiaohu Zhang, Michael Abdelmalak, M. Benidris, Di Shi","doi":"10.1109/PMAPS47429.2020.9183519","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183519","url":null,"abstract":"This paper proposes a smart charging/discharging-based method to evaluate the expected maximum hosting capacity (EMHC) of power systems to electric vehicles (EVs). The rapid growth in the use of EVs increases the challenges to satisfy their charging demand using existing power system resources. Therefore, a method to quantify the EMHC of power systems to EVs is required to plan for system improvements and ensure maximum utilization of resources. In this work, a method to calculate the EMHC of power systems to EVs is developed based on variable charging/discharging rates. The EMHC is calculated for charging stations at both homes and workplaces. The charging/discharging rates are varied based on daily energy demand and parking durations of EVs and network constraints. The parking duration is calculated based on probability distribution functions (PDFs) of arrival and departure times. The energy required to travel each mile and PDF of daily travel distances are used to calculate the daily energy demand of EVs. The optimization problem to maximize the hosting capacity is formulated using a linearized AC power flow model. The Monte Carlo simulation is used to calculate the EMHC. The proposed method is demonstrated on the modified IEEE 33-bus system. The results show that the daily EMHC of the modified IEEE 33-bus system varies between 20-41 cars for selected nodes.","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":"125647274","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/PMAPS47429.2020.9183420
R. Claeys, C. Protopapadaki, D. Saelens, J. Desmet
This paper presents an assessment and improvement of stochastic load modeling for district-level analyses with integration of photovoltaic panels (PV), by comparison with measurement data. Stochastic load profiles for individual households were produced using the bottom-up ‘Stochastic Residential Occupancy Behavior’ (StROBe) model. The self-consumption of households with PV installations and the district-level peak demand are examined as properties relevant for the estimation of PV hosting capacity and accompanying grid-related problems. The comparison shows that while the synthetic profiles produce reasonable estimates of simultaneity and summer peak demand, they insufficiently represent the seasonal variations. In addition, self-consumption is overestimated by the model. The observed discrepancies can be traced back to inaccurate modeling of the peak timing and seasonal variation in individual peak load and simultaneity. Furthermore, vacant homes in the measured data are found to contribute significantly to discrepancies in holiday periods. Adjusting the stochastic modeling to account for these vacant homes results in improved performance of the model. This research demonstrates that harvesting the full potential of bottom-up stochastic load modeling would require more up-to-date information on residential electricity use patterns.
{"title":"A Data-Driven Approach to Assessing and Improving Stochastic Residential Load Modeling for District-Level Simulations and PV Integration","authors":"R. Claeys, C. Protopapadaki, D. Saelens, J. Desmet","doi":"10.1109/PMAPS47429.2020.9183420","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183420","url":null,"abstract":"This paper presents an assessment and improvement of stochastic load modeling for district-level analyses with integration of photovoltaic panels (PV), by comparison with measurement data. Stochastic load profiles for individual households were produced using the bottom-up ‘Stochastic Residential Occupancy Behavior’ (StROBe) model. The self-consumption of households with PV installations and the district-level peak demand are examined as properties relevant for the estimation of PV hosting capacity and accompanying grid-related problems. The comparison shows that while the synthetic profiles produce reasonable estimates of simultaneity and summer peak demand, they insufficiently represent the seasonal variations. In addition, self-consumption is overestimated by the model. The observed discrepancies can be traced back to inaccurate modeling of the peak timing and seasonal variation in individual peak load and simultaneity. Furthermore, vacant homes in the measured data are found to contribute significantly to discrepancies in holiday periods. Adjusting the stochastic modeling to account for these vacant homes results in improved performance of the model. This research demonstrates that harvesting the full potential of bottom-up stochastic load modeling would require more up-to-date information on residential electricity use patterns.","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":"129336467","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/PMAPS47429.2020.9183590
A. Helseth, B. Mo, Hans Olaf Hågenvik
Environmental constraints in hydropower systems serve to ensure sustainable use of water resources. Through accurate treatment in hydropower scheduling, one seeks to respect such constraints in the planning phase while optimizing the utilization of hydropower. However, many environmental constraints introduce state-dependencies and even nonconvexities to the scheduling problem, making them challenging to capture. This paper describes how the recently developed stochastic dual dynamic integer programming (SDDiP) method can incorporate nonconvex environmental constraints in the medium- and longterm scheduling of a hydropower system in a liberalized market context. A mathematical model is presented and tested in a multireservoir case study, emphasizing on the improvements observed when accurately modelling a particular type of nonconvex environmental constraint.
{"title":"Nonconvex Environmental Constraints in Hydropower Scheduling","authors":"A. Helseth, B. Mo, Hans Olaf Hågenvik","doi":"10.1109/PMAPS47429.2020.9183590","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183590","url":null,"abstract":"Environmental constraints in hydropower systems serve to ensure sustainable use of water resources. Through accurate treatment in hydropower scheduling, one seeks to respect such constraints in the planning phase while optimizing the utilization of hydropower. However, many environmental constraints introduce state-dependencies and even nonconvexities to the scheduling problem, making them challenging to capture. This paper describes how the recently developed stochastic dual dynamic integer programming (SDDiP) method can incorporate nonconvex environmental constraints in the medium- and longterm scheduling of a hydropower system in a liberalized market context. A mathematical model is presented and tested in a multireservoir case study, emphasizing on the improvements observed when accurately modelling a particular type of nonconvex environmental constraint.","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":"124576031","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/PMAPS47429.2020.9183613
Elyas Ahmed, Daniel Sohm
The declining costs for various distributed energy resources such as solar and energy storage is driving an increase in the penetration level of these resources at the grid’s edge. The electricity market operator must account for these changes to effectively plan the system’s demand, supply, and adequacy for various scenarios. This paper proposes a simplified methodology to create a probabilistic model of demand and supply which can be used to model resource adequacy as a function of temperature. This adequacy model is then translated to describe adequacy by duration of need. This description can then inform the duration of service needed from limited energy storage resources to reduce the probability of load being unserved. We first use a Bayesian additive model to infer the relationship between demand and available capacity as function of temperature. We then calculate the probability for when demand will be greater than supply for each unit increment of temperature. This probability can be described as a binomial random variable of demand being greater than supply for that hour. Finally, we estimate the duration of need by approximating the sum of binomial random variables for the day. With this methodology, one can rapidly simulate various supply mixes by fuel type to understand its effects on the final duration of need.
{"title":"Temperature Driven Bayesian Probabilistic Modelling of Electricity Demand, Capacity, and Adequacy","authors":"Elyas Ahmed, Daniel Sohm","doi":"10.1109/PMAPS47429.2020.9183613","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183613","url":null,"abstract":"The declining costs for various distributed energy resources such as solar and energy storage is driving an increase in the penetration level of these resources at the grid’s edge. The electricity market operator must account for these changes to effectively plan the system’s demand, supply, and adequacy for various scenarios. This paper proposes a simplified methodology to create a probabilistic model of demand and supply which can be used to model resource adequacy as a function of temperature. This adequacy model is then translated to describe adequacy by duration of need. This description can then inform the duration of service needed from limited energy storage resources to reduce the probability of load being unserved. We first use a Bayesian additive model to infer the relationship between demand and available capacity as function of temperature. We then calculate the probability for when demand will be greater than supply for each unit increment of temperature. This probability can be described as a binomial random variable of demand being greater than supply for that hour. Finally, we estimate the duration of need by approximating the sum of binomial random variables for the day. With this methodology, one can rapidly simulate various supply mixes by fuel type to understand its effects on the final duration of need.","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":"125461839","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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