Pub Date : 2020-08-01DOI: 10.1109/PMAPS47429.2020.9183485
E. Moreira, E. Chagas, A. Rodrigues, M. da Guia da Silva
The problem of power flow in islanded operation of microgrids is more complex than in interconnected mode due to the absence of a slack bus. In addition, the power flow must consider uncertainties in a probabilistic framework. Probabilistic power flow has a high computational cost due to the need to assess a large number of states and the use of iterative methods for nonlinear systems. Consequently, it is interesting to develop linear power flow algorithms for islanded microgrids. The main aim of this paper is to propose a linear power flow algorithm based on the Gauss Zbus method for islanded microgrids. This algorithm was used to evaluate voltages and angular frequency in a probabilistic power flow method based on Monte Carlo Simulation. The test results with the proposed method demonstrated that it achieves high accuracy and significant computational cost savings (about of 99%) over the Newton-Raphson Method.
{"title":"Linear Probabilistic Power Flow for Islanded Microgrids","authors":"E. Moreira, E. Chagas, A. Rodrigues, M. da Guia da Silva","doi":"10.1109/PMAPS47429.2020.9183485","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183485","url":null,"abstract":"The problem of power flow in islanded operation of microgrids is more complex than in interconnected mode due to the absence of a slack bus. In addition, the power flow must consider uncertainties in a probabilistic framework. Probabilistic power flow has a high computational cost due to the need to assess a large number of states and the use of iterative methods for nonlinear systems. Consequently, it is interesting to develop linear power flow algorithms for islanded microgrids. The main aim of this paper is to propose a linear power flow algorithm based on the Gauss Zbus method for islanded microgrids. This algorithm was used to evaluate voltages and angular frequency in a probabilistic power flow method based on Monte Carlo Simulation. The test results with the proposed method demonstrated that it achieves high accuracy and significant computational cost savings (about of 99%) over the Newton-Raphson Method.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117274724","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/PMAPS47429.2020.9183429
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":"31 1","pages":"0"},"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":"1 1","pages":"0"},"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":"33 1","pages":"0"},"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/PMAPS47429.2020.9183398
Boyuan Wei, G. Deconinck
In order to tackle to the rising difficulties on modeling and information acquisition in modern low voltage distribution networks (LVDN), a model-free distributed approach to seek the approximate optimal control trajectory of users is proposed. The proposed approach employs Mean Field Theory to simplify information acquisition, which reduces communication burden. Besides, Hamilton-Jacob-Bellman (HJB) equation is introduced, to make users figure out their control trajectory individually by solving a personalized partial differential equation. Different from classical HJB applications, the system dimension is reduced by a broadcast signal, which relieves the computation burden. The case study is done with a 103 nodes realistic LVDN, with a benchmark done by centralized optimization algorithm under ideal conditions, which proves the effectiveness of the proposed approach.
{"title":"Distributed Model-free Control in Low Voltage Distribution Networks: A Mean Field Approach","authors":"Boyuan Wei, G. Deconinck","doi":"10.1109/PMAPS47429.2020.9183398","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183398","url":null,"abstract":"In order to tackle to the rising difficulties on modeling and information acquisition in modern low voltage distribution networks (LVDN), a model-free distributed approach to seek the approximate optimal control trajectory of users is proposed. The proposed approach employs Mean Field Theory to simplify information acquisition, which reduces communication burden. Besides, Hamilton-Jacob-Bellman (HJB) equation is introduced, to make users figure out their control trajectory individually by solving a personalized partial differential equation. Different from classical HJB applications, the system dimension is reduced by a broadcast signal, which relieves the computation burden. The case study is done with a 103 nodes realistic LVDN, with a benchmark done by centralized optimization algorithm under ideal conditions, which proves the effectiveness of the proposed approach.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134356244","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/PMAPS47429.2020.9183515
Atri Bera, N. Nguyen, Saad Alzahrani, Khalil Sinjari, J. Mitra
Integration of wind energy into the grid poses serious challenges to the system reliability due to its intermittent nature. Variability of wind can be mitigated using various methods including deployment of energy storage systems (ESS), aggregation of geographically diverse wind, and the use of flexible loads. This paper proposes a novel method for reducing the variability of wind power by both deploying ESS and aggregating geographically diverse wind production. Although the aggregation of geographically diverse wind can reduce its intermittency to some extent, the benefits of this approach are limited due to a number of factors which are discussed in this paper. ESS, on the other hand, have been widely used for variability mitigation of wind and achieving reliability targets. However, ESS projects are expensive. In this context, this paper studies the impact of reliability enhancement of a system and the reduction in storage size by aggregating wind power from geographically diverse wind farms. The proposed approach is validated by performing sequential Monte Carlo simulation (MCS) using the IEEE Reliability Test System data. Results show that aggregation of geographically diverse wind can significantly reduce the size of ESS required for improving the reliability of the system.
{"title":"Variability Reduction of Wind Power using Aggregation and Energy Storage","authors":"Atri Bera, N. Nguyen, Saad Alzahrani, Khalil Sinjari, J. Mitra","doi":"10.1109/PMAPS47429.2020.9183515","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183515","url":null,"abstract":"Integration of wind energy into the grid poses serious challenges to the system reliability due to its intermittent nature. Variability of wind can be mitigated using various methods including deployment of energy storage systems (ESS), aggregation of geographically diverse wind, and the use of flexible loads. This paper proposes a novel method for reducing the variability of wind power by both deploying ESS and aggregating geographically diverse wind production. Although the aggregation of geographically diverse wind can reduce its intermittency to some extent, the benefits of this approach are limited due to a number of factors which are discussed in this paper. ESS, on the other hand, have been widely used for variability mitigation of wind and achieving reliability targets. However, ESS projects are expensive. In this context, this paper studies the impact of reliability enhancement of a system and the reduction in storage size by aggregating wind power from geographically diverse wind farms. The proposed approach is validated by performing sequential Monte Carlo simulation (MCS) using the IEEE Reliability Test System data. Results show that aggregation of geographically diverse wind can significantly reduce the size of ESS required for improving the reliability of the system.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132076501","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/PMAPS47429.2020.9183692
André Milhorance, A. M. Leite da Silva, Érica Telles, A. Street
This paper proposes a probabilistic load flow (PLF) based approach, via Monte Carlo simulation (MCS) and cross-entropy (CE) method, for evaluating possible risks associated with contracting the amount of transmission system usage (ATSU). Distribution companies (DISCOs) and the Brazilian ISO establish these contracts on yearly bases. The application of PLF via MCS-CE provides a risk assessment analysis tool to adequately manage possible penalties due to over/under ATSU contracting when several uncertainties are taken into account. The proposed tool is applied to Brazilian DISCOs considering uncertainties on demand, generation, and electric network topology, i.e., contingencies on transmission elements.
{"title":"Risk Assessment for the Amount of Transmission System Usage Penalties via Probabilistic Load Flow","authors":"André Milhorance, A. M. Leite da Silva, Érica Telles, A. Street","doi":"10.1109/PMAPS47429.2020.9183692","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183692","url":null,"abstract":"This paper proposes a probabilistic load flow (PLF) based approach, via Monte Carlo simulation (MCS) and cross-entropy (CE) method, for evaluating possible risks associated with contracting the amount of transmission system usage (ATSU). Distribution companies (DISCOs) and the Brazilian ISO establish these contracts on yearly bases. The application of PLF via MCS-CE provides a risk assessment analysis tool to adequately manage possible penalties due to over/under ATSU contracting when several uncertainties are taken into account. The proposed tool is applied to Brazilian DISCOs considering uncertainties on demand, generation, and electric network topology, i.e., contingencies on transmission elements.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132799182","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":"1 1","pages":"0"},"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/PMAPS47429.2020.9183590
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":"63 1","pages":"0"},"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":"22 1","pages":"0"},"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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