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":"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":"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/PMAPS47429.2020.9183519
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":"13 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":"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/PMAPS47429.2020.9183486
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":"21 2","pages":"0"},"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/PMAPS47429.2020.9183451
Mostafa Nazemi, P. Dehghanian, Zijiang Yang
Despite remarkable growth in penetration of renewable energy resources in power grids, most recovery and restoration strategies cannot fully harness the potentials in such resources due to their inherent uncertainty and stochasticity. We propose a resilient disaster recovery scheme to fully unlock the flexibility of the distribution system (DS) through reconfiguration practices and efficient utilization of mobile power sources (MPS) across the system. A novel optimization framework is proposed to model the MPSs dispatch while considering a set of scenarios to capture the uncertainties in distributed energy resources in the system. The optimization model is then convexified equivalently and linearized into a mixed-integer linear programming formulation to reduce the computational complexity and achieve a global optimality. The numerical results verify a notable recovery speed and an improved power system resilience and survivability to severe extremes with devastating consequences.
{"title":"Swift Disaster Recovery for Resilient Power Grids: Integration of DERs with Mobile Power Sources","authors":"Mostafa Nazemi, P. Dehghanian, Zijiang Yang","doi":"10.1109/PMAPS47429.2020.9183451","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183451","url":null,"abstract":"Despite remarkable growth in penetration of renewable energy resources in power grids, most recovery and restoration strategies cannot fully harness the potentials in such resources due to their inherent uncertainty and stochasticity. We propose a resilient disaster recovery scheme to fully unlock the flexibility of the distribution system (DS) through reconfiguration practices and efficient utilization of mobile power sources (MPS) across the system. A novel optimization framework is proposed to model the MPSs dispatch while considering a set of scenarios to capture the uncertainties in distributed energy resources in the system. The optimization model is then convexified equivalently and linearized into a mixed-integer linear programming formulation to reduce the computational complexity and achieve a global optimality. The numerical results verify a notable recovery speed and an improved power system resilience and survivability to severe extremes with devastating consequences.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"27 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":"127860990","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/PMAPS47429.2020.9183623
K. Thorvaldsen, Sigurd Bjarghov, H. Farahmand
Scheduling a residential building short-term to optimize the electricity bill can be difficult with the inclusion of capacity-based grid tariffs. Scheduling the building based on a proposed measured-peak (MP) grid tariff, which is a cost based on the highest peak power over a period, requires the user to consider the impact the current decision-making has in the future. Therefore, the authors propose a mathematical model using stochastic dynamic programming (SDP) that tries to represent the long-term impact of current decision-making. The SDP algorithm calculates non-linear expected future cost curves (EFCC) for the building based on the peak power backwards for each day over a month. The uncertainty in load demand and weather are considered using a discrete Markov chain setup. The model is applied to a case study for a Norwegian building with smart control of flexible loads, and compared against methods where the MP grid tariff is not accurately represented, and where the user has perfect information of the whole month. The results showed that the SDP algorithm performs 0.3 % better than a scenario with no accurate way of presenting future impacts, and performs 3.6 % worse compared to a scenario where the user had perfect information.
{"title":"Representing Long-term Impact of Residential Building Energy Management using Stochastic Dynamic Programming","authors":"K. Thorvaldsen, Sigurd Bjarghov, H. Farahmand","doi":"10.1109/PMAPS47429.2020.9183623","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183623","url":null,"abstract":"Scheduling a residential building short-term to optimize the electricity bill can be difficult with the inclusion of capacity-based grid tariffs. Scheduling the building based on a proposed measured-peak (MP) grid tariff, which is a cost based on the highest peak power over a period, requires the user to consider the impact the current decision-making has in the future. Therefore, the authors propose a mathematical model using stochastic dynamic programming (SDP) that tries to represent the long-term impact of current decision-making. The SDP algorithm calculates non-linear expected future cost curves (EFCC) for the building based on the peak power backwards for each day over a month. The uncertainty in load demand and weather are considered using a discrete Markov chain setup. The model is applied to a case study for a Norwegian building with smart control of flexible loads, and compared against methods where the MP grid tariff is not accurately represented, and where the user has perfect information of the whole month. The results showed that the SDP algorithm performs 0.3 % better than a scenario with no accurate way of presenting future impacts, and performs 3.6 % worse compared to a scenario where the user had perfect information.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"374 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":"122605397","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/PMAPS47429.2020.9183683
Daniel L. Donaldson, Manuel S. Alvarez‐Alvarado, D. Jayaweera
Rising electric vehicle (EV) adoption is introducing new challenges to the operation and planning of the electric grid. Currently power system planners perform analysis to ensure adequate levels of reliability following contingencies such as loss of a substation. However, existing planning standards do not explicitly mandate studies of the redistribution of EV charging demand that would take place in the case of extreme events. Planning to serve the charging demand from EVs during extreme events is paramount to ensure the resiliency of the grid. This paper presents a novel framework for power system planners to reflect the impact of EV evacuations on grid resiliency during wildfire events. The method consists of resiliency analysis coupled with probabilistic models of load redistribution taking into account potential evacuation routes. A case study using the 2019 update to the IEEE 24 bus Reliability Test System (RTS) is performed to demonstrate the efficacy of the proposed strategy. The framework results in a more specific resiliency trapezoid that reflects a more realistic resiliency behaviour of the system.
{"title":"Power System Resiliency During Wildfires Under Increasing Penetration of Electric Vehicles","authors":"Daniel L. Donaldson, Manuel S. Alvarez‐Alvarado, D. Jayaweera","doi":"10.1109/PMAPS47429.2020.9183683","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183683","url":null,"abstract":"Rising electric vehicle (EV) adoption is introducing new challenges to the operation and planning of the electric grid. Currently power system planners perform analysis to ensure adequate levels of reliability following contingencies such as loss of a substation. However, existing planning standards do not explicitly mandate studies of the redistribution of EV charging demand that would take place in the case of extreme events. Planning to serve the charging demand from EVs during extreme events is paramount to ensure the resiliency of the grid. This paper presents a novel framework for power system planners to reflect the impact of EV evacuations on grid resiliency during wildfire events. The method consists of resiliency analysis coupled with probabilistic models of load redistribution taking into account potential evacuation routes. A case study using the 2019 update to the IEEE 24 bus Reliability Test System (RTS) is performed to demonstrate the efficacy of the proposed strategy. The framework results in a more specific resiliency trapezoid that reflects a more realistic resiliency behaviour of the system.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"43 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":"117078359","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/PMAPS47429.2020.9183471
Mukesh Gautam, N. Bhusal, M. Benidris, C. Singh, J. Mitra
This paper presents a sensitivity-based approach for the placement of distributed energy resources (DERs) in power systems. The approach is based on the fact that most planning studies utilize some form of optimization, and solutions to these optimization problems provide insights into the sensitivity of many system variables to operating conditions and constraints. However, most of the existing sensitivity-based planning criteria do not capture ranges of effectiveness of these solutions (i.e., ranges of the effectiveness of Lagrange multipliers). The proposed method detects the ranges of effectiveness of Lagrange multipliers and uses them to determine optimal solution alternatives. Profiles for existing generation and loads, and transmission constraints are taken into consideration. The proposed method is used to determine the impacts of DERs at different locations, in presence of a stochastic element (load variability). This method consists of sequentially calculating Lagrange multipliers of the dual solution of the optimization problem for various load buses for all load scenarios. Optimal sizes and sites of resources are jointly determined in a sequential manner based on the validity of active constraints. The effectiveness of the proposed method is demonstrated through several case studies on various test systems including the IEEE reliability test system (IEEE RTS), the IEEE 14 and 30 bus systems. In comparison with conventional sensitivity-based approaches (i.e., without considering ranges of validity of Lagrange multipliers), the proposed approach provides more accurate results for active constraints.
{"title":"A Sensitivity-based Approach for Optimal Siting of Distributed Energy Resources","authors":"Mukesh Gautam, N. Bhusal, M. Benidris, C. Singh, J. Mitra","doi":"10.1109/PMAPS47429.2020.9183471","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183471","url":null,"abstract":"This paper presents a sensitivity-based approach for the placement of distributed energy resources (DERs) in power systems. The approach is based on the fact that most planning studies utilize some form of optimization, and solutions to these optimization problems provide insights into the sensitivity of many system variables to operating conditions and constraints. However, most of the existing sensitivity-based planning criteria do not capture ranges of effectiveness of these solutions (i.e., ranges of the effectiveness of Lagrange multipliers). The proposed method detects the ranges of effectiveness of Lagrange multipliers and uses them to determine optimal solution alternatives. Profiles for existing generation and loads, and transmission constraints are taken into consideration. The proposed method is used to determine the impacts of DERs at different locations, in presence of a stochastic element (load variability). This method consists of sequentially calculating Lagrange multipliers of the dual solution of the optimization problem for various load buses for all load scenarios. Optimal sizes and sites of resources are jointly determined in a sequential manner based on the validity of active constraints. The effectiveness of the proposed method is demonstrated through several case studies on various test systems including the IEEE reliability test system (IEEE RTS), the IEEE 14 and 30 bus systems. In comparison with conventional sensitivity-based approaches (i.e., without considering ranges of validity of Lagrange multipliers), the proposed approach provides more accurate results for active constraints.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"44 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":"115241319","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/PMAPS47429.2020.9183473
Aleksandar Jovici, G. Hug
In this paper, a linear framework for the combined state and parameter estimation of an electric power grid observed both by conventional and synchrophasor measurements is proposed. The method can be used for estimating parameters of transmission lines, tap-changers and shunts, while providing unbiased estimates of the bus voltages. The network components with incorrect parameters are identified via measurement residuals. The accuracy of the proposed method is evaluated for various cases of bad parameters using the IEEE 118 bus test system.
{"title":"Linear State and Parameter Estimation for Power Transmission Networks","authors":"Aleksandar Jovici, G. Hug","doi":"10.1109/PMAPS47429.2020.9183473","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183473","url":null,"abstract":"In this paper, a linear framework for the combined state and parameter estimation of an electric power grid observed both by conventional and synchrophasor measurements is proposed. The method can be used for estimating parameters of transmission lines, tap-changers and shunts, while providing unbiased estimates of the bus voltages. The network components with incorrect parameters are identified via measurement residuals. The accuracy of the proposed method is evaluated for various cases of bad parameters using the IEEE 118 bus test 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":"125563863","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/PMAPS47429.2020.9183412
S. Awadallah, J. Milanović, P. Jarman
The paper proposes a method to derive an equivalent age from asset condition scores in order to incorporate asset condition into existing reliability assessment techniques. The method is related to end-of-life failure to inform replacement decision-making process. The paper projects the age cumulative distribution function (CDF) of a fleet of power transformers into the cumulative distribution function (CDF) of their condition scores. A relationship between condition score and age was formulated by using curve fitting techniques. Case studies were performed on a generic test system to compare system and load point reliability indices using the chronological age and the derived equivalent age. The results showed that using equivalent age resulted in different critical load points than the ones identified when using chronological age.
{"title":"Deriving Transformer Equivalent Age for Power System Reliability Assessment from Asset Condition Score","authors":"S. Awadallah, J. Milanović, P. Jarman","doi":"10.1109/PMAPS47429.2020.9183412","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183412","url":null,"abstract":"The paper proposes a method to derive an equivalent age from asset condition scores in order to incorporate asset condition into existing reliability assessment techniques. The method is related to end-of-life failure to inform replacement decision-making process. The paper projects the age cumulative distribution function (CDF) of a fleet of power transformers into the cumulative distribution function (CDF) of their condition scores. A relationship between condition score and age was formulated by using curve fitting techniques. Case studies were performed on a generic test system to compare system and load point reliability indices using the chronological age and the derived equivalent age. The results showed that using equivalent age resulted in different critical load points than the ones identified when using chronological age.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"27 2 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":"127785084","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/PMAPS47429.2020.9183678
Mikhail Skalyga, Quiwei Wu
The combined operation of integrated energy systems is increasingly becoming a crucial topic for renewable energy dominated power systems operation. Flexibility from the district heating system could be used to deal with the uncertainty of renewable energy sources. We formulate a distributionally robust optimization problem for co-optimizing energy and reserve dispatch of the integrated electricity and heating system with a moment-based ambiguity set. The reserve allocation has been modeled through the participation vectors of the controllable generation units. The total reserve capacity has been defined implicitly and is a function of the uncertainty. The proposed model has been transformed into a second-order cone programming (SOCP) optimization problem by applying convex relaxation and linearization of the district heating network equations. Case studies on the integrated six-bus and seven-node system to demonstrate the efficacy of the proposed model.
{"title":"Distributionally Robust Co-Optimization of Energy and Reserve Dispatch of Integrated Electricity and Heat System","authors":"Mikhail Skalyga, Quiwei Wu","doi":"10.1109/PMAPS47429.2020.9183678","DOIUrl":"https://doi.org/10.1109/PMAPS47429.2020.9183678","url":null,"abstract":"The combined operation of integrated energy systems is increasingly becoming a crucial topic for renewable energy dominated power systems operation. Flexibility from the district heating system could be used to deal with the uncertainty of renewable energy sources. We formulate a distributionally robust optimization problem for co-optimizing energy and reserve dispatch of the integrated electricity and heating system with a moment-based ambiguity set. The reserve allocation has been modeled through the participation vectors of the controllable generation units. The total reserve capacity has been defined implicitly and is a function of the uncertainty. The proposed model has been transformed into a second-order cone programming (SOCP) optimization problem by applying convex relaxation and linearization of the district heating network equations. Case studies on the integrated six-bus and seven-node system to demonstrate the efficacy of the proposed model.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":"32 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":"126630681","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：