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":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":"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/PMAPS47429.2020.9183692
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":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":"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/PMAPS47429.2020.9183398
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":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":"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":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":"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/PMAPS47429.2020.9183623
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":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":"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":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":"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":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":"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":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":"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":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":"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":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":"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}
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