Pub Date : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281443
Armando D.T. Acosta, A. Perilla, E. Rakhshani, J. R. Torres, M. Meijden
The connection of offshore wind turbines to the European grid has been growing in the recent years. Many European countries are adopting this renewable energy and are increasing the number of wind power plant additions into their electrical transmission networks. In this paper, the impacts of harmonic frequencies introduced by the wind parks in a low-inertia grid are studied. Despite of classical methods which are mainly based on single-input single-output (SISO) systems, a novel approach, based on Singular Value Decomposition (SVD) techniques, considering a multiple-input multiple-output (MIMO) system is presented and discussed. The proposed SVD is a powerful mathematical tool to discover the harmonic frequencies. It can be used to analyse the system at a certain harmonic frequency and show which input(s) of the system will have more influence in the system dynamics and which output(s) will be the most affected by that input(s). According to the presented study, an SVD based methodology is provided to model any electrical network via its passive electrical elements, and to perform a harmonic analysis.
{"title":"Impact Assessment of Power Electronic-based Generation Units on Harmonic Response of Power Systems Using SVD based Method","authors":"Armando D.T. Acosta, A. Perilla, E. Rakhshani, J. R. Torres, M. Meijden","doi":"10.1109/PESGM41954.2020.9281443","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281443","url":null,"abstract":"The connection of offshore wind turbines to the European grid has been growing in the recent years. Many European countries are adopting this renewable energy and are increasing the number of wind power plant additions into their electrical transmission networks. In this paper, the impacts of harmonic frequencies introduced by the wind parks in a low-inertia grid are studied. Despite of classical methods which are mainly based on single-input single-output (SISO) systems, a novel approach, based on Singular Value Decomposition (SVD) techniques, considering a multiple-input multiple-output (MIMO) system is presented and discussed. The proposed SVD is a powerful mathematical tool to discover the harmonic frequencies. It can be used to analyse the system at a certain harmonic frequency and show which input(s) of the system will have more influence in the system dynamics and which output(s) will be the most affected by that input(s). According to the presented study, an SVD based methodology is provided to model any electrical network via its passive electrical elements, and to perform a harmonic analysis.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131570805","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9282121
Mohammad Jafarian, Alireza Soroudi, A. Keane
For DER management systems (DERMS) to manage and coordinate the DER units, awareness of distribution system topology is necessary. Most of the approaches developed for the identification of distribution network topology rely on the accessibility of network model and load forecasts, which are logically not available to DERMS. In this paper, the application of deep neural networks in pattern recognition is availed for this purpose, relying only on the measurements available to DERMS. IEEE 123 node test feeder is used for simulation. Six switching configurations and operation of two protective devices are considered, resulting in 24 different topologies. Monte Carlo simulations are conducted to explore different DER production and load values. A two-hidden layer feed-forward deep neural network is used to classify different topologies. Results show the proposed approach can successfully predict the switching configurations and status of protective devices. Sensitivity analysis shows that the positive and negative sequence components of the voltage (from DER units and substation) have the most contribution to discrimination among different switching configurations.
{"title":"Distribution System Topology Identification for DER Management Systems Using Deep Neural Networks","authors":"Mohammad Jafarian, Alireza Soroudi, A. Keane","doi":"10.1109/PESGM41954.2020.9282121","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9282121","url":null,"abstract":"For DER management systems (DERMS) to manage and coordinate the DER units, awareness of distribution system topology is necessary. Most of the approaches developed for the identification of distribution network topology rely on the accessibility of network model and load forecasts, which are logically not available to DERMS. In this paper, the application of deep neural networks in pattern recognition is availed for this purpose, relying only on the measurements available to DERMS. IEEE 123 node test feeder is used for simulation. Six switching configurations and operation of two protective devices are considered, resulting in 24 different topologies. Monte Carlo simulations are conducted to explore different DER production and load values. A two-hidden layer feed-forward deep neural network is used to classify different topologies. Results show the proposed approach can successfully predict the switching configurations and status of protective devices. Sensitivity analysis shows that the positive and negative sequence components of the voltage (from DER units and substation) have the most contribution to discrimination among different switching configurations.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128940624","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281837
Bo Chen, Z. Ye, Chen Chen, Jianhui Wang
Large-scale blackouts and extreme weather events in recent decades raise the concern for improving the resilience of electric power infrastructures. Distribution service restoration (DSR), a fundamental application in outage management systems, provides restoration solutions for system operators when power outages happen. As distribution generators (DGs) and remotely controllable devices are increasingly installed in distribution systems, an advanced DSR framework is needed to perform optimally coordinated restoration that can achieve maximal restoration performance. This paper introduces a DSR modeling framework, which can generate optimal switching sequences and estimated time of restoration in the presence of remotely controllable switches, manually operated switches, and dispatchable DGs. Two mathematical models, a variable time step model and a fixed time step model, are presented and compared. The proposed models are formulated as a mixed-integer linear programming model, and their effectiveness is evaluated via the IEEE 123 node test feeder.
{"title":"Toward a MILP Modeling Framework for Distribution System Restoration","authors":"Bo Chen, Z. Ye, Chen Chen, Jianhui Wang","doi":"10.1109/PESGM41954.2020.9281837","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281837","url":null,"abstract":"Large-scale blackouts and extreme weather events in recent decades raise the concern for improving the resilience of electric power infrastructures. Distribution service restoration (DSR), a fundamental application in outage management systems, provides restoration solutions for system operators when power outages happen. As distribution generators (DGs) and remotely controllable devices are increasingly installed in distribution systems, an advanced DSR framework is needed to perform optimally coordinated restoration that can achieve maximal restoration performance. This paper introduces a DSR modeling framework, which can generate optimal switching sequences and estimated time of restoration in the presence of remotely controllable switches, manually operated switches, and dispatchable DGs. Two mathematical models, a variable time step model and a fixed time step model, are presented and compared. The proposed models are formulated as a mixed-integer linear programming model, and their effectiveness is evaluated via the IEEE 123 node test feeder.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130952902","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281703
S. Bahrami, Y. Chen, V. Wong
Direct load control enables load aggregators in distribution networks to remotely curtail customers’ appliances during peak time periods. This paper proposes a direct load control algorithm for residential customers, while accounting for the uncertainties in the customers’ discomfort from curtailing their demand as well as the operational constraints imposed by the distribution network. We model the load control problem as a Markov decision process (MDP). Solving such an MDP is challenging due to the ac power flow equations and the unknown dynamics of the system states (i.e., price, demand, and customer’s discomfort). We develop a deep reinforcement learning algorithm based on the actor-critic method that enables the load aggregator to consider the distribution network constraints and the consequences of its past decisions to update the neural network parameters for the policy and value function without any knowledge of the system dynamics. Simulations are performed on an IEEE 85-bus test feeder with 59 households. Results show that the load aggregator learns to reduce the peak load by 16.7%, while taking into account the distribution network constraints. Also, the customers’ cost is decreased by 26.6% on average; thereby reaching a win-win outcome.
{"title":"Deep Reinforcement Learning for Direct Load Control in Distribution Networks","authors":"S. Bahrami, Y. Chen, V. Wong","doi":"10.1109/PESGM41954.2020.9281703","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281703","url":null,"abstract":"Direct load control enables load aggregators in distribution networks to remotely curtail customers’ appliances during peak time periods. This paper proposes a direct load control algorithm for residential customers, while accounting for the uncertainties in the customers’ discomfort from curtailing their demand as well as the operational constraints imposed by the distribution network. We model the load control problem as a Markov decision process (MDP). Solving such an MDP is challenging due to the ac power flow equations and the unknown dynamics of the system states (i.e., price, demand, and customer’s discomfort). We develop a deep reinforcement learning algorithm based on the actor-critic method that enables the load aggregator to consider the distribution network constraints and the consequences of its past decisions to update the neural network parameters for the policy and value function without any knowledge of the system dynamics. Simulations are performed on an IEEE 85-bus test feeder with 59 households. Results show that the load aggregator learns to reduce the peak load by 16.7%, while taking into account the distribution network constraints. Also, the customers’ cost is decreased by 26.6% on average; thereby reaching a win-win outcome.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133505179","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281706
Jing Zhong Tee, I. Lim, Keliang Zhou, O. Anaya‐Lara
The integration of offshore floating wind turbine generation (WTG) with offshore O&G platforms in the off-grid configuration which is a business model that is in the process of developing in the North Sea. As such, an integrated system consisting of an offshore floating WTG and O&G production platforms with onboard battery energy storage system (BESS) is proposed in this paper. In simulation, there are 4 different scenarios and results show that conventional system has high transient stability which do not meet the IEC standards for O&G platforms. The simulation has shown proposed system 2 which has incorporated with BESS 1 and BESS 2, has a reduction in transient deviation that can meets the IEC standards. In addition, capital expenditure (CapEx) and operational expenditure (OpEx) of proposed system 2 is presented in this paper.
{"title":"Transient Stability Analysis of Offshore Wind With O&G Platforms and an Energy Storage System","authors":"Jing Zhong Tee, I. Lim, Keliang Zhou, O. Anaya‐Lara","doi":"10.1109/PESGM41954.2020.9281706","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281706","url":null,"abstract":"The integration of offshore floating wind turbine generation (WTG) with offshore O&G platforms in the off-grid configuration which is a business model that is in the process of developing in the North Sea. As such, an integrated system consisting of an offshore floating WTG and O&G production platforms with onboard battery energy storage system (BESS) is proposed in this paper. In simulation, there are 4 different scenarios and results show that conventional system has high transient stability which do not meet the IEC standards for O&G platforms. The simulation has shown proposed system 2 which has incorporated with BESS 1 and BESS 2, has a reduction in transient deviation that can meets the IEC standards. In addition, capital expenditure (CapEx) and operational expenditure (OpEx) of proposed system 2 is presented in this paper.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133570117","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281861
Rahul Chakraborty, A. Chakrabortty, E. Farantatos, Mahendra Patel, H. Hooshyar
We propose a new hierarchical frequency control design for multi-area power system models integrated with renewable energy resources. Primary control is proposed based on fast re-dispatch of available headroom in the renewable capacity following a contingency. Secondary control is applied using a new optimization-based approach named Area Prioritized Power Flow (APPF). The APPF methodology prioritizes and maximizes the utilization of area-specific Inverter Based Resources (IBRs). Results are validated using simulations of a 9-machine, 6-IBR, 33-bus, 3-area power system model to show that APPF ensures better steady-state performance, while the hierarchical actuation of IBR setpoints improves the dynamic frequency response performance. The overall scheme mitigates a disturbance faster and more efficiently by prioritizing the use of local area-resources.
{"title":"Hierarchical Frequency Control in Multi-Area Power Systems with Prioritized Utilization of Inverter Based Resources","authors":"Rahul Chakraborty, A. Chakrabortty, E. Farantatos, Mahendra Patel, H. Hooshyar","doi":"10.1109/PESGM41954.2020.9281861","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281861","url":null,"abstract":"We propose a new hierarchical frequency control design for multi-area power system models integrated with renewable energy resources. Primary control is proposed based on fast re-dispatch of available headroom in the renewable capacity following a contingency. Secondary control is applied using a new optimization-based approach named Area Prioritized Power Flow (APPF). The APPF methodology prioritizes and maximizes the utilization of area-specific Inverter Based Resources (IBRs). Results are validated using simulations of a 9-machine, 6-IBR, 33-bus, 3-area power system model to show that APPF ensures better steady-state performance, while the hierarchical actuation of IBR setpoints improves the dynamic frequency response performance. The overall scheme mitigates a disturbance faster and more efficiently by prioritizing the use of local area-resources.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132139852","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281732
Haijun Xing, H. Fan, Ranlong Guo, Jing Wang
Distributed energy storage system (DESS) becomes more and more popular in the distribution network, especially in the era with largely increased the renewable energies. This paper proposed the day-ahead coordinated operation method of active distribution network considering distributed energy storage system integration and network reconfiguration. The objective is to minimize the energy loss of distribution network within one day. The DESS optimal operation model is proposed. The network reconfiguration considered in this paper is a multiple time segment reconfiguration. The purpose is to find a topology structure which can adapt to the load and DG variety within 24 hours. The proposed model and algorithm is verified with a TPC 84-bus system, the different switch operation times and different substation voltages are discussed. The results show the adaptability of the proposed methodology and the necessity to include the DESS and network reconfiguration in the coordinated operation.
{"title":"The Distribution Network Coordinated Operation Considering Distributed Energy Storage System Integration","authors":"Haijun Xing, H. Fan, Ranlong Guo, Jing Wang","doi":"10.1109/PESGM41954.2020.9281732","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281732","url":null,"abstract":"Distributed energy storage system (DESS) becomes more and more popular in the distribution network, especially in the era with largely increased the renewable energies. This paper proposed the day-ahead coordinated operation method of active distribution network considering distributed energy storage system integration and network reconfiguration. The objective is to minimize the energy loss of distribution network within one day. The DESS optimal operation model is proposed. The network reconfiguration considered in this paper is a multiple time segment reconfiguration. The purpose is to find a topology structure which can adapt to the load and DG variety within 24 hours. The proposed model and algorithm is verified with a TPC 84-bus system, the different switch operation times and different substation voltages are discussed. The results show the adaptability of the proposed methodology and the necessity to include the DESS and network reconfiguration in the coordinated operation.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132158259","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281440
Z. Hou, H. Ren, Heng Wang, P. Etingov
Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to the big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns in PMU signals and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple PMU attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, and weather extremes are all from real-world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.
{"title":"Spatiotemporal Pattern Recognition in the PMU Signals in the WECC system","authors":"Z. Hou, H. Ren, Heng Wang, P. Etingov","doi":"10.1109/PESGM41954.2020.9281440","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281440","url":null,"abstract":"Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to the big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns in PMU signals and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple PMU attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, and weather extremes are all from real-world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132440923","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281792
Maikel H. P. Klerx, Jeroen van Tongeren, J. Morren, H. Slootweg
Investigation of both capacity and quality of low-voltage networks is becoming more important. Previous research showed that condition assessment of low voltage (LV) grids is promising, but not sufficient yet to base asset management decisions on – due to limitations in data availability and quality and a low amount of failures. This paper presents a method which combines a condition assessment approach with a method to investigate the capacity of LV networks. The capacity method uses a bottom-up approach to predict if and when LV assets get overloaded. The combined approach ranks assets on the basis of expected condition and capacity bottlenecks. Results of a case study show among others an overlap of 45% of condition and capacity bottlenecks. It is proved that the presented combined approach is a useful, well-functioning and applicable approach for the asset management of LV distribution grids. This result follows up on and expands previous research.
{"title":"Automated Integrated Analysis of Condition and Capacity of Low-Voltage Networks","authors":"Maikel H. P. Klerx, Jeroen van Tongeren, J. Morren, H. Slootweg","doi":"10.1109/PESGM41954.2020.9281792","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281792","url":null,"abstract":"Investigation of both capacity and quality of low-voltage networks is becoming more important. Previous research showed that condition assessment of low voltage (LV) grids is promising, but not sufficient yet to base asset management decisions on – due to limitations in data availability and quality and a low amount of failures. This paper presents a method which combines a condition assessment approach with a method to investigate the capacity of LV networks. The capacity method uses a bottom-up approach to predict if and when LV assets get overloaded. The combined approach ranks assets on the basis of expected condition and capacity bottlenecks. Results of a case study show among others an overlap of 45% of condition and capacity bottlenecks. It is proved that the presented combined approach is a useful, well-functioning and applicable approach for the asset management of LV distribution grids. This result follows up on and expands previous research.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127653888","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 : 2020-08-02DOI: 10.1109/PESGM41954.2020.9281541
I. Onugha, S. Dehghan, P. Aristidou
The flexibility of the end users in the electricity markets is becoming more pertinent with the evolution of market mechanisms allowing consumers to participate actively. The advent of Distributed Energy Resources (DERs) and energy storage systems is gradually and continuously changing the roles of the market operators. The impact of the uncertainty of DERs and load demands on community-based market structures has not been fully investigated. In this paper, we propose a robust solution to community-based market operations under uncertainty and compare the optimal decisions on energy trades with deterministic, stochastic, and opportunistic models. Also, we employ the Taguchi’s orthogonal array testing (TOAT) to generate proficient scenarios from uncertain variables of prosumers. The proposed method is tested on a community-based microgrid with 15 prosumers assuming a single-rate tariff structure. Simulation results demonstrate the cost of robustness and the impact of uncertainty.
{"title":"Rethinking Consumer-Centric Markets Under Uncertainty: A Robust Approach to Community-Based Energy Trades","authors":"I. Onugha, S. Dehghan, P. Aristidou","doi":"10.1109/PESGM41954.2020.9281541","DOIUrl":"https://doi.org/10.1109/PESGM41954.2020.9281541","url":null,"abstract":"The flexibility of the end users in the electricity markets is becoming more pertinent with the evolution of market mechanisms allowing consumers to participate actively. The advent of Distributed Energy Resources (DERs) and energy storage systems is gradually and continuously changing the roles of the market operators. The impact of the uncertainty of DERs and load demands on community-based market structures has not been fully investigated. In this paper, we propose a robust solution to community-based market operations under uncertainty and compare the optimal decisions on energy trades with deterministic, stochastic, and opportunistic models. Also, we employ the Taguchi’s orthogonal array testing (TOAT) to generate proficient scenarios from uncertain variables of prosumers. The proposed method is tested on a community-based microgrid with 15 prosumers assuming a single-rate tariff structure. Simulation results demonstrate the cost of robustness and the impact of uncertainty.","PeriodicalId":106476,"journal":{"name":"2020 IEEE Power & Energy Society General Meeting (PESGM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127804139","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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