Pub Date : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033024
M. Pasetti, M. Longo, S. Rinaldi, P. Ferrari, E. Sisinni, A. Flammini
Battery Electric Vehicles (BEVs) are called to play a relevant role in the decarbonization of the urban mobility. However, the actual sustainability of BEVs remains doubtful, particularly if the energy stored in the batteries is produced from fossil fuels rather from renewable sources. In this scenario, the presence of renewable energy resources, such as Photovoltaic (PV) systems, could help to increase the rate of renewable energy used to recharge the BEVs. But how much is the actual potential of distributed PV systems to reduce the indirect environmental impact of BEVs? How can we foster the sustainable charging of BEVs? This study tries to answer these questions by estimating the indirect GHG emissions and charging costs of BEVs in a prosumer’s network equipped with a PV system, depending on the time the BEV is connected to the charging outlet. The results show that, for the considered use case, potential savings of 84.5% of BEV GHG emissions could be obtained. In addition, the study highlights how the use of smart charging functions, combined with price-based or incentive-based demand response programs, will be crucial to foster the sustainable use of BEVs.
{"title":"On the Sustainable Charging of Electric Vehicles in the Presence of Distributed Photovoltaic Generation","authors":"M. Pasetti, M. Longo, S. Rinaldi, P. Ferrari, E. Sisinni, A. Flammini","doi":"10.1109/iSPEC54162.2022.10033024","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033024","url":null,"abstract":"Battery Electric Vehicles (BEVs) are called to play a relevant role in the decarbonization of the urban mobility. However, the actual sustainability of BEVs remains doubtful, particularly if the energy stored in the batteries is produced from fossil fuels rather from renewable sources. In this scenario, the presence of renewable energy resources, such as Photovoltaic (PV) systems, could help to increase the rate of renewable energy used to recharge the BEVs. But how much is the actual potential of distributed PV systems to reduce the indirect environmental impact of BEVs? How can we foster the sustainable charging of BEVs? This study tries to answer these questions by estimating the indirect GHG emissions and charging costs of BEVs in a prosumer’s network equipped with a PV system, depending on the time the BEV is connected to the charging outlet. The results show that, for the considered use case, potential savings of 84.5% of BEV GHG emissions could be obtained. In addition, the study highlights how the use of smart charging functions, combined with price-based or incentive-based demand response programs, will be crucial to foster the sustainable use of BEVs.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122845827","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 : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10032998
Milad Andalibi, S. Madani, C. Ziebert, F. Naseri, Mojtaba Hajihosseini
Electric vehicles are equipped with a large number of lithium-ion battery cells. To achieve superior performance and guarantee safety and longevity, there is a fundamental requirement for a Battery Management System (BMS). In the BMS, accurate prediction of the State-of-Charge (SOC) is a crucial task. The SOC information is needed for monitoring, controlling, and protecting the battery, e.g. to avoid hazardous over-charging or over-discharging. Nonetheless, the SOC is an internal cell variable and cannot be straightforwardly obtained. This paper presents a Kalman Filter (KF) approach based on an optimized second-order Rc equivalent circuit model to carefully account for model parameter changes. An effective machine learning technique based on Proximal Policy optimization (PPO) is applied to train the algorithm. The results confirm the high robustness of the proposed method to varying operating conditions.
{"title":"A Model-Based Approach for Voltage and State-of-Charge Estimation of Lithium-ion Batteries","authors":"Milad Andalibi, S. Madani, C. Ziebert, F. Naseri, Mojtaba Hajihosseini","doi":"10.1109/iSPEC54162.2022.10032998","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10032998","url":null,"abstract":"Electric vehicles are equipped with a large number of lithium-ion battery cells. To achieve superior performance and guarantee safety and longevity, there is a fundamental requirement for a Battery Management System (BMS). In the BMS, accurate prediction of the State-of-Charge (SOC) is a crucial task. The SOC information is needed for monitoring, controlling, and protecting the battery, e.g. to avoid hazardous over-charging or over-discharging. Nonetheless, the SOC is an internal cell variable and cannot be straightforwardly obtained. This paper presents a Kalman Filter (KF) approach based on an optimized second-order Rc equivalent circuit model to carefully account for model parameter changes. An effective machine learning technique based on Proximal Policy optimization (PPO) is applied to train the algorithm. The results confirm the high robustness of the proposed method to varying operating conditions.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122557011","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 : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033005
Jiawei Zhou, Ming-Ming Cheng
Torque ripple problem is a major constraint to the use of the flux-switching permanent magnet machine (FSPM) in electric vehicle (EV) applications. To solve this problem, this paper firstly analyzed the magnetic field characteristics of the FSPM based on the general airgap field modulation theory (GAFMT) and modeled the torque ripple, especially the cogging torque. Then, a modified disturbance observer (MDOB), which contains a series-connected resonator to enhance the response characteristics in a given frequency, is proposed to achieve a smoother electromagnetic torque and reduce the impact of torque ripple on the EV system. Finally, the finite element analysis (FEA) simulation is given to verify the accuracy of the proposed theoretical torque ripple model, the MATLAB/Simulink simulation is proposed to verify the effectiveness of the proposed control method.
{"title":"Cogging Torque Modelling and Suppression for FSPM in EV application","authors":"Jiawei Zhou, Ming-Ming Cheng","doi":"10.1109/iSPEC54162.2022.10033005","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033005","url":null,"abstract":"Torque ripple problem is a major constraint to the use of the flux-switching permanent magnet machine (FSPM) in electric vehicle (EV) applications. To solve this problem, this paper firstly analyzed the magnetic field characteristics of the FSPM based on the general airgap field modulation theory (GAFMT) and modeled the torque ripple, especially the cogging torque. Then, a modified disturbance observer (MDOB), which contains a series-connected resonator to enhance the response characteristics in a given frequency, is proposed to achieve a smoother electromagnetic torque and reduce the impact of torque ripple on the EV system. Finally, the finite element analysis (FEA) simulation is given to verify the accuracy of the proposed theoretical torque ripple model, the MATLAB/Simulink simulation is proposed to verify the effectiveness of the proposed control method.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126055379","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 : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033029
Jiaqi Huang, Chenye Wu
Load profile synthesis is a commonly used technique for preserving smart meter data privacy. Recent efforts have successfully integrated advanced generative models, such as the Generative Adversarial Networks (GAN), to synthesize high-quality load profiles. Such methods are becoming increasingly popular for conducting privacy-preserving load data analytics. It is commonly believed that performing analyses on synthetic data can ensure certain privacy.In this paper, we examine this common belief. Specifically, we reveal the privacy leakage issue in load profile synthesis enabled by GAN. We first point out that the synthesis process cannot provide any provable privacy guarantee, highlighting that directly conducting load data analytics based on such data is extremely dangerous. The sample re-appearance risk is then presented under different volumes of training data, which indicates that the original load data could be directly leaked by GAN without any intentional effort from adversaries. Furthermore, we discuss potential approaches that might address this privacy leakage issue.
{"title":"Privacy Leakage in GAN Enabled Load Profile Synthesis","authors":"Jiaqi Huang, Chenye Wu","doi":"10.1109/iSPEC54162.2022.10033029","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033029","url":null,"abstract":"Load profile synthesis is a commonly used technique for preserving smart meter data privacy. Recent efforts have successfully integrated advanced generative models, such as the Generative Adversarial Networks (GAN), to synthesize high-quality load profiles. Such methods are becoming increasingly popular for conducting privacy-preserving load data analytics. It is commonly believed that performing analyses on synthetic data can ensure certain privacy.In this paper, we examine this common belief. Specifically, we reveal the privacy leakage issue in load profile synthesis enabled by GAN. We first point out that the synthesis process cannot provide any provable privacy guarantee, highlighting that directly conducting load data analytics based on such data is extremely dangerous. The sample re-appearance risk is then presented under different volumes of training data, which indicates that the original load data could be directly leaked by GAN without any intentional effort from adversaries. Furthermore, we discuss potential approaches that might address this privacy leakage issue.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117154447","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 : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033059
Jasmeen Patel, N. Das, Syed Islam
In power system the stability is an important property that varies on the operational condition and the interruption to which it is exposed. The power system network endangered to the similar disruption can be stable at one operational situation (e.g., in off-peak hours) and not stable at another (e.g., at peak times). Similarly, a network at one operational situation can be stable to one disturbance and not stable to another. Consequently, stability reports generally involve the analysis of several cases, in order to cover various disruptions of interest and the key points of operation of the system. This research recommends various stabilizers such as, Power system stabilizers (PSS), Proportional Integration Differentiation (PID) and Fractional Order PID (FOPID) to decrease oscillations due to small signal disruption. The PSS-Voltage stabilizer generates impulses at the time of speed-change which usually results in positive PSS output. Using the FOPID procedure in relation with PSS, this approach can decrease these impulses. Using this approach, the generated impulses are finally decreased. Genetic Algorithm, particle swarm optimization and cultural algorithm are used for parameter fine-tuning of all the stabilizers. All these algorithms will help to tune parameters at soft computing level and then again tuned by two stabilizers which are FOPID and PSS. Now, again two stabilizers will tune and provide output needs to compare and average output to get required tuned values. Finally, the average of two stabilizers will field circuit and drive rotor according to the value given to it.
{"title":"Design of Hybrid Power System Stabilizer for Dynamic Stability Improvement using Cultural Algorithm","authors":"Jasmeen Patel, N. Das, Syed Islam","doi":"10.1109/iSPEC54162.2022.10033059","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033059","url":null,"abstract":"In power system the stability is an important property that varies on the operational condition and the interruption to which it is exposed. The power system network endangered to the similar disruption can be stable at one operational situation (e.g., in off-peak hours) and not stable at another (e.g., at peak times). Similarly, a network at one operational situation can be stable to one disturbance and not stable to another. Consequently, stability reports generally involve the analysis of several cases, in order to cover various disruptions of interest and the key points of operation of the system. This research recommends various stabilizers such as, Power system stabilizers (PSS), Proportional Integration Differentiation (PID) and Fractional Order PID (FOPID) to decrease oscillations due to small signal disruption. The PSS-Voltage stabilizer generates impulses at the time of speed-change which usually results in positive PSS output. Using the FOPID procedure in relation with PSS, this approach can decrease these impulses. Using this approach, the generated impulses are finally decreased. Genetic Algorithm, particle swarm optimization and cultural algorithm are used for parameter fine-tuning of all the stabilizers. All these algorithms will help to tune parameters at soft computing level and then again tuned by two stabilizers which are FOPID and PSS. Now, again two stabilizers will tune and provide output needs to compare and average output to get required tuned values. Finally, the average of two stabilizers will field circuit and drive rotor according to the value given to it.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125619517","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 : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033061
Julia Schulz, Magdalena Paul, Stefan Roth, Valerie M. Scharmer, Lukas Bank, M. F. Zaeh
The ongoing energy transition to renewable energies heavily impacts even non-energy-intensive manufacturing companies (NEIMCs). This progress comes with fluctuations in electricity availability; and ultimately rising costs. Facing the transformation by applying demand-side energy flexibility measures, so far, has considered rather energy-intensive companies while leaving NEIMCs out of focus. Despite their number and economic importance in the European terrain, their demand-side potential is usually underestimated while it may be central to an overall successful adaptation. In this work, energy transition challenges posed to NEIMCs are funneled from several surveys with eight companies in the southern German manufacturing sector. They were evaluated to extract individual requirements for implementing energy-oriented manufacturing; from a NEIMC’s perspective. Experiences from the process industry were adapted custom-oriented to guideline potentially specific solutions for NEIMCs related to the energy transition using IT solutions. More broadly, this approach reflects opportunities and challenges in the NEIMC environment and outlines promising avenues to exploit energy flexibility.
{"title":"Assessing energy flexibility in non-energy-intensive manufacturing companies","authors":"Julia Schulz, Magdalena Paul, Stefan Roth, Valerie M. Scharmer, Lukas Bank, M. F. Zaeh","doi":"10.1109/iSPEC54162.2022.10033061","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033061","url":null,"abstract":"The ongoing energy transition to renewable energies heavily impacts even non-energy-intensive manufacturing companies (NEIMCs). This progress comes with fluctuations in electricity availability; and ultimately rising costs. Facing the transformation by applying demand-side energy flexibility measures, so far, has considered rather energy-intensive companies while leaving NEIMCs out of focus. Despite their number and economic importance in the European terrain, their demand-side potential is usually underestimated while it may be central to an overall successful adaptation. In this work, energy transition challenges posed to NEIMCs are funneled from several surveys with eight companies in the southern German manufacturing sector. They were evaluated to extract individual requirements for implementing energy-oriented manufacturing; from a NEIMC’s perspective. Experiences from the process industry were adapted custom-oriented to guideline potentially specific solutions for NEIMCs related to the energy transition using IT solutions. More broadly, this approach reflects opportunities and challenges in the NEIMC environment and outlines promising avenues to exploit energy flexibility.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"421 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128973496","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 : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033038
Liaqat Ali, Jan Peters, M. I. Azim, E. Pashajavid, V. Bhandari, Anand Menon, Vinod Tiwari, Arindam Ghosh, Jemma Green
This paper performs a case study to analyse the impacts of variable spot prices on retailer income in the National Electricity Market (NEM). Further, the effectiveness of a peer-to-peer (P2P) trading-based local energy market (LEM) to address those negative impacts is evaluated. The LEM is operated under a single substation consisting of consumers and prosumers with solar PVs and batteries. Energy trading is performed among the sellers and buyers based on real-world data from an Australian town. Two different scenarios of high and low spot prices are considered to analyse the effect of volatile spot prices. The energy buying and selling by the retailer is explored through the metrics of five different parameters. Eventually, simulation results of the P2P and traditional business-as-usual (BAU) trading are analysed. It is found that LEM helps the retailer to reduce the impact of higher spot prices with more stable demand. Additionally, LEM improves the self-sufficiency and self-consumption of the distribution network.
{"title":"How P2P Trading Helps an Electricity Retailer Exposed to Volatile Spot Prices: A Case Study","authors":"Liaqat Ali, Jan Peters, M. I. Azim, E. Pashajavid, V. Bhandari, Anand Menon, Vinod Tiwari, Arindam Ghosh, Jemma Green","doi":"10.1109/iSPEC54162.2022.10033038","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033038","url":null,"abstract":"This paper performs a case study to analyse the impacts of variable spot prices on retailer income in the National Electricity Market (NEM). Further, the effectiveness of a peer-to-peer (P2P) trading-based local energy market (LEM) to address those negative impacts is evaluated. The LEM is operated under a single substation consisting of consumers and prosumers with solar PVs and batteries. Energy trading is performed among the sellers and buyers based on real-world data from an Australian town. Two different scenarios of high and low spot prices are considered to analyse the effect of volatile spot prices. The energy buying and selling by the retailer is explored through the metrics of five different parameters. Eventually, simulation results of the P2P and traditional business-as-usual (BAU) trading are analysed. It is found that LEM helps the retailer to reduce the impact of higher spot prices with more stable demand. Additionally, LEM improves the self-sufficiency and self-consumption of the distribution network.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129951701","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 : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10032993
Vadim Avkhimenia, Matheus Gemignani, P. Musílek, Timothy M. Weis
Battery energy storage in utility-scale transmission grids provides the benefit of fast response, however, efficient battery control in multi-battery multi-bus systems can be challenging. We present here a battery operation strategy based on forecasted load and line ampacity. The forecasted load is serviced via conventional generators in combination with battery energy storage whose outputs are computed using non-linear programming with the objective of minimizing total battery charging and discharging. The operating strategy takes into account battery degradation, line outages, and dynamic line rating. The forecasting model is based on attention convolutional neural network architecture with bidirectional long-short term memory layers forecasting over the range calculated using the sliding windows. The strategy is tested on 24-bus reliability test system and is shown to be effective at predicting battery action.
{"title":"Deep Learning Control of Transmission System with Battery Storage and Dynamic Line Rating","authors":"Vadim Avkhimenia, Matheus Gemignani, P. Musílek, Timothy M. Weis","doi":"10.1109/iSPEC54162.2022.10032993","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10032993","url":null,"abstract":"Battery energy storage in utility-scale transmission grids provides the benefit of fast response, however, efficient battery control in multi-battery multi-bus systems can be challenging. We present here a battery operation strategy based on forecasted load and line ampacity. The forecasted load is serviced via conventional generators in combination with battery energy storage whose outputs are computed using non-linear programming with the objective of minimizing total battery charging and discharging. The operating strategy takes into account battery degradation, line outages, and dynamic line rating. The forecasting model is based on attention convolutional neural network architecture with bidirectional long-short term memory layers forecasting over the range calculated using the sliding windows. The strategy is tested on 24-bus reliability test system and is shown to be effective at predicting battery action.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130994076","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 : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10032984
Qain Ma, Liang Zhang, Xiuli Wang, Ziqiang Wang, He Huang, Peng Li
Aiming at the coordinated optimal dispatching problem of interconnected power system, a distributed interregional economic dispatching method based on alternating direction method of multipliers (ADMM) is proposed. Firstly, considering the constraints such as the upper and lower limits of units’ output, ramping rate and reserve within the regions and the power transaction plan of tie line, a centralized dispatching model of inter-regional interconnected power system is established. Then, the centralized model is reconstructed based on ADMM to establish a distributed dispatching model with sub regions as the basic units. Only the information of tie-line power is exchanged among regions, and there is no need to deliver the units’ and load’s information within regions, which effectively protects the privacy data. In order to cope with the uncertainty of load and renewable energy, the method of conditional value at risk (CVaR) is adopted to control the fluctuation of cost. Finally, the proposed method is applied to improved IEEE-118 bus system for simulation, and the results verify the effectiveness of the proposed method.
{"title":"Distributed Inter-Regional Dispatching Method Based on Alternating Direction Method of Multipliers","authors":"Qain Ma, Liang Zhang, Xiuli Wang, Ziqiang Wang, He Huang, Peng Li","doi":"10.1109/iSPEC54162.2022.10032984","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10032984","url":null,"abstract":"Aiming at the coordinated optimal dispatching problem of interconnected power system, a distributed interregional economic dispatching method based on alternating direction method of multipliers (ADMM) is proposed. Firstly, considering the constraints such as the upper and lower limits of units’ output, ramping rate and reserve within the regions and the power transaction plan of tie line, a centralized dispatching model of inter-regional interconnected power system is established. Then, the centralized model is reconstructed based on ADMM to establish a distributed dispatching model with sub regions as the basic units. Only the information of tie-line power is exchanged among regions, and there is no need to deliver the units’ and load’s information within regions, which effectively protects the privacy data. In order to cope with the uncertainty of load and renewable energy, the method of conditional value at risk (CVaR) is adopted to control the fluctuation of cost. Finally, the proposed method is applied to improved IEEE-118 bus system for simulation, and the results verify the effectiveness of the proposed method.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132634220","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 : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033028
Adithya Ravikumar, S. Deilami, Foad Taghizadeh
Electric vehicles (EVs) are the possible solution to reach for the goal of reliable and sustainable environment and electrifying the transportation system. EV integration is widely done by introducing the virtual power plant (VPP) concept in which the EVs can be clustered and controlled together. By this way one single VPP or aggregator model can be used to solve the challenges in the grid such as power quality, systems losses, and peak demand management. This paper will first analyze the conventional single VPP model and its application. The research work will then propose a new strategy to overcome its limitation for flexible use of EVs by introducing a dynamic virtual power plant (DVPP) algorithm. This algorithm is able to cluster the EVs into different virtual power plants based on the EVs’ present state of charge (SOC) and plug-out time. After the formation of different VPP clusters, the EV coordination and vehicle to grid (V2G) optimization of each VPP cluster are formulated as a mixed integer nonlinear optimization model while subjected to grid constraints. The proposed methodology is evaluated by MATLAB and Open-DSS simulation and the results indicate that the proposed approach has better grid performance than the conventional single fixed VPP model.
{"title":"Advanced Dynamic Virtual Power Plants with Electric Vehicle Integration","authors":"Adithya Ravikumar, S. Deilami, Foad Taghizadeh","doi":"10.1109/iSPEC54162.2022.10033028","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033028","url":null,"abstract":"Electric vehicles (EVs) are the possible solution to reach for the goal of reliable and sustainable environment and electrifying the transportation system. EV integration is widely done by introducing the virtual power plant (VPP) concept in which the EVs can be clustered and controlled together. By this way one single VPP or aggregator model can be used to solve the challenges in the grid such as power quality, systems losses, and peak demand management. This paper will first analyze the conventional single VPP model and its application. The research work will then propose a new strategy to overcome its limitation for flexible use of EVs by introducing a dynamic virtual power plant (DVPP) algorithm. This algorithm is able to cluster the EVs into different virtual power plants based on the EVs’ present state of charge (SOC) and plug-out time. After the formation of different VPP clusters, the EV coordination and vehicle to grid (V2G) optimization of each VPP cluster are formulated as a mixed integer nonlinear optimization model while subjected to grid constraints. The proposed methodology is evaluated by MATLAB and Open-DSS simulation and the results indicate that the proposed approach has better grid performance than the conventional single fixed VPP model.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133781729","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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