Pub Date : 2020-02-01DOI: 10.1109/PECI48348.2020.9064640
Manuel García, V. Oxilia, Ricardo Careaga, F. Fernández
In the advent of the single most important geopolitical negotiation between the owners of the largest hydroelectric power plant in the planet, Paraguay and Brazil, which involves new energy trade prices, this paper proposes a methodology to reach an agreement favorable for both parties, and reveals strategies to strengthen Paraguay’s position at the negotiation table. A bilateral negotiation analysis based on interests applying game theory in finite and infinite play schemes is conducted, considering the real costs of production, compensation royalties and current energy market prices. The resulting diagnose recognizes time as a fundamental criterion, as well as autonomy, to build up leverage. Finally, by comparing both mathematical models we demonstrate that initiating the negotiation as soon as possible will render the best possible outcome for Paraguay.
{"title":"Interest Based Negotiation with a Strategic Approach: Annex C ITAIPU Binational Case Study","authors":"Manuel García, V. Oxilia, Ricardo Careaga, F. Fernández","doi":"10.1109/PECI48348.2020.9064640","DOIUrl":"https://doi.org/10.1109/PECI48348.2020.9064640","url":null,"abstract":"In the advent of the single most important geopolitical negotiation between the owners of the largest hydroelectric power plant in the planet, Paraguay and Brazil, which involves new energy trade prices, this paper proposes a methodology to reach an agreement favorable for both parties, and reveals strategies to strengthen Paraguay’s position at the negotiation table. A bilateral negotiation analysis based on interests applying game theory in finite and infinite play schemes is conducted, considering the real costs of production, compensation royalties and current energy market prices. The resulting diagnose recognizes time as a fundamental criterion, as well as autonomy, to build up leverage. Finally, by comparing both mathematical models we demonstrate that initiating the negotiation as soon as possible will render the best possible outcome for Paraguay.","PeriodicalId":285806,"journal":{"name":"2020 IEEE Power and Energy Conference at Illinois (PECI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122100212","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 : 2020-02-01DOI: 10.1109/PECI48348.2020.9064654
Mohammad Safayet Hossain, H. Mahmood
In this paper, two forecasting models using long short term memory neural network (LSTM NN) are developed to predict short-term electrical load. The first model predicts a single step ahead load, while the other predicts multi-step intraday rolling horizons. The time series of the load is utilized in addition to weather data of the considered geographic area. A rolling time-index series including a time of the day index, a holiday flag and a day of the week index, is also embedded as a categorical feature vector, which is shown to increase the forecasting accuracy significantly. Moreover, to evaluate the performance of the LSTM NN, the performance of other machines, namely a generalized regression neural network (GRNN) and an extreme learning machine (ELM) is also shown. Hourly load data from the electrical reliability council of Texas (ERCOT) is used as benchmark data to evaluate the proposed algorithms.
{"title":"Short-Term Load Forecasting Using an LSTM Neural Network","authors":"Mohammad Safayet Hossain, H. Mahmood","doi":"10.1109/PECI48348.2020.9064654","DOIUrl":"https://doi.org/10.1109/PECI48348.2020.9064654","url":null,"abstract":"In this paper, two forecasting models using long short term memory neural network (LSTM NN) are developed to predict short-term electrical load. The first model predicts a single step ahead load, while the other predicts multi-step intraday rolling horizons. The time series of the load is utilized in addition to weather data of the considered geographic area. A rolling time-index series including a time of the day index, a holiday flag and a day of the week index, is also embedded as a categorical feature vector, which is shown to increase the forecasting accuracy significantly. Moreover, to evaluate the performance of the LSTM NN, the performance of other machines, namely a generalized regression neural network (GRNN) and an extreme learning machine (ELM) is also shown. Hourly load data from the electrical reliability council of Texas (ERCOT) is used as benchmark data to evaluate the proposed algorithms.","PeriodicalId":285806,"journal":{"name":"2020 IEEE Power and Energy Conference at Illinois (PECI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129883771","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 : 2020-02-01DOI: 10.1109/PECI48348.2020.9064642
Ali Alshawish, S. Eshtaiwi, R. Ahmadi, Sepehr Saadatmand
One of the most important reliability concerns for Solid-State Transformers (SSTs) is related to high voltage side switches. High voltage stress on the switches, together with the fact that most modern SST topologies are comprised of a large number of power switches in the high voltage side, contribute to a higher probability of a switch fault occurrence. This paper proposes a new SST topology in conjunction with a fault-tolerant operation strategy that can fully restore operation of the proposed SST in case of the mentioned fault scenario. Preliminary theoretical and simulation results are provided to support the proposed idea.
{"title":"A New Fault-Tolerant Topology and Operation Scheme for the High Voltage Stage in a Three-Phase Solid-State Transformer","authors":"Ali Alshawish, S. Eshtaiwi, R. Ahmadi, Sepehr Saadatmand","doi":"10.1109/PECI48348.2020.9064642","DOIUrl":"https://doi.org/10.1109/PECI48348.2020.9064642","url":null,"abstract":"One of the most important reliability concerns for Solid-State Transformers (SSTs) is related to high voltage side switches. High voltage stress on the switches, together with the fact that most modern SST topologies are comprised of a large number of power switches in the high voltage side, contribute to a higher probability of a switch fault occurrence. This paper proposes a new SST topology in conjunction with a fault-tolerant operation strategy that can fully restore operation of the proposed SST in case of the mentioned fault scenario. Preliminary theoretical and simulation results are provided to support the proposed idea.","PeriodicalId":285806,"journal":{"name":"2020 IEEE Power and Energy Conference at Illinois (PECI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126714357","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 : 2019-11-18DOI: 10.1109/PECI48348.2020.9064636
Ali Parsa Sirat, Hossein Mehdipourpicha, Niloofar Zendehdel, H. Mozafari
Loss minimization in distribution networks (DN) is of great significance since the trend to the distributed generation (DG) requires the most efficient operating scenario possible for economic viability variations. Moreover, voltage instability in DNs is a critical phenomenon and can lead to a major blackout in the system. The decreasing voltage stability level restricts the increase of load served by distribution companies. DG can be used to improve DN capabilities and brings new opportunities to traditional DNs. However, installation of DG in non-optimal places can result in an increase in system losses, voltage problems, etc. In this paper, genetic algorithm (GA), harmony search algorithm (HSA) and improved HSA have been applied to determine the optimal location of DGs. Simulation results for an IEEE 33 bus network are compared for different algorithms, and the best algorithm is stated for minimum losses.
{"title":"Sizing and Allocation of Distributed Energy Resources for Loss Reduction using Heuristic Algorithms","authors":"Ali Parsa Sirat, Hossein Mehdipourpicha, Niloofar Zendehdel, H. Mozafari","doi":"10.1109/PECI48348.2020.9064636","DOIUrl":"https://doi.org/10.1109/PECI48348.2020.9064636","url":null,"abstract":"Loss minimization in distribution networks (DN) is of great significance since the trend to the distributed generation (DG) requires the most efficient operating scenario possible for economic viability variations. Moreover, voltage instability in DNs is a critical phenomenon and can lead to a major blackout in the system. The decreasing voltage stability level restricts the increase of load served by distribution companies. DG can be used to improve DN capabilities and brings new opportunities to traditional DNs. However, installation of DG in non-optimal places can result in an increase in system losses, voltage problems, etc. In this paper, genetic algorithm (GA), harmony search algorithm (HSA) and improved HSA have been applied to determine the optimal location of DGs. Simulation results for an IEEE 33 bus network are compared for different algorithms, and the best algorithm is stated for minimum losses.","PeriodicalId":285806,"journal":{"name":"2020 IEEE Power and Energy Conference at Illinois (PECI)","volume":"70 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124109569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：