Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065993
R. Jha, Kush Khanna, N. Senroy, B. K. Panigrahi
Conventional methods for economic load dispatch do not include dynamic security constraints into the optimization problem; therefore, an insecure generation dispatch may create a blackout scenario under a certain contingency. Such scenarios can be avoided by including dynamic constraints in the optimization problem in the form of voltage stability, small signal stability, transient stability, etc. Transient stability constrained optimal power flow (TSC-OPF) is proposed in this paper to compute dispatch for different generators economically. The proposed TSC-OPF reshuffle generation of machines by withdrawing dispatch from critical machines (threatening the system security) and economically distributing them among non-critical machines. This generation reshuffling is based on deviated normalized kinetic energy of individual machine from the mean value of normalized kinetic energy of all machines in the system at the instant of instability. The proposed method is tested and verified for different test systems such as IEEE 39 bus test system and IEEE 68 bus test system for different contingencies.
{"title":"Normalized Kinetic Energy based Generation Reshuffling to Improve Dynamic Security Constrained Optimal Power Flow","authors":"R. Jha, Kush Khanna, N. Senroy, B. K. Panigrahi","doi":"10.1109/ISAP48318.2019.9065993","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065993","url":null,"abstract":"Conventional methods for economic load dispatch do not include dynamic security constraints into the optimization problem; therefore, an insecure generation dispatch may create a blackout scenario under a certain contingency. Such scenarios can be avoided by including dynamic constraints in the optimization problem in the form of voltage stability, small signal stability, transient stability, etc. Transient stability constrained optimal power flow (TSC-OPF) is proposed in this paper to compute dispatch for different generators economically. The proposed TSC-OPF reshuffle generation of machines by withdrawing dispatch from critical machines (threatening the system security) and economically distributing them among non-critical machines. This generation reshuffling is based on deviated normalized kinetic energy of individual machine from the mean value of normalized kinetic energy of all machines in the system at the instant of instability. The proposed method is tested and verified for different test systems such as IEEE 39 bus test system and IEEE 68 bus test system for different contingencies.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127196629","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 : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065984
Md Umar Hashmi, Deepjyoti Deka, A. Bušić, Lucas Pereira, S. Backhaus
This paper presents a new co-optimization formulation for energy storage for performing energy arbitrage and power factor correction (PFC) in the time scale of minutes to hours, along with peak demand shaving in the time scale of a month. While the optimization problem is non-convex, we present an efficient penalty based convex relaxation to solve it. Furthermore, we provide a mechanism to increase the storage operational life by tuning the cycles of operation using a friction coefficient. To demonstrate the effectiveness of energy storage performing multiple tasks simultaneously, we present a case study with real data for a time scale of several months. We are able to show that energy storage can realistically correct power factor without significant change in either arbitrage gains or peak demand charges. We demonstrate a real-time Model Predictive Control (MPC) based implementation of the proposed formulation with AutoRegressive forecasting of net-load and electricity price. Numerical results indicate that arbitrage gains and peak demand shaving are more sensitive to parameter uncertainty for faster ramping battery compared to slower ramping batteries. However, PFC gains are insensitive to forecast inaccuracies.
{"title":"Co-optimizing Energy Storage for Prosumers using Convex Relaxations","authors":"Md Umar Hashmi, Deepjyoti Deka, A. Bušić, Lucas Pereira, S. Backhaus","doi":"10.1109/ISAP48318.2019.9065984","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065984","url":null,"abstract":"This paper presents a new co-optimization formulation for energy storage for performing energy arbitrage and power factor correction (PFC) in the time scale of minutes to hours, along with peak demand shaving in the time scale of a month. While the optimization problem is non-convex, we present an efficient penalty based convex relaxation to solve it. Furthermore, we provide a mechanism to increase the storage operational life by tuning the cycles of operation using a friction coefficient. To demonstrate the effectiveness of energy storage performing multiple tasks simultaneously, we present a case study with real data for a time scale of several months. We are able to show that energy storage can realistically correct power factor without significant change in either arbitrage gains or peak demand charges. We demonstrate a real-time Model Predictive Control (MPC) based implementation of the proposed formulation with AutoRegressive forecasting of net-load and electricity price. Numerical results indicate that arbitrage gains and peak demand shaving are more sensitive to parameter uncertainty for faster ramping battery compared to slower ramping batteries. However, PFC gains are insensitive to forecast inaccuracies.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125864560","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 : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065981
U. Amin, M. J. Hossain, Edstan Fernandez, K. Mahmud, Guo Tiezheng
This paper proposes an approach to categorize electricity suppliers (ESs) for energy trading between ESs and a single aggregator. A principal-agents game model is developed to model the interactions between an aggregator and different categories of ESs by considering the benefits of both parties. In a proposed game, the aggregator as a principal will purchase a certain amount of power from different-category ESs with the cheapest pricing options available, and at the same time the ESs, acting as agents will maximize their utilities by selling their power to the aggregator instead of feeding the grid at a low rate. The developed optimal contract-based scheme, which can be implemented distributed manner, allows different-category ESs to sell their power at different prices based on their unit production cost to maximize their benefits, and the total cost to the aggregator is minimized. Numerical analysis confirms the effectiveness of the proposed ESs categorizing framework in the development of a contract-based incentive mechanism for energy trading.
{"title":"A Contract-based Trading Model for Electricity Suppliers in Smart Grids","authors":"U. Amin, M. J. Hossain, Edstan Fernandez, K. Mahmud, Guo Tiezheng","doi":"10.1109/ISAP48318.2019.9065981","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065981","url":null,"abstract":"This paper proposes an approach to categorize electricity suppliers (ESs) for energy trading between ESs and a single aggregator. A principal-agents game model is developed to model the interactions between an aggregator and different categories of ESs by considering the benefits of both parties. In a proposed game, the aggregator as a principal will purchase a certain amount of power from different-category ESs with the cheapest pricing options available, and at the same time the ESs, acting as agents will maximize their utilities by selling their power to the aggregator instead of feeding the grid at a low rate. The developed optimal contract-based scheme, which can be implemented distributed manner, allows different-category ESs to sell their power at different prices based on their unit production cost to maximize their benefits, and the total cost to the aggregator is minimized. Numerical analysis confirms the effectiveness of the proposed ESs categorizing framework in the development of a contract-based incentive mechanism for energy trading.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122279052","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 : 2019-11-11DOI: 10.1109/ISAP48318.2019.9065991
Anish K. Mathew, D. P., Sahely Bhadra, N. Pindoriya, A. Kiprakis, S. N. Singh
The uptake of solar power generation is on the rise. This necessitates more research into developing data-driven intelligent methods that can perform effective analytics over power generation data to inform strategies to improve solar power generation systems. In this paper, we consider the utility of time series representation learning for analytics over power generation data. WaRTEm, a representation learning method that focuses on learning time series representations that are invariant to local phase shifts, is the focus of our investigations in this paper. We identify two metadata attributes for power generation sequences, month and CellID, as attributes that embed useful notions of semantic similarity between time series sequences. We evaluate the effectiveness of WaRTEm representations, as against using the raw time series sequences, in alignment to the month and CellID labellings, using accuracy over 1NN retrieval as an evaluation framework. Through empirical evaluations, we identify that WaRTEm embeddings are consistently able to achieve better representations when evaluated on 1NN accuracy. We also identify some features of WaRTEm that are more suited for time series representation learning, which provides promising directions for future work.
{"title":"Time Series Representation Learning Applications for Power Analytics","authors":"Anish K. Mathew, D. P., Sahely Bhadra, N. Pindoriya, A. Kiprakis, S. N. Singh","doi":"10.1109/ISAP48318.2019.9065991","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065991","url":null,"abstract":"The uptake of solar power generation is on the rise. This necessitates more research into developing data-driven intelligent methods that can perform effective analytics over power generation data to inform strategies to improve solar power generation systems. In this paper, we consider the utility of time series representation learning for analytics over power generation data. WaRTEm, a representation learning method that focuses on learning time series representations that are invariant to local phase shifts, is the focus of our investigations in this paper. We identify two metadata attributes for power generation sequences, month and CellID, as attributes that embed useful notions of semantic similarity between time series sequences. We evaluate the effectiveness of WaRTEm representations, as against using the raw time series sequences, in alignment to the month and CellID labellings, using accuracy over 1NN retrieval as an evaluation framework. Through empirical evaluations, we identify that WaRTEm embeddings are consistently able to achieve better representations when evaluated on 1NN accuracy. We also identify some features of WaRTEm that are more suited for time series representation learning, which provides promising directions for future work.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126961499","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：