Pub Date : 2018-10-01DOI: 10.1109/EPEC.2018.8598279
O. Palizban, K. Kauhaniemi
Energy storage systems play a significant role in power management systems and control of the modern grid. One of the most challenging issues is controlling storage units in distributed form. This paper presents a possible means of controlling Energy Storage Systems (ESS) through a decentralized approach. Moreover, the balancing and equalization of stored energy in different storage units presents other challenges in such systems; to deal with this, the paper discuss here the factors that affect energy balancing and the speed of the energy balance convergence. A Proportional-Integral (PI) controller is used in the upper control level to generate an accurate reference value for the State of Charge (SoC), and a modified droop control is employed on the lower control level to equalize the energy on the basis of the SoC. To evaluate the control algorithm and to investigate the factors that affect the speed of equalization, this paper considers the result of a case study with three battery storage units.
{"title":"Operative Factors Affecting Energy Balancing and Speed of Equalization in Battery Storage System","authors":"O. Palizban, K. Kauhaniemi","doi":"10.1109/EPEC.2018.8598279","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598279","url":null,"abstract":"Energy storage systems play a significant role in power management systems and control of the modern grid. One of the most challenging issues is controlling storage units in distributed form. This paper presents a possible means of controlling Energy Storage Systems (ESS) through a decentralized approach. Moreover, the balancing and equalization of stored energy in different storage units presents other challenges in such systems; to deal with this, the paper discuss here the factors that affect energy balancing and the speed of the energy balance convergence. A Proportional-Integral (PI) controller is used in the upper control level to generate an accurate reference value for the State of Charge (SoC), and a modified droop control is employed on the lower control level to equalize the energy on the basis of the SoC. To evaluate the control algorithm and to investigate the factors that affect the speed of equalization, this paper considers the result of a case study with three battery storage units.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128032702","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 : 2018-10-01DOI: 10.1109/EPEC.2018.8598287
Ehsan Davari-nejad, A. Ameli, E. El-Saadany
As far as continuous supply and reliable power delivery to the distribution system customers are concerned, finding a practical and cost-effective method for locating faults after their occurrence is of high importance. In this study, a method is proposed to determine the optimal number and location of power quality monitors (PQMs) to make the distribution network fault-observable, that means, to be able to locate faults as precisely as possible. Moreover, as placement of PQMs is highly dependent on the network topology, the proposed method considers the most probable configurations of the network for optimization. The error of measuring equipment and its effect on number of PQMs is also taken into consideration. The defined objective functions of this study aim to minimize the cost of installing PQMs while minimizing the number of blind-pairs and maximizing the fault-observability level of the network. These objective functions are optimized using Multi-Objective Particle Swarm Optimization (MOPSO) technique. Additionally, to have a better economic evaluation, two scenarios are defined based on the accuracy class of monitoring equipment. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 123-bus distribution test system.
{"title":"Fault-Observability Enhancement in Distribution Networks Using Power Quality Monitors","authors":"Ehsan Davari-nejad, A. Ameli, E. El-Saadany","doi":"10.1109/EPEC.2018.8598287","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598287","url":null,"abstract":"As far as continuous supply and reliable power delivery to the distribution system customers are concerned, finding a practical and cost-effective method for locating faults after their occurrence is of high importance. In this study, a method is proposed to determine the optimal number and location of power quality monitors (PQMs) to make the distribution network fault-observable, that means, to be able to locate faults as precisely as possible. Moreover, as placement of PQMs is highly dependent on the network topology, the proposed method considers the most probable configurations of the network for optimization. The error of measuring equipment and its effect on number of PQMs is also taken into consideration. The defined objective functions of this study aim to minimize the cost of installing PQMs while minimizing the number of blind-pairs and maximizing the fault-observability level of the network. These objective functions are optimized using Multi-Objective Particle Swarm Optimization (MOPSO) technique. Additionally, to have a better economic evaluation, two scenarios are defined based on the accuracy class of monitoring equipment. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 123-bus distribution test system.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125122347","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 : 2018-10-01DOI: 10.1109/EPEC.2018.8598328
Abdelrahman Ayad, Mohsen Khalaf, E. El-Saadany
Maintaining the power system frequency around its nominal value is a very critical issue for the system stability. This operation is performed by the Automatic Generation Control (AGC) system. A cyber attack on the AGC system may affect the whole stability and economic operation of the power system. This paper proposes a method using Recurrent Neural Networks to detect False Data Injection (FDI) attacks in AGC systems. The novelty of this work over other approaches is that the nonlinearities of the AGC system are considered, which make it difficult to use the conventional approaches to detect FDI in case of considering the nonlinearities. The AGC of a two-area power system is used and the results show that the proposed approach succeeded to detect FDI in AGC system with an accuracy of 94%.
{"title":"Detection of False Data Injection Attacks in Automatic Generation Control Systems Considering System Nonlinearities","authors":"Abdelrahman Ayad, Mohsen Khalaf, E. El-Saadany","doi":"10.1109/EPEC.2018.8598328","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598328","url":null,"abstract":"Maintaining the power system frequency around its nominal value is a very critical issue for the system stability. This operation is performed by the Automatic Generation Control (AGC) system. A cyber attack on the AGC system may affect the whole stability and economic operation of the power system. This paper proposes a method using Recurrent Neural Networks to detect False Data Injection (FDI) attacks in AGC systems. The novelty of this work over other approaches is that the nonlinearities of the AGC system are considered, which make it difficult to use the conventional approaches to detect FDI in case of considering the nonlinearities. The AGC of a two-area power system is used and the results show that the proposed approach succeeded to detect FDI in AGC system with an accuracy of 94%.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124311456","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 : 2018-10-01DOI: 10.1109/EPEC.2018.8598301
Y. Uemura, Shuhei Obara, T. Kawasaki
Since the electricity demand and the thermal demand in the cold district are large compared to the warm region, so emission of greenhouse gases by mass consumption of fossil fuels becomes a problem. For this reason, it is necessary to construct an energy system based on renewable energy. This research proposes a power generation system that utilizes unique state change characteristics by thermal cycle of carbon dioxide hydrate. The energy necessary for operating the proposed system is only heat, outside air temperature (low temperature heat source) is used in cold areas in winter, low temperature heat emission (high temperature heat source) of buildings and factories is used. In the trial system, electricity is obtained from the pressure difference during the phase change of CO2gas hydrate by the scroll type expander. In this paper, it is expected that the power generation efficiency reaches 11.8% by appropriately using the heat exchanger which is the reaction vessel of the prototype system.
{"title":"Development of a gas hydrate power generation system for cold district using low temperature heat emission","authors":"Y. Uemura, Shuhei Obara, T. Kawasaki","doi":"10.1109/EPEC.2018.8598301","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598301","url":null,"abstract":"Since the electricity demand and the thermal demand in the cold district are large compared to the warm region, so emission of greenhouse gases by mass consumption of fossil fuels becomes a problem. For this reason, it is necessary to construct an energy system based on renewable energy. This research proposes a power generation system that utilizes unique state change characteristics by thermal cycle of carbon dioxide hydrate. The energy necessary for operating the proposed system is only heat, outside air temperature (low temperature heat source) is used in cold areas in winter, low temperature heat emission (high temperature heat source) of buildings and factories is used. In the trial system, electricity is obtained from the pressure difference during the phase change of CO2gas hydrate by the scroll type expander. In this paper, it is expected that the power generation efficiency reaches 11.8% by appropriately using the heat exchanger which is the reaction vessel of the prototype system.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121851123","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 : 2018-10-01DOI: 10.1109/EPEC.2018.8598295
Ehab Sayed, Peter Azer, M. Kordic, J. Reimers, B. Bilgin, M. Bakr, A. Emadi
This paper discusses the design of a switched reluctance motor (SRM) for pump jacks that are commonly used in oil extraction industry. An SRM is designed as an alternative to a 10-hp induction motor. It is designed to have the same volume so it can utilize the same NEMA frame. Optimization through the number of turns per phase, stator and rotor pole arc angles, and conduction angles is carried out. Selection of conduction angles for the motor drive is based on a multi-objective constrained genetic algorithm optimization to achieve the required torque with the same phase current as the reference induction machine. Thermal analysis of the proposed machine is conducted to demonstrate its suitability for extended continuous operation.
{"title":"Design of a Switched Reluctance Motor for a Pump Jack Application","authors":"Ehab Sayed, Peter Azer, M. Kordic, J. Reimers, B. Bilgin, M. Bakr, A. Emadi","doi":"10.1109/EPEC.2018.8598295","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598295","url":null,"abstract":"This paper discusses the design of a switched reluctance motor (SRM) for pump jacks that are commonly used in oil extraction industry. An SRM is designed as an alternative to a 10-hp induction motor. It is designed to have the same volume so it can utilize the same NEMA frame. Optimization through the number of turns per phase, stator and rotor pole arc angles, and conduction angles is carried out. Selection of conduction angles for the motor drive is based on a multi-objective constrained genetic algorithm optimization to achieve the required torque with the same phase current as the reference induction machine. Thermal analysis of the proposed machine is conducted to demonstrate its suitability for extended continuous operation.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126261450","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 : 2018-10-01DOI: 10.1109/EPEC.2018.8598355
P. Dash, Kshirasagar Naik
Estimating appliance specific power consumption using a single measuring device, known as Non-Intrusive Load Monitoring (NILM), is a challenging Blind Signal source Separation (BSS) problem. For the past two decades, numerous mathematical and pattern recognition techniques, including Fractional Hidden Markov Model (FHMM), Gaussian Mixture Model (GMM) and Mean Shift Based Clustering Techniques (MSBCT) have been proposed to decompose the total power consumption of a household into appliance specific power signals. The measurement sampling rate, operating characteristic of individual appliances and an unknown number of mixed signals create a big challenge in separating them. The main challenge is to design an algorithm that can learn appliance features accurately, before applying the algorithm to disaggregate the main power signals. To address this problem, A Very Deep One dimensional Convolutional Neural Network (VDOCNN) for appliance power signature classification is proposed in this research. As a first step, we have applied VDOCNN in learning appliance features from a given set of labeled training data. VDOCNN has achieved accuracy up to 98% in detecting appliance from its power signature using a UK Domestic Appliance-Level Electricity (UK-DALE) dataset. Using this algorithm, we are working towards disaggregation of power signatures for different appliances from a single power signal in future research.
{"title":"A Very Deep One Dimensional Convolutional Neural Network (VDOCNN) for Appliance Power Signature Classification","authors":"P. Dash, Kshirasagar Naik","doi":"10.1109/EPEC.2018.8598355","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598355","url":null,"abstract":"Estimating appliance specific power consumption using a single measuring device, known as Non-Intrusive Load Monitoring (NILM), is a challenging Blind Signal source Separation (BSS) problem. For the past two decades, numerous mathematical and pattern recognition techniques, including Fractional Hidden Markov Model (FHMM), Gaussian Mixture Model (GMM) and Mean Shift Based Clustering Techniques (MSBCT) have been proposed to decompose the total power consumption of a household into appliance specific power signals. The measurement sampling rate, operating characteristic of individual appliances and an unknown number of mixed signals create a big challenge in separating them. The main challenge is to design an algorithm that can learn appliance features accurately, before applying the algorithm to disaggregate the main power signals. To address this problem, A Very Deep One dimensional Convolutional Neural Network (VDOCNN) for appliance power signature classification is proposed in this research. As a first step, we have applied VDOCNN in learning appliance features from a given set of labeled training data. VDOCNN has achieved accuracy up to 98% in detecting appliance from its power signature using a UK Domestic Appliance-Level Electricity (UK-DALE) dataset. Using this algorithm, we are working towards disaggregation of power signatures for different appliances from a single power signal in future research.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129686313","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 : 2018-10-01DOI: 10.1109/EPEC.2018.8598297
Sreedevi Valsan Kandenkavil, Kankar Bhattacharya
State estimation is widely used in power transmission systems for obtaining a real time network model where measurements of bus voltages and line power flows are available. On the other hand, in distribution systems, with limited availability of measurements, and additional measurements being expensive, careful selection of location for the placement of meters becomes important. The measurement meters typically considered are phasor measurement units (PMUs) and power (PQ) meters. In this work, optimization based approaches are proposed to address the optimal meter placement problem considering different objectives such as minimization of cost, weighted least square (WLS) residual estimate, and a multi-objective function comprising cost and WLS, and the average root mean square error (ARMSE) of the estimated state vector. The various optimization models are tested on a 33-bus distribution feeder, and compared based on meter placement cost and ARMSE of voltage estimates.
{"title":"Optimization Approaches to Distribution System State Estimation for Optimal Meter Placement","authors":"Sreedevi Valsan Kandenkavil, Kankar Bhattacharya","doi":"10.1109/EPEC.2018.8598297","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598297","url":null,"abstract":"State estimation is widely used in power transmission systems for obtaining a real time network model where measurements of bus voltages and line power flows are available. On the other hand, in distribution systems, with limited availability of measurements, and additional measurements being expensive, careful selection of location for the placement of meters becomes important. The measurement meters typically considered are phasor measurement units (PMUs) and power (PQ) meters. In this work, optimization based approaches are proposed to address the optimal meter placement problem considering different objectives such as minimization of cost, weighted least square (WLS) residual estimate, and a multi-objective function comprising cost and WLS, and the average root mean square error (ARMSE) of the estimated state vector. The various optimization models are tested on a 33-bus distribution feeder, and compared based on meter placement cost and ARMSE of voltage estimates.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132952072","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 : 2018-10-01DOI: 10.1109/EPEC.2018.8598339
H. Lahiji, J. Mohammadi, F. B. Ajaei, Ryan E. Boudreau
An improved Proportional-Derivative (PD) droop control strategy is proposed in this paper for stable operation and improved disturbance response of the islanded inverter-dominated AC microgrid. The proposed strategy (i) is simple and easy to implement., (ii) does not require communication between distributed energy resources, and (iii) significantly improves microgrid dynamic response and stability. Performance of the proposed improved PD droop control strategy is investigated using a detailed and realistic study system, and its effectiveness is verified using extensive simulation studies in the PSCAD/EMTDC software. The study results indicate that the proposed control strategy enables effective voltage and frequency regulation, i.e., limited deviations, in the inverter-dominated microgrid without causing power oscillations between inverters.
{"title":"Damping Power Oscillations in the Inverter-Dominated Microgrid","authors":"H. Lahiji, J. Mohammadi, F. B. Ajaei, Ryan E. Boudreau","doi":"10.1109/EPEC.2018.8598339","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598339","url":null,"abstract":"An improved Proportional-Derivative (PD) droop control strategy is proposed in this paper for stable operation and improved disturbance response of the islanded inverter-dominated AC microgrid. The proposed strategy (i) is simple and easy to implement., (ii) does not require communication between distributed energy resources, and (iii) significantly improves microgrid dynamic response and stability. Performance of the proposed improved PD droop control strategy is investigated using a detailed and realistic study system, and its effectiveness is verified using extensive simulation studies in the PSCAD/EMTDC software. The study results indicate that the proposed control strategy enables effective voltage and frequency regulation, i.e., limited deviations, in the inverter-dominated microgrid without causing power oscillations between inverters.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116320398","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 : 2018-10-01DOI: 10.1109/EPEC.2018.8598282
Radhakrishnan Angamuthu Chinnathambi, Mitch Campion, A. S. Nair, P. Ranganathan
This paper investigates three types of feature selection techniques such as relative importance using Linear Regression (LR), Multivariate Adaptive Regression Splines (MARS), and Random forest (RF) to reduce the forecasts error for the hourly spot price of the Iberian electricity markets. Two pricing datasets of durations three and six months were used to validate the performance of the model. Three different set of features (17, 4, 2) for three and six months duration were used in this study. These selected features were applied to the two-stage hybrid model such as ARIMA-GLM, ARIMA-SVM, and ARIMA- RF. Finally, three variables (or features) that are commonly matched were selected and tested. Considerable reduction in Mean Absolute Percentage Errors (MAPE) values were observed for both three and six-month datasets.
{"title":"Investigation of Price-Feature Selection Algorithms for the Day-Ahead Electricity Markets","authors":"Radhakrishnan Angamuthu Chinnathambi, Mitch Campion, A. S. Nair, P. Ranganathan","doi":"10.1109/EPEC.2018.8598282","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598282","url":null,"abstract":"This paper investigates three types of feature selection techniques such as relative importance using Linear Regression (LR), Multivariate Adaptive Regression Splines (MARS), and Random forest (RF) to reduce the forecasts error for the hourly spot price of the Iberian electricity markets. Two pricing datasets of durations three and six months were used to validate the performance of the model. Three different set of features (17, 4, 2) for three and six months duration were used in this study. These selected features were applied to the two-stage hybrid model such as ARIMA-GLM, ARIMA-SVM, and ARIMA- RF. Finally, three variables (or features) that are commonly matched were selected and tested. Considerable reduction in Mean Absolute Percentage Errors (MAPE) values were observed for both three and six-month datasets.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116382366","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 : 2018-10-01DOI: 10.1109/EPEC.2018.8598288
T. Chu, Alejandro Mayoral-Banos, S. Chen, R. Hartani
Renewable energy penetration can be increased by the use of IoT connected devices. The focus of optimization should be less on individual devices, but rather on the networked effect of millions of devices together. By implementing global optimization on electrical loads, it may be possible to eliminate the need for fossil fuel-based electricity generation in certain jurisdictions.
{"title":"Increasing Renewable Energy Penetration through Strategic Deployment of IoT Devices","authors":"T. Chu, Alejandro Mayoral-Banos, S. Chen, R. Hartani","doi":"10.1109/EPEC.2018.8598288","DOIUrl":"https://doi.org/10.1109/EPEC.2018.8598288","url":null,"abstract":"Renewable energy penetration can be increased by the use of IoT connected devices. The focus of optimization should be less on individual devices, but rather on the networked effect of millions of devices together. By implementing global optimization on electrical loads, it may be possible to eliminate the need for fossil fuel-based electricity generation in certain jurisdictions.","PeriodicalId":265297,"journal":{"name":"2018 IEEE Electrical Power and Energy Conference (EPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133742848","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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