Pub Date : 2017-04-01DOI: 10.1109/ISGT.2017.8086027
Mohamed Abuella, B. Chowdhury
To mitigate the uncertainty of variable renewable resources, two off-the-shelf machine learning tools are deployed to forecast the solar power output of a solar photovoltaic system. The support vector machines generate the forecasts and the random forest acts as an ensemble learning method to combine the forecasts. The common ensemble technique in wind and solar power forecasting is the blending of meteorological data from several sources. In this study though, the present and the past solar power forecasts from several models, as well as the associated meteorological data, are incorporated into the random forest to combine and improve the accuracy of the day-ahead solar power forecasts. The performance of the combined model is evaluated over the entire year and compared with other combining techniques.
{"title":"Random forest ensemble of support vector regression models for solar power forecasting","authors":"Mohamed Abuella, B. Chowdhury","doi":"10.1109/ISGT.2017.8086027","DOIUrl":"https://doi.org/10.1109/ISGT.2017.8086027","url":null,"abstract":"To mitigate the uncertainty of variable renewable resources, two off-the-shelf machine learning tools are deployed to forecast the solar power output of a solar photovoltaic system. The support vector machines generate the forecasts and the random forest acts as an ensemble learning method to combine the forecasts. The common ensemble technique in wind and solar power forecasting is the blending of meteorological data from several sources. In this study though, the present and the past solar power forecasts from several models, as well as the associated meteorological data, are incorporated into the random forest to combine and improve the accuracy of the day-ahead solar power forecasts. The performance of the combined model is evaluated over the entire year and compared with other combining techniques.","PeriodicalId":296398,"journal":{"name":"2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114483973","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 : 2017-04-01DOI: 10.1109/ISGT.2017.8086066
B. Palmintier, Bruce Bugbee, P. Gotseff
Capturing technical and economic impacts of solar photovoltaics (PV) and other distributed energy resources (DERs) on electric distribution systems can require high-time resolution (e.g. 1 minute), long-duration (e.g. 1 year) simulations. However, such simulations can be computationally prohibitive, particularly when including complex control schemes in quasi-steady-state time series (QSTS) simulation. Various approaches have been used in the literature to down select representative time segments (e.g. days), but typically these are best suited for lower time resolutions or consider only a single data stream (e.g. PV production) for selection. We present a statistical approach that combines stratified sampling and bootstrapping to select representative days while also providing a simple method to reassemble annual results. We describe the approach in the context of a recent study with a utility partner. This approach enables much faster QSTS analysis by simulating only a subset of days, while maintaining accurate annual estimates.
{"title":"Representative day selection using statistical bootstrapping for accelerating annual distribution simulations","authors":"B. Palmintier, Bruce Bugbee, P. Gotseff","doi":"10.1109/ISGT.2017.8086066","DOIUrl":"https://doi.org/10.1109/ISGT.2017.8086066","url":null,"abstract":"Capturing technical and economic impacts of solar photovoltaics (PV) and other distributed energy resources (DERs) on electric distribution systems can require high-time resolution (e.g. 1 minute), long-duration (e.g. 1 year) simulations. However, such simulations can be computationally prohibitive, particularly when including complex control schemes in quasi-steady-state time series (QSTS) simulation. Various approaches have been used in the literature to down select representative time segments (e.g. days), but typically these are best suited for lower time resolutions or consider only a single data stream (e.g. PV production) for selection. We present a statistical approach that combines stratified sampling and bootstrapping to select representative days while also providing a simple method to reassemble annual results. We describe the approach in the context of a recent study with a utility partner. This approach enables much faster QSTS analysis by simulating only a subset of days, while maintaining accurate annual estimates.","PeriodicalId":296398,"journal":{"name":"2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125244961","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 : 2017-04-01DOI: 10.1109/ISGT.2017.8085978
Zhiyuan Yang, C. Ten
Islanding has been an emerging issue of a power grid separation which can lead to instability. This paper analyzes islanding issues based on the hypothesized cyberattack through multiple IP-based substations that may trigger consequential tripping by the existing protection infrastructure. We first analyze a power grid using AC power flow to associate contingencies with compromised substations that may lead to instability. The DC power flow is then employed to verify the nonconvergent power flow solutions on the islanding cases where this would immediately determine the instability level of a given grid. A weighting factor is introduced to assess the combinations of potential worst cases that are derived from the initial results of AC power flow analysis. The proposed quantification metric is verified using IEEE test systems.
{"title":"Assessment of hypothesized substation cyberattack using linearized power flow approach","authors":"Zhiyuan Yang, C. Ten","doi":"10.1109/ISGT.2017.8085978","DOIUrl":"https://doi.org/10.1109/ISGT.2017.8085978","url":null,"abstract":"Islanding has been an emerging issue of a power grid separation which can lead to instability. This paper analyzes islanding issues based on the hypothesized cyberattack through multiple IP-based substations that may trigger consequential tripping by the existing protection infrastructure. We first analyze a power grid using AC power flow to associate contingencies with compromised substations that may lead to instability. The DC power flow is then employed to verify the nonconvergent power flow solutions on the islanding cases where this would immediately determine the instability level of a given grid. A weighting factor is introduced to assess the combinations of potential worst cases that are derived from the initial results of AC power flow analysis. The proposed quantification metric is verified using IEEE test systems.","PeriodicalId":296398,"journal":{"name":"2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125412926","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 : 2017-04-01DOI: 10.1109/ISGT.2017.8085993
M. Kamal, Jin Wei
This paper proposes an attack-resilient energy management architecture for a hybrid emergency power system of More-Electric Aircrafts (MEAs). Our proposed architecture develops an Adaptive Neuro-Fuzzy Interference System (ANFIS)-based method to evaluate the integrity of the power output of the fuel-cell in the fuel-cell based hybrid auxiliary power unit (APU), which is vulnerable to the cyber-attacks and critical for the effective energy management and emergency control. Our ANFIS-based method achieves the integrity evaluation by leveraging the real-time measures on the State of Charge (SOC) of the battery, power output of the ultra-capacitor and the load profile. In our simulation, we evaluate the performance of our proposed ANFIS-based method to support the integrity of the Energy Management Strategies (EMSs) used in hybrid emergency power system for more-electric aircrafts by using MATLAB/Simulink. Our simulation results illustrate the effectiveness of our proposed method in effectively evaluating the integrity of critical data and achieving resilient control.
{"title":"Attack-resilient energy management architecture of hybrid emergency power system for more-electric aircrafts","authors":"M. Kamal, Jin Wei","doi":"10.1109/ISGT.2017.8085993","DOIUrl":"https://doi.org/10.1109/ISGT.2017.8085993","url":null,"abstract":"This paper proposes an attack-resilient energy management architecture for a hybrid emergency power system of More-Electric Aircrafts (MEAs). Our proposed architecture develops an Adaptive Neuro-Fuzzy Interference System (ANFIS)-based method to evaluate the integrity of the power output of the fuel-cell in the fuel-cell based hybrid auxiliary power unit (APU), which is vulnerable to the cyber-attacks and critical for the effective energy management and emergency control. Our ANFIS-based method achieves the integrity evaluation by leveraging the real-time measures on the State of Charge (SOC) of the battery, power output of the ultra-capacitor and the load profile. In our simulation, we evaluate the performance of our proposed ANFIS-based method to support the integrity of the Energy Management Strategies (EMSs) used in hybrid emergency power system for more-electric aircrafts by using MATLAB/Simulink. Our simulation results illustrate the effectiveness of our proposed method in effectively evaluating the integrity of critical data and achieving resilient control.","PeriodicalId":296398,"journal":{"name":"2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128640515","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 : 2017-04-01DOI: 10.1109/ISGT.2017.8085985
Ramin Moslemi, Ali Hooshmand, Ratnesh K. Sharma
In recent years, developments of the behind the meter energy managements systems (BTM-EMSs) has been considered as an effective approach to manage the energy usage of the industrial/commercial units. As the one of the most important missions, BTM-EMSs are responsible to reduce the customers' monthly demand peaks which is rewarded by significant decrease in the monthly demand charge. However, the unpredicted behavior of individual electricity loads casts a shadow over the profitability of installing BTM storage units. In this paper, a data driven demand charge management solution (DCMS) is proposed to find and realize the minimum achievable monthly demand peak, also called demand charge threshold (DCT), by appropriate battery storage charging and discharging. The proposed approach uses the last few months load profile to calculate time series of DCTs and then searches for the similar DCT time series of other observed loads stored in the data set. Finally, the obtained similar time series are employed to forecast the DCT of the coming month. The efficiency of the proposed approach is validated through the simulation studies on the real value load data.
{"title":"A data-driven demand charge management solution for behind-the-meter storage applications","authors":"Ramin Moslemi, Ali Hooshmand, Ratnesh K. Sharma","doi":"10.1109/ISGT.2017.8085985","DOIUrl":"https://doi.org/10.1109/ISGT.2017.8085985","url":null,"abstract":"In recent years, developments of the behind the meter energy managements systems (BTM-EMSs) has been considered as an effective approach to manage the energy usage of the industrial/commercial units. As the one of the most important missions, BTM-EMSs are responsible to reduce the customers' monthly demand peaks which is rewarded by significant decrease in the monthly demand charge. However, the unpredicted behavior of individual electricity loads casts a shadow over the profitability of installing BTM storage units. In this paper, a data driven demand charge management solution (DCMS) is proposed to find and realize the minimum achievable monthly demand peak, also called demand charge threshold (DCT), by appropriate battery storage charging and discharging. The proposed approach uses the last few months load profile to calculate time series of DCTs and then searches for the similar DCT time series of other observed loads stored in the data set. Finally, the obtained similar time series are employed to forecast the DCT of the coming month. The efficiency of the proposed approach is validated through the simulation studies on the real value load data.","PeriodicalId":296398,"journal":{"name":"2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127694081","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 : 2017-04-01DOI: 10.1109/ISGT.2017.8085976
Yichen Zhang, A. Melin, S. Djouadi, M. Olama
In this paper, a model reference control based inertia emulation strategy is proposed. Desired inertia can be precisely emulated through this control strategy so that guaranteed performance is ensured. A typical frequency response model with parametrical inertia is set to be the reference model. A measurement at a specific location delivers the information of disturbance acting on the diesel-wind system to the reference model. The objective is for the speed of the diesel-wind system to track the reference model. Since active power variation is dominantly governed by mechanical dynamics and modes, only mechanical dynamics and states, i.e., a swing-engine-governor system plus a reduced-order wind turbine generator, are involved in the feedback control design. The controller is implemented in a three-phase diesel-wind system feed microgrid. The results show exact synthetic inertia is emulated, leading to guaranteed performance and safety bounds.
{"title":"Performance guaranteed inertia emulation for diesel-wind system feed microgrid via model reference control","authors":"Yichen Zhang, A. Melin, S. Djouadi, M. Olama","doi":"10.1109/ISGT.2017.8085976","DOIUrl":"https://doi.org/10.1109/ISGT.2017.8085976","url":null,"abstract":"In this paper, a model reference control based inertia emulation strategy is proposed. Desired inertia can be precisely emulated through this control strategy so that guaranteed performance is ensured. A typical frequency response model with parametrical inertia is set to be the reference model. A measurement at a specific location delivers the information of disturbance acting on the diesel-wind system to the reference model. The objective is for the speed of the diesel-wind system to track the reference model. Since active power variation is dominantly governed by mechanical dynamics and modes, only mechanical dynamics and states, i.e., a swing-engine-governor system plus a reduced-order wind turbine generator, are involved in the feedback control design. The controller is implemented in a three-phase diesel-wind system feed microgrid. The results show exact synthetic inertia is emulated, leading to guaranteed performance and safety bounds.","PeriodicalId":296398,"journal":{"name":"2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","volume":"39 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120917204","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 : 2017-04-01DOI: 10.1109/ISGT.2017.8086089
I. L. Ortega Rivera, C. R. F. Esquivel, C. Camacho, G. Heydt, V. Vittal
Small signal stability enhancement in power systems is often accomplished using supplementary controls. The usual technique is to utilize a power system stabilizer to generate a control signal that is applied to the excitation of a large synchronous generator. In this paper, a different approach is taken: a control signal is obtained using estimated signals from a ‘dynamic state estimator’, and the supplementary control is implemented using a static VAr compensator in the transmission circuit adjacent to a synchronous generator. The details of the design of the dynamic state estimator are given. A simple example is used to illustrate the concept. The paper is intended to show feasibility of a dynamic state estimator to provide a control signal for damping enhancement.
{"title":"A dynamic state estimator for the development of a control signal for power system damping enhancement","authors":"I. L. Ortega Rivera, C. R. F. Esquivel, C. Camacho, G. Heydt, V. Vittal","doi":"10.1109/ISGT.2017.8086089","DOIUrl":"https://doi.org/10.1109/ISGT.2017.8086089","url":null,"abstract":"Small signal stability enhancement in power systems is often accomplished using supplementary controls. The usual technique is to utilize a power system stabilizer to generate a control signal that is applied to the excitation of a large synchronous generator. In this paper, a different approach is taken: a control signal is obtained using estimated signals from a ‘dynamic state estimator’, and the supplementary control is implemented using a static VAr compensator in the transmission circuit adjacent to a synchronous generator. The details of the design of the dynamic state estimator are given. A simple example is used to illustrate the concept. The paper is intended to show feasibility of a dynamic state estimator to provide a control signal for damping enhancement.","PeriodicalId":296398,"journal":{"name":"2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125728394","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 : 2017-04-01DOI: 10.1109/ISGT.2017.8086052
Donghan Shi, Chi Jin, Zhe Zhang, F. Choo, L. Koh, Peng Wang
This paper presents the implementation of a hardware-in-the-loop simulation (HIL) workbench for hybrid AC/DC micro grid. The hybrid AC/DC micro grid integrates both distributed sources and loads in AC/DC systems to respective links directly. For DC system, photovoltaic arrays with boost converter (PVBC), battery with bi-directional DC/DC converter and DC loads are covered. Diesel generator (DGs) and conventional AC loads are included in AC grid. Between AC and DC girds, a four-quadrant operating three phase converter is applied which can act as either an inverter or a rectifier to maintain power balance between two systems. The power units of this hybrid micro grid are simulated with Opal-RT real-time simulator while the control units are implemented on TI TMS320F28335 DSPs. This hardware-in-the-loop simulation workbench offers a good platform for system level control algorithm design and verification in micro grid. And it serves as a basis for future power-hardware-in-the-loop simulation (PHIL) with power stage involved.
{"title":"Implementation of hardware-in-the-loop simulation workbench for a hybrid AC/DC microgrid","authors":"Donghan Shi, Chi Jin, Zhe Zhang, F. Choo, L. Koh, Peng Wang","doi":"10.1109/ISGT.2017.8086052","DOIUrl":"https://doi.org/10.1109/ISGT.2017.8086052","url":null,"abstract":"This paper presents the implementation of a hardware-in-the-loop simulation (HIL) workbench for hybrid AC/DC micro grid. The hybrid AC/DC micro grid integrates both distributed sources and loads in AC/DC systems to respective links directly. For DC system, photovoltaic arrays with boost converter (PVBC), battery with bi-directional DC/DC converter and DC loads are covered. Diesel generator (DGs) and conventional AC loads are included in AC grid. Between AC and DC girds, a four-quadrant operating three phase converter is applied which can act as either an inverter or a rectifier to maintain power balance between two systems. The power units of this hybrid micro grid are simulated with Opal-RT real-time simulator while the control units are implemented on TI TMS320F28335 DSPs. This hardware-in-the-loop simulation workbench offers a good platform for system level control algorithm design and verification in micro grid. And it serves as a basis for future power-hardware-in-the-loop simulation (PHIL) with power stage involved.","PeriodicalId":296398,"journal":{"name":"2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125455243","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 : 2017-04-01DOI: 10.1109/ISGT.2017.8085988
Fathalla Eldali, T. Kirk, David Pinney
Advanced Metering Infrastructure (AMI) has been deployed at over 70% of rural electric cooperatives, and this new data source offers opportunities for valuable applications beyond billing. The National Rural Electric Cooperative Association (NRECA) has developed open source computer software to perform anomaly detection and dynamic power flow analysis with AMI data for the cooperatives (consumer-owned, not-for-profit utilities). We describe this software, discuss challenges to collecting and working with AMI data, and discuss further potential applications such as theft detection and technical loss estimation.
{"title":"Application of AMI data to anomaly detection and dynamic power flow analysis","authors":"Fathalla Eldali, T. Kirk, David Pinney","doi":"10.1109/ISGT.2017.8085988","DOIUrl":"https://doi.org/10.1109/ISGT.2017.8085988","url":null,"abstract":"Advanced Metering Infrastructure (AMI) has been deployed at over 70% of rural electric cooperatives, and this new data source offers opportunities for valuable applications beyond billing. The National Rural Electric Cooperative Association (NRECA) has developed open source computer software to perform anomaly detection and dynamic power flow analysis with AMI data for the cooperatives (consumer-owned, not-for-profit utilities). We describe this software, discuss challenges to collecting and working with AMI data, and discuss further potential applications such as theft detection and technical loss estimation.","PeriodicalId":296398,"journal":{"name":"2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132432476","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 : 2017-04-01DOI: 10.1109/ISGT.2017.8086070
Sven van der Kooij, P. Kempker, H. V. D. Berg, S. Bhulai
In this paper, we consider a residential cluster in which some of the households own home batteries. The battery owners have forecasts of future prices for optimally utilizing the long-term flexibility of the battery. These forecasts become increasingly uncertain the further we look into the future. The home batteries are individually too small to influence prices, collectively however, they have enough capacity to have an influence. We study three possible scenarios: (i) Each household controls its own battery to maximize its own profits; (ii) The battery owners coordinate their strategies to maximize the collective battery profits; (iii) The battery owners coordinate their strategies to maximize the overall cluster profits. For (i) we formulate an algorithm for a single price taker battery based on Stochastic Dynamic Programming. Through simulation with realistic data we find that this solution performs well for one isolated home battery and remains stable when used by every battery in the cluster. Additionally, we formulate an algorithm based on Stochastic Dynamic Programming for scenarios (ii) and (iii). Using simulation with realistic data we find that scenarios (ii) and (iii) outperform scenario (i), and that from a cluster perspective, scenario (iii) is more beneficial than scenario (ii). We conclude that incentives have to be put in place to promote the right use of storage in the future grid.
{"title":"Optimal battery charging in smart grids with price forecasts","authors":"Sven van der Kooij, P. Kempker, H. V. D. Berg, S. Bhulai","doi":"10.1109/ISGT.2017.8086070","DOIUrl":"https://doi.org/10.1109/ISGT.2017.8086070","url":null,"abstract":"In this paper, we consider a residential cluster in which some of the households own home batteries. The battery owners have forecasts of future prices for optimally utilizing the long-term flexibility of the battery. These forecasts become increasingly uncertain the further we look into the future. The home batteries are individually too small to influence prices, collectively however, they have enough capacity to have an influence. We study three possible scenarios: (i) Each household controls its own battery to maximize its own profits; (ii) The battery owners coordinate their strategies to maximize the collective battery profits; (iii) The battery owners coordinate their strategies to maximize the overall cluster profits. For (i) we formulate an algorithm for a single price taker battery based on Stochastic Dynamic Programming. Through simulation with realistic data we find that this solution performs well for one isolated home battery and remains stable when used by every battery in the cluster. Additionally, we formulate an algorithm based on Stochastic Dynamic Programming for scenarios (ii) and (iii). Using simulation with realistic data we find that scenarios (ii) and (iii) outperform scenario (i), and that from a cluster perspective, scenario (iii) is more beneficial than scenario (ii). We conclude that incentives have to be put in place to promote the right use of storage in the future grid.","PeriodicalId":296398,"journal":{"name":"2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128123712","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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