2017 IEEE Power & Energy Society General Meeting, 2017
With the increasing of electric vehicle (EV) adoption in recent years, the impact of EV charging ... more With the increasing of electric vehicle (EV) adoption in recent years, the impact of EV charging activities to the power grid becomes more and more significant. In this article, an optimal scheduling algorithm which combines smart EV charging and V2G gird service is developed to integrate EVs into power grid as distributed energy resources, with improved system cost performance. Specifically, an optimization problem is formulated and solved at each EV charging station according to control signal from aggregated control center and user charging behavior prediction by mean estimation and linear regression. The control center collects distributed optimization results and updates the control signal, periodically. The iteration continues until it converges to optimal scheduling. Experimental result shows this algorithm helps fill the valley and shave the peak in electric load profiles within a microgrid, while the energy demand of individual driver can be satisfied.
2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), 2018
As electric vehicle (EV) adoption is growing year after year, there is no doubt that EVs will occ... more As electric vehicle (EV) adoption is growing year after year, there is no doubt that EVs will occupy a significant portion of transporting vehicle in the near future. Although EVs have benefits for environment, large amount of un-coordinated EV charging will affect the power grid and degrade power quality. To alleviate negative effects of EV charging load and turn them to opportunities, a decentralized real-time control algorithm is developed in this paper to provide optimal scheduling of EV bidirectional charging. To evaluate the performance of the proposed algorithm, numerical simulation is performed based on real-world EV user data, and power flow analysis is carried out to show how the proposed algorithm improve power grid steady state operation.. The results show that the implementation of proposed algorithm can effectively coordinate bi-directional charging by 30% peak load shaving, more than 2% of voltage drop reduction, and 40% transmission line current decrease.
2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06)
A multi-level hierarchical content organization and on-demand delivery framework is presented in ... more A multi-level hierarchical content organization and on-demand delivery framework is presented in this paper. This framework enables engineering content representation and exchange in Mobile Computing Environment. Engineering documents generated by CAD/EDA software are transformed into XML based vector graphics format, decomposed and organized into a semantically grouped hierarchical structure. The client selectively can download and render the decomposed contents based on various rules. The evaluation shows that the proposed framework increases the reliability of content exchange and improves the user-application experience.
2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), 2018
Electric vehicles (EVs) are controllable loads from which distribution grid operator can benefit ... more Electric vehicles (EVs) are controllable loads from which distribution grid operator can benefit in order to minimize the load profile variations. In this paper, we proposed a hierarchical distributed optimization framework such that EV management system (EVMS), as a part of distribution grid management system, minimizes the load variance of the grid in communication with the EV aggregators which control EV charging load of the distribution system feeders. The hierarchical distributed framework, based on alternative direction method of multipliers (ADMM), increases the scalability of the EV charging infrastructure while decreases computational burden. In our proposed approach, each EV aggregator schedules the EV charging profiles of its feeder in a distributed fashion which avoids sharing the EV owners' desired charging profile information and enables privacy preserving. To show the performance of our approach, we apply it to a case study with 100% EV penetration, including 4 feeders and 60 EVs, and show how the load variance of the system and charging cost of individual EVs decrease.
Trending integration of distributed energy resources calls for state estimation at the distributi... more Trending integration of distributed energy resources calls for state estimation at the distribution level for providing reliable power systems information. In contrast to transmission systems, distribution systems are sparsely monitored, and consequently difficult to estimate states. To address measurement scarcity problem in distribution systems, this paper proposes a distribution system state estimation framework that relies on robust pseudo-measurement modeling. User-level metering data is used to train gradient boosting tree models for generating pseudo-measurements. A ladder iterative state estimator is then applied on the pseudo-measurements to solve for system states. Simulation studies are performed on the IEEE 13-bus and 123-bus test feeders. Numerical results demonstrate that the proposed state estimation scheme outperform two benchmark approaches in terms of accuracy (error), consistency (error variance) and robustness (high accuracy subject to load changes).
ļ· Users may download and print one copy of any publication from the public portal for the purpose... more ļ· Users may download and print one copy of any publication from the public portal for the purpose of private study or research. ļ· You may not further distribute the material or use it for any profit-making activity or commercial gain ļ· You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
IET Generation, Transmission & Distribution, 2018
This study, based on a novel control strategy, proposes a sizing method for battery energy storag... more This study, based on a novel control strategy, proposes a sizing method for battery energy storage systems (ESSs), which makes the wind power system more dispatchable. The main objective of the proposed control-based sizing method is to facilitate robust unit commitment by smoothing the output power of wind according to a desired reference. Owing to the energy conversion loss, the controller closely monitors the battery state of charge (SOC) to prevent the battery from being fully discharged during the smoothing procedure. The proposed controller can automatically revise the battery dispatching reference maintaining the SOC constant. In each dispatch interval, model predictive control is used in real time to ensure that the output power follows the generated reference. The ESS is sized according to the simulation results solved by the proposed control strategy. Meanwhile, the performance of the proposed control strategy is validated over actual wind power data.
Journal of Communications Software and Systems, 2014
With an increased number of plug-in electric vehicles (PEVs) on the roads, PEV charging infrastru... more With an increased number of plug-in electric vehicles (PEVs) on the roads, PEV charging infrastructure is gaining an ever-more important role in simultaneously meeting the needs of drivers and those of the local distribution grid. However, the current approach to charging is not well suited to scaling with the PEV market. If PEV adoption continues, charging infrastructure will have to overcome its current shortcomings such as unresponsiveness to grid constraints, low degree of autonomy, and high cost, in order to provide a seamless and configurable interface from the vehicle to the power grid. Among the tasks a charging station will have to accomplish will be PEV identification, charging authorization, dynamic monitoring, and charge control. These will have to be done with a minimum of involvement at a maximum of convenience for a user. The system proposed in this work allows charging stations to become more responsive to grid constraints and gain a degree of networked autonomy by a...
The trending integrations of Battery Energy Storage System (BESS, stationary battery) and Electri... more The trending integrations of Battery Energy Storage System (BESS, stationary battery) and Electric Vehicles (EV, mobile battery) to distribution grids call for advanced Demand Side Management (DSM) technique that addresses the scalability concerns of the system and stochastic availabilities of EVs. Towards this goal, a stochastic DSM is proposed to capture the uncertainties in EVs. Numerical approximation is then used to make the problem tractable. To accelerate the computational speed, the proposed DSM is tightly relaxed to a convex form using second-order cone programming. Furthermore, in light of the continuous increasing problem scale, we use a distributed method with a guaranteed convergence to shift the computational burden to local controllers. To verify the proposed DSM, we use real-world EV data collected on UCLA campus and test the DSM in a modified IEEE distribution benchmark test system. Numerical results demonstrates the correctness and merits of the proposed approach.
2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), 2016
Integration of Electric Vehicles (EV) and Renewable Energy Sources (RES) with Energy Management S... more Integration of Electric Vehicles (EV) and Renewable Energy Sources (RES) with Energy Management System (EMS) in microgrid has been widely discussed. In this paper, we present a two-tier EMS system currently in operation on campus of University of California, Los Angeles. The upper level system, called Super Control Center (SCC), processes grid-wide energy coordination while the lower level systems manage local EV charging and other micro-scale services. We use the concept of Solar-to-Vehicle (S2V) to demonstrate the combined function of the EMS system. For lower level system, we present two queuing algorithms with user priority calculated from user's Solar-Friendliness Index (SFI), i.e., solar composition of user's energy consumption profile. The algorithms have shown capabilities to increase RES utilization, while actively encouraging users to change consumption behavior and promoting higher RES utilization. Simulation results have shown that SCC can reduce up to 73% of the EV load. The lower level EV coordination algorithms are able to increase the RES utilization from 0.504 to 1.
2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), 2016
Energy price has increased rapidly in recent years. As energy being the second greatest cost asid... more Energy price has increased rapidly in recent years. As energy being the second greatest cost aside from raw material, factory owners have shown an increasing awareness of overall energy consumption, with a strong desire to have it reduced. The emergence technologies like solar, wind, and energy storage system have brought a significant saving potential for energy cost but also made it more complex for energy use and management. So far, some solutions have been purposed with a specific quantitative focus while others present general qualitative analysis. However, a quantitative solution with plug-and-play property has not been proposed. Here we discuss key requirements of such system and propose an architecture of manufacturing system that is based on Internet-of-Things (IoT) with a focus on improving energy efficiency through quantitative analysis and production planning. A production planning model has been constructed with various constraints concerning distributed energy resources as well as actual work conditions. The results show that implementation of such system introduce great saving potentials.
The randomness of user behaviors plays a significant role in Electric Vehicle (EV) scheduling pro... more The randomness of user behaviors plays a significant role in Electric Vehicle (EV) scheduling problems, especially when the power supply for Electric Vehicle Supply Equipment (EVSE) is limited. Existing EV scheduling methods do not consider this limitation and assume charging session parameters, such as stay duration and energy demand values, are perfectly known, which is not realistic in practice. In this paper, based on real-world implementations of networked EVSEs on UCLA campus, we developed a predictive scheduling framework, including a predictive control paradigm and a kernel-based session parameter estimator. Specifically, the scheduling service periodically computes for cost-efficient solutions, considering the predicted session parameters, by the adaptive kernel-based estimator with improved estimation accuracies. We also consider the power sharing strategy of existing EVSEs and formulate the virtual load constraint to handle the future EV arrivals with unexpected energy demand. To validate the proposed framework, 20-fold cross validation is performed on the historical dataset of charging behaviors for over one-year period. The simulation results demonstrate that average unit energy cost per kWh can be reduced by 29.42% with the proposed scheduling framework and 66.71% by further integrating solar generations with the given capacity, after the initial infrastructure investment. The effectiveness of kernel-based estimator, virtual load constraint and event-based control scheme are also discussed in detail.
2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), 2016
This paper considers a typical solar installations scenario with limited sensing resources. In th... more This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods that are not suitable for applications requiring longer term prediction. We propose a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hour) solar generation prediction using only the historical power data. In global-tier, we examine two popular heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN). In local-tier, the global-tier results are adaptively updated using real-time analytical residual analysis. The proposed method is validated using the UCLA Microgrid with 35kW of solar generation capacity. Experimental results show that the proposed two-tier prediction method achieves higher accuracy compared to day-ahead predictions while providing the same prediction length. The difference in the overall prediction performance using either weighted k-NN based or NN based in the global-tier are carefully discussed and reasoned. Case studies with a typical sunny day and a cloudy day are carried out to demonstrate the effectiveness of the proposed two-tier predictions.
2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2015
Uncontrolled Electric Vehicle (EV) and Plug-in Hybrid Electric Vehicle (PHEV) charging within a l... more Uncontrolled Electric Vehicle (EV) and Plug-in Hybrid Electric Vehicle (PHEV) charging within a local distribution grid may cause unexpected high load, which further results in power quality degradation. However, coordinating charging behaviors of a number of EVs is a challenging task, which involves not only the deterministic schedule computing but also nondeterministic EV driver behaviors with random arrival time and energy demands. Previous researches in this area rarely consider these random behaviors for real EV users. In this paper, an implementable event-based cost optimal scheduling algorithm (ECSA) is developed, which solves EV scheduling problem by dynamically estimating the stay duration and energy demand for each participating EV user. Datasets, including charging records and time series meter data collected from Electric Vehicle Supply Equipments (EVSEs) in UCLA campus, are utilized for feature extraction. Based on that, proper inference technique is employed to determine parameters within each charging session. In addition, solar generation integration into EVSEs is also considered in our problem formulation. The proposed approaches are tested and validated by real EV charging schedules of users in UCLA campus. The results from simulation experiment demonstrate that the proposed algorithm has a better performance in cost minimization and load shifting compared to existing equal-sharing scheduling algorithm (ESSA).
This paper investigates the Demand Side Management (DSM) in a commercial building microgrid with ... more This paper investigates the Demand Side Management (DSM) in a commercial building microgrid with solar generation, stationary Battery Energy Management System (BESS) and gridable (V2G) Electric Vehicle (EV) integration. Taking into consideration of a comprehensive pricing model, we first formulate a deterministic DSM as a mixed integer linear programming problem, assuming perfect knowledge of the uncertainties in the system. A twostage stochastic DSM is further developed that addresses the stochastic nature in solar generation, loads, EV availabilities and EV energy demands. The proposed DSMs are validated with real solar generation, loads, BESS and EV data using sample average approximation. Detailed case studies show that the stochastic DSM outperforms its deterministic counterpart for cost saving for a wide range of prices, though at the expense of higher computational time. Computational results also demonstrate that moderate number of EVs helps to cut down the overall operation cost, which sheds light on the benefit of future large scale EV integration to smart buildings.
Although various Collaborative Systems have successfully improved enterprise work efficiency, Mob... more Although various Collaborative Systems have successfully improved enterprise work efficiency, Mobile Collaborative Systems, which allow collaboration via wireless network and mobile devices, still lack robust functionality and content representation support. This paper proposes a novel framework for multimedia content generation, representation and delivery, for Mobile Collaboration. A unified file format and a message queue management middleware for heterogeneous computing environment designed and employed are also discussed.
Journal of Computing and Information Science in Engineering, 2006
A two-dimensional (2D) graphics hierarchical representation framework and an on-demand content de... more A two-dimensional (2D) graphics hierarchical representation framework and an on-demand content delivery mechanism for facilitating mobile engineering collaboration are presented in this paper. Multi-level graphics content sub-division is utilized to transform large engineering graphics into multiple levels of Scalable Vector Graphics (SVG) content. The hierarchical structure of the SVG content that maintains the relationship between the sub-divided content is formed during the process of sub-division. The divided content is selectively delivered and rendered on the mobile devices in an on-demand fashion. A prototypical system of the proposed approach is implemented and the performance of the framework is evaluated.
2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2014
A microgrid can be characterized by its integration of distributed energy resources and controlla... more A microgrid can be characterized by its integration of distributed energy resources and controllable loads. Such integration brings unique challenges to the microgrid management and control which can be significantly different from conventional power systems. Therefore, a conventional energy management system (EMS) needs to be redesigned with consideration of the unique characteristics of microgrids. To this end, we propose a microgrid EMS named a microgrid platform (MP). We take into account all the functional requirements of a microgrid EMS (i.e., forecast, optimization, data analysis, and human-machine interface) and address the engineering challenges (i.e., flexibility, extensibility, and interoperability) in the design and development of the MP. Moreover, we deploy the prototype system and conduct experiments to evaluate the microgrid management and control in real-world settings at the UCLA Smart Grid Energy Research Center. Our experimental results demonstrate that the MP is able to manage various devices in the testbed, interact with the external systems, and perform optimal energy scheduling and demand response.
2017 IEEE Power & Energy Society General Meeting, 2017
With the increasing of electric vehicle (EV) adoption in recent years, the impact of EV charging ... more With the increasing of electric vehicle (EV) adoption in recent years, the impact of EV charging activities to the power grid becomes more and more significant. In this article, an optimal scheduling algorithm which combines smart EV charging and V2G gird service is developed to integrate EVs into power grid as distributed energy resources, with improved system cost performance. Specifically, an optimization problem is formulated and solved at each EV charging station according to control signal from aggregated control center and user charging behavior prediction by mean estimation and linear regression. The control center collects distributed optimization results and updates the control signal, periodically. The iteration continues until it converges to optimal scheduling. Experimental result shows this algorithm helps fill the valley and shave the peak in electric load profiles within a microgrid, while the energy demand of individual driver can be satisfied.
2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), 2018
As electric vehicle (EV) adoption is growing year after year, there is no doubt that EVs will occ... more As electric vehicle (EV) adoption is growing year after year, there is no doubt that EVs will occupy a significant portion of transporting vehicle in the near future. Although EVs have benefits for environment, large amount of un-coordinated EV charging will affect the power grid and degrade power quality. To alleviate negative effects of EV charging load and turn them to opportunities, a decentralized real-time control algorithm is developed in this paper to provide optimal scheduling of EV bidirectional charging. To evaluate the performance of the proposed algorithm, numerical simulation is performed based on real-world EV user data, and power flow analysis is carried out to show how the proposed algorithm improve power grid steady state operation.. The results show that the implementation of proposed algorithm can effectively coordinate bi-directional charging by 30% peak load shaving, more than 2% of voltage drop reduction, and 40% transmission line current decrease.
2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06)
A multi-level hierarchical content organization and on-demand delivery framework is presented in ... more A multi-level hierarchical content organization and on-demand delivery framework is presented in this paper. This framework enables engineering content representation and exchange in Mobile Computing Environment. Engineering documents generated by CAD/EDA software are transformed into XML based vector graphics format, decomposed and organized into a semantically grouped hierarchical structure. The client selectively can download and render the decomposed contents based on various rules. The evaluation shows that the proposed framework increases the reliability of content exchange and improves the user-application experience.
2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), 2018
Electric vehicles (EVs) are controllable loads from which distribution grid operator can benefit ... more Electric vehicles (EVs) are controllable loads from which distribution grid operator can benefit in order to minimize the load profile variations. In this paper, we proposed a hierarchical distributed optimization framework such that EV management system (EVMS), as a part of distribution grid management system, minimizes the load variance of the grid in communication with the EV aggregators which control EV charging load of the distribution system feeders. The hierarchical distributed framework, based on alternative direction method of multipliers (ADMM), increases the scalability of the EV charging infrastructure while decreases computational burden. In our proposed approach, each EV aggregator schedules the EV charging profiles of its feeder in a distributed fashion which avoids sharing the EV owners' desired charging profile information and enables privacy preserving. To show the performance of our approach, we apply it to a case study with 100% EV penetration, including 4 feeders and 60 EVs, and show how the load variance of the system and charging cost of individual EVs decrease.
Trending integration of distributed energy resources calls for state estimation at the distributi... more Trending integration of distributed energy resources calls for state estimation at the distribution level for providing reliable power systems information. In contrast to transmission systems, distribution systems are sparsely monitored, and consequently difficult to estimate states. To address measurement scarcity problem in distribution systems, this paper proposes a distribution system state estimation framework that relies on robust pseudo-measurement modeling. User-level metering data is used to train gradient boosting tree models for generating pseudo-measurements. A ladder iterative state estimator is then applied on the pseudo-measurements to solve for system states. Simulation studies are performed on the IEEE 13-bus and 123-bus test feeders. Numerical results demonstrate that the proposed state estimation scheme outperform two benchmark approaches in terms of accuracy (error), consistency (error variance) and robustness (high accuracy subject to load changes).
ļ· Users may download and print one copy of any publication from the public portal for the purpose... more ļ· Users may download and print one copy of any publication from the public portal for the purpose of private study or research. ļ· You may not further distribute the material or use it for any profit-making activity or commercial gain ļ· You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
IET Generation, Transmission & Distribution, 2018
This study, based on a novel control strategy, proposes a sizing method for battery energy storag... more This study, based on a novel control strategy, proposes a sizing method for battery energy storage systems (ESSs), which makes the wind power system more dispatchable. The main objective of the proposed control-based sizing method is to facilitate robust unit commitment by smoothing the output power of wind according to a desired reference. Owing to the energy conversion loss, the controller closely monitors the battery state of charge (SOC) to prevent the battery from being fully discharged during the smoothing procedure. The proposed controller can automatically revise the battery dispatching reference maintaining the SOC constant. In each dispatch interval, model predictive control is used in real time to ensure that the output power follows the generated reference. The ESS is sized according to the simulation results solved by the proposed control strategy. Meanwhile, the performance of the proposed control strategy is validated over actual wind power data.
Journal of Communications Software and Systems, 2014
With an increased number of plug-in electric vehicles (PEVs) on the roads, PEV charging infrastru... more With an increased number of plug-in electric vehicles (PEVs) on the roads, PEV charging infrastructure is gaining an ever-more important role in simultaneously meeting the needs of drivers and those of the local distribution grid. However, the current approach to charging is not well suited to scaling with the PEV market. If PEV adoption continues, charging infrastructure will have to overcome its current shortcomings such as unresponsiveness to grid constraints, low degree of autonomy, and high cost, in order to provide a seamless and configurable interface from the vehicle to the power grid. Among the tasks a charging station will have to accomplish will be PEV identification, charging authorization, dynamic monitoring, and charge control. These will have to be done with a minimum of involvement at a maximum of convenience for a user. The system proposed in this work allows charging stations to become more responsive to grid constraints and gain a degree of networked autonomy by a...
The trending integrations of Battery Energy Storage System (BESS, stationary battery) and Electri... more The trending integrations of Battery Energy Storage System (BESS, stationary battery) and Electric Vehicles (EV, mobile battery) to distribution grids call for advanced Demand Side Management (DSM) technique that addresses the scalability concerns of the system and stochastic availabilities of EVs. Towards this goal, a stochastic DSM is proposed to capture the uncertainties in EVs. Numerical approximation is then used to make the problem tractable. To accelerate the computational speed, the proposed DSM is tightly relaxed to a convex form using second-order cone programming. Furthermore, in light of the continuous increasing problem scale, we use a distributed method with a guaranteed convergence to shift the computational burden to local controllers. To verify the proposed DSM, we use real-world EV data collected on UCLA campus and test the DSM in a modified IEEE distribution benchmark test system. Numerical results demonstrates the correctness and merits of the proposed approach.
2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), 2016
Integration of Electric Vehicles (EV) and Renewable Energy Sources (RES) with Energy Management S... more Integration of Electric Vehicles (EV) and Renewable Energy Sources (RES) with Energy Management System (EMS) in microgrid has been widely discussed. In this paper, we present a two-tier EMS system currently in operation on campus of University of California, Los Angeles. The upper level system, called Super Control Center (SCC), processes grid-wide energy coordination while the lower level systems manage local EV charging and other micro-scale services. We use the concept of Solar-to-Vehicle (S2V) to demonstrate the combined function of the EMS system. For lower level system, we present two queuing algorithms with user priority calculated from user's Solar-Friendliness Index (SFI), i.e., solar composition of user's energy consumption profile. The algorithms have shown capabilities to increase RES utilization, while actively encouraging users to change consumption behavior and promoting higher RES utilization. Simulation results have shown that SCC can reduce up to 73% of the EV load. The lower level EV coordination algorithms are able to increase the RES utilization from 0.504 to 1.
2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), 2016
Energy price has increased rapidly in recent years. As energy being the second greatest cost asid... more Energy price has increased rapidly in recent years. As energy being the second greatest cost aside from raw material, factory owners have shown an increasing awareness of overall energy consumption, with a strong desire to have it reduced. The emergence technologies like solar, wind, and energy storage system have brought a significant saving potential for energy cost but also made it more complex for energy use and management. So far, some solutions have been purposed with a specific quantitative focus while others present general qualitative analysis. However, a quantitative solution with plug-and-play property has not been proposed. Here we discuss key requirements of such system and propose an architecture of manufacturing system that is based on Internet-of-Things (IoT) with a focus on improving energy efficiency through quantitative analysis and production planning. A production planning model has been constructed with various constraints concerning distributed energy resources as well as actual work conditions. The results show that implementation of such system introduce great saving potentials.
The randomness of user behaviors plays a significant role in Electric Vehicle (EV) scheduling pro... more The randomness of user behaviors plays a significant role in Electric Vehicle (EV) scheduling problems, especially when the power supply for Electric Vehicle Supply Equipment (EVSE) is limited. Existing EV scheduling methods do not consider this limitation and assume charging session parameters, such as stay duration and energy demand values, are perfectly known, which is not realistic in practice. In this paper, based on real-world implementations of networked EVSEs on UCLA campus, we developed a predictive scheduling framework, including a predictive control paradigm and a kernel-based session parameter estimator. Specifically, the scheduling service periodically computes for cost-efficient solutions, considering the predicted session parameters, by the adaptive kernel-based estimator with improved estimation accuracies. We also consider the power sharing strategy of existing EVSEs and formulate the virtual load constraint to handle the future EV arrivals with unexpected energy demand. To validate the proposed framework, 20-fold cross validation is performed on the historical dataset of charging behaviors for over one-year period. The simulation results demonstrate that average unit energy cost per kWh can be reduced by 29.42% with the proposed scheduling framework and 66.71% by further integrating solar generations with the given capacity, after the initial infrastructure investment. The effectiveness of kernel-based estimator, virtual load constraint and event-based control scheme are also discussed in detail.
2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), 2016
This paper considers a typical solar installations scenario with limited sensing resources. In th... more This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods that are not suitable for applications requiring longer term prediction. We propose a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hour) solar generation prediction using only the historical power data. In global-tier, we examine two popular heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN). In local-tier, the global-tier results are adaptively updated using real-time analytical residual analysis. The proposed method is validated using the UCLA Microgrid with 35kW of solar generation capacity. Experimental results show that the proposed two-tier prediction method achieves higher accuracy compared to day-ahead predictions while providing the same prediction length. The difference in the overall prediction performance using either weighted k-NN based or NN based in the global-tier are carefully discussed and reasoned. Case studies with a typical sunny day and a cloudy day are carried out to demonstrate the effectiveness of the proposed two-tier predictions.
2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2015
Uncontrolled Electric Vehicle (EV) and Plug-in Hybrid Electric Vehicle (PHEV) charging within a l... more Uncontrolled Electric Vehicle (EV) and Plug-in Hybrid Electric Vehicle (PHEV) charging within a local distribution grid may cause unexpected high load, which further results in power quality degradation. However, coordinating charging behaviors of a number of EVs is a challenging task, which involves not only the deterministic schedule computing but also nondeterministic EV driver behaviors with random arrival time and energy demands. Previous researches in this area rarely consider these random behaviors for real EV users. In this paper, an implementable event-based cost optimal scheduling algorithm (ECSA) is developed, which solves EV scheduling problem by dynamically estimating the stay duration and energy demand for each participating EV user. Datasets, including charging records and time series meter data collected from Electric Vehicle Supply Equipments (EVSEs) in UCLA campus, are utilized for feature extraction. Based on that, proper inference technique is employed to determine parameters within each charging session. In addition, solar generation integration into EVSEs is also considered in our problem formulation. The proposed approaches are tested and validated by real EV charging schedules of users in UCLA campus. The results from simulation experiment demonstrate that the proposed algorithm has a better performance in cost minimization and load shifting compared to existing equal-sharing scheduling algorithm (ESSA).
This paper investigates the Demand Side Management (DSM) in a commercial building microgrid with ... more This paper investigates the Demand Side Management (DSM) in a commercial building microgrid with solar generation, stationary Battery Energy Management System (BESS) and gridable (V2G) Electric Vehicle (EV) integration. Taking into consideration of a comprehensive pricing model, we first formulate a deterministic DSM as a mixed integer linear programming problem, assuming perfect knowledge of the uncertainties in the system. A twostage stochastic DSM is further developed that addresses the stochastic nature in solar generation, loads, EV availabilities and EV energy demands. The proposed DSMs are validated with real solar generation, loads, BESS and EV data using sample average approximation. Detailed case studies show that the stochastic DSM outperforms its deterministic counterpart for cost saving for a wide range of prices, though at the expense of higher computational time. Computational results also demonstrate that moderate number of EVs helps to cut down the overall operation cost, which sheds light on the benefit of future large scale EV integration to smart buildings.
Although various Collaborative Systems have successfully improved enterprise work efficiency, Mob... more Although various Collaborative Systems have successfully improved enterprise work efficiency, Mobile Collaborative Systems, which allow collaboration via wireless network and mobile devices, still lack robust functionality and content representation support. This paper proposes a novel framework for multimedia content generation, representation and delivery, for Mobile Collaboration. A unified file format and a message queue management middleware for heterogeneous computing environment designed and employed are also discussed.
Journal of Computing and Information Science in Engineering, 2006
A two-dimensional (2D) graphics hierarchical representation framework and an on-demand content de... more A two-dimensional (2D) graphics hierarchical representation framework and an on-demand content delivery mechanism for facilitating mobile engineering collaboration are presented in this paper. Multi-level graphics content sub-division is utilized to transform large engineering graphics into multiple levels of Scalable Vector Graphics (SVG) content. The hierarchical structure of the SVG content that maintains the relationship between the sub-divided content is formed during the process of sub-division. The divided content is selectively delivered and rendered on the mobile devices in an on-demand fashion. A prototypical system of the proposed approach is implemented and the performance of the framework is evaluated.
2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2014
A microgrid can be characterized by its integration of distributed energy resources and controlla... more A microgrid can be characterized by its integration of distributed energy resources and controllable loads. Such integration brings unique challenges to the microgrid management and control which can be significantly different from conventional power systems. Therefore, a conventional energy management system (EMS) needs to be redesigned with consideration of the unique characteristics of microgrids. To this end, we propose a microgrid EMS named a microgrid platform (MP). We take into account all the functional requirements of a microgrid EMS (i.e., forecast, optimization, data analysis, and human-machine interface) and address the engineering challenges (i.e., flexibility, extensibility, and interoperability) in the design and development of the MP. Moreover, we deploy the prototype system and conduct experiments to evaluate the microgrid management and control in real-world settings at the UCLA Smart Grid Energy Research Center. Our experimental results demonstrate that the MP is able to manage various devices in the testbed, interact with the external systems, and perform optimal energy scheduling and demand response.
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