IEEE Green Energy and Smart Systems Conference
Long Beach, CA


Call for Papers: IEEE Green Energy and Smart Systems Conference (IGESSC 2019)

Date: November 4-5, 2019

Location: Pyramid, CSULB, Long Beach, CA 90840, USA

The proposed conference intends to bring together researchers and practitioners from relevant fields to present and disseminate ongoing research for smart systems, sustainable and green energy technologies. This year the focus would be on the application of data science in smart grid and smart systems, Internet of Things, Sustainable and Secured Systems, Optimized Power Grids with Battery, and Intelligent Transportation Systems.

Conference has the following topics:

Smart Systems

  • Machine Learning for Smart Systems: Smart Building, Smart Campus, and Smart City
  • Communication, Networking, and Digital Signal Processing
  • Internet-of-things and Green Computation
  • Smart Grid, Big Data, Security Cloud Computing
  • Intelligent Transportation Systems
  • Data-Driven Complex Systems for Intrusion, Detection, and Optimization

Green Energy and Power

  • Safe and Resiliency through Community-Scale Micro-Grids
  • Modeling Cyber-physical Smart Grids and Demand Response Management
  • Energy Storage, Fuel Cell Technologies, and Trends
  • Electrical Vehicles, Grid to Vehicle (G2V) and Vehicle to Grid (V2G)
  • Solar Power Systems, Energy and Water Management and Sustainability
  • Secure and Advanced Metering Infrastructure


Submission & Publication: All accepted papers in Technical Track will be published in IGESSC proceedings by IEEE. All submissions should be formatted according to the IEEE standard Word or Latex Template.

Technical Track:

Full paper (4-6 pages) submission deadline: June 10 July 31, 2019 (Mon)

Full paper acceptance notification: Aug. 19, 2019 (Mon)

Camera-ready version: Oct 14, 2019 (Mon)

Student Track for Poster Presentation Only (Not for publication in IEEE Xplore):

High quality research papers are encouraged to submit to Technical Track
Submission deadline for student paper for posters (2-4 pages): June 10, 2019 (Mon)
Acceptance notification: Aug. 19, 2019 (Mon)
Poster-Ready Version: Oct 14, 2019 (Mon)

1-day Workshop 11/5/2019 (Tu): Smart and/or Green Systems (Presentation and/or Hands-on only)

IGESSC 2019 Final Program

 Conference and Workshop

November 4-5, 2019 (Monday)
8:30 AM – 5:00 PM
Location:      The Pointe, Pyramid, California State University, Long Beach


Track A

Time Paper# Title – Authors Session Session chair
9:00 Keynote Innovations in Signals and Systems

Kung Yao (University of California – Los Angeles)

10:00 AM 1570562442 MiniDES – Lightweight Python Simulation Framework for Interconnected Renewable Resources

– Jonas Schlund; Julian Reisenweber; Reinhard German (Friedrich-Alexander-University Erlangen-N¨urnberg, Germany)

Power Systems Tracy Toups
10:25 AM Break
10:45 AM 1570568152 Designing a Dynamic Balancing Compensator for Unbalanced Loads in a Three Phase Power System

– Tracy Toups (California State University, Sacramento, USA)

Power Systems Tracy Toups
11:10 AM 1570561898 Programmable Turbine Failsafe System for Pico-Hydroelectric Power in the Nepal Himalayas

– Hsi-Jen James Yeh; Rick Sturdivant (Azusa Pacific University, USA); Mark Stambaugh (RIDS-USA); Alex Zahnd (RIDS-Switzerland)

Power Systems Tracy Toups
11:35 AM 1570564949 Analyzing a Compenastor for Unbalanced Loading due to Electric Vehicle Integration

– Tracy Toups (California State University, Sacramento, USA)

Power Systems Tracy Toups
12:00 PM Lunch
12:30 PM Greeting Toward  greener and Cleaner Campus

– Dr. Jane Conoley,  President of CSULB

12:45 PM Luncheon Speaker #1 Analytics for Industrial Internet Applications

Piero P. Bonissone (Piero P Bonissone Analytics, LLC, USA)

1:25 PM Luncheon Speaker #2 A Walk with Neural Networks: Interesting Behaviors in Training, Inference, Pruning, and Controlling

– Rosanne Liu, (Uber AI, USA)

2:05 PM Break
2:20 PM 1570561143 Dynamic Simulation and Control of a Battery Energy Storage System

Md Arifujjaman and Manuel Avendaño (Southern California Edison, USA)

Energy Storage Systems Hossein Jula
2:45 PM 1570566367 Multi-function Energy Storage System for Smart Grid

– Jianlong Bai(China State Shipbuilding Corp., China)

Energy Storage Systems Hossein Jula
3:10 PM 1570572131 Energy Management for a Community Microgrid in the city of Boston

– Sohad Abu-elzait and Robert Parkin (University of Massachusetts, Lowell, USA)

Energy Storage Systems Hossein Jula
3:35 PM 1570570263 Stand-Alone Electric Vehicle Charging Station Using FPGA Microcontroller

– Ammar Natsheh, Eman AlShammar, Maryam Alkhaja, Noora AlBlooshi, Nguyen Hai, Salama Almheiri, Salma AlAsaad, and Shamma Ismail (Dubai Women’s College, United Arab Emirates)

Energy Storage Systems Hossein Jula
4:00 PM 1570561145 Dynamic Simulation and Control of an Induction Generator Based Wind Energy Conversion System

Md Arifujjaman and Manuel Avendaño (Southern California Edison, USA)

Power Systems Hossein Jula
4:25 PM Student Poster Award, Best Paper Award, and Service Award Ceremony Hui Yuan,

Greg Smith


Track B

Time Paper # Title – Authors Session Session chair
10:00 AM 1570569953 Deep Convolutional Neural Networks for Shark Behavior Analysis

– Wenlu Zhang; Anthony Martinez; Emily Meese; Christopher G. Lowe; Yu Yang; Hen-Geul Yeh (California State University, Long Beach, USA)



Yu Yang
10:25 AM Break
10:45 AM 1570570333 Classification of Shark Behaviors using K-Nearest Neighbors

– Sainesh Karan, Emily Meese, Yu Yang, Hen-Geul Yeh, Christopher G. Lowe, Wenlu Zhang   (California State University, Long Beach, USA)



Wenlu Zhang
11:10 AM 1570569089 Statistical and Deep Learning Methods for Electric Load Forecasting in Multiple Water Utility Sites

– J Yusuf; Rumana Binte Faruque; A S M Jahid Hasan; Sadrul Ula

(University of California, Riverside, USA)



Wenlu Zhang
11:35 AM 1570570034 Performance Comparison of Machine Learning Methods with Distinct Features to Estimate Battery SOC

– ASM Jahid Hasan; J Yusuf; Rumana Binte Faruque (University of California, Riverside, USA)



Wenlu Zhang
12:00 Noon Lunch
12:30 PM Greeting


Toward  greener and Cleaner Campus

– Dr. Jane Conoley,  President of CSULB

12:45 PM Luncheon Speaker #1 Analytics for Industrial Internet Applications – Piero P. Bonissone (Piero P Bonissone Analytics, LLC, USA)
1:25 PM Luncheon Speaker #2 Rosanne Liu, (Uber AI, USA)
2:05 PM Break
2:20 PM 1570566256 AI-enabled IoT, Network Complexity and 5G

– Igor Alvarado (National Instruments Corp., USA)

Communications & Digital Signal Processing Sean Kwon
2:45 PM 1570572165 Achievable Capacity of Multi-Polarization MIMO (MP-MIMO) toward 6G Wireless Communications

– Son Doan; Sean (Seok-Chul) Kwon (CSULB, USA)

Communications & Digital Signal Processing Sean Kwon
3:10 PM 1570583781 A traveling wave fault location system based on wavelet transformation

– Yanghao Zheng (Shenzhen University, P.R. China); Yuan Xu and Zhiyong Xiao (Shenzhen Technology University, P.R. China)



Sean Kwon
3:35 PM  


 Transportation emissions and EVs in Smart cities:A study of multi-dimensional emission data

– Ahmed Lasisi, Nii Attoh-Okine, Grace Ashley (University of Delaware, USA), Fuad Ali (Amtrak Railway, USA)



Sean Kwon
4:00 PM 1570562053 Designing a Robust Gasoline Blending Recipe Via Scenario-based Optimization

– Yu Yang and Loren dela Rosa (California State University, Long Beach, USA)



Sean Kwon



Workshop – November 5, 2019 (Tuesday)

9:00 AM – 5:00 PM at Engineering and Computer Science (ECS) 312, California State University, Long Beach


Time Workshop Presentation Title Session Session Chair
9:00 AM 1 A case study of implementation of California Energy Commission (CEC) and California Air Resources Board (CARB) grants to promote Zero emission Vehicles at the Port of Long Beach

– William Stone (Long Beach Port, USA)

Smart Systems Sean Kwon
9:35 AM 2 Rapid Prototyping of Wireless Communications Systems with the NI LabVIEW Communications System Design Suite

– Igor Alvarado ( National Instruments Corp., USA)

Smart Systems Sean Kwon
10:10am Break
10:20 AM 3 Modeling of Distribution Volt/VAR Control

– Navid Ahmadiyeh (Southern California Edison, USA)

Power Systems Sam Jalali
10:55 AM 4 Simulating the Operation of Emerging Distribution Businesses

– Manuel Avendaño (Southern California Edison, USA)

Power Systems Sam Jalali
11:30 AM 5 PV Curtailment as a Mitigation Strategy in Distribution Feeders

– Matthew Kedis (Southern California Edison, USA)

Power Systems Sam Jalali
12:05 PM Lunch
1:00 PM 6 Machine learning using MATLAB

– Reece Teramoto (MathWorks, USA)

Smart Systems Henry Yeh
3:30 PM Break
3:45 PM 7 Deep Learning using MATLAB

– Reece Teramoto (MathWorks, USA)

Smart Systems Henry Yeh
4:45PM 8 Machine learning and Deep Learning using MATLAB, Open Q &A

– Reece Teramoto (MathWorks, USA)

Smart Systems Henry Yeh


Keynote Speaker:

Innovations in Signals and Systems


In recent years, tremendous progress has been made in fast computational resources, high speed wireless communications, and smart system operations and optimizations. Since many of these operations are being conducted on portable devices, low-power and low-energy requirements are of necessity.

In this talk, we will discuss some of these issues already available and will be even more prevalent in the near-future. In the last seventy years, theoretical advances in coding, detection, and estimation both at the single user as well as at the web-based levels can achieve near-optimum transmission and processing results.

Given a weak signal in the presence of moderately strong noisy or interference disturbances, full classical maximum-likelihood decoding and detection and estimation techniques, not to mention more modern adaptive machine-learning and non-convex optimized algorithms can be used. Computational requirements of these tools that were considered too demanding in terms of computer memories or processing speeds can be achieved with readily available pipelined/parallel processors. If a data or signal is worthy of extraction, it can be obtained with modest efforts. Data (including a large amount of data) can be moved either by latest optical fiber channels or near-future multi-hop 5G wireless systems. To achieve all these capabilities, not only do we need “smart” algorithms and processing, but also require low-power devices presently available or will be available soon.

As a student of system-theory (fifty or sixty years ago), these optimum or near-optimum decoding, detection and estimation tools were available only on main-frame computer processing running some higher-languages operating systems (using Fortran, C-‘s, and Matlabs.). Today, we can almost perform many of these operations on battery-based processors; if not today, but will be in the near-future.

Large amount of science-motivated field collected data on land or over the sea can be transmitted to a node with work-station processing capabilities interpreting the scientific efforts of these data.
In conclusion, smart algorithms and low-power and low-energy devices are revolutionizing the technical world and probably will be benefiting human lives.

Speaker’s Biography:


Kung Yao received the B.S. (Summa Cum Laude) and Ph.D. degrees in EE from Princeton University. He was a NAS-NRC Post-Doctoral Fellow at the University of California, Berkeley.  He has served as an Assistant Dean of the School of Engineering at UCLA.  Presently, he is a Distinguished Professor Emeritus in the ECE Department at UCLA.  His professional interests include wireless fading channel modelling, digital communication theory, acoustic beamforming, sensor array system, and simulation.  He received the IEEE Signal Processing Society’s 1993 Award in VLSI Signal Processing, the 2008 IEEE Communications Society/Information Theory Society Joint Paper Award, and the 2012 JCN Journal Best Paper Award.  He is a co-author of “Detection and Estimation in Communication and Radar Systems”, Cambridge Univ. Press, in 2013 and the author of “Signal Processing Algorithms for Communication and Radar Systems,” Cambridge Univ. Press, 2019.  He also has extensive practical system experiences in sensor networks, satellite-wireless communications, radar system, and systolic and microphone array designs.  He is a Life Fellow of the IEEE.


Luncheon Speakers:

Topic: Toward greener and Cleaner Campus

Abstract and Bio:

Dr. Jane Close Conoley serves as the seventh President of Long Beach State University.  Prior to assuming this role she was the interim Chancellor of the University of California Riverside.  She has also held leadership positions at the University of California Santa Barbara, Texas A&M University, and at the University of Nebraska Lincoln.  Dr. Conoley is the author, co-author or editor of over one hundred thirty books, articles, and book chapters.   Her latest book “Positive Psychology and Family Therapy” was co-authored with her husband, Dr. Collie W. Conoley.

She serves on numerous journal editorial and community service boards.  She has received research, teaching and service honors during her career.  Both the American Psychological Association and the Association of Psychological Science honored Dr. Conoley with fellow status.  Her research and development efforts in school safety, teacher quality, and student achievement have been supported by over 50 million dollars in external federal, state, and private funds.

Topic: Analytics for Industrial Internet Applications

Abstract: The Industrial Internet is the third disruptive wave, after the Industrial and the Internetrevolutions. It is transforming our industries, just like the Internet revolution transformed our commerce.  In this new context, we face a combination of hyper-connected intelligent machines, interacting with other machines and people, and generating large amounts data that need to be analyzed by descriptive, predictive, and prescriptive models.  As a result, we see the resurgence of analytics as a key differentiator for creating new services, the emergence of cloud computing as an enabling technology for service delivery, and the growth of crowdsourcing as a new phenomenon in which people play critical roles in creating information and shaping decisions in a variety of problems. We explore the intersection of these three concepts from the perspective of a machine-learning researcher and show how his job and roles have evolved over time.

In the past, analytic model creation was an artisanal process, as models were handcrafted by experienced, knowledgeable model-builders. More recently, the use of meta-heuristics, such as evolutionary algorithms, has provided us with limited levels of automation in model building and maintenance.  In the short future, we expect data-driven analytic models to become a commodity. We envision having access to a large number of data-driven models, obtained by a combination of crowdsourcing, cloud-based evolutionary algorithms, outsourcing, in-house development, and legacy models. In this context, the critical issue will be model ensemble selection and fusion, rather than model generation.

First, we will review the application of data-driven analytic models to assets diagnostics and prognostics, such as aircraft engines, medical imaging devices, and locomotives.  We will cover a case study on prediction of remaining useful life for each unit in a fleet of locomotives using fuzzy models.

Then we will explore the evolution of analytic models with the advent of cloud computing, and propose the use of customized model ensembles on demand, inspired by Lazy Learning. This approach is agnostic with respect to the origin of the models, making it scalable and suitable for a variety of applications.  We successfully tested this approach in a regression problem for a power plant management application, using two different sources of models: bootstrapped neural networks, and GP-created symbolic regression models evolved in the cloud. We will also present results on the fusion of models for FlyQuest, a GE-sponsored Kaggle competition in which we crowd-sourced the generation of models predicting the estimated runway and gateway arrival (ERA, EGA) over a month of US flights.

Finally, we will explore research trends, challenges and opportunities for Machine Learning techniques in this emerging context of big data and cloud computing.



Dr. Piero P. Bonissone is an independent consultant specialized in the use of analytics for Industrial Internet applications. He provides consulting services in machine learning (ML) and analytic applications, ranging from project definition and risk abatement, project evaluation, transition from development to deployment, and model maintenance. During 2018, he was an Advanced Analytics advisor for Stanley Black Decker (SBD). In 2017, he defined and shaped new projects for GE Oil & Gas, prior to their integration with Baker Hughes Inc. (BHI). During the previous two years, he was an Advanced Analytics Advisor for Schlumberger (SLB), where he played a key role in SLB Digital Transformation, such as part forecasting, market intelligence, PHM projects related to equipment reliability, etc. He was also a consultant for DIGILE and Ford Motor Co.

A former Chief Scientist at GE Global Research (GE GR), where he retired in 2014 after 34 years of service, Dr. Bonissone has been a pioneer in the field of analytics, machine learning, fuzzy logic, AI, and soft computing applications.

He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), the Association for the Advancement of Artificial Intelligence (AAAI), the International Fuzzy Systems Association (IFSA), and a Coolidge Fellow at GE Global Research. He received the 2012 Fuzzy Systems Pioneer Award from the IEEE Computational Intelligence Society (CIS). From 2010 to 2015, he chaired the Scientific Committee of the European Centre for Soft Computing. In 2008 he received the II Cajastur International Prize for Soft Computing from the European Centre of Soft Computing. In 2005 he received the Meritorious Service Award from the IEEE CIS. He has received two Dushman Awards from GE Global Research. He served as Editor-in-Chief of the International Journal of Approximate Reasoning for 13 years. He is in the editorial board of five technical journals and is Editor at Large of the IEEE Computational Intelligence Magazine. He co-edited six books and has 150+ publications in refereed journals, book chapters, and conference proceedings, with 10,000+ citations, an H-Index of 52 and an i10-index of 160 (by Google Scholar). He received 73 patents issued by the US Patent Office (and 10+ pending patents). From 1982 until 2005 he has been an Adjunct Professor at Rensselaer Polytechnic Institute, in Troy NY, where he supervised 5 PhD theses and 34 Master theses. He co-chaired 12 scientific conferences focused on Multi-Criteria Decision-Making, Fuzzy sets, Diagnostics, Prognostics, and Uncertainty Management in AI. He has been a member of the IEEE Fellow Committee in 2007-09; 2012-14, and 2016-19. In 2002, while serving as President of the IEEE Neural Networks Society (now CIS), he was a member of the IEEE TAB.. He was an ExCom/AdCom member of NNC/NNS/CIS society in 1993-2012 and 2016-18 and an IEEE CIS Distinguished Lecturer in 2004-14, and in 2017-19.


Topic: A Walk with Neural Networks: Interesting Behaviors in Training, Inference, Pruning, and Controlling

Abstract: In the recent decade, new advancement in AI has been largely enabled by the development of neural networks, in computer vision, natural language processing, reinforcement learning, robotics, and many other areas. While many neural networks deliver superior performance at their tasks, networks are fundamentally complex systems, and their training and operation is still poorly understood. In this talk, imagine we take a walk with the fantastic beasts — neural networks, and have conversations around a number of active topics dedicated to the understanding of their behaviors.

How is the training process like under microscopic inspection — how to measure each parameter’s contribution to loss change?

How to define and measure intrinsic network complexity, and what does that measure tell us?

How to uncover hidden flaws in a popular model, and is there a better alternative?

How to speed up both training and inference, from more natural input spaces of images?

How to deconstruct the mystical success in pruning networks to develop better architectures and initializations, and what are the implications?

And lastly, we will cover a lightweight, flexible control mechanism in language generation with large transformer-based models.




Dr. Rosanne Liu is a senior research scientist, and a founding member of Uber AI. She obtained her PhD in Computer Science at Northwestern University, where she used neural networks to help discover novel materials. She is currently working on the multiple fronts where machine learning and neural networks are mysterious. She has multiple publications at NeurIPS, ICLR, ICML, and other top machine learning venues, and her work had been featured in MIT Tech Review, WIRED, TechCrunch and Fortune. She was named 30 Rising stars in AI by ReWork in 2019, and 30 Influential Women Advancing AI in San Francisco in 2018.


Student Posters:


Authors with affiliation and country


Density Functional Theory Study of Catalytic Capabilities

of Nitrogen doped Graphene

Tran Nguyen; Abby Parker (California State University, Long Beach, USA)

Assessment of Distributed Solar PV Curtailment in

Southern California


Christina Braga (California State University, Long Beach and Southern California Edison, USA)


Comparing the oxygen reduction reaction on different carbon-based catalysts for fuel cell

Oziel Palma (California State University, Long Beach, USA)
Limbi Alondra J Vivas (California State University, Long Beach, USA)


Deep Learning networks for Visual Question Answering Wei Chieh Tseng (California State University, Long Beach, USA)


Student posters will be evaluated by faculty members during the lunch hour.  Students should be available to present the research and answer questions about the poster.  Faculty judges will evaluate the posters according to the criteria

  • Relevance to the conference theme of green energy and smart systems
  • Presentation by the speaker or speakers
  • Organization and clarity of the poster content
  • Technical content of the poster

Awards will be made at the closing ceremonies of the conference.  Students shall present at the closing ceremonies to receive their awards.  The awards will be Amazon gift cards as follows (variant each year based on the founding):

  • First place for ~$175
  • Second place for  ~$150
  • Third place for ~$125
  • Fourth place for ~$100



Topic: Rapid Prototyping of Wireless Communications Systems with the NI LabVIEW Communications System Design Suite

Abstract:Most complex systems, and especially those classified as cyber-physical systems include some type of wireless connectivity and increasingly require advanced capabilities (e.g. 5G and beyond). These new wireless communications systems use new coding/decoding, modulation/demodulation schemes and waveforms with much more complex numerology to provide service “slices” that are optimized for specific applications (e.g. Ultra-Reliable, Low-Latency Control-URLLC for autonomous vehicles and smart transportation systems). New algorithms are to be deployed on hardware using FPGAs, requiring floating-to-fix-point conversion (and vice-versa) for a deterministic, parallel execution of advanced algorithms. Designing and prototyping these new wireless communications systems under tight time/cost/performance/resources constraints is becoming a challenge for system engineers. The LabVIEW Communications System Design Suite (LabVIEW Comms) is an integrated, hardware-aware design environment for prototyping communications systems using software-defined radios (SDRs). Using an integrated hardware-software approach, LabVIEW Comms and USRP SDRs allows students, engineers and researchers to rapidly prototype their wireless communications systems while taking advantage of onboard FPGAs and embedded CPUs. Best practices for designing, prototyping and developing wireless communication system using host-target or distributed architectures, while optimizing the algorithm design flow are presented.



Igor Alvarado (Academic Research Manager, National Instruments): Igor Alvarado is the Business Development Manager for Academic Research at National Instruments ( where he help to develop collaborations and strategic partnerships with leading universities in the U.S. in such areas as Cyber-Physical Systems, Smart Energy Systems, Medical Imaging/Devices, Advanced Manufacturing and RF/Wireless Communications to advance scientific research and accelerate innovation with support from NSF, NIH, DoD, DoE and other funding agencies.  Mr. Alvarado is a Mechanical Engineer (Kansas State University, ’84) and has been with NI since 1999. He is a NSF Innovation-Corps mentor and has more than 30 years of practical experience in successfully developing and growing markets for high-technology products and services in the U.S. and Latin America. He has led the design, development and deployment of real-time, measurement and intelligent control systems that involve advanced numerical methods and algorithms using high-performance embedded platforms. He is an active member of the Institute of Electrical and Electronics Engineers (IEEE), the Society of Industrial and Applied Mathematics (SIAM), the International Society of Automation (ISA), the American Physical Society (APS), the American Society of Mechanical Engineering (ASME), the American Association for the Advancement of Science (AAAS), the National Organization of Research Development Professionals (NORDP) and the Ibero-American Science and Technology Education Consortium (ISTEC). Mr. Alvarado has published technical papers and has taught courses to engineers and scientists on advanced instrumentation, control and automation applications in industry and academia; he has also been an invited keynote speaker at leading at national/international conferences and has served as a consultant and advisory board member for academic institutions, corporations and research laboratories. In 2017, Mr. Alvarado received the prestigious Electrical and Computer Engineering Department Heads Association’s (ECEDHA) Industry Award for his contributions to the ECE discipline and to engineering education.

Topic: Simulating the Operation of Emerging Distribution Businesses

Abstract: As we move to a clean energy future increasingly powered by renewables and distributed energy resources (DERs) the systems needed to make the grid work effectively are becoming more complex. At Southern California Edison (SCE), we are meeting these challenges by modernizing our distribution business, including leveraging advanced hardware and software to enable advanced grid management; connecting DERs to markets to maximize the value of those DERs; and empowering customers to partner in making the grid more reliable, efficient, and clean.

This workshop presentation will discuss the development and application of ProsumerGridTM DSO Simulation Studio, a simulation tool capable of evaluating the operation of emerging distribution business models. The tool accounts for physical and market constraints, as well as information and coordination requirements. The software is based on a Transmission and Distribution (T&D) co-simulation platform that enables several capabilities, including DER scheduling and decentralized optimization, locational value analysis, and advanced web-based visualization.

The focus of the presentation will be on the development and testing of use cases based on SCE’s existing network models; T&D co-simulation under high DER penetration, while also accounting for Distribution Locational Marginal Pricing (DLMP); and the impact of implementing emerging distribution business market designs and functions, including interaction with the California Independent System Operator (CAISO).



Dr Manuel Avendaño is the Senior Engineering Manager of Emerging Technologies Evaluation at Southern California Edison, the primary electricity supply company for much of Southern California. He is responsible for leading SCE’s effort to understand and test emerging smart grid technologies and determine their feasibility for demonstration projects and their potential impact to SCE’s Grid Modernization plan. Dr Avendaño earned a bachelor’s degree and a master’s degree in Electrical Engineering in Mexico and the PhD in Electrical Engineering in United Kingdom. Dr Avendaño has been a member of IEEE since 2006 and currently serves as Chair of the IEEE Distribution Subcommittee and Editor for the IEEE Transactions on Power Delivery.


Topic: PV Curtailment as a Mitigation Strategy in Distribution Feeders

Abstract: A comprehensive assessment to estimate the solar PV curtailment in distribution feeders with significant solar PV penetration and no traditional distribution upgrades will be presented. This talk will discuss the methodology behind the study and the results of estimating the magnitude of curtailment, i.e., the number of PV curtailment events and the total energy curtailed (MWh) in SCE’s feeders with rising grid penetrations of solar PV systems that can have adverse distribution power quality, reliability, and safety impacts.

Biography: Matthew Kedis is an engineer with Southern California Edison’s Emerging Technology & Valuation group. Matthew has four years industry experience working in energy storage and power systems modeling. He has worked on modeling and analytics projects involving energy storage voltage regulation and solar integration. Matthew earned his Bachelor of Science degree in Electrical Engineering from California State University, Long Beach.

Topic: Modeling of Distribution Volt/VAR Control



This presentation workshop will discuss a novel methodology developed to model Southern California Edison’s field implementation of the Distribution Voltage and VAR Control (DVVC) algorithm.  DVVC aims to maintain voltages on distribution feeders within the acceptable voltage range and allows for more effective regulation of voltage and VAr levels.  The model helped SCE determine optimal setpoints for a DVVC field pilot and identify anomalies that were adversely impacting DVVC performance in the field.  The methodology presented herein can be applied to similar modelling efforts at other electric utilities.


Navid Ahmadiyeh is an engineer with Southern California Edison’s Emerging Technology Valuation group.   Navid has 10 years industry experience working in distribution planning, system planning & engineering, customer engineering and renewable integration.   Navid has worked on various modeling and analytics projects, evaluated new technologies for combating wildfire risks and has led the implementation of a company-wide GIS and Advanced Vehicle Location system.  Navid earned his Bachelor of Science degrees in electrical engineering from the University of Texas at San Antonio.


Topic:  A case study of implementation of California Energy Commission (CEC) and California Air Resources Board (CARB) grants to promote Zero emission Vehicles at the Port of Long Beach (POLB).


The POLB is second busiest Port in the United States. With the goal of significantly reducing health risks posed by air pollution form port-related mobile sources including ships, trains, trucks and terminal operating equipment. The Ports recently updated the Plan in 2017 with the stated purpose of becoming even greener by setting the target date of 2030 to become zero emission free for port infrastructure goods movement.

This talk will discuss the various projects being designed and commissioned at the POLB and the lessons learned in implementing these projects. The various projects look at different manufacturers and methods of electrification of Utility Tractor Rigs (UTR), forklifts and Rubber Tire Gantry Cranes. The primary purpose of these initial studies is to document how the various pieces of equipment function in a Port facility with the limitations and demands required to operate in a functioning Port facility and supply the Terminal operators with the electrical infrastructure to add additional electrified equipment.

Biography: William Stone, PE, LC (Senior Electrical Engineer, Port of Long Beach) The Port of Long Beach is the second largest Container Terminal in the United States. The Port of Long Beach is working towards having all Cargo Handling Equipment Zero emissions by 2030.  He is responsible for evaluating advances in Zero emissions technology and determine their feasibility in the Port Environment. Mr. Stone Graduated from California State University, Long Beach with a Bachelor of Science in Electrical Engineering, emphasis in Power, and is a registered professional Electrical Engineer in the State of California. Mr. Stone is also the Treasurer of the Orange County Joint Chapter of the Institute of Electrical and Electronic Engineers (EEE) Industrial Applications Society (IAS) and Power and Energy Society (PES).







Professional Society Sponsorship

IEEE Systems Council Chapter

IEEE Coastal Los Angele Section

IEEE Women in Engineering

AIChE LA/OC Section

IEEE Region 6



College of Engineering

IEEE Student Branch

AIChE Student Branch



Student Career and Development Center, CSULB