|| First Name
|| Last Name
|| Environmental Defense Fund
|| USENSYS and GENESYS: open source capacity expansion energy system optimization models for US and the World with R and energyRt
|| The US ENergy SYStem (USENSYS) and Global ENErgy SYStem (GENESYS) are two new open source energy modeling initiatives for US and the Globe with the focus on renewable energy and transition to zero-carbon economy. The models are based on 'energyRt' package for R which facilitates development and application of Reference System (or capacity expansion) models using a set of classes and methods in R. The optimization model is currently formulated in GAMS and GLPK/MathProg. Julia/JuMP version is planned. The USENSYS model will be uploaded on GitHub by the workshop.
|| Stony Brook University
|| Modeling China's Power System
|| The SWITCH-China Model was first developed in the Renewable and Appropriate Energy Laboratory at UC Berkeley as my PhD dissertation and I continue working on using the model to study the challenges facing China's clean power transition: soaring variable renewable energy penetration and curtailment; pressing air pollution, water stress, climate change, and human health challenges by coal consumption. Through the modeling efforts, I'm very familiar with the data availability, accessibility, and credibility of China's power systems, and the strategies to address those data and model challenges.
|| Carnegie Institution for Science & Department of Global Ecology
|| Characterization and utility of an extremely simple energy model
|| We discuss the motivation and use cases for a simple energy model that optimizes for a least-cost electricity system meeting hourly demand. There is significant uncertainty in future energy demand, technology costs, and government regulations. Our group has developed an open source, computationally cheap model capable of exploring a range of scenarios. The model can consider rare weather and demand events that are critical for system design by optimizing over multiple years. Our goal is to explore endogenous system dynamics and ultimately to inform policy and research and development goals.
|| reducing accessibility barriers to grid planning models/tools for stakeholders in electric infrastructure proceedings
|| Due to the closed nature of existing capacity expansion and production cost modeling tools in public stakeholder proceedings to vet electric infrastructure investments, stakeholders must invest significant time and resources to adequately evaluate IOU infrastructure investments. These stakeholders do not always have the resources to invest in such efforts and as a result, unnecessary infrastructure investments, which often favor traditional solutions that may not remain effective for their useful life, can be approved.
|| ReEDS 2.0
|| Over the past two years we've re-developed the Regional Energy Deployment System (ReEDS), an electricity capacity expansion model of North America. On September 1st, 2019 we will be open-sourcing the model.
|| Asia Pacific Energy Research Centre
|| Asia Pacific Energy Model
|| The Asia Pacific Energy Research Centre (APERC) is developing an energy systems model to project supply, transformation, and demand across APEC's 21 member economies. In this talk, I will share the scope of our modeling capability, tools, status, and challenges to start broader discussion with the group.
|| Los Alamos National Laboratory
|| InfrastructureModels.jl: Multi-Infrastructure Mathematical Programming in Julia
|| The significant penetration of distributed renewable energy resources and increasingly dynamic fuel prices present significant challenges for the design and operation of critical infrastructure systems. To help explore how optimization algorithms can address these emerging challenges this work presents a collection of Julia packages for infrastructure optimization. These packages provide a variety of tools for, reproducing state-of-the-art results, developing new problem formulations, and benchmarking novel algorithms using standardized data formats and test cases.
|| Sandia National Labs
|| Adverse-Weather Impact Scenarios for Power System Resilience Modeling
|| Resilient Operations Models (ROM) move beyond standard production cost models (PCM) to simulate system behavior during a disruptive event. In order to test out the new capabilities needed to move from PCM to ROM, we have developed a number of adverse-weather event impact scenarios. The scenarios impose changing weather conditions onto a region of interest over time, and we model plausible subsequent component failures (generation and transmission), load impacts, and restoration. Here, we present several of these impact scenarios and simulation results from applications on the RTS-GMLC network.
|| Sandia National Laboratories
|| This talk will be an overview of the unit commitment capabilities in the EGRET software package for electrical grid optimization, including the modular base generator model, ancillary service stack specification, and transmission constraint handling.
|| UC Berkeley
|| LITS.jl - An Open-Source Julia based Simulation Toolbox for Low-Inertia Power Systems
|| The integration of power electronics-interfaced generation from renewable energy sources poses additional challenges to the stability and transient behavior of electric power systems. In this work, we develop an open source simulation, LITS.jl, to study transient responses when there is a high penetration of converter-interfaced generation. It features multi-machine modeling capability, inverter parameter scaling to study different CIG penetration scenarios and device ratings, and a rich library of synchronous generators components (AVR, PSS, Governor, etc.)
|| Sandia National Laboratories
|| An introduction to EGRET: Electrical Grid Research and Engineering Tools
|| EGRET is am open-source python package based on Pyomo for different aspects of electrical grid optimization. EGRET includes capabilities for a large number of unit commitment formulations, as well as linear and nonlinear optimal power flow including DCOPF, DCOPF with losses, PTDF, PTDF with losses and several ACOPF formulations. This presentation will discuss the capabilities and use of EGRET, and cover current development (e.g., ACOPF relaxations).
|| RTI International
|| The Micro-level Economic and Environmental Detail of Electricity dataset (MEEDE)
|| The Micro-level Economic and Environmental Detail of Electricity dataset provides unit-level environmental, engineering, and economic data on electricity. MEEDE has annual information on fuel and generation quantities; engineering configurations for generation and control equipment; estimates for capital, fixed and variable O&M, and revenue by generation and control equipment; and emissions estimates for PM, NOX, SO2, Hg, CO2, CH4, N2O, and F-gases. Updates will include a time series, adding hourly emissions and system costs, adding FERC Form-1 Data, and making it open.
|| Carnegie Science
|| A method for cleaning and validation of time series data used in energy models: demonstrated using hourly electricity demand data
|| Energy models use time series data to constrain electricity demand and solar and wind availability. Data sets can contain missing and potentially erroneous values. We present a method for identifying erroneous data and imputing missing and erroneous values. This method is validated using multiple techniques. As a demonstration, we focus on the U.S. Energy Information Administration√¢??s (EIA) hourly electricity demand data gathered from 56 regional entities; the EIA data is available to users √¢??as is√¢?¬ù. We provide the cleaned and validated data for use in region-specific analyses.
|| Catalyst Cooperative
|| Public Utility Data Liberation (PUDL) - the quest for open and accessible energy data
Public Utility Data Liberation - striving for accessible data
| Catalyst Cooperative is organizing public data to help shape policies and tell stories about the ongoing transformation of the U.S. energy sector. We have been busy building an open source tool that combines information from the Energy Information Administration, the Federal Energy Regulatory Commission, the Environmental Protection Agency and other agencies. We√¢??ll talk through what is available through PUDL now, what we are planning to add in the next year, and an overview on how to use the tool. We√¢??ll also explore what other data sets are being used by participants.
|| Carbon Impact Consulting
|| PowerGenome: An open-source data platform for power system models
|| Assembling the data needed for a power system model is time consuming, and comparing inputs across models can be even harder. Clean Air Task Force is building PowerGenome, an open-source data platform for building US power system model inputs. We use - and are contributing new data to - Catalyst Cooperative√¢??s PUDL project as our main data repository. PUDL currently provides access to clean and normalized EIA/EPA/FERC data. Once a user specifies model regions (single and/or aggregations of IPM regions) and a handful of other parameters, PowerGenome creates the inputs needed for a model run.
|| Akin Olcum
|| Environmental Defense Fund
|| The U.S. state-level open-source data and modeling framework
|| The WiNDC provides an open source build routine for generating micro-consistent state-level SAMs based on national US data from the BEA. For energy and emissions accounting, an energy-environment sub-routine called blueNOTE ensures that the state-level input-output accounts incorporate information on physical energy demands, supplies and prices based on the SEDS Database. This allows us to study the US state economies under various environmental and energy regulations. Our first application is NYISO's proposal for additional carbon adder on NY power generators.
|| UC Davis
|| We are interested in creating forecast or power scenarios when the|
underlying forecast methodology is modeled as being more (or less)
accurate than it has been. This can be used in studies that extend
into the future and may need to consider the possibility that forecast
technology will improve. An open-source software implementation of
the methods described here is available. It is called mapemaker.
|| Jacqueline A.
|| California Institute of Technology
|| Generation of a multi-decadal, hourly based wind and solar availability data set
|| NASA provides public access to the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) version of the GEOS-5 atmospheric general circulation model. The reanalysis model assimilates a wide array of observational data to deliver an internally consistent historical representation of weather. We present a method to derive solar and wind capacity factors for any region on Earth from this reanalysis data. This multi-decadal time series with hourly resolution can be used to inform energy policy and investment decisions for wind and solar heavy electricity systems.
|| Design, Analysis, and Operations Toolkit: Concentrating Solar Power Central Receiver Design and Operations Optimization
|| The Design, Analysis, and Operations Toolkit (DAO-Tk) is a software package that allows users to explore design optimization, operations decision, and performance characterization of concentrating solar power tower plants. Users choose from a list of variables such as tower height, solar multiple, design-point irradiance, thermal storage size, etc., and specify information about the system using a list of parameters. The software can then optimize the specified variables to reduce the cost of energy produced by the system while meeting certain production requirements.
|| University of Natural Resources and Life Sciences, Vienna
|| Classified: The top secret price of wind turbines
|| We comment on the frequently taken for granted issue of modelling costs of renewable energy.|
While, todays power system models succeed in quantifying the power output of various energy sources in increasing temporal & spatial detail, converting between MWh and $ remains often a highly challenging subject for researchers.
We briefly recap the state of investment cost estimates for wind turbines and summarize current challenges and possible ways to improve upon them.
Furthermore, we believe that similar concerns are relevant for most energy sources, be they wind, renewable or conventional.
|| Carnegie Institution for Science (Stanford University)
|| Generating a framework for inter-model comparisons in energy systems modeling
|| There are many distinct models in the field of energy systems modeling that use differing cost, demand, and technical inputs with varying temporal and spatial resolutions to predict the future energy mix. Results from these models are difficult to compare because underlying assumptions vary and it is unclear where differences originate. There is an evident need for a framework for inter-model comparison that allows us to pinpoint differences between a range of models to contextualize their outputs. Our work focuses on an initial analysis from two open-source models using a simple framework.
|| NC State University
|| Tools for Energy Model and Analysis (Temoa)
|| Tools for Energy Model Optimization and Analysis (Temoa) is an open source energy systems optimization model. Temoa development began nearly a decade ago to address two prevailing shortcomings in the community: (1) results produced with closed models couldn't be replicated, and (2) little sensitivity and uncertainty analysis was being performed with large, complex models. This talk will briefly summarize how Temoa is working to address these challenges and introduce a new effort funded by the Sloan Foundation to create an Open Energy Outlook for the United States.
|| Council for Scientific and Industrial Research (CSIR)
|| Modelling framework choices for long-term national/regional level energy planning in developing countries
|| What process is typically followed to choose a modelling framework(s)?|
Is more than one a good idea?
Who chooses the energy modelling framework(s)? (local institutions, domestic/international consultants, development funding agencies)
What are the key dimensions to consider when choosing an energy modelling framework(s)?
What are the typical skill sets needed to support maintenance and updating of established energy models? i.e. ensuring longevity.
What infrastructure (hardware/software) is needed to run increasingly complex long-term energy models?