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Our Research

  • Virtual Utility Plant, Utility energy trading solution for the companies with various resource demand & supply

    Participating Organizations

    Thingspire, PIELDS-engineering, WOOWON MNE, Institute for Advanced Engineering, GemVaxLink, Iaan, Enitt, Teraplatform, Korea Industrial Complex Corporation, GS E&R


    To check and build a proofcase of whether providing utility resource trading within Source and Sink companies in a distributed environment through collective pooling of resources under a common IT infrastructure.


    Develop utility energy trading technology within source and sink companies

    • Source companies: Companies with highly efficient facilities that can share their own post-demand surplus resources

    • Sink companies: Firms with low-efficiency facilities that have an advantage in purchasing external resources


    • Steam Energy: 7% Saving

    • Compressed Energy: 10% Saving

    • Infrastructure Cost: 15% Saving

    • Research Organization

      PIELDS-engineering, Thingspire, Elchemtech, Hyundai Motor Group, Korea Institute of Science and Technology, Korea Institute of Energy Research


      • Expanding domestic performance and exporting overseas through a system that can store intermittent and volatile renewable energy to stably supply heat and electricity


      • Development of modular/mobile 1MWh+ hydrogen storage and hot water production/electricity generation systems

      • Development of integrated control systems that respond to various demand sources (EMS, H-EMS, T-EMS)

      Expected Achievement

      • Establishing demonstration-phase hydrogen storage system and fuel cell operating conditions

      • Establishing operating conditions for hydrogen storage materials in the demonstration phase

      • Development of system operation logic - System test evaluation

    • Research Organization

      • HD Hyundai Electric, Hyundai Motor Group, Haeseong-eng, NuriFlex, SEP Cooperative, Envest, Chemtopia, Taesan Solutions, Thingspire, Teraplatform, TUKOREA Industry-Academic Cooperation Foundation, Ecoeye, Korea Testing Laboratory.


      • Promoting carbon neutrality and RE100 by building a renewable energy power plant and integrated energy management system in Banwol-Sihwa Smart Green Industrial Complex


      • Building a low-carbon, high-efficiency smart green industrial complex by expanding renewable energy distributed power sources and establishing energy supply and demand optimization infrastructure

      • Establishment/operation of renewable energy power plants and integrated energy management system for Banwol-Sihwa Industrial Complex in accordance with the 2050 Net zero declaration

      • Establishment of energy self-sufficiency infrastructure that enables industrial complex tenants to switch to green energy use and participate in RE100

      • Expanded and operated the CEMS system in connection with the Smart Energy Platform(SEP) project in Banwol-Sihwa Industrial Complex

      • Corporate RE100 participation and regional carbon neutrality foundation in conjunction with energy infrastructure projects

      • Establishment of electric vehicle charging infrastructure

      Expected Achievement

      • Self-sufficiency in renewable energy generation: 23,214 MWh/year

      • 5% reduction in energy usage within Green Energy Activities: 2,500MWh/year

      • Supporting trade of small-scale carbon credits: 2,612 tons of CO2 reduction

    • Research Organization

      Thingspire, KAIST


      • Increasing need for estimating accurate GHG emissions to set effective reduction targets (Park et al., 2022)

      • Scope 3 emissions account for about 84% of all GHG emissions from businesses (Huang et al., 2009)

      • To date, most companies have only reported on Scope 1 and 2 emissions in their assessment reports, but there is now growing pressure to include an assessment of Scope 3 emissions (Stenzel and Waichman, 2023).

      • There are two main methods for calculating Scope 3 emission factors: Life Cycle Assessment (LCA) and Environmentally Extended Input-Output (EEIO) methods. LCA allow for more detailed analysis but has the limitation of being highly cost due to process-specific data collection (Filimonau et al., 2011; Wiedmann et al., 2011), while EEIO methods are easier to calculate and have valid results for emissions over a relatively large geographic area and industry (Demeter et al., 2021; World Resource Institute, 2013).

      • Various companies have adopted the EEIO method to calculate GHG emission factors in the scope of Scope 3 (Demeter et al., 2021; Tian et al., 2018; Yang et al., 2017)

      • This study was conducted due to the lack of EEIO-based emission factors that can be applied in detail across Korean industries.


      • Review of related research on calculating Scope 3 emission factors using existing EEIOs in Korea and abroad

      • Calculating emissions in relate to Scope 1, 2, and 3 and emission factors for 381 domestic sectors based on the 2019 Basic Sector Industry Association Table

      • Elaboration of emission factors based on Korean standard industry classification

      • Comparison and verification of emission factors by testing on sample companies

      Expected Achievement

      • Facilitate the calculation of Scope 3 GHG emissions for a wide range of organizations.

      • Derive detailed emission factors applicable to Scope 3 Categories 1, 2, 4, and 15.

  • Intelligent Process Automation (IPA)

    IPA is a technology that combines robotic process automation (RPA) with artificial intelligence (AI) to increase the level of automation. It helps businesses increase operational efficiency by automating inefficient manual tasks, and provides assistance capabilities to support human decision-making by learning from various corporate documents. IPA also includes technologies to measure, analyze, and automate reports on a company's carbon footprint, collect and integrate information from various data sources, and provide missing/outlier detection and restoration to ensure data consistency.

    Advanced Predictive Analytics (APA):

    APA is a technique used in a variety of industries to predict future events, behaviors, and trends using advanced data analysis techniques. It estimates or forecasts unobtainable metrics in a scientific and consistent way, recognizing patterns based on historical data and predicting future carbon emissions. APA can be used to set emissions reduction targets for a company and assess the likelihood of achieving them.

    AI Agents

    AI agents are technologies that can make decisions and act independently to achieve a given goal. They provide proactive insights for decision-making through data collection, processing and refinement, storage and management, and intelligent analytics. AI agents can help you develop strategies to reduce greenhouse gas emissions, support reporting to meet legislation and sustainability goals, and provide simulations of how to optimize energy use and process efficiency to minimize carbon emissions.

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