Empowering the Southeast Asia's Energy Transition Journey . 123

Southeast Asia Information Platform for Energy Transition (SIPET) is a one-stop platform for information and knowledge exchange about the energy transition in the region. SIPET aims to be a central launch pad for energy transition stake holders to facilitate dialogue and promote coordination in the Southeast Asia power sector.

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TRACKING THE ENERGY TRANSITION

EXPLORE INFORMATION AND ACTIVITIES RELATED TO THE ENERGY TRANSITION IN SOUTHEAST ASIA

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Malaysia Coming soon

Brunei Darussalam Coming soon

Singapore Coming soon

SIPET CAN CHANGE HOW PEOPLE PERCEIVE RENEWABLE energy

SHARE YOUR EXPERTISE AND DIVE INTO IN-DEPTH KNOWLEDGE OF ENERGY SECTOR IN SEA

Southeast Asia (SEA XYZ) is one of the fastest-developing regions in the world, with energy demand projected to grow by 60% over the next 20 years. While the region has been undergoing a clean energy transition in recent years, several barriers hinder the acceleration of this, process including inconsistent energy planning, under-investment in modernization of the power grid infrastructure, and a lack of easy collaboration and knowledge-sharing among key stakeholders.

SIPET aims to engage with multiple stakeholders (government, development, private sector, civil society, research communities) and be a platform to discuss the crosscutting issues as well act as a key hub for knowledge and dialogue for its users.

TRACKING THE ENERGY TRANSITION IN DEMO

MAPPING ENERGY TRANSITION ACTIVITY SOUTHEAST ASIA

Multiple initiatives, funded by donor agencies, non-profit organizations and public authorities,are under way across the Southeast Asian region, with the aim to facilitate the process of shift to clean energy. SIPET provides a comprehensive, dynamic database of such initiatives, which are either active or being planned information on each project is provided and maintained by SIPET Members, who are donors, companies, investors, or civil society organizations.

Users of SIPET can see project details including name of donors, political partners, implementing partners, project budgets and timelines, list of key activities and areas of intervention, to name a few.

To explore projects, go to PROJECT MAPPING TOOL

June2024 - July2024

DEMO PROJECT

DEMO PROJECT DESCRIPTION

August2023 - November2023

Policy predictability for EV adoption needed to make investm .. Read more at: http://timesofindia.indiatimes.com/articleshow/103745254.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst

With the FAME-II (Faster Adoption of Manufacturing of Electric Vehicles in India) scheme coming to an end in March next year, Ravneet S Phokela said Ather Energy is happy with the government support and the current levels of subsidy but hoped that it would be extended for another three to five years for acceleration of EV adoption. Policy predictability is needed to make investment plans for accelerating electric mobility in the two-wheeler segment, Ravneet S Phokela, Chief Business Officer at Ather Energy, told PTI in an interview. "Two requirements, extend the period of time and importantly, let there be policy predictability," he told PTI when asked about the company's wishlist for FAME III.

Read more at: https://yourstory.com/2023/09/ev-subsidy-should-be-extended-for-3-5-years-for-better-adoption-ather-energy

September2023 - October2023

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ADB is helping Viet Nam expand and improve electricity services for ethnic minority communities in poor, remote areas. The project will develop five to 10 mini hydropower plants to serve communes in mountainous areas in the north. It will also support the rural electrification program and expand power coverage to five provinces and provide electricity connections to about 100,000 poor households. Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam. Electricity connections will be provided to about 60-75 villages in poor, remote ethnic minority areas in northern Viet Nam, and minimum 100 gigawatt hours (GWh) of hydropower energy to the national grid. Component 2 of the Project will support the Government's ongoing special program of supporting rural electrification of poor provinces inhabited by ethnic minorities. The Government requested assistance to extend this program to five provinces: Dien Bien and Lai Chau in Northern region; Tra Vinh and Soc Trang in Mekong Delta; and Quang Tri in central Viet Nam. Approximately 100,000 poor households in about 1,000 villages are expected to receive electricity under this component. The Project will include an associated technical assistance (TA) grant for capacity building for renewable energy development. It will also support the rural electrification program and expand power coverage to five provinces and provide electricity connections to about 100,000 poor households. Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam. It will also support the rural electrification program and expand power coverage to five provinces and provide electricity connections to about 100,000 poor households. It will also support the rural electrification program and expand power coverage to five provinces and provide electricity connections to about 100,000 poor households. It will also support the rural electrification program and expand power coverage to five provinces and provide electricity connections to about 100,000 poor households.

Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam

ADB is helping Viet Nam expand and improve electricity services for ethnic minority communities in poor, remote areas. The project will develop five to 10 mini hydropower plants to serve communes in mountainous areas in the north. It will also support the rural electrification program and expand power coverage to five provinces and provide electricity connections to about 100,000 poor households. Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam. Electricity connections will be provided to about 60-75 villages in poor, remote ethnic minority areas in northern Viet Nam, and minimum 100 gigawatt hours (GWh) of hydropower energy to the national grid. Component 2 of the Project will support the Government's ongoing special program of supporting rural electrification of poor provinces inhabited by ethnic minorities. The Government requested assistance to extend this program to five provinces: Dien Bien and Lai Chau in Northern region; Tra Vinh and Soc Trang in Mekong Delta; and Quang Tri in central Viet Nam. Approximately 100,000 poor households in about 1,000 villages are expected to receive electricity under this component. The Project will include an associated technical assistance (TA) grant for capacity building for renewable energy development. It will also support the rural electrification program and expand power coverage to five provinces and provide electricity connections to about 100,000 poor households. Component 1 of the Project will develop about 5-10 grid-connected run-of-river mini-hydropower plants of 7.5 megawatts (MW) or less capacity, totaling 30 MW in several provinces in northern and central Viet Nam. It will also support the rural electrification program and expand power coverage to five provinces and provide electricity connections to about 100,000 poor households. It will also support the rural electrification program and expand power coverage to five provinces and provide electricity connections to about 100,000 poor households. It will also support the rural electrification program and expand power coverage to five provinces and provide electricity connections to about 100,000 poor households.

January2020 - December2030

Capacity Building of Renewable Energy Development

The primary objective of the sector Project is to develop rural electrification and renewable energy in Viet Nam to benefit ethnic minority communities inhabiting remote and poorer parts of the country. The Project consists of two investment components: (i) development of mini-hydropower plants in mountain provinces, and (ii) network expansion and rehabilitation of distribution networks serving poor provinces.

June2023 - June2023

Solar Energy Management System Project

Whenever the energy from the sun falls on the solar panel, then the energy can be absorbed through the photovoltaic cells. The energy conversion from solar to electrical in the solar cells can be done with the help of silicon semiconductors using the effect of PV. The converted energy is in the form of DC so that it can directly charge the battery.

September2023 - March2023

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September2023 - March2023

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March2023 - April2023

Renewable Energy and Energy Efficiency (4E) Phase II

The main objective of the 4E Project is to further develop the legal, regulatory and institutional preconditions as well as related capacities to channel investment towards renewable energy and energy efficiency. The energy sector governance is enhanced, and the regulatory framework and legal basis are in place for the large-scale expansion of renewable energy as well as the implementation of energy efficiency measures and for the compliance with the climate change commitments of Viet Nam. Further depth has been added to the specialist knowledge of decision-makers for the successful formulation and implementation of policy guidelines in the sector.

PUBLICATIONS, VIDEOS AND EVENTS

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01-08-2024 Vietnam

Videos

Use case 2 by Energy Transition Expert Dr. Siripha Junlakarn, researcher at the Energy Research Institute (ERI) at Chulalongkorn University, , commended the standardized approach to data in the database, as it improved data conformity and facilitated efficient comparative analysis.

SIPET Power Sector Resource Data Browser

Samart Kumar, program manager at Agora Energiewende, and Judit Hecke, climate policy analyst at NewClimate Institute, introduced SIPET’s Power Sector Data Browser and complementary Power Sector Snapshots. During the presentation, Samart explained the database-building process, which utilized primary data sources from ministries, governments, utilities, and generation companies, while Judit provided a virtual walkthrough of the Data Browser and Snapshots

Energy Efficiency (4E) Phase II Under the BMZ-funded second phase of the 4E Project, further development of RE and EE is focused on strengthening the legal framework for RE and EE as well as enhancing the human capacity of the related active stakeholders. The second phase of 4E includes a co-financing component with the EU “Technical Assistance (TA) for the Implementation of the EU-Vietnam Energy Facility” (in short: TA for EVEF), which thematically reinforces and complements 4E BMZ component’s fields of action. The EVEF is aimed at supporting the Ministry of Industry and Trade and non-Ministry institutions in the energy sector with technical assistance focusing on regulatory framework improvement, strengthening technical and methodological capacities in RE, EE, energy access and energy data and statistics.

Preferential Dispatch of all Renewable Energy Plants in the WESM

This publication reports on the effects of preferential dispatch of all existing geothermal, biomass, and impounding hydroelectric power plants, in strengthening the Philippine's energy self-sufficiency. Findings show that preferential dispatch for all renewable energy (RE) will increase the use of indigenous RE resources, diversify the energy mix, and reduce market clearing prices in the spot market.

The Preferential Dispatch report serves as evidence to the Department Circular No. DC2022-10-0031 of the Philippine Department of Energy. The report is authored by the Institute for Climate and Sustainable Cities (ICSC)

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SIPET Power Sector Resource Data Browser

Preferential Dispatch of all Renewable Energy Plants in the WESM

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LATEST NEWS AND UPDATES DEMO

SIPET NEWS HIGHLIGHTS

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We, our partners, and users of our services deploy cookies and similar technologies to record certain information, as well as the options you as Data Subject (“means any identified or identifiable individual to whom personal data relates”) have to control them. What are cookies? Cookies are small pieces of data, stored in text files, that are stored on your computer or another device when websites are loaded in a browser to make the user’s experience more efficient. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe.

Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe.Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. Data abounds, but it is not all the same quality. Some data is dirty (filled with mistakes and omissions). Some data is flat out wrong and yet others are fictional. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. This is particularly true if you rely on public domain data. Some datasets contain bias - which can create major risks for businesses if used in an AI. Some simply contain mistakes - as outlined in a recent project by MIT. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe. A solid understanding of where the data comes from is essential to knowing whether the insights the data generates are valuable or even safe.

Sustainable Finance Advice for the ASEAN Low Carbon Energy Program

IISD is a technical partner in the ASEAN Low Carbon Energy Programme (LCEP), which aims to drive inclusive growth and poverty reduction through increased energy efficiency and the adoption of low-carbon energy. The ASEAN LCEP is a GBP 15 million overseas development program under the auspices of the United Kingdom’s Prosperity Fund, which is targeted specifically at six countries: Indonesia, Malaysia, the Philippines, Thailand, and Vietnam. The program seeks to help these countries reap the sustainable development opportunities that can arise from the deployment of low-carbon energy, in particular through green finance and energy efficiency. The main project partners are Ernst & Young, IMC worldwide, and the Carbon Trust. The ASEAN LCEP program runs until 2022.

Sustainable Finance Advice for the ASEAN Low Carbon Energy Program

USAID Launches PHP1.6 Billion Project to Promote Clean Energy in the Philippines

Manila, June 28, 2021—The U.S. government, through the U.S. Agency for International Development (USAID), launched on Monday, June 28, its flagship project to support a more competitive, secure, and resilient Philippine energy sector. The five-year, Php1.6-billion ($34 million) Energy Secure Philippines (ESP) project will promote the country’s key energy sector priorities and support its climate mitigation goals.

Sustainable Finance Advice for the ASEAN Low Carbon Energy Program

Automated Testing is Better

Renewable energy, often referred to as clean energy, comes from natural sources or processes that are constantly replenished. For example, sunlight and wind keep shining and blowing, even if their availability depends on time and weather.

While renewable energy is often thought of as a new technology, harnessing nature’s power has long been used for heating, transportation, lighting, and more. Wind has powered boats to sail the seas and windmills to grind grain. The sun has provided warmth during the day and helped kindle fires to last into the evening. But over the past 500 years or so, humans increasingly turned to cheaper, dirtier energy sources, such as coal and fracked gas.

Now that we have innovative and less-expensive ways to capture and retain wind and solar energy, renewables are becoming a more important power source, accounting for more than 12 percent of U.S. energy generation. The expansion in renewables is also happening at scales large and small, from giant offshore wind farms to rooftop solar