Japan lends Vietnam $761 mln mostly for energy

By Reuters


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Japan, Vietnam's biggest single provider of aid, said recently it would extend up to 79.33 billion yen ($761 million) in yen loans to Hanoi this year for infrastructure projects, with the bulk going for energy plants.

The total is just a little smaller than the $872.1 million.

Tokyo is expected to dispense to Vietnam's large communist neighbour China, which has seen Japan lending decrease in the last three years.

Vietnam is in dire need of financing to ramp up energy supplies in order to meet demand that is growing at more than twice its annual GDP rate of seven percent.

Some $448 million of the yen loans will go toward engine construction at the O Mon thermal power plant and the building of the Dai Ninh hydropower station, a statement from the Japanese embassy in Hanoi said.

The rest will be divided among six projects that include upgrading bridges, improving the safety of Vietnam's main railway line and improving the water supply system in two provinces.

Japanese Ambassador Norio Hattori said the loan total for Vietnam this year was about the same as that in 2003 even though Japan's official development aid budget had shrunk. He said that showed the importance of Japan's relationship with Vietnam.

India is Japan's top recipient of aid in Asia. It is slated to receive 120 billion yen in loans this year to fight poverty and improve its infrastructure.

Tokyo has provided loans to Vietnam annually since 1992, with the amount remaining constant in the last 10 years. Hanoi has said it needs to build 60 more power plants by 2020 to meet its needs and that it requires energy investments of around $2 billion a year.

Some will come from foreign aid but it is also hoping for private bank loans. Around 73 percent of Vietnam's electricity is provided by hydropower and coal-fired plants; the rest comes from gas-fired plants. ($1

104.25 yen)

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Quebec premier inaugurates La Romaine hydroelectric complex

La Romaine Hydroelectric Complex anchors Quebec's hydropower expansion, showcasing Hydro-Québec ingenuity, clean energy, electrification, and grid capacity gains along the North Shore's Romaine River to power industry and nearly 470,000 homes.

 

Key Points

A four-station, $7.4B hydro project on Quebec's Romaine River producing 8 TWh a year for electrification and industry.

✅ Generates 8 TWh yearly, powering about 470,000 homes

✅ Largest Quebec hydro build since James Bay project

✅ Key to clean energy, grid capacity, and electrification

 

Quebec Premier François Legault has inaugurated the la Romaine hydroelectric complex on the province's North Shore.

The newly inaugurated Romaine hydroelectric complex could serve as a model for future projects, such as the Carillon Generating Station investment now planned in the province, Legault said.

"It brings me a lot of pride. It is truly the symbol of Quebec ingenuity," he said as he opened the vast power plant.

Legault was accompanied at today's event by Jean Charest, who was Quebec premier when construction began in 2009, as well as Hydro-Québec president and CEO Michael Sabia. 

La Romaine is comprised of four power stations and is the largest hydro project constructed in the province since the Robert Bourassa generation facility, which was commissioned in 1979. It is the biggest hydro installation since the James Bay project, bolstering Hydro-Québec's hydropower capacity across the grid today.

The construction work for Romaine-4 was supposed to finish in 2020, but it was delayed the COVID-19 pandemic, the death of four workers due to security flaws and soil decomposition problems. 

The $7.4-billion la Romaine complex can produce eight terawatt hours of electricity per year, enough to power nearly 470,000 homes.

It generates its power from the Romaine River, located north of Havre-St-Pierre, Que., near the Labrador border, where long-standing Newfoundland and Labrador tensions over Quebec's projects sometimes resurface today.

Legault said that Quebec still doesn't have enough electricity to meet demand from industry, including recent allocations of electricity for industrial projects across the province, and Quebecers need to consider more ways to boost the province's ability to power future projects. The premier has said previously that demand is expected to surge by an additional 100 terawatt-hours by 2050 — half the current annual output of the provincially owned utility.

Legault's environmental plan of reducing greenhouse gases and achieving carbon neutrality by 2050 hinges on increased electrification and a strategy to wean off fossil fuels provincewide, so the electricity needs for transport and industry will be massive.

An updated strategic plan from Hydro-Quebec will be presented in November outlining those needs, president and CEO Michael Sabia told reporters on Thursday, after recent deals with NB Power underscored interprovincial demand.

Legault said the report will trigger a broader debate on energy transition and how the province can be a leader in the green economy. He said he wasn't ruling out any potential power sources — except for a return to nuclear power at this stage.

 

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Big prizes awarded to European electricity prediction specialists

Electricity Grid Flow Prediction leverages big data, machine learning, and weather analytics to forecast power flows across smart grids, enhancing reliability, reducing blackouts and curtailment, and optimizing renewable integration under EU Horizon 2020 innovation.

 

Key Points

Short-term forecasting of power flows using big data, weather inputs, and machine learning to stabilize smart grids.

✅ Uses big data, weather, and ML for 6-hour forecasts

✅ Improves reliability, cuts blackouts and energy waste

✅ Supports smart grids, renewables, and grid balancing

 

Three European prediction specialists have won prizes worth €2 million for developing the most accurate predictions of electricity flow through a grid

The three winners of the Big Data Technologies Horizon Prize received their awards at a ceremony on 12th November in Austria.

The first prize of €1.2 million went to Professor José Vilar from Spain, while Belgians Sofie Verrewaere and Yann-Aël Le Borgne came in joint second place and won €400,000 each.

The challenge was open to individuals groups and organisations from countries taking part in the EU’s research and innovation programme, Horizon 2020.

Carlos Moedas, Commissioner for Research, Science and Innovation, said: “Energy is one of the crucial sectors that are being transformed by the digital grid worldwide.

“This Prize is a good example of how we support a positive transformation through the EU’s research and innovation programme, Horizon 2020.

“For the future, we have designed our next programme, Horizon Europe, to put even more emphasis on the merger of the physical and digital worlds across sectors such as energy, transport and health.”

The challenge for the applicants was to create AI-driven software that could predict the likely flow of electricity through a grid taking into account a number of factors including the weather and the generation source (i.e. wind turbines, solar cells, etc).

Using a large quantity of data from electricity grids, EU smart meters, combined with additional data such as weather conditions, applicants had to develop software that could predict the flow of energy through the grid over a six-hour period.

Commissioner for Digital Economy and Society Mariya Gabriel said: “The wide range of possible applications of these winning submissions could bring tangible benefits to all European citizens, including efforts to tackle climate change with machine learning across sectors.”

The decision to focus on energy grids for this particular prize was driven by a clear market need, including expanding HVDC technology capabilities.

Today’s energy is produced at millions of interconnected and dispersed unpredictable sites such as wind turbines, solar cells, etc., so it is harder to ensure that electricity supply matches the demand at all times.

This complexity means that huge amounts of data are produced at the energy generation sites, in the grid and at the place where the energy is consumed.

Being able to make accurate, short-term predictions about power grid traffic is therefore vital to reduce the risks of blackouts or, by enabling utilities to use AI for energy savings, limit waste of energy.

Reliable predictions can also be used in fields such as biology and healthcare. The predictions can help to diagnose and cure diseases as well as to allocate resources where they are most needed.

Ultimately, the winning ideas are set to be picked up by the energy sector in the hopes of creating smarter electricity infrastructure, more economic and more reliable power grids.

 

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Solar Plus Battery Storage Cheaper Than Conventional Power in Germany

Germany Solar-Plus-Storage Cost Parity signals grid parity as solar power with battery storage undercuts conventional electricity. Falling LCOE, policy incentives, and economies of scale accelerate the energy transition and decarbonization across Germany's power market.

 

Key Points

The point at which solar power with battery storage is cheaper than conventional grid electricity across Germany.

✅ Lower LCOE from tech advances and economies of scale

✅ EEG incentives and streamlined installs cut total costs

✅ Enhances energy security, reduces fossil fuel dependence

 

Germany, a global leader in renewable energy adoption, with clean energy supplying about half of its electricity in recent years, has reached a significant milestone: the cost of solar power combined with battery storage has now fallen below that of conventional electricity sources. This development marks a transformative shift in the energy landscape, showcasing the increasing affordability and competitiveness of renewable energy technologies and reinforcing Germany’s position as a pioneer in the transition to sustainable energy.

The decline in costs for solar power paired with battery storage represents a breakthrough in Germany’s energy sector, especially amid the recent solar power boost during the energy crisis, where the transition from traditional fossil fuels to cleaner alternatives has been a central focus. Historically, conventional power sources such as coal, natural gas, and nuclear energy have dominated electricity markets due to their established infrastructure and relatively stable pricing. However, the rapid advancements in solar technology and energy storage solutions are altering this dynamic, making renewable energy not only environmentally preferable but also economically advantageous.

Several factors contribute to the cost reduction of solar power with battery storage:

  1. Technological Advancements: The technology behind solar panels and battery storage systems has evolved significantly over recent years. Solar panel efficiency has improved, allowing for greater energy generation from smaller installations. Similarly, cheaper batteries have advanced, with reductions in cost and increases in energy density and lifespan. These improvements mean that solar installations can produce more electricity and store it more effectively, enhancing their economic viability.

  2. Economies of Scale: As demand for solar and battery storage systems has grown, manufacturers have scaled up production, leading to economies of scale. This scaling has driven down the cost of both solar panels and batteries, making them more affordable for consumers. As the market for these technologies expands, prices are expected to continue decreasing, further enhancing their competitiveness.

  3. Government Incentives and Policies: Germany’s commitment to renewable energy has been supported by robust government policies and incentives. The country’s Renewable Energy Sources Act (EEG) and other supportive measures, alongside efforts to remove barriers to PV in Berlin that could accelerate adoption, have provided financial incentives for the adoption of solar power and battery storage. These policies have encouraged investment in renewable technologies and facilitated their integration into the energy market, contributing to the overall reduction in costs.

  4. Falling Installation Costs: The cost of installing solar power systems and battery storage has decreased as the industry has matured. Advances in installation techniques, increased competition among service providers, and streamlined permitting processes have all contributed to lower installation costs. This reduction in upfront expenses has made solar with battery storage more accessible and financially attractive to both residential and commercial consumers.

The economic benefits of solar power with battery storage becoming cheaper than conventional power are substantial. For consumers, this shift translates into lower electricity bills and reduced reliance on fossil fuels. Solar installations with battery storage allow households and businesses to generate their own electricity, store it for use during times of low sunlight, and even sell excess power back to the grid, reflecting how solar is reshaping electricity prices in Northern Europe as markets adapt. This self-sufficiency reduces exposure to fluctuating energy prices and enhances energy security.

For the broader energy market, the decreasing cost of solar power with battery storage challenges the dominance of conventional power sources. As renewable energy becomes more cost-effective, it creates pressure on traditional energy providers to adapt and invest in cleaner technologies, including responses to instances of negative electricity prices during renewable surpluses. This shift can accelerate the transition to a low-carbon energy system and contribute to the reduction of greenhouse gas emissions.

Germany’s achievement also has implications for global energy markets. The country’s success in making solar with battery storage cheaper than conventional power serves as a model for other nations pursuing similar energy transitions. As the cost of renewable technologies continues to decline, other countries can leverage these advancements to enhance their own energy systems, reduce carbon emissions, and achieve energy independence amid over 30% of global electricity now from renewables trends worldwide.

The impact of this development extends beyond economics. It represents a significant step forward in addressing climate change and promoting sustainability. By reducing the cost of renewable energy technologies, Germany is accelerating the shift towards a cleaner and more resilient energy system. This progress aligns with the country’s ambitious climate goals and reinforces its role as a leader in global efforts to combat climate change.

Looking ahead, several challenges remain. The integration of renewable energy into existing energy infrastructure, grid stability, and the management of energy storage are all areas that require continued innovation and investment. However, the decreasing cost of solar power with battery storage provides a strong foundation for addressing these challenges and advancing the transition to a sustainable energy future.

In conclusion, the fact that solar power with battery storage in Germany has become cheaper than conventional power is a groundbreaking development with wide-ranging implications. It underscores the technological advancements, economic benefits, and environmental gains associated with renewable energy technologies. As Germany continues to lead the way in clean energy adoption, this achievement highlights the potential for renewable energy to drive global change and reshape the future of energy.

 

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Purdue: As Ransomware Attacks Increase, New Algorithm May Help Prevent Power Blackouts

Infrastructure Security Algorithm prioritizes cyber defense for power grids and critical infrastructure, mitigating ransomware, blackout risks, and cascading failures by guiding utilities, regulators, and cyber insurers on optimal security investment allocation.

 

Key Points

An algorithm that optimizes security spending to cut ransomware and blackout risks across critical infrastructure.

✅ Guides utilities on optimal security allocation

✅ Uses incentives to correct human risk biases

✅ Prioritizes assets to prevent cascading outages

 

Millions of people could suddenly lose electricity if a ransomware attack just slightly tweaked energy flow onto the U.S. power grid, as past US utility intrusions have shown.

No single power utility company has enough resources to protect the entire grid, but maybe all 3,000 of the grid's utilities could fill in the most crucial security gaps if there were a map showing where to prioritize their security investments.

Purdue University researchers have developed an algorithm to create that map. Using this tool, regulatory authorities or cyber insurance companies could establish a framework for protecting the U.S. power grid that guides the security investments of power utility companies to parts of the grid at greatest risk of causing a blackout if hacked.

Power grids are a type of critical infrastructure, which is any network - whether physical like water systems or virtual like health care record keeping - considered essential to a country's function and safety. The biggest ransomware attacks in history have happened in the past year, affecting most sectors of critical infrastructure in the U.S. such as grain distribution systems in the food and agriculture sector and the Colonial Pipeline, which carries fuel throughout the East Coast, prompting increased military preparation for grid hacks in the U.S.

With this trend in mind, Purdue researchers evaluated the algorithm in the context of various types of critical infrastructure in addition to the power sector, including electricity-sector IoT devices that interface with grid operations. The goal is that the algorithm would help secure any large and complex infrastructure system against cyberattacks.

"Multiple companies own different parts of infrastructure. When ransomware hits, it affects lots of different pieces of technology owned by different providers, so that's what makes ransomware a problem at the state, national and even global level," said Saurabh Bagchi, a professor in the Elmore Family School of Electrical and Computer Engineering and Center for Education and Research in Information Assurance and Security at Purdue. "When you are investing security money on large-scale infrastructures, bad investment decisions can mean your power grid goes out, or your telecommunications network goes out for a few days."

Protecting infrastructure from hacks by improving security investment decisions

The researchers tested the algorithm in simulations of previously reported hacks to four infrastructure systems: a smart grid, industrial control system, e-commerce platform and web-based telecommunications network. They found that use of this algorithm results in the most optimal allocation of security investments for reducing the impact of a cyberattack.

The team's findings appear in a paper presented at this year's IEEE Symposium on Security and Privacy, the premier conference in the area of computer security. The team comprises Purdue professors Shreyas Sundaram and Timothy Cason and former PhD students Mustafa Abdallah and Daniel Woods.

"No one has an infinite security budget. You must decide how much to invest in each of your assets so that you gain a bump in the security of the overall system," Bagchi said.

The power grid, for example, is so interconnected that the security decisions of one power utility company can greatly impact the operations of other electrical plants. If the computers controlling one area's generators don't have adequate security protection, as seen when Russian hackers accessed control rooms at U.S. utilities, then a hack to those computers would disrupt energy flow to another area's generators, forcing them to shut down.

Since not all of the grid's utilities have the same security budget, it can be hard to ensure that critical points of entry to the grid's controls get the most investment in security protection.

The algorithm that Purdue researchers developed would incentivize each security decision maker to allocate security investments in a way that limits the cumulative damage a ransomware attack could cause. An attack on a single generator, for instance, would have less impact than an attack on the controls for a network of generators, which sophisticated grid-disruption malware can target at scale, rather than for the protection of a single generator.

Building an algorithm that considers the effects of human behavior

Bagchi's research shows how to increase cybersecurity in ways that address the interconnected nature of critical infrastructure but don't require an overhaul of the entire infrastructure system to be implemented.

As director of Purdue's Center for Resilient Infrastructures, Systems, and Processes, Bagchi has worked with the U.S. Department of Defense, Northrop Grumman Corp., Intel Corp., Adobe Inc., Google LLC and IBM Corp. on adopting solutions from his research. Bagchi's work has revealed the advantages of establishing an automatic response to attacks, and analyses like Symantec's Dragonfly report highlight energy-sector risks, leading to key innovations against ransomware threats, such as more effective ways to make decisions about backing up data.

There's a compelling reason why incentivizing good security decisions would work, Bagchi said. He and his team designed the algorithm based on findings from the field of behavioral economics, which studies how people make decisions with money.

"Before our work, not much computer security research had been done on how behaviors and biases affect the best defense mechanisms in a system. That's partly because humans are terrible at evaluating risk and an algorithm doesn't have any human biases," Bagchi said. "But for any system of reasonable complexity, decisions about security investments are almost always made with humans in the loop. For our algorithm, we explicitly consider the fact that different participants in an infrastructure system have different biases."

To develop the algorithm, Bagchi's team started by playing a game. They ran a series of experiments analyzing how groups of students chose to protect fake assets with fake investments. As in past studies in behavioral economics, they found that most study participants guessed poorly which assets were the most valuable and should be protected from security attacks. Most study participants also tended to spread out their investments instead of allocating them to one asset even when they were told which asset is the most vulnerable to an attack.

Using these findings, the researchers designed an algorithm that could work two ways: Either security decision makers pay a tax or fine when they make decisions that are less than optimal for the overall security of the system, or security decision makers receive a payment for investing in the most optimal manner.

"Right now, fines are levied as a reactive measure if there is a security incident. Fines or taxes don't have any relationship to the security investments or data of the different operators in critical infrastructure," Bagchi said.

In the researchers' simulations of real-world infrastructure systems, the algorithm successfully minimized the likelihood of losing assets to an attack that would decrease the overall security of the infrastructure system.

Bagchi's research group is working to make the algorithm more scalable and able to adapt to an attacker who may make multiple attempts to hack into a system. The researchers' work on the algorithm is funded by the National Science Foundation, the Wabash Heartland Innovation Network and the Army Research Lab.

Cybersecurity is an area of focus through Purdue's Next Moves, a set of initiatives that works to address some of the greatest technology challenges facing the U.S. Purdue's cybersecurity experts offer insights and assistance to improve the protection of power plants, electrical grids and other critical infrastructure.

 

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Thermal power plants’ PLF up on rising demand, lower hydro generation

India Coal Power PLF rose as capacity utilisation improved on rising peak demand and hydropower shortfall; thermal plants lifted plant load factor, IPPs lagged, and generation beat program targets amid weak rainfall and slower snowmelt.

 

Key Points

Coal plant load factor in India rose in May on higher demand and weak hydropower, with generation beating targets.

✅ PLF rose to 65.3% as demand climbed

✅ Hydel generation fell 14% YoY on poor rainfall

✅ IPP PLF at 57.8%, below 60% debt comfort

 

Capacity utilisation levels of coal-based power plants improved in May because of a surge in electricity demand and lower generation from hydroelectric sources. The plant load factor (PLF) of thermal power plants went up to 65.3% in the month, 1.7 percentage points higher than the year-ago period.

While PLFs of central and state government-owned plants were 75.5% and 64.5%, respectively, the same for independent power producers (IPPs) stood at 57.8%, even as coal and electricity shortages eased across the market. Though PLFs of IPPs were higher than May 2017 levels, it failed to cross the 60% mark, which eases debt servicing capabilities of power generation assets.

Thermal power plants generated 96,580 million units (MU) in May, 4% more than the programme set for the month and 5.2% higher than last year, partly supported by higher imported coal volumes in the market. On the other hand, hydel plants produced 10,638 MU, 10% lower than the target, reflecting a 14% decline from last year.

#google#

Peak demand of power on the last day of the month was 1,62,132 MW, 4.3% higher than the demand registered in the same day a year ago, underscoring India's position as the third-largest electricity producer globally.

According to sources, hydropower plants have been generating lesser than expected electricity due to inadequate rainfall and snow melting at a slower pace than previous years, even as the US reported a power generation jump year on year. Data for power generation from renewable sources have not been made available yet.

 

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The Cool Way Scientists Turned Falling Raindrops Into Electricity

Raindrop Triboelectric Energy Harvesting converts falling water into electricity using Teflon (PTFE) on indium tin oxide and an aluminum electrode, forming a transient water bridge; a low frequency nanogenerator for renewable, static electricity harvesting.

 

Key Points

A method using PTFE, ITO, and an aluminum electrode to turn raindrop impacts into low frequency electrical power.

✅ PTFE on ITO boosts charge transfer efficiency.

✅ Water bridge links electrodes for rapid discharge.

✅ Low frequency output suits continuous energy harvesting.

 

Scientists at the City University of Hong Kong have used a Teflon-coated surface and a phenomenon called triboelectricity to generate a charge from raindrops. “Here we develop a device to harvest energy from impinging water droplets by using an architecture that comprises a polytetrafluoroethylene [Teflon] film on an indium tin oxide substrate plus an aluminium electrode,” they explain in their new paper in Nature as a step toward cheap, abundant electricity in the long term.

Triboelectricity itself is an old concept. The word means “friction electricity”—from the Greek tribo, to rub or wear down, which is why a diatribe tires you out—and dates back a long, long time. Static electricity is the most famous kind of triboelectric, and related work has shown electricity from the night sky can be harvested as well in niche setups. In most naturally occurring kinds, scientists have studied triboelectric in order to avoid its effects, like explosions inside of grain silos or hospital workers touching off pure oxygen. (Blowing sand causes an electric field, and NASA even worries about static when astronauts eventually land on Mars.)

One of the most studied forms of intentional and useful triboelectric is in systems such as ocean wave generators where the natural friction of waves meets nanogenerators of triboelectric energy. These even already use Teflon, which has natural conductivity that makes it ideal for this job. But triboelectricity is chaotic, and harnessing it generally involves a bunch of complicated, intersecting variables that can vary with the hourly weather. Promises of static electricity charging devices have often been, well, so much hot, sandy wind.

The scientists at City University of Hong Kong used triboelectric ideas to turn falling raindrops into energy. They say previous versions of the same idea were not very efficient, with materials that didn’t allow for high-fidelity transfer of electrical charge. (Many sources of renewable energy aren’t yet as efficient to turn into power, both because of developing technology and because their renewability means even less efficient use could be better than, for example, fossil fuels, and advances in renewable energy storage could help.)

“[A]chieving a high density of electrical power generation is challenging,” the team explains in its paper. “Traditional hydraulic power generation mainly uses electromagnetic generators that are heavy, bulky, and become inefficient with low water supply.” Diversifying how power is generated by water sources such as oceans and rivers is good for the existing infrastructure as well as new installations.

The research team found that as simulated raindrops fell on their device, the way the water accumulated and spread created a link between their two electrodes, one Teflon-coated and the other aluminum. This watery de facto wire link closes the loop and allows accumulated energy to move through the system. Because it’s a mechanical setup, it’s not limited to salty seawater, and because the medium is already water, its potential isn’t affected by ambient humidity either.

Raindrop energy is very low frequency, which means this tech joins many other existing pushes to harvest continuously available, low frequency natural energy, including underwater 'kites' that exploit steady currents. To make an interface that increases “instantaneous power density by several orders of magnitude over equivalent devices,” as the researchers say they’ve done here, could represent a major step toward feasibility in triboelectric generation.

 

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