Farmers growing power along with their crops

IESO Fictitious Demand Error inflated HOEP in the Ontario electricity market, after embedded generation was mis-modeled; the OEB says double-counted load lifted wholesale prices and shifted costs via the Global Adjustment.

Key Points

An IESO modeling flaw that double-counted load, inflating HOEP and charges in Ontario's wholesale market.

✅ Double-counted unmetered load from embedded generation

✅ Inflated HOEP; shifted costs via Global Adjustment

✅ OEB flagged transparency; exporters paid more

For almost a year, the operator of Ontario’s electricity system erroneously counted enough phantom demand to power a small city, causing prices to spike and hundreds of millions of dollars in extra charges to consumers, according to the provincial energy regulator.

The Independent Electricity System Operator (IESO) also failed to tell anyone about the error once it noticed and fixed it.

The error likely added between $450 million and $560 million to hourly rates and other charges before it was fixed in April 2017, according to a report released this month by the Ontario Energy Board’s Market Surveillance Panel.

It did this by adding as much as 220 MW of “fictitious demand” to the market starting in May 2016, when the IESO started paying consumers who reduced their demand for power during peak periods. This involved the integration of small-scale embedded generation (largely made up of solar) into its wholesale model for the first time.

The mistake assumed maximum consumption at such sites without meters, and double-counted that consumption.

The OEB said the mistake particularly hurt exporters and some end-users, who did not benefit from a related reduction of a global adjustment rate applicable to other customers.

“The most direct impact of the increase in HOEP (Hourly Ontario Energy Price) was felt by Ontario consumers and exporters of electricity, who paid an artificially high HOEP, to the benefit of generators and importers,” the OEB said.

The mix-up did not result in an equivalent increase in total system costs, because changes to the HOEP are offset by inverse changes to a electricity cost allocation mechanism such as the Global Adjustment rate, the OEB noted.

A chart from the OEB's report shows the time of day when fictitious demand was added to the system, and its influence on hourly rates.

Peak time spikes
The OEB said that the fictitious demand “regularly inflated” the hourly price of energy and other costs calculated as a direct function of it.

For almost a year, Ontario's electricity system operator @IESO_Tweets erroneously counted enough phantom demand to power a small city, causing price spikes and hundreds of millions in charges to consumers, @OntEnergyBoard says. @5thEstate reports.

It estimated the average increase to the HOEP was as much as $4.50/MWh, but that price spikes, compounded by scheduled OEB rate changes, would have been much higher during busier times, such as the mid-morning and early evening.

“In times of tight supply, the addition of fictitious demand often had a dramatic inflationary impact on the HOEP,” the report said.

That meant on one summer evening in 2016 the hourly rate jumped to $1,619/MWh, it said, which was the fourth highest in the history of the Ontario wholesale electricity market.

“Additional demand is met by scheduling increasingly expensive supply, thus increasing the market price. In instances where supply is tight and the supply stack is steep, small increases in demand can cause significant increases in the market price.

The OEB questioned why, as of September this year, the IESO had failed to notify its customers or the broader public, amid a broader auditor-regulator dispute that drew political attention, about the mistake and its effect on prices.

“It's time for greater transparency on where electricity costs are really coming from,” said Sarah Buchanan, clean energy program manager at Environmental Defence.

“Ontario will be making big decisions in the coming years about whether to keep our electricity grid clean, or burn more fossil fuels to keep the lights on,” she added. “These decisions need to be informed by the best possible evidence, and that can't happen if critical information is hidden.”

In a response to the OEB report on Monday, the IESO said its own initial analysis found that the error likely pushed wholesale electricity payments up by $225 million. That calculation assumed that the higher prices would have changed consumer behaviour, while upcoming electricity auctions were cited as a way to lower costs, it said.

In response to questions, a spokesperson said residential and small commercial consumers would have saved $11 million in electricity costs over the 11-month period, even as a typical bill increase loomed province-wide, while larger consumers would have paid an extra $14 million.

That is because residential and small commercial customers pay some costs via time-of-use rates, including a temporary recovery rate framework, the IESO said, while larger customers pay them in a way that reflects their share of overall electricity use during the five highest demand hours of the year.

The IESO said it could not compensate those that had paid too much, given the complexity of the system, and that the modelling error did not have a significant impact on ratepayers.

While acknowledging the effects of the mistake would vary among its customers, the IESO said the net market impact was less than $10 million, amid ongoing legislation to lower electricity rates in Ontario.

It said it would improve testing of its processes prior to deployment and agreed to publicly disclose errors that significantly affect the wholesale market in the future.

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Puerto Rico Tesla Solar Power enables resilient microgrids using batteries, renewable energy, and energy storage to rebuild the hurricane-damaged grid, reduce fossil fuels, cut costs, and accelerate recovery with scalable solar-plus-storage solutions.

Key Points

A solar-plus-storage plan using Tesla microgrids and batteries to restore Puerto Rico's cleaner, resilient power.

✅ Microgrids cut diesel reliance and harden critical facilities.

✅ Batteries stabilize the grid and shave peak demand costs.

✅ Scalable solar enables faster, modular disaster recovery.

Puerto Rico’s governor Ricardo Rossello has said that he will speak to Elon Musk after the Tesla inventor said his innovative solar and battery systems could be used to restore electricity on the island.

Mr Musk was mentioned in a tweet, referencing an article discussing ways to restore Puerto Rico’s power grid, which was knocked out by Hurricane Maria on September 20.

Restoring the ageing and already-weakened network has proved slow: as of Friday 90 per cent of the island remained without power. The island’s electricity company was declared bankrupt in July.

Mr Musk was asked: “Could @ElonMusk go in and rebuild #PuertoRico’s electricity system with independent solar & battery systems?”

The South African entrepreneur replied: “The Tesla team has done this for many smaller islands around the world, but there is no scalability limit, so it can be done for Puerto Rico too.

“Such a decision would be in the hands of the PR govt, PUC, any commercial stakeholders and, most importantly, the people of PR.”

His suggestion was seized upon by Mr Rossello, who then tweeted: “@ElonMusk Let's talk. Do you want to show the world the power and scalability of your #TeslaTechnologies?

“PR could be that flagship project.”

Mr Musk replied that he was happy to talk.

Restoring power to the battered island is a priority for the government, and improving grid resilience remains critical, with hospitals still running on generators and the 3.5 million people struggling with a lack of refrigeration or air conditioning.

Radios broadcast messages advising people how to keep their insulin cool, and doctors are concerned about people not being able to access dialysis.

And, with its power grid wiped out, the Caribbean island could totally rethink the way it meets its energy needs, drawing on examples like a resilient school microgrid built locally. 

“This is an opportunity to completely transform the way electricity is generated in Puerto Rico and the federal government should support this,” said Judith Enck, the former administrator for the region with the environmental protection agency.

“They need a clean energy renewables plan and not spending hurricane money propping up the old fossil fuel infrastructure.”

Forty-seven per cent of Puerto Rico’s power needs were met by burning oil last year - a very expensive and outdated method of electricity generation. For the US as a whole, petroleum accounted for just 0.3 per cent of all electricity generated in 2016 even as the grid isn’t yet running on 100% renewable energy nationwide.

The majority of the rest of Puerto Rico’s energy came courtesy of coal and natural gas, with renewables, which later faced pandemic-related setbacks, accounting for only two per cent of electricity generation.

“In that time of extreme petroleum prices, the utility was borrowing money and buying oil in order to keep those plants operating,” said Luis Martinez, a lawyer at natural resources defense council and former special aide to the president of Puerto Rico’s environmental quality board.

“That precipitated the bankruptcy that followed. It was in pretty poor shape before the storm. Once the storm got there, it finished the job.”

But Mr Martinez told the website Earther that it might be difficult to secure the financing for rebuilding Puerto Rico with renewables from FEMA (Federal Emergency Management Agency) funds.

“A lot of distribution lines were on wood poles,” he said.

“Concrete would make them more resistant to winds, but that would potentially not be authorized under the use of FEMA funds.

"We’re looking into if some of those requirements can be waived so rebuilding can be more resilient.”

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AI Load Forecasting for Utilities leverages machine learning, smart meters, and predictive analytics to balance energy demand during COVID-19 disruptions, optimize grid reliability, support demand response, and stabilize rates for residential and commercial customers.

Key Points

AI predicts utility demand with ML and smart meters to improve reliability and reduce costs.

✅ Adapts to rapid demand shifts with accurate short term forecasts

✅ Optimizes demand response and distributed energy resources

✅ Reduces outages risk while lowering procurement and operating costs

The spread of the novel coronavirus that causes COVID-19 has prompted state and local governments around the U.S. to institute shelter-in-place orders and business closures. As millions suddenly find themselves confined to their homes, the shift has strained not only internet service providers, streaming platforms, and online retailers, but the utilities supplying power to the nation’s electrical grid, which face longer, more frequent outages as well.

U.S. electricity use on March 27, 2020 was 3% lower than it was on March 27, 2019, a loss of about three years of sales growth. Peter Fox-Penner, director of the Boston University Institute for Sustainable Energy, asserted in a recent op-ed that utility revenues will suffer because providers are halting shutoffs and deferring rate increases. Moreover, according to research firm Wood Mackenzie, the rise in household electricity demand won’t offset reduced business electricity demand, mainly because residential demand makes up just 40% of the total demand across North America.

Some utilities are employing AI and machine learning for the energy transition to address the windfalls and fluctuations in energy usage resulting from COVID-19. Precise load forecasting could ensure that operations aren’t interrupted in the coming months, thereby preventing blackouts and brownouts. And they might also bolster the efficiency of utilities’ internal processes, leading to reduced prices and improved service long after the pandemic ends.

Innowatts
Innowatts, a startup developing an automated toolkit for energy monitoring and management, counts several major U.S. utility companies among its customers, including Portland General Electric, Gexa Energy, Avangrid, Arizona Public Service Electric, WGL, and Mega Energy. Its eUtility platform ingests data from over 34 million smart energy meters across 21 million customers in more than 13 regional energy markets, while its machine learning algorithms analyze the data to forecast short- and long-term loads, variances, weather sensitivity, and more.

Beyond these table-stakes predictions, Innowatts helps evaluate the effects of different rate configurations by mapping utilities’ rate structures against disaggregated cost models. It also produces cost curves for each customer that reveal the margin impacts on the wider business, and it validates the yield of products and cost of customer acquisition with models that learn the relationships between marketing efforts and customer behaviors (like real-time load).

Innowwatts told VentureBeat that it observed “dramatic” shifts in energy usage between the first and fourth weeks of March. In the Northeast, “non-essential” retailers like salons, clothing shops, and dry cleaners were using only 35% as much energy toward the end of the month (after shelter-in-place orders were enacted) versus the beginning of the month, while restaurants (excepting pizza chains) were using only 28%. In Texas, conversely, storage facilities were using 142% as much energy in the fourth week compared with the first.

Innowatts says that throughout these usage surges and declines, its clients took advantage of AI-based load forecasting to learn from short-term shocks and make timely adjustments. Within three days of shelter-in-place orders, the company said, its forecasting models were able to learn new consumption patterns and produce accurate forecasts, accounting for real-time changes.

Innowatts CEO Sid Sachdeva believes that if utility companies had not leveraged machine learning models, demand forecasts in mid-March would have seen variances of 10-20%, significantly impacting operations.

“During these turbulent times, AI-based load forecasting gives energy providers the ability to … develop informed, data-driven strategies for future success,” Sachdeva told VentureBeat. “With utilities and energy retailers seeing a once-in-a-lifetime 30%-plus drop in commercial energy consumption, accurate forecasting has never been more important. Without AI tools, utilities would see their forecasts swing wildly, leading to inaccuracies of 20% or more, placing an enormous strain on their operations and ultimately driving up costs for businesses and consumers.”

Autogrid
Autogrid works with over 50 customers in 10 countries — including Energy Australia, Florida Power & Light, and Southern California Edison — to deliver AI-informed power usage insights. Its platform makes 10 million predictions every 10 minutes and optimizes over 50 megawatts of power, which is enough to supply the average suburb.

Flex, the company’s flagship product, predicts and controls tens of thousands of energy resources from millions of customers by ingesting, storing, and managing petabytes of data from trillions of endpoints. Using a combination of data science, machine learning, and network optimization algorithms, Flex models both physics and customer behavior, automatically anticipating and adjusting for supply and demand patterns through virtual power plants that coordinate distributed assets.

Autogrid also offers a fully managed solution for integrating and utilizing end-customer installations of grid batteries and microgrids. Like Flex, it automatically aggregates, forecasts, and optimizes capacity from assets at sub-stations and transformers, reacting to distribution management needs while providing capacity to avoid capital investments in system upgrades.

Autogrid CEO Dr. Amit Narayan told VentureBeat that the COVID-19 crisis has heavily shifted daily power distribution in California, where it’s having a “significant” downward impact on hourly prices in the energy market. He says that Autogrid has also heard from customers about transformer failures in some regions due to overloaded circuits, which he expects will become a problem in heavily residential and saturated load areas during the summer months (as utilities prepare for blackouts across the U.S. when air conditioning usage goes up).

“In California, [as you’ll recall], more than a million residents faced wildfire prevention-related outages in PG&E territory in 2019,” Narayan said, referring to the controversial planned outages orchestrated by Pacific Gas & Electric last summer. “The demand continues to be high in 2020 in spite of the COVID-19 crisis, as residents prepare to keep the lights on and brace for a similar situation this summer. If a 2019 repeat happens again, it will be even more devastating, given the health crisis and difficulty in buying groceries.”

AI making a difference
AI and machine learning isn’t a silver bullet for the power grid — even with predictive tools at their disposal, utilities are beholden to a tumultuous demand curve and to mounting climate risks across the grid. But providers say they see evidence the tools are already helping to prevent the worst of the pandemic’s effects — chiefly by enabling them to better adjust to shifted daily and weekly power load profiles.

“The societal impact [of the pandemic] will continue to be felt — people may continue working remotely instead of going into the office, they may alter their commute times to avoid rush hour crowds, or may look to alternative modes of transportation,” Schneider Electric chief innovation officer Emmanuel Lagarrigue told VentureBeat. “All of this will impact the daily load curve, and that is where AI and automation can help us with maintenance, performance, and diagnostics within our homes, buildings, and in the grid.”

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Floatgen Floating Offshore Wind Turbine exports first kWh to France's grid from SEM-REV off Le Croisic, showcasing Ideol's concrete floating foundation by Bouygues and advancing marine renewable energy leadership ambitions.

Key Points

A grid-connected demo turbine off Le Croisic, proving Ideol's floating foundation at SEM-REV.

✅ First power exported to French grid from SEM-REV site

✅ Ideol concrete floating base built by Bouygues

✅ Demonstrator can supply up to 5,000 inhabitants

Floating offshore wind turbine Floatgen, the first offshore wind turbine installed off the French coast, exported its first KWh to the electricity grid, echoing the offshore wind power milestone experienced by U.S. customers recently.

The connection of the electricity export cable, similar in ambition to the UK's 2 GW substation program, and a final series of tests carried out in recent days enabled the Floatgen wind turbine, which is installed 22 km off Le Croisic (Loire-Atlantique), to become fully operational on Tuesday 18 September.

This announcement is a highly symbolic step for the partners involved in this project. This wind turbine is the first operational unit of the floating foundation concept patented by Ideol and built in concrete by Bouygues Travaux Publics. A second unit of the Ideol foundation will soon be operational off Japan. For Centrale Nantes, this is the first production tool and the first injection of electricity into its export cable at its SEM-REV test site dedicated to marine renewable energies, alongside projects such as the Scotland-England subsea power link that expand transmission capacity (third installation after tests on acoustic sensors and cable weights).

This announcement is also symbolic for France since Floatgen lays the foundation for an industrial offshore wind energy sector and represents a unique opportunity to become the global leader in floating wind, as major clean energy corridors like the Canadian hydropower line to New York illustrate growing demand.

With its connection to the grid, SEM-REV will enable the wind turbine to supply electricity to 5000 inhabitants, and similar integrated microgrid initiatives show how local reliability can be enhanced.

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Energy Pricing Factors span electricity generation, transmission, and distribution costs, plus natural gas supply-demand, renewables, seasonal peaks, and wholesale pricing effects across residential, commercial, and industrial customers, usage patterns, weather, and grid constraints.

Key Points

They are the costs and market forces driving electricity and natural gas prices, from generation to delivery and demand.

✅ Generation, transmission, distribution shape electricity rates

✅ Gas prices hinge on supply, storage, imports/exports

✅ Demand shifts: weather, economy, and fuel alternatives

There are a lot of factors that affect energy prices globally. What’s included in the price to heat homes and supply them with electricity may be a lot more than some people may think.

Electricity
Generating electricity is the largest component of its price, according to the U.S. Energy Information Administration (EIA). Generation accounts for 56% of the price of electricity, while distribution and transmission account for 31% and 13% respectively.

Homeowners and businesses pay more for electricity than industrial companies, and U.S. electricity prices have recently surged, highlighting broader inflationary pressures. This is because industrial companies can take electricity at higher voltages, reducing transmission costs for energy companies.

“Industrial consumers use more electricity and can receive it at higher voltages, so supplying electricity to these customers is more efficient and less expensive. The price of electricity to industrial customers is generally close to the wholesale price of electricity,” EIA explains.

NYSEG said based on the average use of 600 kilowatt-hours per month, its customers spent the most money on delivery and transition charges in 2020, 57% or about $42, and residential electricity bills increased 5% in 2022 after inflation, according to national data. They also spent on average 35% (~$26) on supply charges and 8% (~$6) on surcharges.

Electricity prices are usually higher in the summer. Why? Because energy companies use sources of electricity that cost more money. It used to be that renewable sources, like solar and wind, were the most expensive sources of energy but increased technological advances have changed this, according to the International Energy Agency’s 2021 World Energy Outlook.

“In most markets, solar PV or wind now represents the cheapest available source of new electricity generation. Clean energy technology is becoming a major new area for investment and employment – and a dynamic arena for international collaboration and competition,” the report said.

Natural gas
The price of natural gas is driven by supply and demand. If there is more supply, prices are generally lower. If there is not as much supply, prices are generally higher the EIA explains. On the other side of the equation, more demand can also increase the price and less demand can decrease the price.

High natural gas prices mean people turn their home thermostats down a few degrees to save money, so the EIA said reduced demand can encourage companies to produce more natural gas, which would in turn help lower the cost. Lower prices will sometimes cause companies to reduce their production, therefore causing the price to rise.

The three major supply factors that affect prices: the amount of natural gas produced, how much is stored, and the volume of gas imported and exported. The three major demand factors that affect price are: changes in winter/summer weather, economic growth, and the broader energy crisis dynamics, as well as how much other fuels are available and their price, said EIA.

To think the price of natural gas is higher when the economy is thriving may sound counterintuitive but that’s exactly what happens. The EIA said this is because of increases in demand.

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Alberta Electricity Market faces energy-only vs capacity debate as transmission, distribution, and administration fees surge; rural rates rise amid a regulated duopoly of investor-owned utilities, prompting calls for competition, innovation, and lower bills.

Key Points

Alberta's electricity market is an energy-only system with rising delivery charges and limited rural competition.

✅ Energy-only design; capacity market scrapped

✅ Delivery charges outpace energy on monthly bills

✅ Rural duopoly limits competition and raises rates

Last week, Alberta’s new Energy Minister Sonya Savage announced the government, through its new electricity rules, would be scrapping plans to shift Alberta’s electricity to a capacity market and would instead be “restoring certainty in the electricity system.”

The proposed transition from energy only to a capacity market is a contentious subject as a market reshuffle unfolds across the province that many Albertans probably don’t know much about. Our electricity market is not a particularly glamorous subject. It’s complicated and confusing and what matters most to ordinary Albertans is how it affects their monthly bills.

What they may not realize is that the cost of their actual electricity used is often just a small fraction of their bill amid rising electricity prices across the province. The majority on an average electricity bill is actually the cost of delivering that electricity from the generator to your house. Charges for transmission, distribution and franchise and administration fees are quickly pushing many Alberta households to the limit with soaring bills.

According to data from Alberta’s Utilities Consumer Advocate (UCA), and alongside policy changes, in 2004 the average monthly transmission costs for residential regulated-rate customers was below $2. In 2018 that cost was averaging nearly $27 a month. The increase is equally dramatic in distribution rates which have more than doubled across the province and range wildly, averaging from as low as $10 a month in 2004 to over $80 a month for some residential regulated-rate customers in 2018.

Where you live determines who delivers your electricity. In Alberta’s biggest cities and a handful of others the distribution systems are municipally owned and operated. Outside those select municipalities most of Alberta’s electricity is delivered by two private companies which operate as a regulated duopoly. In fact, two investor-owned utilities deliver power to over 95 per cent of rural Alberta and they continue to increase their share by purchasing the few rural electricity co-ops that remained their only competition in the market. The cost of buying out their competition is then passed on to the customers, driving rates even higher.

As the CEO of Alberta’s largest remaining electricity co-op, I know very well that as the price of materials, equipment and skilled labour increase, the cost of operating follows. If it costs more to build and maintain an electricity distribution system there will inevitably be a cost increase passed on to the consumer. The question Albertans should be asking is how much is too much and where is all that money going with these private- investor-owned utilities, as the sector faces profound change under provincial leadership?

The reforms to Alberta’s electricity system brought in by Premier Klein in the late 1900s and early 2000s contributed to a surge in investment in the sector and led to an explosion of competition in both electricity generation and retail. 

More players entered the field which put downward pressure on electricity rates, encouraged innovation and gave consumers a competitive choice, even as a Calgary electricity retailer urged the government to scrap the overhaul. But the legislation and regulations that govern rural electricity distribution in Alberta continue to facilitate and even encourage the concentration of ownership among two players which is certainly not in the interests of rural Albertans.

It is also not in the spirit of the United Conservative Party platform commitment to a “market-based” system. A market-based system suggests more competition. Instead, what we have is something approaching a monopoly for many Albertans. The UCP promised a review of the transition to a capacity market that would determine which market would be best for Alberta, and through proposed electricity market changes has decided that we will remain an energy-only market.
Consumers in rural Alberta need electricity to produce the goods that power our biggest industries. Instead of regulating and approving continued rate increases from private multinational corporations, we need to drive competition and innovation that can push rates down and encourage growth and investment in rural-based industries and communities.

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