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Covid-19 Energy Transition and Machine Learning reshape climate change policy, electricity planning, and grid operations, from demand forecasting and decarbonization strategies in Europe to scalable electrification modeling and renewable integration across Africa.

Key Points

How the pandemic reshapes energy policy and how ML improves planning, demand forecasts, and grid reliability in Africa.

✅ Pandemic-driven demand shifts strain grid operations and markets

✅ Policy momentum risks rollback; favor future-oriented decarbonization

✅ ML boosts demand prediction, electrification, and grid reliability in Africa

The impact of Covid-19 on the energy system was discussed in an online climate change workshop that also considered how machine learning can help electricity planning in Africa.

This year’s International Conference on Learning Representations event included a workshop held by the Climate Change AI group of academics and artificial intelligence industry representatives, which considered how machine learning can help tackle climate change and highlighted advances by European electricity prediction specialists working in this field.

Bjarne Steffen, senior researcher at the energy politics group at ETH Zürich, shared his insights at the workshop on how Covid-19 and the accompanying economic crisis are affecting recently introduced ‘green’ policies. “The crisis hit at a time when energy policies were experiencing increasing momentum towards climate action, especially in Europe, and in proposals to invest in smarter electricity infrastructure for long-term resilience,” said Steffen, who added the coronavirus pandemic has cast into doubt the implementation of such progressive policies.

The academic said there was a risk of overreacting to the public health crisis, as far as progress towards climate change goals was concerned.

Lobbying

“Many interest groups from carbon-intensive industries are pushing to remove the emissions trading system and other green policies,” said Steffen. “In cases where those policies are having a serious impact on carbon-emitting industries, governments should offer temporary waivers during this temporary crisis, instead of overhauling the regulatory structure.”

However, the ETH Zürich researcher said any temptation to impose environmental conditions to bail-outs for carbon-intensive industries should be resisted. “While it is tempting to push a green agenda in the relief packages, tying short-term environmental conditions to bail-outs is impractical, given the uncertainty in how long this crisis will last,” he said. “It is better to include provisions that will give more control over future decisions to decarbonize industries, such as the government taking equity shares in companies.”

Steffen shared with pv magazine readers an article published in Joule which can be accessed here, and which articulates his arguments about how Covid-19 could affect the energy transition.

Covid-19 in the U.K.

The electricity system in the U.K. is also being affected by Covid-19, even as the U.S. electric grid grapples with climate risks, according to Jack Kelly, founder of London-based, not-for-profit, greenhouse gas emission reduction research laboratory Open Climate Fix.

“The crisis has reduced overall electricity use in the U.K.,” said Kelly. “Residential use has increased but this has not offset reductions in commercial and industrial loads.”

Steve Wallace, a power system manager at British electricity system operator National Grid ESO recently told U.K. broadcaster the BBC electricity demand has fallen 15-20% across the U.K. The National Grid ESO blog has stated the fall-off makes managing grid functions such as voltage regulation more challenging.

Open Climate Fix’s Kelly noted even events such as a nationally-coordinated round of applause for key workers was followed by a dramatic surge in demand, stating: “On April 16, the National Grid saw a nearly 1 GW spike in electricity demand over 10 minutes after everyone finished clapping for healthcare workers and went about the rest of their evenings.”

Climate Change AI workshop panelists also discussed the impact machine learning could have on improving electricity planning in Africa. The Electricity Growth and Use in Developing Economies (e-Guide) initiative funded by fossil fuel philanthropic organization the Rockefeller Foundation aims to use data to improve the planning and operation of electricity systems in developing countries.

E-Guide members Nathan Williams, an assistant professor at the Rochester Institute of Technology (RIT) in New York state, and Simone Fobi, a PhD student at Columbia University in NYC, spoke about their work at the Climate Change AI workshop, which closed on Thursday. Williams emphasized the importance of demand prediction, saying: “Uncertainty around current and future electricity consumption leads to inefficient planning. The weak link for energy planning tools is the poor quality of demand data.”

Fobi said: “We are trying to use machine learning to make use of lower-quality data and still be able to make strong predictions.”

The market maturity of individual solar home systems and PV mini-grids in Africa mean more complex electrification plan modeling is required, similar to integrating AI data centers into Canada's grids at scale.

Modeling

“When we are doing [electricity] access planning, we are trying to figure out where the demand will be and how much demand will exist so we can propose the right technology,” added Fobi. “This makes demand estimation crucial to efficient planning.”

Unlike many traditional modeling approaches, machine learning is scalable and transferable. Rochester’s Williams has been using data from nations such as Kenya, which are more advanced in their electrification efforts, to train machine learning models to make predictions to guide electrification efforts in countries which are not as far down the track.

Williams also discussed work being undertaken by e-Guide members at the Colorado School of Mines, which uses nighttime satellite imagery and machine learning to assess the reliability of grid infrastructure in India, where new algorithms to prevent ransomware-induced blackouts are also advancing.

Rural power

Another e-Guide project, led by Jay Taneja at the University of Massachusetts, Amherst – and co-funded by the Energy and Economic Growth program on development spending based at Berkeley – uses satellite imagery to identify productive uses of electricity in rural areas by detecting pollution signals from diesel irrigation pumps.

Though good quality data is often not readily available for Africa, Williams added, it does exist.

“We have spent years developing trusting relationships with utilities,” said the RIT academic. “Once our partners realize the value proposition we can offer, they are enthusiastic about sharing their data … We can’t do machine learning without high-quality data and this requires that organizations can effectively collect, organize, store and work with data. Data can transform the electricity sector, as shown by Canadian projects to use AI for energy savings, but capacity building is crucial.”

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Minnesota 100% Carbon-Free Electricity advances renewable energy: wind, solar, hydropower, hydrogen, biogas from landfill gas and anaerobic digestion; excludes incineration in environmental justice areas; uses renewable energy credits and streamlined permitting.

Key Points

Minnesota's mandate requires utilities to deliver 100% carbon-free power by 2040 with targets and EJ safeguards.

✅ Utilities must hit 90% carbon-free by 2035; 100% by 2040.

✅ Incineration in EJ areas excluded; biogas, wind, solar allowed.

✅ Compliance via renewable credits; streamlined permitting.

Minnesota Gov. Tim Walz, D, is expected to soon sign a bill establishing a clean electricity standard requiring utilities in the state to provide electricity from 100% carbon-free sources by 2040. The bill also calls for utilities to generate at least 55% of their electricity from renewable energy sources by 2035, a trajectory similar to New Mexico's clean electricity push underway this decade.

Electricity generated from landfill gas and anaerobic digestion are named as approved renewable energy technologies, but electricity generated from incinerators operating in “environmental justice areas”, reflecting concerns about renewable facilities violating pollution rules in some states, will not be counted toward the goal. Wind, solar, and certain hydropower and hydrogen energy sources are also considered renewable in the bill. 

The bill defines EJ areas as places where at least 40% of residents are not white, 35% of households have an income that’s below 200% of the federal poverty line, and 40% or more of residents over age 5 have “limited” English proficiency. Areas the U.S. state defines as “Indian country” are also considered EJ areas.

Some of the state’s largest electric utilities, like Xcel Energy and Minnesota Power, have already pledged to move to carbon-free energy, and utilities such as Alliant Energy have outlined carbon-neutral plans in the region, but this bill speeds up that goal by 10 years, Minnesota Public Radio reported. The bill calls for public utilities operating in the state to be 80% carbon-free and other electric utilities to be 60% carbon-free by 2030. All utilities must be 90% carbon-free by 2035 before ultimately hitting the 100% mark in 2040, according to the bill.  

The bill gives utilities some leniency if they demonstrate to state regulators that they can’t offer affordable power while working toward the benchmarks, acknowledging reliability challenges seen in places like California's grid during the clean energy transition. It also allows utilities to buy renewable energy credits to meet the standard instead of generating the energy themselves. 

Patrick Serfass, executive director of the American Biogas Council, said the bill will incentivize more biogas-related electricity projects, “which means the recycling of more organic material and more renewable electricity in the state. Those are all good things,” he said. ABC sees significant potential for biogas production in Minnesota, though the federal climate law has delivered mixed results for accelerating clean power deployment.

The bill also aims to streamline the permitting process for new energy projects in the state, even as some states consider limits on clean energy that would constrain utility use, and calls for higher minimum wage requirements for workers.

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Egypt Renewable Energy Expansion targets solar and wind power projects to diversify the energy mix, adding 6.6 GW by 2020 and reaching 8,200 MW, with UK cooperation, grid upgrades, and investment in the electricity sector.

Key Points

A plan to boost solar and wind by 6.6 GW by 2020, reaching 8,200 MW and diversifying Egypt's energy mix.

✅ Adds 6.6 GW by 2020; targets 8,200 MW total capacity

✅ Focus on solar, wind, grid upgrades, and investment

✅ UK-Egypt cooperation in electricity sector projects

Egypt is planning to expand into renewable energy projects in a bid to increase its contribution to the energy mix, in step with global records being set in renewables, and amid Saudi Arabia’s 60 GW drive in the region, the country’s minister of electricity and renewable energy Mohamed Shaker said.

Renewable power is expected to add 6.6 gigawatts (GW) by the end of 2020, a scale comparable to Saudi wind expansion underway, with plans to reach 8,200 megawatts (MW) after the completion of the renewable energy projects currently under consideration, reflecting gains seen since IRENA’s 2016 record year for renewables, Shaker added in a statement on Tuesday, even as regional challenges persist.

This came during the minister’s video-conference meeting with the British ambassador to Egypt Geoffrey Adams to explore the potential means for cooperation between the two countries in the electricity sector, including lessons from the UK project backlog now affecting investments and from Ireland’s green-electricity goals being pursued.

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Commercial Electric Tariffs explain utility rate structures, peak demand charges, kWh vs kW pricing, time-of-use periods, voltage, delivery, capacity ratchets, and riders, guiding facility managers in tariff analysis for accurate energy savings.

Key Points

Commercial electric tariffs define utility pricing for energy, demand, delivery, time-of-use periods, riders, and ratchet charges.

✅ Separate kWh charges from kW peak demand fees.

✅ Verify time-of-use windows and demand interval length.

✅ Review riders, capacity ratchets, and minimum demand clauses.

Especially during these tough economic times, as major changes to electric bills are debated in some states, facility executives who don’t understand how their power is priced have been disappointed when their energy projects failed to produce expected dollar savings. Here’s how not to be one of them.

Your electric rate is spelled out in a document called a “tariff” that can be downloaded from your utility’s web page. A tariff should clearly spell out the costs for each component that is part of your rate, reflecting cost allocation practices in your region. Don’t be surprised to learn that it contains a bunch of them. Unlike residential electric rates, commercial electric bills are not based solely on the quantity of kilowatt-hours (kWh) consumed in a billing period (in the United States, that’s a month). Instead, different rates may apply to how your power is supplied, how it is delivered via electricity delivery charges, when it was consumed, its voltage, how fast it was used (in kW), and other factors.

If a tariff’s lingo and word structure are too opaque, spend some time with a utility account rep to translate it. Many state utility commissions also have customer advocates that may assist as they explore new utility rate designs that affect customers. Alternatively, for a fee, facility managers can privately chat with an energy consultant.

Common mistakes

Many facility managers try to estimate savings based on an averaged electric rate, i.e., annual electric spend divided by annual kWh. However, in markets where electricity demand is flat, such a number may obscure the fastest rising cost component: monthly peak demand charges, measured in dollars per kW (or kilo-volt-amperes, kVA).

This charge is like a monthly speeding ticket, based solely on the highest speed you drove during that time. In some areas, peak demand charges now account for 30 to 60 percent of a facility’s annual electric spend. When projecting energy cost savings, failing to separately account for kW peak demand and kWh consumption may result in erroneous results, and a lot of questions from the C-suite.

How peak demand charges are calculated varies among utilities. Some base it on the highest average speed of use across one hour in a month, while others may use the highest average speed during a 15- or 30-minute period. Others may average several of the highest speeds within a defined time period (for example, 8 a.m. to 6 p.m. on weekdays). It is whatever your tariff says it is.

Because some power-consuming (or producing) devices, including those tied to smart home electricity networks, vary in their operation or abilities, they may save money on a few — but not all — of those rate components. If an equipment vendor calculates savings from its product by using an average electric rate, take pause. Tell the vendor to return after the proposal has been redone using tariff-based numbers.

When a vendor is the only person calculating potential savings from using a product, there’s also a built-in conflict of interest: The person profiting from an equipment sale should not also be the one calculating its expected financial return. Before signing any energy project contracts, it’s essential that someone independent of the deal reviews projected savings. That person (typically an energy or engineering consultant) should be quite familiar with your facility’s electric tariff, including any special provisions, riders, discounts, etc., that may pertain. When this doesn’t happen, savings often don’t occur as planned. 

For example, some utilities add another form of demand charge, based on the highest kW in a year. It has various names: capacity, contract demand, or the generic term “ratchet charge.” Some utilities also have a minimum ratchet charge which may be based on a percent of a facility’s annual kW peak. It ensures collection of sufficient utility revenue to cover the cost of installed transmission and distribution even when a customer significantly cuts its peak demand.

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Manitoba Hydro capacity constraints challenge clean energy growth as industrial demand, hydrogen projects, EV batteries, and electrification strain the grid; limited surplus, renewables, storage, and transmission bottlenecks hinder new high-load connections.

Key Points

Limited surplus power blocks new energy-intensive loads until added generation and transmission expand Manitoba's grid.

✅ No firm commitments for new energy-intensive industrial customers

✅ Single large load could consume remaining surplus capacity

✅ New renewables need transmission; gas, nuclear face trade-offs

Manitoba Hydro lacks the capacity to provide electricity to any new "energy intensive" industrial customers, the Crown corporation warns in a confidential briefing note that undercuts the idea this province can lure large businesses with an ample supply of clean, green energy, as the need for new power generation looms for the utility.

On July 28, provincial economic development officials unveiled an "energy roadmap" that said Manitoba Hydro must double or triple its generating capacity, as electrical demand could double over the next two decades in order to meet industrial and consumer demand for electricity produced without burning fossil fuels.

Those officials said 18 potential new customers with high energy needs were looking at setting up operations in Manitoba — and warned the province must be careful to choose businesses that provide the greatest economic benefit as well as the lowest environmental impact.

In a briefing note dated Sept. 13, obtained by CBC News, Manitoba Hydro warns it doesn't have enough excess power to hook up any of these new heavy electricity-using customers to the provincial power grid.

There are actually 57 proposals to use large volumes of electricity, Hydro says in the note, including eight projects already in the detailed study phase and nine where the proponents are working on construction agreements.

"Manitoba Hydro is unable to offer firm commitments to prospective customers that may align with Manitoba's energy roadmap and/or provincial economic development objectives," Hydro warns in the note, explaining it is legally obliged to serve all existing customers who need more electricity.

"As such, Manitoba Hydro cannot reserve electric supply for particular projects."

Hydro says in the note its "near-term surplus electricity supply" is so limited amid a Western Canada drought that "a single energy-intensive connection may consume all remaining electrical capacity."

Adding more electrical generating capacity won't be easy, even with new turbine investments underway, and will not happen in time to meet demands from customers looking to set up shop in the province, Hydro warns.

The Crown corporation goes on to say it's grappling with numerous requests from existing and prospective energy-intensive customers, mainly for producing hydrogen, manufacturing electric vehicle batteries and switching from fossil fuels to electricity, such as to use electricity for heat in buildings.

In a statement, Hydro said it wants to ensure Manitobans know the corporation is not running out of power — just the ability to meet the needs of large new customers, and continues to provide clean energy to neighboring provinces today.

"The size of loads looking to come to Manitoba are significantly larger than we typically see, and until additional supply is available, that limits our ability to connect them," Hydro spokesperson Bruce Owen said in a statement.

Adding wind power or battery storage, for example, would require the construction of more transmission lines, and deals such as SaskPower's purchase depend on that interprovincial infrastructure as well.

Natural gas plants are relatively inexpensive to build but do not align with efforts to reduce carbon emissions. Nuclear power plants require at least a decade of lead time to build, and tend to generate local opposition.

Hydro has also ruled out building another hydroelectric dam on the Nelson River, where the Conawapa project was put on hold in 2014.

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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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