In November 2019, the first case of Covid-19 was reported in Wuhan, China. During the early days of the outbreak, local authorities attempted to clamp down on sharing information about the virus, but as the transmission strengthened in the region, the government imposed lockdown measures across China’s Hubei province to control the spread of Covid-19. On January 22, Wuhan became the first major city under quarantine, and in the months that followed, many cities followed suit that caused a shock to the global economy. In June, the OECD projected that the global economy would contract by 6% this year if the second wave of the virus is avoided. However, in the case of a second outbreak, the global economy would contract to 7.6%.
Amid the Covid-19 outbreak, policymakers and scientists leveraged data insights to track and contain the virus in cities. As the economy begins to reopen, cities must continue to use data to not only monitor Covid-19 but also initiate a sustainable recovery from the Covid-19 crisis. The World Bank has reported that cities generate more than 80% of global GDP but consume over two-thirds of the world’s energy and account for more than 70% of global CO2 emissions. As cities will play an integral part in achieving a sustainable recovery, they need to capitalise on three data-driven solutions to incorporate more clean energy into the grid system, optimise charging of electric vehicles, and reduce energy consumption in buildings. By doing so, countries will not only create highly skilled employment but also accelerate their transition towards a low-carbon economy.
1) Intelligently align the grid’s energy demand with supply from renewables
In a recent report, the International Energy Agency (IEA) noted that over 90% of the world’s data was created in the past two years. This exponential growth – coupled with rising global commitments towards a low-carbon economy – has enabled data to play a critical role in driving clean energy growth. In the grid system, digital advancement is helping the supply of renewable energy align with electricity demand patterns. Previously, renewable sources like wind and solar came with the challenge of being intermittent. Now, with the right hardware and software in place, along with the proliferation of energy storage, utilities can forecast energy demand and better manage the integration of renewable assets into the grid system. For cities, this presents a unique opportunity to reduce carbon emissions. By understanding the grid’s demand, utilities can intelligently manage their energy storage capacities and optimise price points to sell into the wholesale energy markets.
As cities look to restart operations and seek ways to manage their carbon emissions, data-driven AI-powered energy solutions are going to be essential tools for local authorities and utilities to incorporate. Nancy Pfund, Founder and Managing Partner of DBL Partners, said in an interview that the application of artificial intelligence and machine learning is able to address the very complex challenge of “managing and optimising storage capacity in combination with the electricity generated by solar and wind at different times of the day”. Ms Pfund added that by leveraging these insights, which had been the missing ‘intelligence layer’, “utilities are now able to determine when and at what price to sell into the wholesale energy markets and cost-effectively deploy more renewable energy”.
However, to maximise the deployment of renewable energy in power grids, Ms Pfund highlighted that investment into “grid technologies needs to continue to optimise the supply of renewable energy”. In June, IEA’s Smart Grid report noted that investment in electricity grids declined for the third consecutive year in 2019, falling 7% from the 2018 level to just under $275 billion. Meanwhile, the report also showed that investment in smart grid infrastructure fell by 20%, from $20 billion in 2018 to $18 billion in 2019.
As the economy begins to reopen, cities will need to be mindful in managing their carbon footprint to meet future energy demand. Ms Pfund recommends electric utility companies invest in AI-powered software solutions like DBL’s Advanced Microgrid Solutions (AMS) “to maximise the value of renewables, batteries and entire asset portfolios” and to regularly meet their electricity demand with clean energy supply. In doing so, the city’s electricity supply can shift to renewables at a much faster rate and improve the planning, operation and control of the power system.
2) Incentivise electric vehicle charging during off-peak periods
Smart mobility is one of the critical topics facing local authorities as they look to reopen cities after Covid-19. In recent months, many cities have embraced the slow street movement to promote alternatives to public transportation and private cars by allocating street space for cyclists and walkers. During the lockdown, urban planners have leveraged the fact that since most trips in the city cover short distances, walking or biking within communities is the most feasible mode of transportation. In the short run, the lockdown and slow street movement have helped reduce carbon emissions. Now, as cities look to ease restrictions, new data is suggesting a rebound in carbon emissions post-lockdown with people starting to use private instead of public transport due to health concerns. As we adjust to social distancing measures for the foreseeable future, cities will need to develop holistic, long-term solutions to drive down emissions in the transportation sector. For short commutes, walking and biking are great solutions. However, longer trips still require vehicles. For this reason, the scaling of Zero-Emission Vehicles (ZEVs) infrastructure will be critical in reducing carbon emissions in cities. Data can help play an essential role in the adaptation of ZEVs. By identifying commuting patterns, cities can locate optimal sites for charging stations and begin to synergise the electrification of the transportation sector with the power grid.
Suvranil Majumdar, Project Lead – Electric Mobility at World Bank Group and Advisor to Urban Radar, said in an interview, “Cities need to focus on charging infrastructure/swapping stations for large-scale adoption of EVs that could decarbonise the transportation sector. The development of such infrastructure would be better implemented by using data to not only locate and deploy charging points around high demand routes but also help cities and power utilities assess the potential impact on the grid in specific locations”. In the United States, Bloomberg reported that fast-charging stations remain the final roadblock in the adaptation of electric vehicles. Meanwhile, in emerging markets, such as India, the installation of charging infrastructure is a vital component that is needed to help propel the industry.
As a result, for cities looking to reopen and reduce their carbon emissions, the scaling of EV charging stations that are integrated with a clean power grid system is going to be a critical next step. The IEA’s Global EV Outlook 2020 reports that higher electric vehicle deployment – coupled with more rapid decarbonisation of electricity generation – can reduce oil demand and well-to-wheel greenhouse gas emissions. The impact would not only help countries decarbonise in line with the Paris Agreement goals but also potentially mitigate the harmful effects of climate change. Moreover, by leveraging data, cities can better manage electricity demand and supply to alleviate the potentially negative impact of electric vehicle charging on power systems. The IEA is projecting 250 million electric cars on the road by 2030, which could increase peak demand in the evening by as much as 4–10% in the leading electric vehicle markets (China, European Union and the United States). To address these issues, the IEA recommends the implementation of end-user programming and/or nighttime tariffs that – combined with dynamic controlled charging and vehicle-to-grid services (V2G) – can encourage consumers to charge during periods of low electricity demand or high renewables-based electricity generation.
3) Uncover hidden energy efficiency measures in buildings
According to C40 Cities, buildings account for roughly 50% of a city’s total carbon emissions, and 70% in major cities like London, Los Angeles, and Paris. For cities to achieve sustainable growth and broader climate goals, they will have to look into innovative solutions to reduce their emissions from the building sector. In 2018, the mayors of 19 cities – representing 130 million people – committed to enacting policies and regulations that will make all new buildings carbon neutral by 2030. It may not be new buildings that are a challenge for cities, but rather improving the energy efficiency in existing buildings.
In OECD countries, buildings that already exist are estimated to account for 65% of all buildings by 2060. Data analytics is already being used in property technology solutions, including software, to track monthly energy use or the performance of individual equipment. Now, advanced AI and machine learning capabilities are unlocking even more possibilities for monitoring and optimising energy use in existing buildings. Carbon Lighthouse, an Energy Savings-as-a-Service company, uses AI-enabled data analytics to reduce emissions in commercial buildings by accurately measuring, analysing and modelling a building’s exact energy usage. The results provide Carbon Lighthouse engineers with a better understanding of baseline energy use and, more importantly, uncover hidden energy inefficiencies based on that particular building’s actual needs.
Brenden Millstein, CEO and Co-founder of Carbon Lighthouse, stated in an interview, “AI-enabled data analytics are only as good as the data being looked at. It’s now possible to capture and analyse even deeper, more valuable data-sets that can provide more meaningful and actionable building insights”. In commercial buildings, Mr Millstein added that Carbon Lighthouse’s CLUES®, a patented AI platform, “formulates models and scenarios to best optimise a building’s energy use and reveal new, never-before-accessible energy reduction scenarios”. In the face of Covid-19 and reopenings throughout the country, data about how buildings operate can inform more resilient commercial real estate strategies – for the short and long term. With occupant safety rightfully top of mind for building operators, many are overwhelmed by existing recommendations on their building’s safety. Extensive recommendations to adjust a building’s heating, ventilation and air conditioning (HVAC) system could increase both energy costs and carbon emissions and may not even be effective in preventing the virus from spreading. To help facilitate and manage the reopenings of buildings, Mr Millstein mentioned that Carbon Lighthouse’s CLUES platform leverages “100 million square feet of building data to help clients take a more science-based approach, one anchored in real, dynamic building data”. As a result, Carbon Lighthouse’s clients are able to evaluate the true impact of Covid-related HVAC strategies on building operations, energy use and cost to help identify the best solutions for each building.
In the future, energy efficiency in buildings is going to become more critical with the growing focus on smart cities, which all include carbon emission regulations for buildings. Most buildings in the United States were built before the Cold War. Thus, many aren’t equipped to meet these upcoming regulations (i.e. Local Law 97 in New York) or the associated fines and tax penalties. Yet, with the right technologies and data insights, even older buildings can successfully monitor and manage their carbon emissions to meet or even exceed regulatory requirements for a more sustainable future.
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