Meeting of the Minds took a few moments to talk with Herrie Schalekamp about new working relationships between researchers and paratransit operators in South Africa and beyond. Herrie is the ACET Research Officer at the University of Cape Town’s Centre for Transport Studies. In addition to his research, teaching and consulting in the fields of paratransit and public transport reform he is involved in specialised educational programmes for paratransit operators and government officials. Herrie’s activities form part of a broader endeavour to investigate and contribute to improved public transport operations and regulation in Sub-Saharan African cities under ACET – the African Centre of Excellence for Studies in Public and Non-motorised Transport.
Intelligent Energy Storage
Machine learning enables Google to present news tailored to your interests; Amazon, books that you want to read; and Netflix, movies that you love. IBM’s advances in machine learning enabled it to beat the world’s top chess masters and Jeopardy champions. Recently, Alphabet beat the world’s best at Go, a game with so many possibilities that pure number crunching would not do.
Now any city with a tight budget can use machine learning to cut high energy bills; commercial users can store low-cost electricity delivered at night and use the electricity during peak hours; and electric utilities can replace aging coal and nuclear plants with distributed advanced batteries.
Cities Cut High Energy Bills
In Silicon Valley, Redwood City is home to 84,000 residents and software giants like Oracle.
“We are not unlike most cities in the State of California. We have limited resources and funding but ambitious environmental goals,” says Vicki Sherman, Redwood City’s Environmental Initiatives Coordinator. “Green Charge Networks offered our City a unique solution that was both environmentally and fiscally sustainable.”
Green Charge Networks installed lithium batteries at key locations and provides cloud-based machine learning software and big data to reduce peak demand charges. Their software learns how much energy will be needed the next day, based on usage patterns, calendars, and utility charge rates. The software knows that more energy is needed on Monday than Sunday; more energy for air conditioning is needed on an August afternoon than a December morning; more electric car charging at nine than five. With machine learning, algorithms get smarter in predicting, recommending action, or automatically taking action to charge batteries when cost is low.
Electric car adoption is high in Silicon Valley municipalities like Redwood City. Faced with high demand charges for the spikes inherent in EV charging, the city took advantage of incentives and a shared savings financing model to install energy storage coupled with EV chargers at two locations: the downtown municipal parking garage near retail and CalTrain and at the city library. The city paid zero up front.
The city is saving $7,000 annually in peak utility charges at each location and drivers like me have been able to conveniently charge their electric cars while shopping, dining, or reading at the library.
Commercial Customers Make Money in Energy Markets
From stores to banks to hotels, commercial enterprises have moved the fastest in deploying hundreds of intelligent storage systems.
After testing intelligent storage at a few locations, Extended Stay now uses large lithium batteries and Stem cloud-based software and big data to reduce energy bills at 68 of its California hotel sites. The system is totally automated, with energy stored during lowest cost night hours and used at peak.
“The Stem system is invisible to our guests and our staff – it’s simply a great way to save on energy costs. In addition, the software provides us with a clear view of electricity usage and activity at our California sites,” said Larry Fichuk, Director of Energy and Sustainability at Extended Stay America. “We look forward to the benefits and impact we can have on the environment from working with Stem.”
Stem has delivered over 200 systems that provide advanced batteries, big data and machine learning cloud-based software to lower energy bills and reduce expensive usage spikes. Stem’s advanced batteries store energy, either delivered from the grid, or from onsite generation, such as solar. Sign a hardware lease and service agreement with Stem, then get everything with monthly payments. Stem partners with leaders in grid battery storage including Panasonic, Tesla, LG Chem and Samsung. Stem can integrate the power electronics of the battery provider, others, or install its own offering. Stem has raised a $135 million project financing fund, so that governments, commercial customers and utilities can install solutions by only committing to monthly payment contracts and avoid capital expenditures.
Monthly utility bills can be a big hit to the budget. A facility can spend thousands based on total kilowatt hours, time of use pricing for those hours, and the demand charge which is based on the maximum power required by the facility. Cut the maximum power and save big. Safeway uses Stem intelligent storage systems to not only shift energy use to low cost hours with battery storage, they have also used big data analytics to understand, then reduce peak demand charges. Insights as simple as not running a chiller at the same time as a water pump, can lower peak usage thereby lowering peak demand charges.
The beauty of machine learning is that as a software provider like Stem, or Green Charge Networks, adds locations of markets, hotels, and cities, it accumulates more data and the energy saving algorithms learn and improve.
Electric Utility of the Future
Some commercial and utility executives are excited about the potential of the 100 kW Tesla Powerpack, priced at a $250/kWh, for zero-net energy (ZNE) buildings and mission critical applications. Utilities can use arrays of Powerpacks for solar and wind farms, microgrids, substations, and peak generation. Utilities replacing reliable 24/7 nuclear and coal plants with intermittent solar and wind see the potential for advanced batteries. What utilities really need are partners and systems with enough intelligence to dispatch megawatts of stored energy with the same ease as a power plant. They need to also use big data and algorithms that aggregate and get smarter.
Southern California Edison (SCE), serving 14 million people, is meeting a growing demand for electricity even as it shuts down two large nuclear power plants. SCE will deploy multiple forms of large scale electricity storage, once all regulatory approvals are secured. Advanced storage provider AES is installing 100 MW of large-scale lithium battery storage in a 20-year power purchase agreement (PPA). Stem, using big data and second-by-second-analytics, will manage 85 MW of distributed, behind the meter, lithium battery storage. Advanced Microgrid Solutions will install 50 MW. Ice Energy Holdings will install 25.6 MW of thermal storage, making ice off peak for use in cooling during peak. The storage will also enable SCE to reduce its use of methane-fueled peaker plants. Unnatural Gas Article.
Colin Cushnie, SCE Vice President of Energy Procurement and Management said, “We don’t want to put peakers on the grid if cleaner-cost options are available.”
Stem is actively participating in the California Independent System Operator (CAISO) day-ahead and real-time markets. Stem aggregates the excess capacities of several customer-sited systems and bids those assets into the CAISO market. Acting as a demand response provider, Stem sets its price target and then its predictive software automatically accepts market bids and dispatches available power to the grid. Upon procurement, payment from CAISO, Stem will pass payments of thousands, and eventually millions, to its participating customers, such as Adobe.
Intelligent energy storage is enabling leading utilities to replace obsolete power plants with renewables, build more reliable grids, and enable large-scale distributed generation.
Intelligent energy storage will rapidly scale, saving governments and corporations millions, and enable a smart and sustainable energy future.
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Spotlighting innovations in urban sustainability and connected technology
Brownfields are sites that are vacant or underutilized due to environmental contamination, real or imagined. There are brownfields of some kind in virtually every city and town in the U.S., usually related to a gas station, dry cleaner, auto repair shop, car dealership or some other ubiquitous local business that once benefited the community it now burdens with environmental hazards or old buildings.
In addressing this issue, technology has not been effectively deployed to promote redevelopment of these sites and catalyze community revitalization. We find that the question around the use of technology and data in advancing the redevelopment of brownfields is twofold:
How can current and future technology advancements be applied to upgrade existing brownfield modeling tools? And then, how can those modeling tools be used to accelerate transformative, sustainable, and smart redevelopment and community revitalization?
Across the country, urban parks are enjoying a renaissance. Dozens of new parks are being built or restored and cities are being creative about how and where they are located. Space under highways, on old rail infrastructure, reclaimed industrial waterfronts or even landfills are all in play as development pressure on urban land grows along with outdoor recreation needs.
These innovative parks are helping cities face common challenges, from demographic shifts, to global competitiveness to changing climate conditions. Mayors and other city officials are taking a fresh look at parks to improve overall community health and sense of place, strengthen local economies by attracting new investments and creating jobs, help manage storm water run-off, improve air quality, and much more. When we think of city parks holistically, accounting for their full role in communities, they become some of the smartest investments we can make.