In-Home Storage: The Virtual Power Plant

Rapid Growth

Solar and wind are considered the most popular renewable resources across the world, but due to their intermittent and unpredictable nature, utilities are still relying on natural gas and coal. However, when renewable technologies are combined with energy storage they smooth out load fluctuations and have the potential to significantly impact the generation mix.
Total energy storage deployment has increased dramatically in the past few years because of low-carbon, clean energy policies, and is anticipated to grow even more in the near-term. By 2022, GTM Research expects the U.S. energy storage market to reach 2.5 GW annually, with residential opportunities contributing around 800 MW.


Source: GTM Research

How Does It Work?

Energy storage works as a three-step process that consists of extracting power from the grid, solar panels, or wind turbines, storing it (charging phase) during the off-peak period when power prices are lower, and returning it (discharging period) at a later stage during the on-peak period when the prices are much higher.


For electric vehicles (EV), most of the charging happens at night and during weekends, when the prices are comparatively lower, and vehicles are not used that much. As EVs continue to enter the mainstream market, they would increase the off-peak prices and contribute to load shifting.
Energy storage devices and EVs can complement each other or they may be competitive. But energy storage is the key element for EV charging during on-peak hours.

Different Market Players

Residential energy storage has been a holy grail for companies like Tesla, Panasonic, LG, Sunverge Energy, and Orison with lithium ion (Li-ion) batteries as the leading technology type. Now with plug-in electric and hybrid vehicles on the rise, automobile companies Tesla, Nissan, Mercedes Benz, BMW, Renault and Audi have also joined the residential market to integrate EV charging stations, battery storage and rooftop solar that in essence has a residence operating as a virtual power plant.
Beginning in December of last year, Arizona Public Service Company deployed Sunverge Energy’s energy storage hardware coupled with advanced, intelligent energy management systems that predict future load requirements and solar generation. Additionally, Tesla is enjoying significant market share, shown recently by Vermont-based Green Mountain Power’s launch of a comprehensive solution to reduce customer electricity bills using Tesla’s cutting edge Powerwall 2 and GridLogic software.
A few other utility companies, especially in Florida and California, are also exploring residential energy storage programs, as shown in the figure below.


Source: Hawaii PUC; General Assembly of Maryland

So, what are some other current thoughts about the pros and cons of in-home energy storage?

  • Energy storage reduces load fluctuations by providing localized ramping services for PV and ensuring constant, combined output (PV plus storage).
  • Improves demand response and reduces the peak demand.
  • Extra savings for customers through net metering systems and end-user bill management.
  • Reduces reliance on the grid; the customer can generate and store the energy during severe outages also.
  • Disposal of Li-ion batteries is not easy, and they are difficult to recycle
  • Automakers, like Nissan and BMW, are implementing second-life batteries, thereby reducing the durability and reliability of the product.


Concluding Thoughts

Clearly, a wider acceptance of energy storage resources would be a game changer in the U.S. power sector. Utilities, consumers, and automakers are profiting from this exponential growth of energy storage. With an increasing number of companies using artificial intelligence and machine learning algorithms for energy management systems, the synergy with energy storage creates a perfect, smart, personal power plant which has tremendous potential to change the landscape of the energy industry.

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Artificial Intelligence and the Future of the Power Grid

Artificial intelligence (AI) has become one of the fastest growing tech sectors, with over five billion dollars invested in AI startups.  Despite Elon Musk’s warnings about its dangers, AI is rapidly advancing and is expected to play a major role in our lives in transportation, healthcare, security, and other sectors.  Artificial intelligence—the ability of machines to perform cognitive functions normally associated with the human mind—has seen enormous advances in the past few years due to a type of AI called deep learning.  And the prevalence of artificial intelligence can already be seen in many everyday experiences; for example, when Facebook automatically recognizes faces in uploaded photos or when Apple’s Siri answers your question, AI is at work.

One of the industries where artificial intelligence is making important inroads is the electricity sector.  On the supply side, numerous companies are using AI to improve power production efficiency.  For example, earlier this year, GE announced AI related technology for wind turbines in Japan expected to increase power output by 5% and lower maintenance costs by 20%.  On the solar front, NEXTracker uses machine learning in its solar trackers, which can increase production by up to 6%.  And AI is not just for renewable resources: Siemens uses artificial intelligence algorithms to improve combustion efficiency, reduce emissions, and lower the wear on gas turbines.  UK-based EDF Energy is testing machine learning to predict demand for the next day more accurately than humans, resulting in energy saving in cogeneration plants up to 15%.  Finally, some coal-fired plants have used AI to increase efficiency and reduce emissions.  For example, Xcel Energy has implemented sophisticated artificial neural networks to make recommendations on how to adjust operations in order to reduce emissions in its Texas coal plants.  Clearly, AI is set to have a significant impact on how power plants operate in the future.

Artificial intelligence also has the potential of making a substantial difference in helping balance demand and supply of the electricity sector as well.  The recent rise of renewable energy, from both power plants and distributed generation, has caused its share of challenges to the power grid for producers, utilities, and consumers.  To help on the consumer side, in the town of Reidholz, Switzerland, forty homes are trying a new technology called Gridsense so that AI can improve how power is used within homes and helping ensure that “the power grid is always operating at optimal load” by adjusting customer energy consumption and coordinating with the photovoltaic generation in the neighborhood.  On a larger scale, Google’s DeepMind is in discussion with one of the UK’s energy providers, National Grid, to use artificial intelligence on their power grid to help balance supply and demand.  DeepMind has already used its program at Google’s data centers to cut electricity usage by 15%.

Another area where AI has the potential of making a big difference on the grid is in the control and operation of demand response.  This is where large consumers of electricity are rewarded when decreasing their energy requirements on short notice to help balance the grid, and this can be cheaper for the operators than turning on very expensive power plants.  Demand response programs have existed for some time now, and improving AI technology may provide significant benefits to consumers hoping to optimize their participation in the program.  As one source states, “Demand management is also seeing an explosion of AI activity with use cases covering areas such as demand response, building energy management systems, overall energy efficiency and DR game theory.”  One company, Upside, is using AI to manage a portfolio of storage assets to provide real-time energy reserves to relieve stress on the grid.  It has developed an Advanced Algorithmic Platform that manages demand response of different devices to be run in parallel.  Another company, Open Energi, uses AI to optimize companies’ assets to save energy and cut costs by choosing what time to run them based on supply and demand fluctuations in the power grid.

The use of Artificial Intelligence is already at work improving efficiency in the electricity sector for power plants, grid operators, and both large and small consumers.  Whatever lies in the future for the power industry, signs are promising that artificial intelligence will play an essential role in improving the overall efficiency on the grid.

Filed under: Artificial Intelligence, Energy Efficency, Power GridTagged with: , , , ,