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: , , , ,

The Fundamentals of Energy Efficiency and Demand Response

What are Energy Efficiency & Demand Response Programs?

Though the Energy Information Administration states, “there does not seem to be a single commonly-accepted definition of energy efficiency,” efficient energy use, sometimes simply called energy efficiency, refers to the reduction in the amount of energy required to provide the equivalent quality of products and services. Examples include improvements to home insulation, installation of fluorescent lighting & efficient appliances, or improving building design to minimize energy waste.

Demand response, according to the Department of Energy, is defined as, “a tariff or program established to motivate changes in electric use by end-use customers in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized.” Utilities can signal demand reduction to consumers, either through price-based incentives or through explicit requests. Unlike energy efficiency, which reduces energy consumption at all times, demand response programs aim to shift load away from peak hours towards hours where demand is lower.

What are the Benefits of Energy Efficiency & Demand Response Programs?

The decreasing and ‘flattening’ of the demand curve can directly contribute to improved system and grid reliability. This ultimately translates to lower energy costs, resulting in a financial cost saving to consumers, assuming the energy savings are greater than the cost of implementing these programs and policies. In 2010, Dan Delurey, then president of the Demand Response and Smart Grid Coalition, pointed out that the top 100 hours (or just over 1% of the hours in a year) account for 10-20% of total electricity costs in the United States. Slashing energy consumption during these high peak hours, or at least shifting demand to off-peak hours, relieves stress on the grid and should make electricity cheaper.

Additionally, decreasing energy consumption directly contributes to the reduction of greenhouse gas emissions. According to the International Energy Agency, improved energy efficiency in buildings, industrial processes and transportation prevented the emission of 10.2 gigatonnes of CO2, helping to minimize global emissions of greenhouse gases.

Lastly, reductions in energy consumption can provide domestic benefits in the forms of avoided energy capital expenditure and increased energy security. The chart below displays the value of avoided imports by country in 2014 due to the investments in energy efficiency since 1990:

Added Volume and Value of Imports Figure 1: Avoided volume and value of imports in 2014 from efficiency investments in IEA countries since 1990. Source

Based on these estimated savings, energy efficiency not only benefits a country’s trade balance, but also reduces their reliance on foreign countries to meet energy needs.

Modeling the Impacts of Energy Efficiency and Demand Response

Using AURORAxmp, we are able to quantify the impact of energy efficiency and demand response programs. In this simple exercise, we compare the difference between California with 2 GW of energy efficiency and 2 GW of demand response versus a case without energy efficiency or demand response from 2016 to 2030. The charts below show the average wholesale electricity prices & system production costs:

average electricity price $-MWhAverage System Cost (000's)

 Figure 2: Note these are 2014 real dollars.

Holding all else equal, adding demand response and energy efficiency programs into the system decreased average wholesale electricity prices by about $2.88 (5.4%) and the average system production cost fell by $496,000,000 (5.1%). This is a simple example in one part of the country, but one can easily include additional assumptions about the grid, resources characteristics, and load shape as they desire.

Both demand response and energy efficiency programs are intended to be more cost effective and efficient mechanisms of meeting power needs than adding generation. Emphasis on the demand side can lead to lower system production costs, increased grid reliability, and cheaper electric bills; all of which lie in the best interest of governments, utilities, and consumers.

Filed under: Energy Efficency, Power Market InsightsTagged with: , , , , ,