Hybridization of solar PV and geothermal.

geothermal energy

Both geothermal power and solar Photovoltaics (PV) are renewable sources of energy, although geothermal unlike solar and wind energy is not intermittent and hence can be used as a base load power just like other conventional sources of energy powers like large hydro or diesel power generators.

Geothermal energy is a type of renewable energy which is generated within the earth and can be used directly for heating or transformed into electricity.  Geothermal power comes in three ways or power plants, that is, dry steam, flash and binary depending on how the hot water is cooled either under high pressure/low pressure to cool it (Flash) or when the hot water is passed through a secondary liquid with a low boiling point to turn it into vapor to drive the turbine. With dry steam power plants, the steam from the ground is used directly to drive the turbine while producing electricity.

Despite, the technology employed to drive the turbine and produce electricity, geothermal is site specific and not all places in the earth are endowed with geothermal resources. However, according to IRENA, geothermal energy can be sourced virtually everywhere although the vast majority of geothermal resources of economic value that are suitable for power generation are located to close areas of volcanic activity – for example, situated along plate boundaries (majority of the pacific), mid-oceanic ridges (such as Iceland and the Azores) and the rift valleys (such as the East African Rift) or near hot spots (such as in Hawaii).

Currently, about 20 countries use geothermal energy with the United States being the largest producer of geothermal in world and with the largest geothermal field. However, Geothermal energy is more prevalent in Iceland which has about 600 hot springs and 200 volcanoes and producing about 25% of its energy from geothermal power. Other countries that are endowed with a great resource of geothermal energy resources include Kenya, New Zealand, Philippines, Indonesia, Japan, Mexico, Italy etc

Solar PV integration with Geothermal Energy

Hybridization of renewable energy technologies is one way of solving, especially the intermittent nature of solar or wind power. In addition to use of artificial intelligence and machine learning to solve grid stability issues for utility scale solar and wind generation, using base load renewable energy systems such as geothermal instead of conventional power sources such as large-hydro and fossil fuels is one way of stabilizing the grid while decarbonizing it at the same time.

Countries rich in the geothermal resource including USA, Kenya, New Zealand, Iceland, Philippines etc, have benefited from this renewable resource while using it as a base load because of its constant and clean power available in all seasons.

However, depending on the geothermal technology employed (i.e. dry steam, flash or binary), the geothermal power plant output can be affected by the increased ambient temperatures during hot summer temperatures as noted by (DiMarzio et al. 2015). DiMarzio et al. 2015 captured one of the first ever geothermal plant in the world to integrate geothermal, solar photovoltaic and solar thermal for the Stillwater geothermal project which is located in Nevada, USA, and owned and operated by Enel Green Power North America, Inc. (EGP-NA).

power grid

DiMarzio et al. 2015 notes that the first phase of the project began with a geothermal plant, a 33 MW gross binary plant which was commissioned in 2009 and later in 2012 an additional 26MW of solar photovoltaic (PV) power was added to increase the output tailored to complement the geothermal plant output degradation during hot summer temperatures. During the hot summer seasons, the solar generated peak for the solar PV augments the capacity for the geothermal generated base load power increasing the power yield for the geothermal solar PV hybrid system.

However, every technology has its own limitations and when it comes to producing electricity from geothermal for example, the geothermal resource aspects such as temperature, pressure, flow or non-condensable gas content may change over time resulting in decreased power yields. Hybridization of geothermal with solar PV is a more cost effective option to mitigating this problem, because reworking the already depleting well may not be succesful while running a new well runs the risk of a dry or unproductive well (DiMarzio et al. 2015).

geothermal springs

When it comes to integrating solar PV with geothermal renewable resource for residential applications, this article captures some of the aspects of how solar PV and a geothermal heat pump can work in tandem to help regulate your home’s temperature using electricity provided by solar PV. Just like at utility scale, these two technologies even at residential scale can be complementary to help provide the power, heating and cooling for your home. For more details, how these two technologies complement each other at residential scale, check out this article.

Reference sources:

DiMarzio et al. 2015 : The Stillwater Triple Hybrid Power Plant: Integrating Geothermal, Solar Photovoltaic and Solar Thermal Power Generation, Proceedings World Geothermal Congress 2015, Melbourne, Australia, 19-25 April 2015.

Grid stabilization with increased renewable energy.

AI and solar PVWith the growing environmental concerns about climate change and the need for decarbonization, many private sector organizations, governments, and civil society have committed to a 100% renewable energy future.

As of late 2016, more than 300 cities, municipalities, and regions including Frankfurt, Vancouver, Sydney, San Francisco, Copenhagen, Oslo, Scotland, Kasese in Uganda, Indonesia’s Sumba island and the Spanish Island of El Hierro have demonstrated that transitioning to 100% RE is a viable political decision.

It is no doubt such ambitious targets to transition to 100% renewables will require new tools, concepts, and technologies to cope with the increased penetration of intermittent renewable energy into the grid. The good news is that technological developments, in the artificial intelligence and analytics space have already created tools and solutions needed to enable the decarbonization of the economy according to the International Renewable Energy Agency (IRENA).

As such, the International Renewable Energy Agency (IRENA) has developed solutions in its recent report on the “Innovation Landscape for a Renewable Powered Future” which provides a toolbox of solutions for policymakers and guidance on how to apply them system-wide in a coherent and mutually-reinforcing way.

In particular, these solutions center around the application of digital technologies such as Artificial Intelligence (AI), big data and analytics in increasing flexibility in the system for larger integration of renewable energy.

According to IRENA, Artificial Intelligence (AI) and big data, the Internet of Things and batteries are innovative solutions that will enable massive solar and wind use and amplify the transformation of the power sector based on renewables.

Why AI, Big-Data, and Analytics?

The increasing electrical loads such as electric cars, energy storage (batteries or pumped hydro) as well as decentralized renewable energy power such as rooftop solar PV systems, commercial solar, and wind power systems will need a more stable grid or a smart grid.

A smart grid is able to learn and adapt based on the load and amount of variable renewable energy put into the grid as a result of having lots of rooftops solar PV, other extra loads to the grid such as electric cars, energy storage (batteries and pumped hydro) and increasing decentralized intermittent renewable energy.

AI and Internet of Things

Without a smart system using artificial intelligence (AI), big data and analytics, grid operators will definitely not cope with the changing electrical loads and the increasing penetration of renewable energy into the grid.

Also, at its core, AI is a series of systems that act intelligently, using complex algorithms to recognize patterns, draw inferences and support decision-making processes through their own cognitive judgment, the way people do.

How can AI support the large integration of renewable energy?

Since renewable energy is very intermittent in nature as we would expect because there is no constant wind or solar generation due to weather changes, renewables such as solar and wind can be unreliable and many utility companies utilize energy storage (batteries or pumped hydro) to deal with this issue.

Excess solar or wind power is stored during low demand times and used when energy demand goes high. As a result, AI can improve the reliability of solar and wind power by analyzing enormous amounts of meteorological data and using this information to make predictions and knowing when to gather, store and distribute wind or solar power.

smart grid AIOn the other hand, AI used in smart grids can be used to balance the grid especially when rooftop solar and other decentralized renewable energy are involved and put into the grid. AI systems utilizing neural networks or complex algorithms to recognize patterns associated with various loads (electric vehicles or energy storage) and increased rooftop solar or other forms of distributed energy (wind or solar) which can make the system to be unstable. The most efficient way to balance this variability in the system is through AI in analyzing grids before and after they absorb smaller units, and in working to reduce congestion.

The IRENA’s report Innovation Landscape for a Renewable Powered Future explains these new AI tools and digital technologies that will support the deployment of renewables as the power sector complexity continues to increase.

According to IRENA, most of the advances currently supported by AI have been in advanced weather and renewable power generation forecasting and in predictive maintenance. However, in the future, AI and big data will further enhance decision-making, planning and supply chain optimization while increasing the overall energy efficiency of the energy systems.

For the renewable energy sector, AI and analytics can support it in several ways such as better monitoring, operation, and maintenance of renewable energy.

Future of solar in a smart building.

smarthomeBecause of the volatility of global oil prices, the cost of energy will continue to increase proportionately and especially when our energy demand continues to depend on finite fossil fuels. Similarly, the cost of energy for an average building in the USA or globally will continue to increase proportionately when the main source is from fossil fuels because the price for energy continues to increase due to volatility of oil prices. Solar PV and increased connectivity is an option that seems very promising and could help to reduce or mitigate the issue of climate change and increasing energy prices.

The advent of AI in energy management

Artificial intelligence technologyThe advent of new technologies such as big data analytics, machine learning and Artificial Intelligence (AI), robotics and blockchain allows for smart building energy management systems that can provide monitoring made possible through the Internet of Things (IoT), advanced data analytics and via wireless connections.

Looking in the future, solar is likely to be sold as a core part of the smart building concept that includes a building energy management system, energy storage, Electric Vehicle (EV) charging and smart appliances. This makes more sense because sourcing all the energy from solar will help to save more money and help to achieve sustainability. Also, EV and smart appliances can help to balance the grid for instance, electric vehicles can be used as temporary storage to connected appliances to reduce power usage when needed.

IoTAlso, in the energy management space, lighting and HVAC integration are the two most common systems integrated into the smart building strategy to reduce the energy footprint, but the IoT industry has opened the door to more sensors and hence increased intelligence through data collection. Some of the most common IoT sensors have applications for smart metering, occupancy sensors, water detection, humidity sensors, contact sensors, and carbon monoxide detection among many others.

Internet of Things

The whole idea of making your building smart is to allow you to make more informed decisions about the building based on the data it provides. Data is aggregated via IoT (Internet of Things) controls and sensors in a web-based platform that can be monitored, controlled and acted upon in real-time or perhaps using your cellphone. The main advantage of having a smart building is to help facility and property managers gain insight into the detailed workings of their locations and gather useful data to improve building performance and efficiency.

Advantages of integrating solar in a smart building:

  •  Smart buildings utilize machine learning algorithms and can be able to forecast your energy consumption and through demand response mechanisms solar consumption by the building can be increased in times of high solar generation and vice-versa. Through IoT smart appliances can be remotely controlled digitally to adopt on-site demand. For instance, heat pumps, heat storage batteries and air conditioning units can be optimized with solar generation and be a way of using excess solar electricity as heat.
  • Battery storage and smart electric vehicle charging when integrated with solar PV could significantly increase solar consumption for some households and businesses and especially when solar PV is combined with battery storage.
  • Deep machine learning and artificial intelligence when integrated with your smart appliances and solar can help to forecast and manage generation and consumption as well as voice activation technology to make systems more user-friendly.
  • Generally, smart buildings through optimization increase energy efficiency, comfort and safety and with solar PV, more energy is saved reducing your energy footprint.

This article explained how the smart building concept can help to reduce energy consumption and allow for the integration of solar PV, EV charging and IoT helping you reduce your energy footprint to achieve sustainability. However, a key question is whether these smart building technologies can currently pay for themselves? Do they currently increase or decrease the return on investment on installation when combined with solar?  EnergySage is a great starting point to help you figure out your energy savings when it comes to going solar.

AI and Sustainability.

 

sustainability

By 2020, the Fourth Industrial Revolution will be characterized by many technological developments, including but not limited to advanced robotics, autonomous transport, AI, and Machine Learning, advanced materials, biotechnology, and genomics. On the other hand, despite these technological advancements, the world is also experiencing some of the most challenging sustainability issues ever experienced by planet earth such as climate change.

Smart technology combined with AI can help individuals and businesses manage their environmental impact. AI and smart technology can help to collect, analyze data using machine learning to help monitor and improve energy consumption, water consumption while increasing operational efficiency because of lowered of reducing costs and material waste. As such, it is worthwhile to learn how big data and AI can be applied in the industrial, energy, agricultural and water sectors to help monitor and reduce material, water, and energy wastage while increasing operational efficiency.

Below we capture some interesting case studies for how some start-up companies (C3, Falkonry, FogHorn, Sight Machine, SparkCognition, Uptake, Zymergen, Foris.io™, etc) in the industrial sector, as well as well-established companies such as ABB, GE, Siemens, IBM, Honeywell, Hewlett Packard Enterprises, are implementing interesting AI concepts in their respective sectors to help achieve sustainability. National Renewable Energy Laboratory (NREL) is a government research body that is also helping to shape AI and its implementation in the energy sector.

Application of AI in the Industrial Sector.

A combination of software and hardware technology with the use of AI and machine learning is beginning to enter the market and some businesses have adopted such technology to help them manage and improve their environmental performance while saving money.data science for sustainability

For instance, in the industrial sector in the US has experienced a high growth of the use of AI-enabled devices to help improve operational efficiency, reduce materials waste, predict interruptions, take advantage of predictive maintenance and optimize resource consumption. The use of AI technology in the industrial sector grows every year at a rate of nearly 65% through 2024.

In the energy sector, the National Renewable Energy Laboratory (NREL) in collaboration with Hewlett Packard Enterprises have developed AI and machine learning technologies to automate and improve operational efficiency, including resiliency and energy usage for data centers. This concept helps to reduce energy consumption and lower operating costs through monitoring and predictive analytics in power and cooling systems for HP data centers.

Application of AI in the Energy Sector.

As such, historical data (about 16 terabytes of data) collected from sensors in NREL”s supercomputers are used to train models for anomaly detection to predict and prevent issues before they occur.

This collaboration will help to address future water and energy consumption as early results based on the models trained with historical data have successfully predicted or identified events that previously occurred and thus can be replicated in other data centers.

Also, AI coupled with smart sensors or devices can help to reduce building energy consumption by up to 30% using accurate sensing and predictive analytics according to Department of Energy (DOE) studies.

Furthermore, ABB has developed machine learning algorithms to predict unplanned peaks in power consumption and identify strategies to prevent them. According to ABB, its Energy Forecasting AI uses neural network methods to identify and learn patterns in a circuit or a building’s energy consumption, while also factoring weather data.

As such, using weather forecasts and historical data, ABB’s Energy forecasting is then able to predict power consumption(kW) for the next 24 hours, updating its forecast every 15 minutes with best-in-class accuracy. This ABB system through accurate power consumption prediction will enable facility managers to take full advantage of Time of Use (TOU) tariffs and to take timely action to reduce unplanned consumption.data science and renewable energy

With regard to renewable energy, the application of AI and machine learning can help to solve the intermittent nature of wind and solar power that may bring about grid stability issues. Currently, pumped hydro or batteries (energy storage) is employed to solve this intermittent nature of renewable energy.

However, the grid will be more stable when AI and machine learning algorithms are employed with renewable energy to predict or forecast when the amount of solar or wind power goes down so that the power stored can kick in. This can help to avoid expensive or use of fossil fuel-powered standby generators.

Application of AI in the Agricultural Sector.

Similarly, smart sensors and AI can be applied by farmers that have connectivity to save water, energy, fertilizer and pesticide usage. One great example of a company that is implementing the Internet of Things (IoT) to bring improvement in energy and sustainability is Foris.io powered with IBM Watson.

Working with IBM Watson™ and IBMCloud™, foris.io™ uses data from precision agriculture tools and grower records, cognitive computing, and analytics to enable holistic field management.

Using probes installed on the soil, smart devices are capable of measuring and transmitting data on moisture, PH level, salinity, temperature and other factors that are fed into IBM’s data storage, processing, and analytics cloud for analysis.

Using AI and machine learning, the data gained from the soil sensors are combined with a variety of other environmental factors, such as weather, geographic location, crop yield statistics, and other data to provide farmers with real-time feedback to inform about how much to water and fertilize according to Foris.io’s motto, “just enough • just in time”.data science and renewable energy

Application of AI in the Water sector

According to UN-Water, water scarcity already affects every continent and water use has been growing globally at more than twice the rate of population increase in the last century, and an increasing number of regions are reaching the limit at which water services can be sustainably delivered, especially in arid regions.

In the US, nearly two trillion gallons of water are lost in the country before it even reaches an end-use due to leaks and pipeline faults according to ABB. To address this, ABB amongst many other companies are implementing flow and sound sensors into water infrastructure to pinpoint leaks and predict and proactively maintain the water supply using AI and machine learning.