Smart Grids: Leveraging AI to Optimise Solar Energy Grids

We Discuss Leveraging AI to Optimise Solar Energy Grids.

By Dr. Eyo Eyo, who runs the website https://eyowhite.com/

According to the World Economic Forum, most of our traditional energy grids face challenges with stability and sustainability. Solar photovoltaic (PV) is becoming an essential component of clean energy sources across the globe, playing a vital role in achieving international energy and climate objectives.

Introduction

While solar energy is becoming increasingly available and widely used as a clean source of electricity generation, its supply can be intermittent and unreliable. This inconsistency poses challenges to the efficiency and reliability of the power grid.

Artificial Intelligence (AI) can certainly be a game-changer in this regard. By integrating AI algorithms, smart solar energy grid can be created that optimises energy generation, distribution, and consumption in real time.

Early research carried out on the use of AI algorithms to improve the efficiency of solar power generation is already becoming successful. In Europe for instances, the International Renewable Agency (IRENA), has highlighted the considerable positive transformations in operating costs that has been occasioned by the use of AI in solar power generation.

One promising example is the EWeLiNE project, a German research initiative that utilises artificial intelligence. Completed in 2017, it focused on predicting power generation by analysing data from solar sensors and weather forecasts. This AI-based approach effectively reduced the need to curtail surplus power production.

Following this achievement, Gridcast, a subsequent project, also employed artificial intelligence in solar sensors and forecast of the weather while aiding a curtailment of excess power generation.

Also, scientists at the University of Gävle in Sweden recently embarked on a project to create innovative AI methods for maximising the efficiency of rooftop solar panels. Through trials conducted in Gothenburg and Uppsala, they have achieved impressive advancements in accurately enhancing energy generation from roofs, surpassing previous efforts.

Although still very much at its developmental stages, these glimpses of success have proven the potential of AI in positively revolutionising the solar energy production levels and by so doing advancing towards the attainment of ecologically responsible objectives and sustainability.

What is Artificial Intelligence (AI)

Artificial Intelligence or AI for short is referred simply as a field of computer science that involves the development of machines with the ability to think and respond in a manner similar to humans. AI encompasses systems that can adapt their behaviour based on observed, collected, and analysed data without requiring explicit programming.

In other words, AI is a series of systems that exhibit intelligence by utilising intricate algorithms to identify patterns, make deductions, and aid in decision-making processes through their own cognitive judgment, much like how humans do.

House with a roof full of solar panels
A grid-connected, residential solar rooftop system near Boston, USA
Credit: Gray Watson

Current Challenges in Solar Energy Generation and Distribution Systems

There is no doubt that the future of energy is heading towards a more decentralised, adaptable, and sustainable power supply. However, it is important to acknowledge that the global power generation industry, which has been around for over a century, often relies on infrastructure that is nearly as old to meet the needs of billions of people and their ever-evolving demands.

Although the future of energy is certainly shifting towards more sustainable means of production such as solar energy, challenges regarding instability and inefficiency persist.

Power distribution grid operators are constantly being confronted with new technical and operational problems during deployment especially as the number of solar photovoltaics (PV) continue to surge.

Changes in the weather which makes reliance on solar power doubtful in some countries. For instance, the temperate regions of the world often face weather variability that challenges the reliability of solar power. These areas experience frequent fluctuations in weather patterns, including cloudy days, rain, and seasonal changes, which can hinder consistent solar energy generation.

Such unpredictability can strain energy grids, requiring backup sources and storage solutions to ensure a steady power supply.

An inherent challenge in integrating solar power into energy grids is the need for surplus storage capacity. Solar energy production is intermittent and depends on the availability of sunlight availability. This does make it crucial to store surplus energy generated during peak sunlight hours for use during periods of low or no sunlight, such as at night or during cloudy days.

Traditional battery technologies like lithium-ion are commonly used, but they may not always provide the scalability or cost-effectiveness required for large-scale grid storage.

In achieving the utmost energy efficiency and cost-saving by optimising solar panels, artificial intelligence (AI) can act as a transformative force that changes standard procedures completely. AI-powered solutions can ensure that solar panels operate at their fullest potential to produce clean energy, which benefits nature and the economy. The optimal use of solar power through specialised algorithms for data analysis based on advanced artificial intelligence techniques can be determined.

Notwithstanding, it is useful to note that the full potential of AI in improving solar energy grids can mostly be unlocked by the generation of big data and increased processing power. AI algorithms rely on vast datasets to train and refine their predictive capabilities. The rapid growth and availability of devices and technologies (smart meters, sensors, and IoT devices) which the solar system would rely on does mean that the energy sector would require substantial real-time data on weather conditions, grid performance, and energy consumption patterns.

Towards Smart Management of Solar Energy Grids

The synergy between AI, big data, and processing power holds the promise of positively transforming solar energy grids by making them more resilient and responsive to changing conditions.

Improved weather forecasting is one of the main AI applications that will improve the usage and integration of solar power into the power system.

For example, in 2015, IBM collaborated with the US Department of Energy’s SunShot Initiative and achieved a remarkable 30% improvement in solar forecasting. This progress was made possible through the integration of extensive historical data and real-time measurements from various sources such as local weather stations, sensor networks, satellites, and sky image cameras into a self-learning weather model and renewable generation forecasting technology.

Secondly, AI can ensure that the power grid always operates at optimal load and can optimise the energy consumption of customers.

The primary goal is to ensure that the electricity produced by the solar PV system in homes or local grids is efficiently used. To achieve this, an algorithm called GridSense can be employed. This algorithm utilises AI to learn user behaviour and effectively control key electricity consumers like heat pumps, boilers, household batteries, and electric vehicle charging stations. In addition, it incorporates measurement data from solar PV systems to optimize grid operations for maximum efficiency.

Thirdly, AI has the potential to enhance safety, reliability, and efficiency in the power grid by automatically identifying disruptions. This technology can enable real-time automated processing of data and swiftly detect emergencies or equipment failures. For instance, researchers have trained AI models with typical instances of system outages to teach the algorithm how to accurately differentiate between normal operational data and specific malfunctions.

The algorithm can then rapidly determine if there is any anomaly or fault, as well as identify the type and location of that disturbance. In case one power plant fails, it is expected that there will be a sudden increase in load on other plants. This increased load causes generators to slow down and results in a decrease in frequency.

Fourth, accurate prediction of demand can be utilised to optimise economic load distribution and enhance efficiency in managing and controlling energy consumption. The data generated by consumers through the power grid provides valuable insights that AI can leverage.

By analysing this data, AI algorithms can accurately forecast network load as well as individual consumption patterns for each consumer. As a result, significant cost savings of up to 40% on utility bills can be achieved by maximising efficiency gains and shifting electricity usage to periods when it is most affordable.

Finally, energy storage systems, such as large-scale batteries, combined small batteries, or electric vehicles connected to the grid, are becoming increasingly important for integrating sustainable energy sources. Artificial intelligence can play a crucial role in optimising the operation of these technologies by improving electricity integration and reducing generation forecast errors. In addition, AI can help minimise local electricity consumption costs and maximise returns for storage system owners.

Stem, a company based in California, has created Athena, an AI-powered solution that analyses energy consumption patterns and enables customers to monitor changes in energy prices. This technology aims to optimise the utilisation of storage systems by efficiently managing fluctuations in energy rates. In Australia, Tesla’s Hornsdale battery served as a catalyst for change, prompting software-as-a-service to take notice and explore similar advancements.

Leveraging AI to Optimise Solar Energy Grids - Solar panels connect to base electric grid
On 140 acres of unused land on Nellis Air Force Base, Nev., 70,000 solar panels are part of a solar photovoltaic array that will generate 15 megawatts of solar power for the base. (U.S. Air Force photo/Airman 1st Class Nadine Y. Barclay)
Public Domain, Source

The Future of AI-Enabled Smart Grid Technology

In the past ten years, there has been a significant rise in the deployment of solar PV systems worldwide, accompanied by a significant decrease in their costs. To achieve our goal of achieving net zero emissions, it is crucial to further expand solar PV on a global scale.

Artificial Intelligence (AI) has the potential to transform this process. By incorporating AI algorithms, we can develop intelligent solar energy grids that optimise the generation, distribution, and utilisation of energy in real-time.

While there have been some reported successes, the use of AI to facilitate achievement is still in its early stages. The hurdles related to integrating solar power and ensuring flexibility are still prevalent.

In addition to the challenges mentioned earlier, other obstacles include navigating through intricate regulatory changes, dealing with the emergence of prosumers (producers and consumers), and managing the influx of new startups in deregulated areas.

Just like any business and digital transformation journey, the transition to smarter grid management begins with taking a few careful steps before picking up speed. There is no doubt that technologies in the utilities sector are driving and facilitating the evolution of this industry. However, for significant change to take place, utilities companies must develop robust strategies for communication, customer engagement, and change management that include thorough planning.

There are several factors that drive the more efficient deployment and utilisation of AI to optimise solar energy grids in the near future namely, the availability and quality of data, technological maturity of AI systems, cybersecurity and training and re-skilling programs for professionals in the energy sector.


About the Author

Dr. Eyo Eyo is lecturer and researcher in sustainable science and engineering at the University of the West of England, United Kingdom. He is also a regular contributor to the Firoz Lalji Institute for Africa (FLIA) at the London School of Economics and Political Science (LSE) blog and the Elephant.info.

Learn more at his website: https://eyowhite.com/

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