In my opinion, AI is inadequate for us to be used to determine the best solutions for carbon-free energy independence. First of all, AI will spit out what we will feed in. It will provide solution based on data we provide. Therefore, if we provide wrong, incomplete and limited data, then it will give us the best wrong, incomplete and limited solution. In my opinion, we will need smart kids, adults and seniors working together as a team that will invest energy and time with a long term planning. I said kids because they care about the future therefore they will be concerned about the trash elders make. I said adults because they will be creating, building and providing the solution. They take the work load. And finally, I said seniors because they have some experience from trials and errors; and sometimes the common sense and old ways are the best solutions than some abstract theory or engineering. Over-engineering can also be harmful. To be more technical, we will need not just electrical engineers, physicists, civil engineers or mechanical engineers. We will also need system engineers that can see the whole picture and that can put all things together so that each subsystem can work smoothly. As a first step, I would start defining the problem and state it clearly with a clear path and goal. Study the history to find similarity of the problem was made in the past. For example, we moved from wood, coal, oil, natural gas and uranium. All of these past solution have correlation. They have an increasing power density, increase cost of infrastructure, increasing cost of fuel production, increasing cost of waste disposal, increasing cost of damage to ecosystem and increasing cost of life due to its toxicity. All of them are centralized. Obviously this a wrong path to continue that way. Therefore one must find alternative path and I would start from that. We should look how other nations solved the similar problem following different path. Take for example, the energy situation in Poland, Canada, U.S. and Japan. Canada, U.S and Japan do not need nuclear energy. Both have plenty of water and real estate to build water dams alone to cover their energy needs. All these nations can use their coast to top up or for extra power generation using wind mills and wave energy. On top of that, they can use solar panels. I believe that with those solutions they can easily double their power output. But this solution I mentioned does not happen because there is no political will and sense of direction or purpose. Their governments are weak. The problem always befalls on ordinary people and some corporations. Regarding Poland, it is more challenging but not impossible to live without nuclear power and coal plants. But engineers in Poland must team up together and work on it the same way people rebuild Warsaw after WWII. This is a problem that requires team work, long term planning and respect to quality of life. Keep in mind that energy crisis is not due to lack of oil or natural gas. The problem is carbon emission and reducing it will not solve the problem and make a better future. Carbon emission is just a symptom. The real problem is waste production. We never spend mental energy or a dime on how to stop or eliminate/recycle waste. Instead we dispose it and hope that nature will do the rest. We want to grow indefinitely therefore we need to consume more energy indefinitely. Doing that we produce waste indefinitely to a point that nature can not keep up with our appetite and our waste production. Thus is why we must change our path. Thus is why AI will not tell us that unless we will feed it with proper and complete data (including historical data). My educated guess says that AI will predict that chips will be harder to produce because they demand purity instead radioactive contamination will be so spread out and so bad that it will be harder to make pure elements used to make more delicate chips. This will also be an interesting research topic for others. AI will have to have holistic data not just specific energy data.

Artificial intelligence (AI) plays a crucial role in designing and modeling renewable and carbon-free energy sources. Here are some ways AI technology contributes to this field:

1-Renewable Energy Generation Prediction:

AI uses data analysis to predict up to 36 hours in advance in which geographic areas renewable energy production will decline and peaks in additional demand will occur.

This allows electricity companies to increase production at other plants with better weather conditions to compensate for the loss, avoiding outages or supply shortages.

2-Optimization of Renewable Parks:

Historical meteorological data at candidate locations is crucial for their selection in designing new wind or solar plants.

AI helps estimate how much a plant would have produced in different locations and choose the most profitable one.

In addition, AI is used to predict how much energy will be produced in parks already installed and in operation, which allows bidding in electricity markets with precision.

3-Smart Electrical Energy Grids:

Renewable energies also challenge the electrical energy transportation and distribution network.

AI assists in the optimal design and operation of renewable-generation batteries and hydrolyzers.

These advances promote the use of renewable energies in the not-so-distant future.

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Artificial intelligence is a concept that was born almost at the same time as computer science. From the start researchers have tried to imitate the way humans think, trying to endow  computers with some form of intelligence. Currently, the most accepted definition of the field is that of the study of “intelligent agents”. “Intelligent agents” are systems that, by some means, are capable of perceiving their surroundings and, in some way, react to them with the intention of achieving a set of goals.

One of the simplest examples of an intelligent agent is a thermostat. It perceives the temperature of a room (its environment), and interacts with it to achieve a specific temperature (its goals). In this case, the environment is part of the real world, but that is not necessarily the case for all intelligent agents. Also, the objectives of this system require relatively simple logic to be achieved, but this would not suffice given an increase in the complexity of the objectives or the environment.

Many techniques and technologies have been proposed over decades, with various degrees of success.  Some have been so successful in their respective fields that they have become commonplace and stopped being considered “intelligence”. Other techniques have had waves of resurgence as technology advances, such as artificial neural networks. These are a subset of “Machine learning”: the design of systems that learn from their experience and adapt to their environment. Other techniques rely on mathematical optimization, statistics or logic systems to achieve this goal

How can AI be used in the renewable energy sector?

In a survey with more than a 100 renewable energy sector workers, over 90% of people said their jobs would benefit from digitalization and automation; that there was some part of their workflow that could be automated by AI, particularly within design, operation and maintenance.

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How can this be done? There are two main ways to apply AI in the energy sector (or any sector): automated decision-making and aided decision-making.

Automated decision-making involves computer systems processing information without human intervention, creating the potential for them to handle complex tasks faster than humans would be able to do in the same amount of time. An example of this are Automated AI systems for the schedule of preventive maintenance of a solar photovoltaic (PV) plant.

The limitation of this approach is that automated decisions can be distorted by different kinds of bias, which may result in incorrect decisions being made. This can cause problems in critical systems and, as such, automated decision-making is not viable for all applications.

Aided decision-making can address this by keeping the responsibility of the actual decision on the human side. As such, the AI is focused on providing insights that enrich the process. This brings with it the added flexibility of humans applying their knowledge and experience to the computer system’s analysis. Aided AI systems can be used for tasks that require nuanced decision-making such as environmental impact analysis for proposed solar and wind farm sites.

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AI in the energy sector: use case examples

Risk and opportunity analysis

Image recognition, a form of AI, has a key role to play in validating data. Investors and developers can use image recognition and other algorithms to identify the possible risks and opportunities of a potential project.

Algorithms can then create preliminary cash-flow models that demonstrate to developers whether a project has good potential. By automating the project modeling process, AI tools can rapidly identify profitable projects and free up teams to focus on completing deals efficiently.

Manage supply and flow

Electricity companies and grid operators can use AI and machine learning to forecast generation, schedule maintenance and manage power flows. At the same time, consumers can get up-to-date information about supply to help manage their consumption, distributed generation, and storage to reduce their energy bills. The increasing adoption of smart meters is generating data that AI algorithms can use to predict demand and network load, shifting the load to optimize consumption from solar and battery systems.

Predict power output

AI systems can use forecasts for wind speeds and irradiance to make predictions for short-term renewable power output in minutes rather than days. In this way they can maintain grid stability and dispatching, to optimize plant availability and schedule maintenance.

Smarter economics

AI programs can combine machine learning weather models, historical datasets, real-time information from local weather stations, satellite imagery, cameras, and sensor networks. Enhanced forecasting can result in more efficient management of conventional generators and reduce the cost of starting and shutting down units, making optimal use of plants by adapting output to changing weather conditions. That can also cut the cost of solar power curtailment.

More accurate forecasting of renewable output allows generators and energy traders to bid in the wholesale and balancing markets.

The long-term impact of AI in renewables

AI applications are revolutionizing the technology and digital sector. They’ve become an integral part of everyone’s day to day interactions with the digital world: Search engines are one of the main access points to the internet; recommendation systems are orchestrating what content is presented by social networks, streaming services and online marketplaces; and virtual assistants are present in almost every platform. Such groundbreaking changes are slowly appearing in many other industries, but there is yet a big untapped potential for artificial intelligence applied to other areas.

The renewable energy industry in particular is one that is constantly reinventing itself, embracing research and innovation to adapt to ever  growing demand. As such, it is in a prime position to successfully adopt the innovations of Artificial Intelligence and adapt them to its particular needs. Already today, new and established tools are using elements of AI to streamline processes and create functionality that was impossible in the past. If the trend continues as today, AI enhanced tools  have the potential to become as important for the future of engineering as computer aided design (CAD) software is today. 

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AI can be used to tackle the complex problems of designing solar plants, which results in cost savings and improved efficiencies,resulting in better investment returns.