Electric Utilities like every other business, exist to provide a service and make a profit. Some problems associated with the electricity industry, include: low productivity and data analysis, customer support and personalization, and fraud. AI can be implemented to carry out tedious, repetitive processes, currently carried out by humans. This improves productivity and complex analytical tasks by automating business processes resulting in efficiency and reduce overheads. Looking at the power plant in particular, in areas like preventative maintenance, AI can be used to predict and prevent issues before they turn into major problems that impact productivity adversely, leading to blackouts, brownouts or breakdowns. AI can detect and fend off security risk and reduce network downtime and mitigates against loss of supply and finances. AI can be used by utilities in cyber-security in a preventative and predictive manner; identify trends based on historical data (and in real-time), provide intelligent prediction to empower future customer behaviour. The results will be improved accuracy, personalization of service and higher efficiency. AI will continue to make inroads in transforming the electric power industry, and already has a foothold in areas like load forecasting and plant condition monitoring. We must be careful about the misconceptions of AI, which are often due to science fiction (SciFi) and extreme entertainment, which have no place in modern reality. All AI right now is narrow AI and the fundamental technologies are: computer vision (CV), natural language processing (NLP) and reinforced learning (RL). AI is transformative and can deliver critical business outcomes for electric utilities.

One of the possible development of applications of artificial intelligence technologies in the future are computerized, remotely managed smart home systems. Currently, as part of the development and improvement of smart home systems, equal technologies of the current fourth technological revolution of Industry 4.0 are implemented, including cloud computing, the Internet of Things, learning machines etc. Currently, the key area of ​​smart home system improvement is the issue of security and management systems various risk categories that occur in households and in residential buildings. Artificial intelligence technology can be implemented to improve risk management processes.

Greetings,

Dariusz Prokopowicz

Applying AI in power system with high rate will improve sensitivity, economic and minimize power interruption.

While biosciences is no less of a noble pursuit, what makes this quote all the more intriguing is the fact that in many respects, AI and energy are converging in ways that – while disruptive – can also mitigate potential negative impacts and facilitate a more orderly energy transformation.

AI algorithms and techniques are widely used nowadays for solving different problems in power systems control for both human uses in house and the industry in general. Many AI solutions include the use of new AI techniques for power sensing, planning, scheduling, and forecasting to meet the high requirements and the increase of load demand.

Artificial intelligence already has a great impact on the processes of generation and consumption of electricity and other energy services such as heating, ventilation and air conditioning; to name a few. The application of artificial intelligence is manifested through concepts such as: learning machines, the internet of things, cloud computing and data science.

All these aspects facilitate the energy optimization of energy generating systems from renewable sources and non-renewable sources. They allow the optimization and integration of thermal and electrical energy accumulation systems, with energy consuming systems such as residential buildings, factories, shopping centers, schools, means of transportation, among others.

Buildings, major energy consumers worldwide, through smart building and green building technologies, or the integration of both technologies; They implement practices that increase energy efficiency and the use of resources such as water, energy carriers, and electricity. All this, while increasing the quality of life and user comfort.

Furthermore, data science techniques have been used to support and improve the basics of energy efficiency in buildings. This innovative and promising practice finds its current applications and future trends in the following points: (i) Predicting the energy demand required for the efficient operation of a building. (ii) Study the economic impact of energy consumption caused by the user of the building. (iii) Optimize the operation of buildings. (iv) Verify the operational status and failures of systems, equipment and networks. (v) Prevent and detect energy fraud.

In this scenario, the importance of artificial intelligence, the Internet of Things and data science is undeniable.

I used computer clouds to simulate 140k fusion reactors without results, then used IA algorithms to remove initial cases and then simulated 900k more reactors, the last ones reached ignition conditions 80% of them. Every reactor can generate 3.5MW that could reduce the energy costs to 20% of a coal plant. I hope in the future they would be used to power drones to move persons

I will comment on the revolution on optimal power systems scheduling. Conventional power system scheduling problems such as economic dispatch, unit commitment and automatic generation control were based on static optimization methods. Static optimization meant that the algorithms used were iterating on a stationary forecasted data profile until the optimal values were achieved. With new AI methods, dynamic scheduling is now very seamless. First, the use of AI eliminates the need for including a forecasting module within the algorithms because AI-based methods can be trained to make instant near-optimal decisions on time-varying system states. This is going to be very important in residential and grid-connected microgrid power/energy scheduling. Furthermore, the introduction of real-time tariff systems in the smart grid paradigm is a feasible way to enhance demand response. Residential power scheduling algorithms can now tell when the tariff is expensive and schedule cheaper generations such as solar and wind if installed in the residential units, or discharge the battery if storage is integrated. Some of the recent work on this topic can be found on my profile.

Mathematical computation technique of power flow point in power lines, terminals and grids is illustrated in "power flow" topic shared by me. With the inclusion of 5G and cloud computing, fast real time power flow computation can be monitored in real time. The additional parameters such as cooling, temperature, valve, current, voltages etc. can be monitored as well in one of my topic "machine learning" to transmit information from the instrument plant or site to the monitoring station. The neural network displayed in the AI machine learning technique exhibit the function to relate current information for better maintenance and prediction of the electrical power system for analysis.

This is a topic that is close to my heart, having completed my PhD in Machine Learning applications to the management of power systems in 1993. Since then, the significant improvement in compute power, along with IIoT, has set a roadmap which will transform the way that a power system can be monitored and managed. We can now, quite practically, look at a power system with fine granularity and resolution (both on the supply and demand side) and sensibly manage the massive amount of data that ensues. AI and ML definitely have come to the fore in recent years, and will play a significant role in managing the increasing complexity in managing power systems, now and in the future.

Artificial intelligence is very important aspect in transforming the existing electrical power system. Mainly power system lags in safety and fault detection aspects.So, with help of AI occurring faults can be detected before major disturbances which leads to fires and even black-outs. AI plays a vital role in communications between the lighting fixtures and other smart appliances in smart homes, which provides a self-reliant and energy efficient operations. AI helps to improve better functioning of the existing systems and operations can be controlled in a appropriate manner

As rightly pointed out by Dariusz Prokopowicz, with Industry 4.0 in vogue, Artificial Intelligence will push Power System as well from Autimatic to Autonomous System with the capabilty to correct itself by taking appropriate decision with proprer adoptability (provided means exist).

Dear Kah Yung Yap,

Power system analysis by conventional techniques becomes more difficult because of (i) Complex, versatile and large amount of information which is used in calculation, diagnosis and learning (ii) Increase in the computational time period and accuracy due to extensive and vast system data handling. Several problems in power systems cannot be solved by conventional techniques are based on several requirements which may not feasible all the time. In these situations, artificial intelligence techniques are the obvious and the only option.

The main feature of power system design and planning is reliability, which had been conventionally evaluated using deterministic methods. Moreover, a conventional technique does not fulfill the probabilistic essence of power systems. This leads to increase in operating and maintenance costs. Plenty of research is performed to utilize the current interest AI for power system applications.

The modern power system operates close to the limits due to the ever increasing energy consumption and the extension of currently existing electrical transmission networks and lines. This situation requires a less conservative power system operation and control operation which is possible only by continuously checking the system states in a much more detail manner than it was necessary. Sophisticated computer tools are now the primary tools in solving the difficult problems that arise in the areas of power system planning, operation, diagnosis and design. Among these computer tools, Artificial Intelligence (AI) has grown predominantly in recent years and has been applied to various areas of power systems.

AI has many roles in applications. There are so many areas of application of AI in power systems are:

(i) Operation of power system like unit commitment, hydro-thermal coordination, economic dispatch, congestion management, maintenance scheduling, state estimation, load and power flow.

(ii) Planning of power system like generation expansion planning, power system reliability, transmission expansion planning, reactive power planning.

(iii) Control of power system like voltage control, stability control, power flow control, load frequency control.

(iv) Control of power plants like fuel cell power plant control, thermal power plant control.

(v) Control of network like location, sizing and control of FACTS devices.

(vi) Electricity markets like strategies for bidding, analysis of electricity markets.

(vii) Automation of power system like restoration, management, fault diagnosis, network security.

(viii) (Applications of distribution system like planning and operation of distribution system, demand side response and demand side management, operation and control of smart grids, network reconfiguration.

(ix) Applications of distributed generation like distributed generation planning, solar photovoltaic power plant control, wind turbine plant control and renewable energy resources.

(x) (x) Forecasting application like short term and long term load forecasting, electricity market forecasting, solar power forecasting, wind power forecasting

AI proposes techniques namely (i) artificial neural networks (ANN) (ii) fuzzy logic (iii) expert systems (iv) genetic algorithms (GA) can transform the existing electrical power system. Out of thesetechniques, using genetic algorithms, the best method which withstands all constraints can be selected as it is the best method among the proposed methods (survival of fittest).

(i) As ANNs operate on biological instincts and perform biological evaluation of real world problems, the problems in generation, transmission and distribution of electricity can be fed to the ANNs so that a suitable solution can be obtained.

(ii) Fuzzy logic can be used for designing the physical components of power systems. They can be used in anything from small circuits to large mainframes. They can be used to increase the efficiency of the components used in power systems. As most of the data used in power system analysis are approximate values and assumptions, fuzzy logic can be of great use to derive a stable, exact and ambiguity-free output.

(iii) Since expert systems are basically computer programs, the process of writing codes for these programs is simpler than actually calculating and estimating the value of parameters used in generation, transmission and distribution. Any modifications even after design can be easily done because they are computer programs.

(iv) As genetic algorithms are based on the principle of survival of fittest, several methods for increasing the efficiency of power system processes and increasing power output can be proposed.

Ashish

Dear Kang,

Thank you.

Ashish

Based on AI, it is possible to build not only a dynamically controlled system, but also integrate various energy resources. AI will allow to build a unified dispatching of gas, water, heat, hydrogen, electricity. The integration of energy and distributed generation based on AI will allow systems to be more secure, energy efficient, flexible, environmentally friendly and sustainable.

AI would transform existing power systems, looking at the risk a power network fault can pose on a large scale power systems. early fault detection is very important to eradicate risk before it happens. Machine learning technique like Anomaly detection could be implemented to help build intelligent power systems.

The question is, what is the expected future prospect and revolution by AI? The basic answer is, we just don't know. Many hypes have come and gone, markets rise and crash, Spanish Influenza came and went; Covid-19 is here and we trust it will disappear in the near future. AI refers to the creation of intelligent systems that can learn from data, extract knowledge, make predictions and decisions, and interact with or act in response to their environment. From this perspective, AI will increasingly have an impact on the electricity industry and application in the electrical power system going forward. Hence it is imperative for every electric utility to adopt AI to be competitive and innovative. A simple example: protection, metering and control 2 decades ago used to be disparate circuits but PMC integration (automation) has transformed those three separate functions into one integrated automated function in distribution system operations.

Electric Utilities like every other business, exist to provide a service and make a profit. Some problems associated with the electricity industry, include: low productivity and data analysis, customer support and personalization, and fraud. AI can be implemented to carry out tedious, repetitive processes, currently carried out by humans. This improves productivity and complex analytical tasks by automating business processes resulting in efficiency and reduce overheads. Looking at the power plant in particular, in areas like preventative maintenance, AI can be used to predict and prevent issues before they turn into major problems that impact productivity adversely, leading to blackouts, brownouts or breakdowns. AI can detect and fend off security risk and reduce network downtime and mitigates against loss of supply and finances. AI can be used by utilities in cyber-security in a preventative and predictive manner; identify trends based on historical data (and in real-time), provide intelligent prediction to empower future customer behaviour. The results will be improved accuracy, personalization of service and higher efficiency. AI will continue to make inroads in transforming the electric power industry, and already has a foothold in areas like load forecasting and plant condition monitoring. We must be careful about the misconceptions of AI, which are often due to science fiction (SciFi) and extreme entertainment, which have no place in modern reality. All AI right now is narrow AI and the fundamental technologies are: computer vision (CV), natural language processing (NLP) and reinforced learning (RL). AI is transformative and can deliver critical business outcomes for electric utilities.

Kah Yung Yap

Artificial intelligence will lead us into a world of rebots and will subsequently increase the unemployment rate. But on the other hand, it will improve the smart grids.

Dear Kah Yung Yap

This topic exists in the smart grid. Really, the smart grid contains ICT, AI, big data and sensors.

I fully agree with the comment of Dariusz Prokopowicz

Implementation of smart grid technology equips the power system with better sensors, communication and control; and that provides us "big data". We can use different Machine Learning and Deep Learning techniques to analyse this data and understand the system in an unprecedented way and develop better and easier ways of fault detection, theft detection, power loss analysis( and thus work on loss mitigation), failure of equipment, load forecasting, load shedding, cost analysis and much more in an intelligent manner.

This has the potential to develop better control and protection techniques as well if different AI techniques are implemented especially in integration with IoT technology.

Dear Kah Yung Yap ,

Welcome!

This question is good. It deals with the application of artificial intelligence in the electrical power systems.

I think the use of artificial intelligence in the controllers of systems leads to a great enhancement in the production and utilization efficiency. There is great losses in every phase of energy production and energy utilization. If intelligent controllers are massively used in all these phases we get a great saving in every production and utilization step of our resources including the electrical energy.

I excited the topic of the utilization efficiency of our products including the energy.

please follow this topic in the link: https://www.researchgate.net/post/Is_the_utilization_efficiency_of_the_consumer_products_sufficiently_considered_in_design_and_manufacturing

Best wishes

AI will also find ready application in data analytics with load modeling of customers and aggregating to provide more accurate load forecasts. Power Engineers should get a sound grasp of AI techniques, but focus more on practical applications and not distracted by sheer mathematical gymnastics that find no place in enhancing the performance of our power systems.

Based on AI, it is possible to build not only a dynamically controlled system, but also integrate various energy resources. AI will allow to build a unified dispatching of gas, water, heat, hydrogen, electricity. The integration of energy and distributed generation based on AI will allow systems to be more secure, energy efficient, flexible, environmentally friendly and sustainable.