View source for Digitalisation and Artificial Intelligence in Power Systems


{| class="wikitable"
|+ Article Information
|-
! Sector
| Power Systems and Energy Transition
|-
! Sub-sector
| Digitalisation, Artificial Intelligence and Grid Modernisation
|-
! Geographic Scope
| Global
|-
! Technologies
| Artificial Intelligence, Digital Platforms, Smart Meters, Sensors, Energy Management Systems and Distributed Energy Resource Management Systems
|-
! Primary Sources
| International Renewable Energy Agency (IRENA), 2025–2026
|-
! Related SDGs
| SDG 7 • SDG 9 • SDG 13
|}

== Key Takeaways ==

* Digitalisation is becoming an important enabler of electricity-system transformation as power systems become more renewable, decentralised and electrified.
* Artificial intelligence can support electricity forecasting, grid operation, asset management, renewable-energy integration and automated energy management.
* IRENA groups digital solutions for power systems into five value clusters: monitoring, forecasting, operational optimisation, end-user automation and transparency.
* Digital technologies can improve system reliability, reduce operating costs, integrate more renewable energy and create new opportunities for consumers and energy-market participants.
* Recent applications include dynamic congestion management, predictive maintenance, non-firm grid connections, interoperability standards, energy management systems, virtual power plants and distributed energy resource management.
* Digitalisation also creates new challenges, particularly around data quality, interoperability, cybersecurity, digital skills, investment incentives and regulatory frameworks.
* Emerging markets and developing economies can benefit from digital solutions, but implementation requires appropriate infrastructure, institutional capacity, financing and locally relevant approaches.

== Introduction ==

Electricity systems are undergoing major structural changes.

The increasing deployment of renewable energy, the electrification of transport and heating, the growth of distributed generation and the emergence of battery storage are creating electricity systems that are more decentralised and more complex to operate.

At the same time, electricity demand is expected to increase as more sectors of the economy become electrified.

These developments create new requirements for electricity-system planning, monitoring and operation. Grid operators increasingly need to manage large numbers of generation units, storage systems, flexible loads and other distributed energy resources while maintaining reliable and affordable electricity supply.

Digital technologies can help address these challenges by collecting, processing and analysing information from across the electricity system.

Artificial intelligence (AI), advanced sensors, smart meters, cloud computing, digital platforms and automated control systems can transform this information into operational decisions.

The International Renewable Energy Agency (IRENA) describes digitalisation in power systems as the integration of digital technologies into system planning, operation and management. Its recent work highlights digitalisation and AI as important enablers of power-system transformation and renewable-energy integration.

== What is Power-System Digitalisation? ==

Power-system digitalisation refers to the increasing use of digital technologies to monitor, analyse, control and optimise electricity systems.

Digitalisation is broader than simply replacing analogue equipment with digital equipment. It involves connecting data, software, communication systems and physical infrastructure so that electricity-system operators and other participants can make better and faster decisions.

Examples include:

* smart electricity meters;
* sensors and monitoring equipment;
* supervisory control and data acquisition (SCADA) systems;
* phasor measurement systems;
* digital substations;
* cloud computing;
* edge computing;
* digital twins;
* artificial intelligence;
* machine learning;
* automated control systems;
* energy management systems;
* distributed energy resource management systems;
* digital electricity-market platforms.

Digitalisation can therefore affect almost every stage of the electricity value chain, from generation and transmission to distribution and end-use.

== Why Digitalisation is Becoming More Important ==

Traditional electricity systems were largely designed around relatively predictable electricity flows from centralised generators through transmission and distribution networks to consumers.

The increasing penetration of variable renewable energy changes this structure.

Solar PV and wind generation are influenced by weather conditions. Distributed solar systems can generate electricity at the distribution level, while batteries and flexible loads can both consume and supply electricity.

Electric vehicles, heat pumps and other electrified technologies can further change the timing and volume of electricity demand.

These developments increase the importance of information.

Grid operators need to know:

* how much electricity is being generated;
* where generation is located;
* how much electricity consumers are using;
* how demand is changing;
* where congestion is developing;
* when renewable generation is likely to increase or decrease;
* which assets may require maintenance;
* how distributed resources can contribute to system operation.

Digital technologies can provide the information and automated decision-making capabilities required to manage these increasingly complex systems.

== Digitalisation and Artificial Intelligence ==

Digitalisation and artificial intelligence are related but not identical concepts.

Digitalisation provides the infrastructure through which data can be collected, communicated, stored and processed. AI uses data and computational methods to identify patterns, make predictions, optimise decisions or automate certain tasks.

For example, a digital electricity system may collect real-time information from thousands of smart meters and grid sensors. An AI model can then analyse this information to forecast electricity demand or identify abnormal system behaviour.

AI applications can include:

* demand forecasting;
* renewable-energy forecasting;
* fault detection;
* predictive maintenance;
* congestion management;
* electricity-market optimisation;
* energy-consumption optimisation;
* automated control;
* distributed-resource coordination.

The usefulness of AI therefore depends heavily on the availability and quality of the underlying digital infrastructure and data.

== A Value-Based Approach to Digitalisation ==

IRENA's work proposes evaluating digital solutions according to the value they can create for electricity systems and their participants.

Five broad value clusters provide a framework for understanding the main applications of digitalisation:

{| class="wikitable"
|+ Five Value Clusters of Digital Solutions
! Value Cluster
! Main Function
|-
| Monitoring
| Collecting and processing information about electricity-system conditions and assets.
|-
| Forecasting
| Predicting electricity demand, renewable generation, equipment behaviour and other system variables.
|-
| Operational Optimisation
| Using data and digital tools to improve system operation and resource allocation.
|-
| End-User Automation
| Automating energy consumption, distributed generation and flexible demand.
|-
| Transparency
| Improving access to information, coordination, accountability and decision-making.
|}

These clusters are not isolated. A single digital application may contribute to several areas simultaneously.

For example, a smart energy-management system may monitor electricity consumption, forecast future demand and automatically adjust appliances or battery charging in response to system conditions.

== Monitoring and Data Collection ==

Monitoring is the foundation of digitalised power systems.

Sensors, smart meters and other digital devices can provide information about generation, electricity flows, voltage, frequency, equipment condition and consumption.

Improved monitoring can help operators identify problems more rapidly and develop a more accurate understanding of system behaviour.

Examples include:

* real-time monitoring of transmission lines;
* smart-meter data collection;
* monitoring of renewable-energy plants;
* transformer condition monitoring;
* battery-management systems;
* distribution-network sensors.

More extensive monitoring can also enable utilities to move from periodic inspections towards continuous assessment of infrastructure.

However, increasing the volume of collected data does not automatically improve system performance. Data must be accurate, accessible, interoperable and appropriately managed.

== Forecasting ==

Forecasting is one of the areas where AI can provide significant value to electricity systems.

Electricity operators need forecasts for both demand and generation.

Demand forecasts help determine how much electricity will be required at different times. Renewable-energy forecasts estimate the future availability of resources such as solar radiation and wind.

Improved forecasting can help operators:

* schedule generation;
* optimise battery charging and discharging;
* manage reserves;
* reduce renewable-energy curtailment;
* reduce balancing costs;
* anticipate congestion;
* improve system reliability.

IRENA highlights examples in which AI-enhanced forecasting has reduced operational costs. One example cited in its 2025 work reported reductions in operating reserve costs of approximately 10–15%, with annual customer savings exceeding USD 9 million in the specific application.

Forecasting technologies continue to develop through advances in machine learning, satellite data, sensors, cloud computing and other digital technologies.

== Operational Optimisation ==

Digitalisation can improve the way electricity networks and energy assets are operated.

Optimisation systems can process large amounts of information and identify operating strategies that would be difficult to calculate manually.

Applications include:

* optimising electricity dispatch;
* managing network congestion;
* coordinating batteries;
* reducing renewable-energy curtailment;
* optimising distributed energy resources;
* managing electricity-market participation;
* improving network utilisation.

Digital optimisation can also allow existing infrastructure to be used more efficiently.

For example, a grid operator may be able to manage transmission capacity dynamically using real-time information about weather, line conditions and electricity flows rather than relying only on conservative static operating limits.

== Digitalisation and Renewable Energy Integration ==

The increasing share of solar and wind power makes digitalisation particularly relevant.

Variable renewable generation introduces uncertainty because electricity production changes according to weather conditions.

Digital forecasting can improve estimates of future renewable output, while automated controls can respond to changes in generation and demand.

Digital tools can therefore support:

* higher renewable-energy penetration;
* improved balancing;
* reduced curtailment;
* better use of transmission capacity;
* improved coordination of storage;
* more efficient electricity markets.

Digitalisation does not eliminate the physical variability of renewable energy, but it can improve the ability of electricity systems to manage that variability.

== Applications of Digitalisation and AI in Power Systems ==

IRENA's 2026 case-study report demonstrates how digitalisation and AI are being applied to practical power-system challenges. The examples cover both grid management and the coordination of distributed energy resources.

The applications illustrate an important characteristic of power-system digitalisation: digital tools do not replace physical electricity infrastructure. Instead, they improve the ability to monitor, operate and coordinate that infrastructure.

== Dynamic Congestion Management ==

Electricity networks can experience congestion when the amount of electricity flowing through a transmission or distribution asset approaches or exceeds its operating limit.

Traditionally, grid operators may rely on conservative assumptions about network conditions when determining how much electricity can safely flow through a line.

Digital monitoring can provide more detailed information about actual system conditions. Advanced systems can combine information about electricity flows, weather and infrastructure conditions to calculate available network capacity more dynamically.

This can allow grid operators to make better use of existing infrastructure.

Dynamic congestion management can potentially:

* increase the usable capacity of existing networks;
* reduce unnecessary congestion;
* improve integration of renewable generation;
* reduce the need for some network reinforcement;
* improve the utilisation of transmission assets.

The approach is particularly relevant as renewable generation expands and electricity flows become more variable.

== Predictive Maintenance ==

Electricity networks contain large numbers of assets, including transformers, transmission lines, substations, switchgear and other equipment.

Traditional maintenance approaches may rely on fixed schedules or inspections after problems have already become apparent.

Predictive maintenance uses data from sensors, historical maintenance records and other information to estimate the condition of equipment and identify potential failures before they occur.

AI and machine-learning techniques can help identify patterns associated with equipment degradation.

Potential benefits include:

* earlier identification of equipment problems;
* reduced unplanned outages;
* better scheduling of maintenance;
* more efficient use of maintenance personnel;
* reduced equipment downtime;
* improved asset utilisation.

Predictive maintenance does not eliminate the need for physical inspection and engineering expertise. Instead, it provides additional information that can help operators prioritise maintenance activities.

== Non-Firm Connections for Renewable Energy ==

Connecting new renewable-energy projects to electricity networks can be delayed when existing grid infrastructure has limited capacity.

One approach explored through digitalisation is the use of '''non-firm connections'''.

Under a non-firm connection arrangement, a renewable generator may be connected to the network without receiving an unconditional guarantee that all of its potential electricity output can always be transported.

Instead, digital monitoring and network-management systems can identify periods when network constraints require the generator's output to be reduced.

This approach can potentially accelerate renewable-energy deployment by allowing projects to connect before major grid reinforcement is completed.

The concept relies on accurate information about network conditions and appropriate rules for managing curtailment.

It can therefore be supported by:

* real-time network monitoring;
* automated congestion management;
* renewable-generation forecasting;
* transparent connection rules;
* appropriate compensation arrangements.

Non-firm connections are not suitable for every network or project. Their feasibility depends on the reliability requirements of the electricity system and the regulatory framework governing grid access.

== Distributed Energy Resources ==

The growth of distributed solar PV, batteries, electric vehicles, flexible loads and other small-scale energy resources is changing the structure of electricity systems.

These resources are collectively referred to as '''distributed energy resources (DERs)'''.

Individually, a household battery or rooftop solar installation may have limited impact on the electricity system. However, thousands of connected devices can represent a significant amount of generation, storage or flexible demand.

Digitalisation makes it possible to coordinate these resources.

A digital platform can receive information from multiple devices and send operating instructions based on grid conditions, electricity prices or customer preferences.

This creates opportunities to turn distributed resources into active participants in electricity-system management.

== Interoperability ==

As the number of connected energy devices increases, different technologies need to communicate with each other.

A major challenge is that equipment from different manufacturers may use different communication protocols, data structures or technical interfaces.

'''Interoperability''' refers to the ability of different systems and devices to exchange information and operate together effectively.

International standards can help address this challenge by establishing common approaches to communication and data exchange.

Interoperability can support:

* integration of equipment from different manufacturers;
* easier deployment of distributed energy resources;
* lower integration costs;
* more flexible energy-management systems;
* improved data exchange;
* greater competition among technology providers.

Without interoperability, digitalisation can result in isolated technology platforms that are difficult to integrate.

== Energy Management Systems ==

Energy management systems (EMS) use digital technologies to monitor and control energy assets and electricity consumption.

An EMS can coordinate resources such as:

* solar PV;
* batteries;
* electric vehicles;
* controllable appliances;
* generators;
* industrial loads;
* heating and cooling systems.

Depending on the application, the system can optimise energy use according to electricity prices, renewable generation, grid conditions or user preferences.

For example, a battery may be charged when renewable generation is abundant and discharged during periods of higher demand.

Similarly, flexible electricity consumption can be shifted away from periods when the electricity system is under stress.

== Virtual Power Plants ==

A '''virtual power plant (VPP)''' is a digital system that coordinates multiple distributed energy resources so that they can operate collectively as a single controllable resource.

A VPP may combine:

* rooftop solar systems;
* batteries;
* electric vehicles;
* commercial loads;
* industrial loads;
* other flexible resources.

The individual resources remain physically distributed, but digital communication and control allow them to participate collectively in electricity-system operations or electricity markets.

VPPs can potentially provide:

* demand response;
* balancing services;
* peak-load reduction;
* renewable-energy integration;
* energy-market participation;
* flexibility services.

The effectiveness of a VPP depends on reliable communication, appropriate control systems, customer participation and market rules that allow distributed resources to provide services.

== Distributed Energy Resource Management Systems ==

A '''distributed energy resource management system (DERMS)''' provides a more comprehensive digital platform for managing distributed energy resources connected to electricity networks.

A DERMS can help utilities monitor, coordinate and optimise distributed generation, storage and flexible demand.

Potential functions include:

* monitoring distributed assets;
* forecasting distributed generation;
* coordinating batteries;
* managing voltage;
* reducing network congestion;
* supporting demand response;
* integrating electric vehicles;
* coordinating renewable-energy resources.

DERMS can therefore become an important interface between distributed energy resources and electricity-network operators.

== Digitalisation and Consumers ==

Digitalisation is not limited to utilities and grid operators.

Consumers can also become active participants in electricity systems.

Smart meters, mobile applications, automated energy-management systems and connected appliances can give consumers greater visibility and control over electricity use.

Consumers may be able to:

* monitor electricity consumption;
* identify high-consumption appliances;
* respond to electricity prices;
* optimise battery operation;
* schedule electric-vehicle charging;
* participate in demand-response programmes;
* increase self-consumption of rooftop solar generation.

This creates a transition from a traditional model in which consumers primarily receive electricity to a more interactive system in which consumers can also provide flexibility to the grid.

== Digitalisation and Renewable-Energy Forecasting ==

Forecasting is particularly important for systems with large shares of variable renewable energy.

Solar and wind generation depend on weather conditions, meaning that grid operators need reliable estimates of future output.

Digital forecasting systems can combine:

* historical generation data;
* weather forecasts;
* satellite observations;
* sensor data;
* numerical weather models;
* machine-learning algorithms.

Improved forecasts can allow operators to schedule generation more efficiently and reduce the amount of reserve capacity that must be maintained.

Better forecasting can also improve the operation of batteries and reduce renewable-energy curtailment.

== Digitalisation and Electricity Markets ==

Digital technologies can improve the operation of electricity markets by allowing more participants and resources to interact with the system.

Automated platforms can process large quantities of information about generation, demand, prices and network conditions.

Distributed resources can potentially participate in markets through aggregators or virtual power plants.

This can create new revenue opportunities for:

* battery owners;
* flexible consumers;
* renewable-energy producers;
* electric-vehicle owners;
* aggregators.

However, market rules need to be designed so that smaller resources can participate without facing disproportionately high administrative or technical barriers.

== Digitalisation in Mini-Grids and Distributed Systems ==

Digitalisation can also improve the management of mini-grids and other decentralised electricity systems.

Remote monitoring can allow operators to track:

* electricity generation;
* battery state of charge;
* electricity consumption;
* equipment condition;
* outages;
* system performance.

Remote management can reduce the need for frequent physical visits, which can be particularly valuable in geographically dispersed systems.

Digital payment and customer-management systems can also improve revenue collection and provide operators with better information about electricity demand.

In renewable-energy-based mini-grids, digital energy-management systems can coordinate solar generation, batteries and electricity demand to improve system utilisation.

== Data as Critical Infrastructure ==

Digitalised electricity systems depend increasingly on data.

The quality of decisions made by AI and other digital tools is strongly influenced by the quality of the information available to them.

Important characteristics include:

* accuracy;
* completeness;
* timeliness;
* consistency;
* accessibility;
* interoperability;
* security.

Poor-quality data can produce inaccurate forecasts, incorrect optimisation decisions or misleading equipment assessments.

Data governance should therefore be treated as an important component of digital power-system development rather than as a separate information-technology issue.

== Cybersecurity ==

Greater digital connectivity also creates additional cybersecurity risks.

As more electricity assets become connected to communication networks, there are more potential points through which malicious actors could attempt to disrupt operations or gain unauthorised access.

Potential risks include:

* unauthorised access;
* malware;
* manipulation of operational data;
* disruption of control systems;
* theft of sensitive information;
* attacks on communication infrastructure.

Cybersecurity measures therefore need to be incorporated into the design and operation of digital electricity systems.

Security should be considered throughout the technology life cycle, from procurement and system design to software updates, maintenance and eventual decommissioning.

== Artificial Intelligence and Human Expertise ==

AI can process large amounts of information and identify patterns that may be difficult to detect manually. However, electricity systems remain physical infrastructure operating under engineering, safety and regulatory constraints.

AI systems therefore need to operate alongside human expertise.

Human oversight remains important for:

* validating AI outputs;
* handling unusual events;
* making safety-critical decisions;
* interpreting uncertain predictions;
* managing system failures;
* ensuring compliance with regulations.

The most useful applications are therefore likely to combine automated analysis with appropriate human supervision rather than replacing system operators completely.

== Benefits of Digitalisation and AI ==

Digitalisation can create benefits across different parts of the electricity system. The value of a particular application depends on the local system, available data, infrastructure and institutional arrangements.

=== Improved System Reliability ===

Better monitoring, forecasting and predictive maintenance can help electricity operators identify problems before they develop into major failures.

Digital systems can provide earlier warnings of equipment deterioration, abnormal electricity flows and unexpected changes in generation or demand.

This can support:

* faster fault detection;
* improved maintenance planning;
* reduced equipment downtime;
* better system visibility;
* improved continuity of electricity supply.

Digitalisation does not eliminate physical failures, but it can improve the ability of operators to anticipate and respond to them.

=== Better Integration of Renewable Energy ===

Digital tools can support higher shares of variable renewable energy by improving forecasting, network management and coordination of flexible resources.

For example, improved forecasts can help operators anticipate periods of high or low solar and wind generation. Batteries and flexible loads can then be coordinated around these expected changes.

Digitalisation can therefore contribute to:

* reduced renewable-energy curtailment;
* improved balancing;
* better use of grid capacity;
* increased renewable-energy integration;
* more efficient storage operation.

=== More Efficient Use of Existing Infrastructure ===

Building new transmission and distribution infrastructure can require substantial time and investment.

Digital technologies can sometimes improve the utilisation of existing infrastructure before new physical assets are constructed.

Dynamic network management, improved monitoring and advanced congestion-management systems can provide operators with more detailed information about the actual condition and utilisation of network assets.

This can help identify opportunities to use existing infrastructure more efficiently.

=== New Services and Business Models ===

Digitalisation can create new opportunities for companies and consumers to participate in electricity markets.

Aggregators and virtual power plants can combine large numbers of small energy resources and make their combined flexibility available to electricity-system operators.

Potential participants include:

* households with batteries;
* rooftop solar owners;
* electric-vehicle owners;
* commercial buildings;
* industrial consumers;
* energy-service companies.

This can create new revenue streams while providing flexibility to electricity systems.

== Challenges to Digitalisation ==

The benefits of digitalisation are accompanied by technical, financial, institutional and social challenges.

=== Data Quality and Availability ===

AI and digital applications depend on reliable data.

Incomplete, inconsistent or inaccurate datasets can produce poor forecasts and unreliable recommendations.

This is particularly challenging in electricity systems where monitoring infrastructure may be limited or where historical datasets are incomplete.

Improving data collection, storage, quality assurance and governance is therefore an important prerequisite for advanced digital applications.

=== Interoperability ===

Electricity systems contain equipment from many manufacturers and generations.

If these systems cannot communicate effectively, utilities may become dependent on isolated technology platforms.

Interoperability standards can reduce this problem by establishing common approaches to communication and data exchange.

However, implementing interoperability may require upgrades to existing equipment and coordination between utilities, technology providers, regulators and standards organisations.

=== Cybersecurity ===

Greater connectivity increases the potential attack surface of electricity infrastructure.

Cybersecurity therefore needs to be integrated into digitalisation programmes from the beginning.

Important measures can include:

* secure communication;
* access controls;
* encryption;
* network segmentation;
* continuous monitoring;
* incident-response procedures;
* regular software updates;
* cybersecurity training.

Cybersecurity should be treated as an ongoing operational requirement rather than a one-time technical intervention.

=== Privacy and Data Governance ===

Smart meters and other digital devices can generate detailed information about electricity consumption.

In some circumstances, consumption data can reveal patterns of behaviour or information about when buildings are occupied.

Appropriate data-governance frameworks are therefore needed to determine:

* who owns or controls data;
* who can access it;
* how it can be used;
* how long it can be retained;
* how sensitive information is protected.

=== Skills and Institutional Capacity ===

Digital power systems require expertise that combines energy-sector knowledge with information technology, data science, cybersecurity and communications.

Utilities and regulators may need to develop new capabilities in areas such as:

* data analytics;
* artificial intelligence;
* software engineering;
* cybersecurity;
* digital asset management;
* digital regulation;
* data governance.

Capacity building is therefore an important part of digital transformation.

=== Investment Requirements ===

Digitalisation requires investment in both hardware and software.

Costs may include:

* sensors;
* smart meters;
* communication networks;
* servers and cloud services;
* software platforms;
* cybersecurity systems;
* system integration;
* staff training;
* maintenance and upgrades.

The business case for digitalisation should therefore consider the expected operational savings and system benefits over the full life of the investment.

== Digitalisation in Developing Countries ==

Digitalisation can provide opportunities for developing countries to improve electricity-system management, but implementation conditions can differ significantly from those in highly digitalised electricity markets.

Some countries may have limited:

* electricity-system monitoring;
* telecommunications infrastructure;
* reliable electricity supply;
* historical energy datasets;
* digital skills;
* access to finance;
* institutional capacity.

This does not mean that developing countries cannot adopt advanced digital solutions.

Instead, digitalisation strategies may need to prioritise solutions that address immediate operational challenges while creating a foundation for future expansion.

Examples include:

* remote monitoring of mini-grids;
* digital payment systems;
* smart metering;
* remote fault detection;
* mobile-based customer services;
* solar and battery monitoring;
* demand forecasting;
* digital asset management.

== Digitalisation in Africa ==

African electricity systems present both significant challenges and opportunities for digital transformation.

Many countries are expanding renewable generation while simultaneously attempting to improve electricity access, strengthen transmission and distribution networks and integrate decentralised energy systems.

Digitalisation can support these objectives by improving visibility of electricity infrastructure and enabling more effective management of distributed resources.

Potential applications include:

* digital monitoring of rural mini-grids;
* smart meters for improved revenue collection;
* renewable-generation forecasting;
* remote monitoring of solar and battery systems;
* digital management of distributed energy resources;
* predictive maintenance of grid equipment;
* demand-side management;
* digital electricity-market platforms.

In areas where conventional electricity infrastructure is still developing, digital technologies can sometimes be introduced alongside new infrastructure rather than being added to legacy systems later.

However, African countries also need to consider the cost of digital infrastructure, cybersecurity, data governance and the availability of specialised technical skills.

== A Practical Approach to Digital Transformation ==

Digital transformation does not need to occur simultaneously across an entire electricity system.

A phased approach can help utilities and policymakers prioritise investments according to their most important operational needs.

A possible sequence is:

# '''Establish data foundations:''' Improve metering, monitoring, communication and data-management systems.
# '''Digitise core operations:''' Introduce digital asset management, remote monitoring and automated reporting.
# '''Develop forecasting capabilities:''' Apply advanced analytics to electricity demand and renewable-generation forecasting.
# '''Introduce optimisation:''' Use digital tools to improve network operation, storage management and renewable integration.
# '''Coordinate distributed resources:''' Develop platforms for batteries, flexible demand, electric vehicles and distributed generation.
# '''Scale AI applications:''' Apply machine learning and other AI technologies where sufficient data and operational capacity exist.
# '''Strengthen cybersecurity and governance:''' Continuously improve security, privacy, interoperability and institutional frameworks.

This approach can help ensure that advanced AI applications are built on reliable digital foundations.

== Conditions for Successful Implementation ==

Successful digitalisation requires more than purchasing new software.

Several conditions are important.

=== Clear Objectives ===

Digital projects should begin with a clearly defined operational problem.

Examples include:

* reducing electricity losses;
* improving renewable forecasting;
* reducing outages;
* improving maintenance;
* increasing network capacity;
* improving revenue collection.

Defining the problem first helps prevent investment in technology without a clear operational purpose.

=== Appropriate Technology ===

Technology should be selected according to the needs and capabilities of the electricity system.

A highly sophisticated platform may provide limited value if the underlying electricity infrastructure or data systems are inadequate.

Technology choices should therefore consider:

* scalability;
* interoperability;
* reliability;
* cybersecurity;
* maintenance requirements;
* local technical capacity;
* total cost of ownership.

=== Human Capacity ===

Digital systems require people who can operate, maintain and evaluate them.

Training should therefore accompany technology deployment.

Utilities may also need to recruit specialists in data science, software engineering and cybersecurity while developing the digital skills of existing energy-sector professionals.

=== Institutional Coordination ===

Digital electricity systems involve multiple stakeholders.

These can include:

* utilities;
* regulators;
* system operators;
* technology companies;
* telecommunications providers;
* consumers;
* distributed-energy companies;
* research institutions.

Coordination between these stakeholders is important for data sharing, interoperability, cybersecurity and market participation.

== Future Outlook ==

Digitalisation is likely to become increasingly important as electricity systems become more renewable, decentralised and interconnected.

Several trends are expected to shape the future.

=== Increasing Use of AI ===

AI applications are likely to expand from forecasting and analytics into increasingly automated system-management functions.

Potential applications include:

* automated fault diagnosis;
* advanced renewable forecasting;
* real-time optimisation;
* autonomous energy management;
* predictive asset management;
* automated demand response.

The use of AI in safety-critical electricity applications will require appropriate testing, validation and human oversight.

=== Digital Twins ===

Digital twins are virtual representations of physical assets or systems that can be updated using real-world data.

In power systems, digital twins can potentially be used to:

* simulate network conditions;
* test operating strategies;
* identify equipment problems;
* optimise asset performance;
* support maintenance planning.

Their effectiveness depends on the quality and frequency of the data used to maintain the digital representation.

=== Growth of Distributed Energy Resources ===

The continued deployment of rooftop solar, batteries, electric vehicles and flexible loads is likely to increase the importance of digital coordination.

Platforms such as virtual power plants and distributed energy resource management systems can help integrate these resources into electricity-system operations.

=== More Automated Electricity Systems ===

As digital technologies improve, more electricity-system decisions may become partially or fully automated.

Automation can improve response times and reduce operational workloads, but it also increases the importance of cybersecurity, system resilience and human oversight.

=== Digitalisation and the Energy Transition ===

Digitalisation is not an energy source by itself. Its value lies in enabling electricity systems to operate more efficiently and flexibly.

As renewable generation, storage, electrification and distributed resources expand, digital systems can help coordinate these technologies and make better use of available infrastructure.

== Conclusion ==

Digitalisation and artificial intelligence are becoming important components of modern power-system transformation.

The increasing deployment of variable renewable energy, energy storage, distributed generation and electrified technologies is creating electricity systems that are more complex and data-intensive than traditional centralised systems.

Digital technologies can help address this complexity through improved monitoring, forecasting, operational optimisation, end-user automation and transparency.

Practical applications already include dynamic congestion management, predictive maintenance, renewable-energy forecasting, non-firm grid connections, interoperability solutions, virtual power plants and distributed energy resource management.

However, digitalisation should not be viewed as a substitute for investment in physical electricity infrastructure. Strong grids, reliable generation, storage, communication networks and appropriately designed markets remain essential.

The effectiveness of AI and other digital tools also depends on data quality, cybersecurity, interoperability, financing and human expertise.

For developing countries and African electricity systems, digitalisation can provide opportunities to improve the management of both centralised and decentralised electricity infrastructure. Remote monitoring, smart metering, digital payments, renewable forecasting and distributed-resource management can be particularly relevant where resources and technical capacity are constrained.

The most successful digitalisation strategies are therefore likely to be those that begin with clearly defined energy-system challenges, build reliable data and digital foundations, develop local capacity and introduce increasingly advanced applications as institutional and technical capabilities grow.

== See Also ==
* [[Grid Integration of Renewable Energy]]

== External Links ==

* [https://www.irena.org/Publications/2026/May/Digitalisation-and-AI-for-Transforming-Power-Systems-Case-Studies-from-IRENA-Innovation-Week-2025 IRENA: Digitalisation and AI for Transforming Power Systems: Case Studies from IRENA Innovation Week 2025]
* [https://www.irena.org/Publications/2025/Oct/Digitalisation-and-AI-for-power-system-transformation-Perspectives-for-the-G7 IRENA: Digitalisation and AI for Power System Transformation: Perspectives for the G7]
* [https://www.irena.org/ IRENA – International Renewable Energy Agency]

== References ==

* International Renewable Energy Agency (IRENA) (2026). ''Digitalisation and AI for Transforming Power Systems: Case Studies from IRENA Innovation Week 2025''. Abu Dhabi: IRENA.
* International Renewable Energy Agency (IRENA) (2025). ''Digitalisation and AI for Power System Transformation: Perspectives for the G7''. Abu Dhabi: IRENA.

== Attribution and Licence ==

This article is an independently structured educational synthesis based primarily on the International Renewable Energy Agency (IRENA) publication ''Digitalisation and AI for Transforming Power Systems: Case Studies from IRENA Innovation Week 2025'' (2026), with supporting concepts drawn from IRENA's ''Digitalisation and AI for Power System Transformation: Perspectives for the G7'' (2025).

The article does not reproduce the source publications. Readers should consult the original IRENA publications for their complete methodologies, case-study details, assumptions and source references.

== Categories ==

[[Category:Nigeria Off-Grid Solar Knowledge Hub]]