Data governance in smart grids: from the meter to the energy strategy

Energy is the engine of our society, a vital resource that powers our lives and the global economy. However, the traditional energy model faces monumental challenges: growing demand, climate urgency, and the prevailing need for a transition to cleaner and more sustainable sources. In this panorama of profound transformation, a silent but powerful actor emerges: data. Not only is "having data" important, but also the ability to govern it properly to transform the energy sector.

In this new energy paradigm, data has become a strategic resource as essential as energy itself. The key is not only in generating and distributing electricity, but in understanding, anticipating and optimizing its use in real time. And to do this, it is necessary to capture the digital pulse of the energy system through millions of measurement and observation points.

So, before addressing how this data is governed, it's worth understanding where it comes from, what kind of information it generates, and how it's quietly transforming how the power grid works.

The digital heartbeat of the network: data from smart meters and sensors

Imagine an electric grid that not only distributes power, but also "listens," "learns," and "reacts." This is the promise of smart grids, a  system that goes far beyond the cables and transformers we see. Asmart grid is an electricity distribution system that uses digital technology to improve grid efficiency, sustainability, and security. At the heart of this revolution are smart meters and a vast network of sensors.

Smart meters, also known as Advanced Metering Infrastructure (AMI), are devices that record electricity consumption digitally, often at very short intervals of time (e.g. every 15 minutes or per hour), and transmit this data to power companies via various communication technologies, such as cellular networks, WiFi,  PLC (Power Line Communication) or radio frequency (RF). This data is not limited to the total amount of energy consumed, but offers a detailed breakdown of consumption patterns, voltage levels, power quality, and even fault detection.

But network intelligence doesn't just lie with the meters. A myriad of sensors distributed throughout the electrical infrastructure monitor critical variables in real time: from transformer temperature and equipment status to environmental conditions and power flow at different points on the grid. These sensors act as the "eyes and ears" of the system, providing a granular and dynamic view of network performance.

The magic happens in the flow of this data. The information from the meters and sensors travels bidirectionally: from the point of consumption or generation to the management platforms of the electricity company and vice versa. This constant communication allows utilities to:

• Check-in accurately
• Implement demand response programs
• Optimize power distribution
• Predict and prevent disruptions
• Efficiently integrate renewable energy sources that are intermittent by their nature

Data governance: the backbone of a connected network

The mere collection of data, however abundant, does not guarantee its value. In fact, without proper management, this heterogeneity of sources can become an insurmountable barrier to the integration and useful analysis of information. This is where data governance comes into play.

Data governance  in the context of smart grids involves establishing a robust set of principles, processes, roles, and technologies to ensure that the data generated is reliable, accessible, useful, and secure. It's the "rule of the game" that defines how data is captured, stored, maintained, used, protected, and deleted throughout its entire lifecycle.

Why is this so crucial?

• Interoperability: A smart grid is not a monolithic system, but a constellation of devices, platforms, and actors (generators, distributors, consumers, prosumer, regulators). For all these elements to "speak the same language", interoperability is essential. Data governance sets standards for nomenclature, formats, encoding, and synchronization, allowing information to flow frictionlessly between disparate systems. Without it, we risk creating fragmented and costly information silos.
• Quality: Artificial intelligence algorithms and machine learning, so vital to smart grids, are only as good as the data they are fed with. Data governance ensures the accuracy, completeness, and consistency of data (and future information and knowledge) by defining business rules, cleaning up duplicates, and managing data errors. Poor quality data can lead to wrong decisions, operational inefficiencies, and unreliable results.
• Security: The interconnection of millions of devices in a smart network exponentially expands the attack surface for cybercriminals. A breach of data security could have catastrophic consequences, from massive power outages to breaches of user privacy. Data governance is the shield that implements robust access controls, encryption protocols, and usage audits, safeguarding the integrity and confidentiality of critical information. Adhering to consolidated security frameworks such as ENS, ISO/IEC 27000, NIST, IEC 62443, and NERC CIP is critical.

Ultimately, effective data governance turns data into critical infrastructure, as important as cables and substations, for decision-making, resource optimization, and intelligent automation.

Data in action: optimising, anticipating and facilitating the energy transition

Governing data is not an end in itself, but the means to unlock vast potential for efficiency and sustainability in the energy sector.

1. Optimisation of consumption and operational efficiency

Exact, complete, consistent, current, credible and real-time data enables multiple advantages in energy management:

• Consumption at the user level: smart meters empower citizens and businesses by providing them with detailed information about their own consumption. This allows them to identify patterns, adjust their habits, and ultimately reduce their energy bills.
• Demand management: Utilities can use data to implement demand response (DR) programs. These programs incentivize consumers to reduce or shift their electricity consumption during periods of high demand or high prices, thereby balancing the load on the grid and avoiding costly investments in new infrastructure.
• Reduced inefficiencies: The availability of accurate and well-integrated data allows utilities to automate tasks, avoid redundant processes, and reduce unplanned downtime in their systems. For example, a generation plant can adjust its production in real-time based on the analysis of performance and demand data.
• Energy monitoring and emission control: real-time monitoring of energy, water or polluting gas emissions reveals hidden inefficiencies and savings opportunities. Smart dashboards, powered by governed data, enable industrial plants and cities to reduce their costs and advance their environmental sustainability goals.

2. Demand anticipation and grid resilience

Smart grids can also foresee the future of energy consumption:

• Demand forecasting: By using advanced artificial intelligence and machine learning algorithms  (such as time series analysis or neural networks), historical consumption data, combined with external factors such as weather, holidays, or special events, allow utilities to forecast demand with astonishing accuracy. This anticipation is vital to optimize resource allocation, avoid overloads, and ensure network stability.
• Predictive maintenance: By combining historical maintenance data with real-time information from sensors on critical equipment, companies can anticipate machine failures before they occur, proactively schedule maintenance, and avoid costly unexpected outages.

3. Facilitating the energy transition

Data governance is an indispensable catalyst for the integration of renewable energy and decarbonization:

• Integration of renewables: sources such as solar and wind energy are intermittent by nature. Real-time data on generation, weather conditions, and grid status are critical to managing this variability, balancing the load, and maximizing the injection of clean energy into the grid.
• Distributed Energy Resources (RED) Management: The proliferation of rooftop solar panels, storage batteries, and electric vehicles (which can charge and discharge energy to the grid) requires sophisticated data management. Data governance ensures the interoperability needed to coordinate these resources efficiently, transforming them into "virtual power plants" that can support grid stability.
• Boosting the circular economy: thanks to the full traceability of a product's life cycle, from design to recycling, data makes it possible to identify opportunities for reuse, recovery of materials and sustainable design. This is crucial to comply with new circular economy regulations and the Digital Product Passport (DPP).
• Digital twins: For a virtual replica of a physical process or system to work, it needs to be powered by accurate and consistent data. Data governance ensures synchronization between the physical and virtual worlds, enabling reliable simulations to optimize the design of new production lines or the arrangement of elements in a factory.

Tangible benefits for citizens, businesses and administrations

Investment in data governance in smart grids generates significant value for all actors in society:

For citizens

• Savings on electricity bills: by having access to real-time consumption data and flexible tariffs (for example, with lower prices in off-peak hours), citizens can adjust their habits and reduce their energy costs.
• Empowerment and control: citizens go from being mere consumers to "prosumers", with the ability to generate their own energy (for example, with solar panels) and even inject the surplus into the grid, being compensated for it. This encourages participation and greater control over their energy consumption.
• Better quality of life: A more resilient and efficient grid means fewer power interruptions and greater reliability, which translates into a better quality of life and uninterrupted essential services.
• Promoting sustainability: By participating in demand response programs and adopting more efficient consumption behaviors, citizens contribute directly to the reduction of the country's carbon footprint and energy transition.

For companies

• Optimization of operations and cost reduction: companies can predict demand, adjust production and perform predictive maintenance of their machinery, reducing losses due to failures and optimizing the use of energy and material resources.
• New business models: The availability of data creates opportunities for the development of new services and products. This includes platforms for energy exchange, intelligent energy management systems for buildings and homes, or the optimization of charging infrastructures for electric vehicles.
• Loss reduction: Intelligent data management allows utilities to minimize losses in power transmission and distribution, prevent overloads, and isolate faults faster and more efficiently.
• Improved traceability: in regulated sectors such as food, automotive or pharmaceuticals, the complete traceability of the product from the raw material to the end customer is not only an added value, but a regulatory obligation. Data governance ensures that this traceability is verifiable and meets standards.
• Regulatory compliance: Robust data management enables companies to comply with increasingly stringent regulations on sustainability, energy efficiency, and emissions, as well as data privacy regulations (such as GDPR).

For Public Administrations

• Smart energy policymaking: Aggregated and anonymised data from the smart grid provides public administrations with valuable information to design more effective energy policies, set ambitious decarbonisation targets and strategically plan the country's energy future.
• Infrastructure planning: with a clear view of consumption patterns and future needs, governments can more efficiently plan grid upgrades and expansions, as well as the integration of distributed energy resources such as smart microgrids.
• Boosting urban resilience: the ability to manage and coordinate locally distributed energy resources, such as in micro-grids, improves the resilience of cities to extreme events or failures in the main grid.
• Promotion of technological and data sovereignty: by encouraging the publication of this data in open data portals together with the creation of national and sectoral data spaces, Administrations ensure that the value generated by data stays in the country and in local companies, boosting innovation and competitiveness at an international level.

Challenges and best practices in smart grid data governance

Despite the immense benefits, implementing effective data governance initiatives in the energy sector presents significant challenges:

• Heterogeneity and complexity of data integration: Data comes from a multitude of disparate sources (meters, sensors, SCADA, ERP, MES, maintenance systems, etc.). Integrating and harmonizing this information is a considerable technical and organizational challenge.
• Privacy and compliance: Energy consumption data can reveal highly sensitive patterns of behavior. Ensuring user privacy and complying with regulations such as the GDPR is a constant challenge that requires strong ethical and legal frameworks.
• Cybersecurity: The massive interconnection of devices and systems expands the attack surface, making smart grids attractive targets for sophisticated cyberattacks. Integrating legacy systems with new technologies can also create vulnerabilities.
• Data quality: Without robust processes, information can be inconsistent, incomplete, or inaccurate, leading to erroneous decisions.
• Lack of universal standards: The absence of uniform cybersecurity practices and regulations across different regions can reduce the effectiveness of security measures.
• Resistance to change and lack of data culture: The implementation of new data governance policies and processes can encounter internal resistance, and a lack of understanding about the importance of data often hampers efforts.
• Role and resource allocation: Clearly defining who is responsible for which aspect of the data and securing adequate financial and human resources is critical to success.
• Scalability: As the volume and variety of data grows exponentially, the governance structure must be able to scale efficiently to avoid bottlenecks and compliance issues.

To overcome these challenges, the adoption of the following best practices is essential:

• Establish a strong governance framework: define clear principles, policies, processes and roles from the outset, with the support of public administrations and senior management. This can be solved with the implementation of the processes of UNE 0077 to 0080,  which includes the definition of data governance, management and quality processes, as well as the definition of organizational structures.
• Ensure data quality: Implement data quality assessment methodologies and processes, such as data asset classification and cataloguing, quality control (validation, duplicate cleanup), and data lifecycle management. All this can be based on the implementation of a quality model following UNE 0081.
• Prioritize cybersecurity and privacy: Implement robust security frameworks (ENS, ISO 27000, NIST, IEC 62443, NERC CIP), secure IoT devices, use advanced threat detection tools (including AI), and build resilient systems with network segmentation and redundancy. Ensure compliance with data privacy regulations (such as GDPR).
• Promote interoperability through standards: adopt open standards for communication and data exchange between systems, such as OPC UA or ISA-95.
• Invest in technology and automation: Use data governance tools that enable automatic data discovery and classification, application of data protection rules, automation of metadata management, and cataloguing of data. Automating routine tasks improves efficiency and reduces errors.
• Collaboration and Information Sharing: Encourage the exchange of threat and best practice information among utilities, government agencies, and other industry stakeholders. In this regard, it is worth highlighting the more than 900 datasets published in the datos.gob.es catalogue  on the subject of Energy, as well as the creation of "Data Spaces" (such as the National Data Space for Energy or Industry in Spain) facilitates the secure and efficient sharing of data between organisations, boosting innovation and sectoral competitiveness.
• Continuous monitoring and improvement: data governance is a continuous process. KPIs should be established to monitor progress, evaluate performance, and make improvements based on feedback and regulatory or strategic changes.

Conclusions: a connected and sustainable future

Energy and data are linked in the future. Smart grids are the manifestation of this symbiosis, and data governance is the key to unlocking its potential. By transforming data from simple records into strategic assets and critical infrastructure, we can move towards a more efficient, sustainable and resilient energy model.

Collaboration between companies, citizens and administrations, driven by initiatives such as the National Industry Data Space in Spain, is essential to build this future. This space not only seeks to improve industrial efficiency, but also to reinforce the country's technological and data sovereignty, ensuring that the value generated by data benefits our own companies, regions and sectors. By investing in strong data governance initiatives and building shared data ecosystems, we are investing in an industry that is more connected, smarter and ready for tomorrow's energy and climate challenges.