Leveraging Agentic AI for Sustainability and Net-Zero in SEC

Leveraging Agentic AI for Sustainability and Net-Zero in SEC

Abstract: Saudi Electricity Company (SEC) faces the dual challenge of meeting surging electricity demand while achieving Vision 2030 sustainability targets and net-zero emissions goals.

Agentic artificial intelligence (AI) – comprising autonomous, learning agents embedded throughout the grid – offers a transformative path to decarbonize SEC’s operations.

This paper analyzes how AI-driven optimization, predictive analytics, and digital automation can reduce SEC’s Scope 1 (direct) emissions from generation, Scope 2 (indirect) emissions from grid losses and purchased energy, and Scope 3 emissions along the value chain. The discussion is grounded in Saudi Arabia’s regulatory framework (Vision 2030, Saudi Green Initiative, NREP, GCC standards) and includes hypothetical SEC case studies (e.g. AI-tuned gas turbine control, smart meter demand response, AI-managed procurement), cost–benefit analyses, and an AI integration architecture (SCADA, GIS, digital twins, cloud/edge). Compliance with IEC/IEEE/CIGRE standards for cybersecurity and AI governance (e.g. IEC 61850 substation automation, ISO 55001 asset management, emerging ISO/IEC AI standards) is addressed.

The results suggest that agentic AI could substantially boost grid efficiency and asset utilization, reduce fuel burn and outages, and engage consumers, all while aligning with Saudi Arabia’s environmental targets.

Keywords: agentic AI, sustainability, net-zero, Saudi Electricity Company, Scope 1 emissions, predictive maintenance, digital substation, smart grid, digital twin, SCADA, GIS, cloud-edge AI, IEC 61850, asset management, Vision 2030, Saudi Green Initiative.

1. Introduction

Saudi Arabia has announced ambitious climate goals: a 50% renewable power target by 2030 and net-zero GHG emissions by 2060. The National Renewable Energy Program (NREP) sets 50% renewable generation by 2030, and the Saudi Green Initiative commits to deep cuts in CO₂ (278 million tonnes by 2030) through a circular carbon economy. Achieving these requires decarbonizing all sectors, with the electric power sector a top priority: it accounts for the majority of Saudi emissions. Saudi Electricity Company (SEC), as the dominant grid operator, must transform its generation fleet, distribution network, and end-user services. Advanced AI – specifically agentic (autonomous, multi-agent) AI – can enable SEC to optimize energy production and consumption intelligently.

Agentic AI refers to systems of autonomous agents that sense the grid environment, learn from data, and act to achieve objectives (e.g. minimize emissions) with minimal human input. In power systems, this can manifest as software agents performing tasks such as real-time dispatch optimization, anomaly detection, or demand-response coordination. With rapidly evolving AI technologies (e.g. neural networks, reinforcement learning), agentic AI promises dynamic, decentralized control of complex energy systems. This paper examines how SEC can apply agentic AI across its operations to meet Scope 1, 2, and 3 sustainability goals.

We first review Saudi regulatory frameworks guiding decarbonization (Vision 2030, Saudi Green Initiative, GCC environmental standards) to contextualize SEC’s goals. Then we analyze AI strategies by emission scope: Scope 1 (on-site generation) including plant optimization, predictive maintenance, and emissions monitoring; Scope 2(transmission/distribution) including loss reduction, digital substation, and smart-meter analytics; and Scope 3 (supply chain and end-use) including green procurement, logistics, and customer engagement. Throughout, we propose hypothetical SEC case studies illustrating AI applications. A cost–benefit analysis estimates the emissions reduction and O&M savings from AI deployment. We outline an AI integration architecture (leveraging SCADA/OT systems, GIS, digital twins, cloud and edge computing) and discuss AI safety and governance in line with IEC/IEEE standards (e.g. IEC 61850, ISO 55001, emerging ISO AI governance). The comprehensive synthesis is aimed at utility executives and engineers seeking cutting-edge solutions for SEC’s sustainability transformation.

2. Regulatory and Policy Framework

SEC’s AI strategy must align with national sustainability mandates. Saudi Vision 2030 and the Saudi Green Initiative (SGI) set the stage. Vision 2030’s energy pillar drives efficiency and diversification. Notably, Saudi pledges 50% of power from renewables by 2030. SGI targets cutting 278 Mtpa of CO₂ by 2030 and net-zero by 2060. As part of SGI, Saudi emphasizes a Circular Carbon Economy (CCE): reduce, reuse, recycle carbon. SEC must therefore reduce emissions intensity in power generation (Scope 1) and losses (Scope 2), even as it expands capacity.

The National Renewable Energy Program (NREP) mandates deployment of ~130 GW renewables (58.7 GW solar, 40 GW wind) by 2030. While this displaces fossil fuel generation (Scope 1), AI can optimize the remaining conventional fleet and integrate variable renewables. The GCC has also begun issuing regional environmental guidelines; for example, GCC countries increasingly adopt international standards (ISO, IEC) for asset management and emissions monitoring, reinforcing Saudi plans. In combination, these frameworks drive SEC to pursue digital innovation for energy efficiency and emissions control.

3. Agentic AI for Scope 1 Emissions

Scope 1 emissions are direct GHGs from SEC-owned generation (fossil-fueled plants, backup generators). Reducing Scope 1 requires improving generator efficiency, optimizing dispatch, and minimizing fuel-based emissions. Agentic AI can contribute in several ways:

• Generation Fleet Optimization: AI algorithms (machine learning, neural networks) can learn from historical and real-time plant data to optimize operating parameters. For example, reinforcement learning could adjust gas-turbine valve settings to balance output and fuel burn, reducing CO₂ per MWh. Model-based AI can optimize combined-cycle power plant schedules (e.g. flexible turbine bypass) to minimize start-up fuel use. While older plants have fixed controls, AI can retrofit via sensor integration. Studies of ML-based control show potential to improve thermal efficiency and ramp-up times, though operational trials remain sparse. In addition, AI can coordinate multi-unit dispatch. An agent at each power plant can negotiate output levels with other agents to meet demand with lowest aggregate emissions.

• Predictive Maintenance: AI-driven predictive maintenance (PdM) uses real-time sensor data (vibration, temperature, etc.) and machine learning to predict component degradation. By preventing failures, AI PdM increases availability and efficiency. According to IEEE, predictive maintenance can prevent up to 70% of equipment breakdowns and reduce downtime by ~50%. Increased uptime improves overall plant output and reduces reliance on inefficient reserve units. One study noted machine availability can rise from 80% to 90% using PdM, with ROI ~33% over five years. In SEC’s context, AI agents could monitor critical assets (boilers, turbines, switchgear) and schedule maintenance before efficiency losses occur, thus cutting unplanned outages and emissions from inefficient operation.

• Emissions Monitoring and Control: AI can enhance emissions control systems. For example, intelligent controllers can adjust flue-gas desulfurization or nitrogen-oxide (NOx) reduction processes in real time. Neural-network models can predict stack emissions based on combustion parameters and instruct corrective actions. Although Saudi’s grid largely uses natural gas (low SOx), NOx and CO₂ are primary concerns. AI-enabled combustion optimization can trim excess oxygen use and stabilize flames, reducing NOx. In emerging units (e.g. future ammonia-cofired plants), AI will be vital to manage new processes under CCE policies. Moreover, AI can analyze satellite or aerial imagery to verify compliance.

Case Study (Fictional): Riyadh South Power Plant (1000 MW Combined Cycle) implements an AI agent that continuously tunes the gas turbine fuel–air mix using a deep neural network trained on historical operating data. The agent autonomously adjusts operations to maintain peak heat rate and minimize fuel use. Simultaneously, an AI maintenance agent predicts bearing wear in the steam turbine, scheduling minor maintenance during planned load reductions. Over a year, the plant reports a 5% fuel savings and 7% lower outage rate, cutting CO₂ by 100,000 tpa compared to baseline (estimates based on plant specs). The development and training cost ($1.5M) was offset by fuel savings in ~2 years, illustrating positive ROI.

4. Agentic AI for Scope 2 Emissions

Scope 2 emissions typically refer to indirect emissions from purchased electricity, but for a vertically integrated utility like SEC, they manifest as losses in transmission and distribution and purchased grid power (if any). Key targets are reducing technical losses and enabling consumers to use electricity more efficiently.

• Grid Loss Minimization: AI can reduce losses via dynamic system optimization. Examples include adaptive voltage regulation: AI agents controlling on-load tap changers and capacitor banks can flatten voltage profiles, reducing I²R losses. Reinforcement learning controllers have been proposed to adjust voltage setpoints in real time. AI can also reconfigure network topology via smart switches to route flows along lower-loss paths. Graph neural networks may analyze the power flow graph to detect loss-intensive patterns. Such AI strategies essentially “rewire” the grid logically to minimize losses under varying loads. While traditional engineering addresses peak losses, agentic AI can continuously learn and adapt to daily and seasonal load patterns, outperforming static schemes.

• Digital Substation Optimization: Modern substations use IEC 61850 communication to connect intelligent electronic devices (IEDs), merging units, and bay controllers. Digital substations enable data-rich condition monitoring and control. AI agents in a substation can analyze real-time sensor data to predict equipment degradation or optimize protection settings. For instance, an AI could fine-tune relay thresholds based on network conditions, improving reliability and security. As ABB’s Digital Systems Center notes, integrated AI can predict the next fault location with ~90% accuracy given historical data. This allows predictive adjustment of protection and switching equipment to mitigate disturbances. Digital substation architecture (Fig. 1) facilitates these capabilities: process-bus merging units digitize analog measurements, and a centralized protection server applies AI analytics. By minimizing switchgear faults and simplifying wiring (reducing 80% of cabling), digital substations cut maintenance costs and outages.

Figure 1: IEC 61850-based digital substation architecture enables centralized protection and advanced monitoring(Source: Shah, 2022)

• Smart Meter Data Analytics: At the distribution level, smart meters provide granular load data. AI can mine this data for energy efficiency and demand response. Agents can cluster consumers by usage patterns and identify inefficiencies (e.g. circuits with high standby load). Machine learning forecasting of aggregate demand enables better dispatch planning upstream. For example, AI-based demand response programs could send signals or price incentives to groups of customers to shift consumption away from peak (using agentic LLM-based home assistants). Recent IEEE Smart Grid studies highlight AI-driven analytics for demand response, enabling up to 20% peak load reduction. While consumer behavior modeling is complex, reinforcement-learning agents in home energy management systems (HEMS) can autonomously control HVAC and appliances to both save money and carbon. Over time, AI-driven automated demand response can shrink peak demand and reduce the need to run peaker plants.

  In addition, AI can assist SEC’s customer engagement (an indirect Scope 3 impact) by providing personalized energy-saving recommendations, thus flattening load curves and reducing emissions indirectly. GCC guidelines encourage utilities to involve customers in energy conservation; agentic AI-enabled platforms (mobile apps with chatbots) can simulate the role of an “energy agent” advising users.

5. Agentic AI for Scope 3 Emissions

Scope 3 emissions include all other indirect impacts, such as supply chain, logistics, and end-user fuel use. SEC’s Scope 3 includes procurement of materials, fuel transport, employee travel, and customer behaviors. Agentic AI applications here are more novel:

• Sustainable Procurement: AI can analyze supply chain data (materials, equipment lifecycle) to choose lower-carbon suppliers and logistics. For example, a procurement AI agent could evaluate vendor carbon intensity scores and optimize orders to minimize shipping (e.g. cluster deliveries, favor local sourcing). Reinforcement learning can simulate trade-offs between cost and carbon footprints. Leading companies use ML tools to optimize supplier mix; SEC could similarly embed AI criteria into procurement processes in line with Vision2030’s sustainability objectives. Standards like ISO 20400 (Sustainable Procurement) and SASB guidelines could be encoded into decision models.

• Logistics Optimization: SEC operates a large vehicle fleet (for patrols, deliveries, field work). AI routing agents (with connectivity) can optimize field service routes to cut fuel usage. Autonomous vehicles (future scenario) could further reduce emissions. Even non-vehicle logistics (e.g. fuel oil delivery to remote diesel generators) can be optimized by AI forecasts of demand vs. fuel levels to minimize trips. Similar AI is used in other sectors: e.g. Amazon uses ML for route planning; SEC could use analogous systems for its field service management.

• End-User Engagement and Energy Behavior: Although customer emissions fall under their control, SEC can indirectly influence Scope 3 via education and programs. AI-driven platforms can personalize energy reports and gamify savings. Agentic chatbots integrated into smart meter interfaces can answer consumer questions (e.g. “how to save energy during Ramadan?”) and nudge conservation. Demand-side management agents can even simulate household energy “avatars” to recommend appliance schedules. Research shows informed customers can cut usage by 5–15%. By leveraging AI to tailor messages (similar to marketing recommendation systems), SEC could indirectly reduce fuel use at customer sites (e.g. home backup generators or industrial plants using gas turbines). In GCC states, evolving regulations may soon require utilities to assist with customer emissions (e.g. mandatory efficiency programs), making these AI tools relevant.

Case Study (Fictional): SEC’s Central Procurement Dept. deploys an AI agent that evaluates vendor bids not only on price but also on lifecycle emissions. For example, when ordering transformers, the AI predicts the embodied emissions of different materials and selects the option with lowest total CO₂ (given similar cost). Over a year, this practice is estimated to reduce procurement-related Scope 3 emissions by ~10%, while maintaining budget. Similarly, a Smart Homes program uses an AI chatbot to coach customers on peak shaving, enrolling 10,000 households; preliminary results show a 3% overall load reduction.

6. AI Integration Architecture

Implementing agentic AI requires a robust IT/OT architecture. Key components include:

• SCADA and OT Systems: SEC’s legacy SCADA (Supervisory Control and Data Acquisition) systems manage grid control. Modernizing SCADA to an open, IP-based architecture allows integration with AI. AI agents need real-time data from sensors and historian databases, and the ability to issue control commands. An architecture might place AI inference engines on edge servers at control centers, connected via secure VPN or TSN networks to RTUs/IEDs. For example, an AI load forecasting module could interface with SCADA to adjust generation setpoints. Importantly, standards like IEC 60870-5-104 (SCADA communications) must be honored.

• GIS and Digital Twins: Geospatial Information Systems (GIS) map the network assets. Combining GIS with digital twin technology creates a live model of the power system. A digital twin (virtual replica) ingests real-time SCADA and sensor data to simulate system behavior. Fig. 2 (above) illustrates a generic energy system digital twin architecture. In SEC, a digital twin of a substation or entire network can enable offline scenario analysis by AI. For instance, before sending crews, an AI agent could simulate fault locations in the twin, improving outage response. Digital twins also support asset health models: a twin of a transformer combines thermal, electrical, and loading models; AI can simulate wear-and-tear and suggest refurbishments. The Saudi grid’s size (90+ GW) makes digital twins valuable for planning under Vision2030.

Figure 2: Example digital twin architecture combining data streams, simulation models, and analytics in a unified platform (Massel & Massel, 2020)

• Cloud–Edge AI Pipelines: Data from SCADA, smart meters, and IoT sensors can be routed to edge servers for low-latency decisions and to cloud platforms for heavy analytics. An edge AI layer (using GPUs/TPUs) can run time-critical models (e.g. real-time grid stability), while cloud hosts longer-term learning (training ML models on historical data). Architecturally, a three-layer approach – cloud, edge, and sensor – is used. In practice, SEC could deploy a hybrid solution: sensitive grid-control AI runs on-premises (edge), whereas big-data analytics (e.g. for procurement optimization) run in AWS/Azure. The cloud layer might use containerized microservices for scalability. Communication between layers must use standardized protocols (MQTT, IEC 61850 GOOSE/SV for substation data).

Importantly, all components require secure design: edge gateways must enforce firewall rules and authentication (e.g. IEC 62443 guidelines), and cloud services must comply with Saudi data laws. At each layer, AI modules can operate as agents communicating via pub/sub or RESTful APIs, orchestrated by an intelligent platform (similar to a multi-agent framework).

7. Hypothetical SEC Case Studies

To illustrate possibilities, we outline three hypothetical case studies within SEC:

7.1. AI-Optimized Combined Cycle Power Plant (Scope 1)
The Riyadh West CCPP installs an agentic AI control system. Agents gather high-fidelity sensor data (exhaust temperature, pressure, grid frequency) and run reinforcement learning to adjust combustion in real time. A predictive maintenance agent monitors heat recovery steam generator (HRSG) tube integrity, scheduling online inspections. Over 3 years, efficiency improves by 4%, reducing fuel costs and CO₂ by ~150,000 t/yr compared to baseline. Maintenance costs drop 20% due to fewer unplanned outages. The project’s cost ($2M) yields a payback in 2.5 years and 10-year net NPV of ~$5M.

7.2. Smart Substation Transformer Management (Scope 2)
At Dammam East 380kV Substation, SEC deploys AI-based asset health monitoring. Digital twin models of transformers run on edge servers, fed by cell-sampling gas sensors and thermal cameras. An AI agent applies neural nets to predict transformer aging and remaining useful life. When it forecasts imminent oil breakdown, it recommends swapping in a spare via the SCADA control. Meanwhile, grid-loss reduction agents in the substation optimize on-load tap changer positions and reactive compensation to minimize I²R losses on connected lines. These combined actions cut substation throughput losses by ~5%, saving roughly 50 GWh/yr (equal to ~25,000 tCO₂/yr).

7.3. AI-Driven Demand Response and Customer Engagement (Scope 3)
SEC pilots a consumer app with an AI agent (chatbot) for residential energy efficiency. The agent analyzes each home’s smart meter data and suggests customized actions (e.g. pre-cooling in the morning, shifting laundry to off-peak). It also implements a learning program: households receive virtual rewards for reducing peak usage. The AI continuously refines recommendations based on past compliance. In one year, participating homes reduce peak demand by an average of 8%. Extrapolated to the city grid, the peak reduction defers building a new 500 MW substation, saving the embodied carbon and financing (~$300M). The initiative aligns with Saudi policy encouraging customer-side conservation.

8. Cost–Benefit Analysis of AI Strategies

A detailed quantitative analysis is needed for full 20,000-word treatment, but illustrative estimates underscore AI’s value:

• Emissions Impact: As shown above, Generation AI agents and PdM could cut fuel use by 5–10% on legacy units. If SEC’s thermal output is ~450 TWh/yr, a 5% efficiency gain saves ~22.5 TWh, roughly 10 MtCO₂ annually (using 0.45 kg/kWh baseline CO₂). Grid-loss AI (Scope 2) reducing losses by 5% on 90 GW grid (~8% typical losses) saves ~7 TWh/yr, ~3 MtCO₂. Demand-response AI (Scope 3) trimming peak by ~8% could reduce generation requirement by ~7 GW-hrs/day peak, saving an estimated ~1–2 MtCO₂/yr. Collectively, AI could plausibly cut SEC’s emissions by an order-of-magnitude percentage, accelerating progress toward SGI targets.

• Operational Savings: IEEE notes AI-based PdM can reduce maintenance costs by ~20% and downtime by up to 50%. For SEC’s large O&M budget ($billions), a 10–20% cut could be hundreds of millions annually. Loss reduction (5% of ~$4B electricity value) yields ~$200M/yr. Substation digitization can cut capital costs: reported savings up to 10–15% via simpler design. Reduced outage-related penalties (Saudi’s strict grid reliability targets) also save money.

• Upfront Costs vs ROI: AI projects require investment (sensors, compute, training data). However, many have sub-3-year payback. For example, the imagined plant project ($2M cost, $0.8M/yr savings) pays back in ~2.5 years, with IRR ~40%. Digital twin systems have high initial cost but lower risk of catastrophic failures, arguably saving more in avoided downtime. The procurement AI is software-centric with low cost, but multiplying its effect across decades yields large abatement of Scope 3 emissions.

In sum, on a rigorous basis, detailed modeling would incorporate SEC’s specific fleet and finances. But even rough metrics support strong net benefits from AI.

9. AI Safety, Governance, and Compliance

Deploying agentic AI in critical infrastructure demands strict governance. SEC must align with international standards:

• AI Safety and Ethics: Emerging ISO/IEC AI standards (e.g. ISO/IEC TR 24028) stress transparency, robustness, and accountability. SEC should enforce explainable AI (XAI) for high-stakes decisions: e.g. permit operators to audit why an agent shut down a unit. Training data must be vetted for bias (e.g. do AI demand predictions treat all customers fairly). A governance body (AI Ethics Board) can supervise AI projects, analogous to ISO 42001 (Innovation Management) frameworks.

• Standards Compliance (IEC 61850, ISO 55001, IEEE): Physical and digital assets must follow IEC and IEEE standards. For example, IEC 61850-6 SCL ensures multi-vendor IED interoperability in digital substations; AI modules should consume data via these standardized models. Asset management (to which AI PdM contributes) aligns with ISO 55001: AI can improve metrics but SEC must maintain documentation and audits per the standard. Cybersecurity standards (IEC 62443, NIST) are critical: AI agents connected to OT must be hardened against tampering. IEEE has standards for asset condition (e.g. IEEE C37.118 for PMU data) and for secure communication (IEEE 1686 for IED security); AI integration must respect these protocols.

• Governance Frameworks: ISO 9001 (quality), 14001 (environment) remain relevant: AI-induced process changes must be integrated into SEC’s quality management. New ISO/IEC 42001 (Innovation Management Systems) guidelines could help structure AI innovation pipeline. Importantly, Saudi’s regulatory bodies (e.g. Electricity & Cogeneration Regulatory Authority – ECRA) may require reporting on AI use and outcomes in grid operation.

Overall, compliance requires cross-disciplinary teams: AI engineers must work with power system engineers, cybersecurity experts, and policy/legal units. Liability issues (e.g. if an AI misprediction causes a blackout) must be contractually and technically addressed. In practice, agentic AI should be introduced progressively, starting with advisory roles (AI suggests an action, human approves) before granting full autonomy.

10. Conclusion

Saudi Electricity Company stands at the threshold of a grid revolution. By embedding agentic AI across its value chain, SEC can greatly accelerate decarbonization. Smart algorithms will optimize thermal generation efficiency, schedule maintenance proactively, and enable adaptive grid control – all contributing to lower Scope 1 emissions. AI-driven monitoring and analytics in substations and distribution will shrink losses and outages, cutting Scope 2 emissions. Outreach and supply-chain AI initiatives will multiply these gains by reducing Scope 3 impacts.

These strategies align with Saudi Vision 2030 and the Saudi Green Initiative, which mandate a 50% renewable mix by 2030 and net-zero by 2060. Our analysis shows plausible multi-megaton CO₂ reductions and significant O&M savings from AI deployment, with robust ROI in most cases. The feasibility is supported by IEEE and industry evidence: for instance, predictive maintenance can halve downtime, and digital substations can cut costs 10–15%.

Implementing agentic AI will require careful architecture (SCADA/GIS integration, cloud-edge pipelines), rigorous adherence to IEC/IEEE standards (61850, 62443, ISO 55001), and strong governance. Hypothetical SEC case studies – optimized plants, AI-managed substations, digital customer programs – illustrate the transformative potential.

In conclusion, agentic AI offers SEC a powerful toolkit to meet sustainability commitments while maintaining reliability. As Saudi Arabia invests in smart grids and renewables, SEC should view AI not just as a digital aid, but as an autonomous partner in achieving net-zero and a model utility for the region.