Automation and AI Risks in Long Duration Energy Storage Systems (LDES): Risk Mitigation and Regulatory Responsibilities
By Dan RICCI
As Long Duration Energy Storage Systems (LDES) become essential to the future of grid resiliency and renewable integration, the infusion of automation and artificial intelligence (AI) into these technologies presents a range of strategic risks. These include cybersecurity vulnerabilities, operational uncertainties, automation-induced failures, and regulatory gaps. This white paper outlines the major categories of risk and identifies key government, regulatory, and standards bodies responsible for managing and mitigating these challenges.
Introduction
LDES technologies are crucial to the future for balancing energy supply and demand, integrating intermittent renewable energy sources, and maintaining long-term grid stability. The adoption of AI and automation accelerates innovation but also introduces significant risk vectors that must be proactively managed.
Key Risks from AI and Automation in LDES
Cybersecurity Risks
AI-augmented automation in control systems, predictive maintenance, and grid integration can significantly expand the attack surface. Intelligent control systems, such as AI-based energy management platforms, may be compromised if they are not adequately secured. This could lead to hazardous outcomes, such as grid instability or storage system failure. Data poisoning attacks are also a threat; adversaries may manipulate sensor data used in training or real-time diagnostics, resulting in incorrect operational decisions. Moreover, unauthorized access to automated systems could allow attackers to alter charge/discharge cycles, disable systems, or manipulate grid interactions. [1][2][3]
Model Uncertainty and Decision-Making Risks
AI models deployed for forecasting demand, optimizing storage use, or predicting renewable availability can be opaque, often functioning as “black boxes.” This lack of transparency is particularly problematic in safety-critical environments. These models may also be trained on biased or incomplete datasets, leading to flawed assumptions about rare but significant events, such as extreme weather. If not properly tuned, models may suffer from overfitting or underfitting, which reduces their effectiveness and leads to inefficiencies, missed revenues, or operational errors. [4][6]
Automation-Induced Systemic Failures
In LDES, where systems are often tightly integrated across thermal, mechanical, or electrochemical domains, automation can quickly propagate errors. A fault in one part of the system might cascade across others due to automated interdependencies. Furthermore, autonomous coordination among distributed LDES units without sufficient human oversight could lead to unforeseen emergent behaviors, such as competing control signals or instability in grid contributions. [5][9]
Operational and Maintenance Challenges
AI-led diagnostics and predictive maintenance systems may misidentify issues, prompting unnecessary equipment replacements or failing to detect critical faults. Additionally, overreliance on automated systems could lead to deskilling of the workforce. The loss of manual expertise may delay recovery during system failures, resulting in increased downtime and repair costs. [9][10]
Regulatory and Compliance Risks
The rapid evolution of AI technologies may outpace existing regulatory frameworks. This can result in non-compliance or legal liabilities if autonomous decisions violate safety, reliability, or environmental standards. Moreover, the lack of transparency in AI systems makes it challenging for auditors and regulators to verify compliance or certify the safety of critical operations. [4][6]
Supply Chain and Data Integrity
LDES development and operation depend heavily on third-party data sources and sensor networks. If these data pipelines are compromised, either intentionally or due to error, the resulting AI output may be misleading or hazardous. Automated design and simulation tools may also propagate errors if they are based on flawed models or biased training data, which can impact the safety and performance of deployed technologies. [4][11]
Responsible Regulatory and Standards Bodies
Cybersecurity and Infrastructure Protection
Responsible Bodies:
U.S. Department of Energy (DOE), Office of Cybersecurity, Energy Security, and Emergency Response (CESER) [2]
Cybersecurity and Infrastructure Security Agency (CISA) [1]
Federal Energy Regulatory Commission (FERC) [3]
North American Electric Reliability Corporation (NERC) [7]
Rationale: These agencies are responsible for overseeing cybersecurity for critical infrastructure, including power grids and energy storage systems. CESER and CISA should establish minimum cybersecurity requirements tailored to AI-integrated operational technology (OT) systems. FERC and NERC already enforce relevant cybersecurity standards such as NERC CIP, which should be expanded to include AI-specific provisions.
AI Risk Management and Explainability
Responsible Bodies:
National Institute of Standards and Technology (NIST) [4]
International Electrotechnical Commission (IEC) [5]
Institute of Electrical and Electronics Engineers (IEEE) [6]
ISO/IEC JTC 1/SC 42 (AI standards committee) [8]
Rationale: NIST’s AI RMF provides a robust foundation for managing AI risks, emphasizing transparency and reliability. IEC and IEEE contribute essential standards for safe AI integration in control systems. ISO/IEC JTC 1/SC 42 focuses on the full AI system lifecycle, from design through decommissioning, addressing fairness, explainability, and accountability.
Automation Governance and Safety Standards
Responsible Bodies:
Occupational Safety and Health Administration (OSHA) [10]
International Safety Standards bodies (e.g., IEC 61508) [5]
U.S. National Renewable Energy Laboratory (NREL) [9]
Rationale: OSHA ensures workplace safety, a critical factor as automation increases in LDES facilities. IEC 61508 governs the functional safety of automated and electronic systems. NREL supports validation and testing for AI-integrated renewable systems.
Grid Integration, System Coordination, and Market Fairness
Responsible Bodies:
FERC (U.S.)
European Union Agency for the Cooperation of Energy Regulators (ACER)
International Energy Agency (IEA)
Rationale: These regulators ensure that LDES units interact safely and fairly with energy markets. They should require that AI systems in LDES follow auditable logic and implement anti-manipulation safeguards in autonomous grid coordination and bidding.
Research, Innovation, and Standards Development
Responsible Bodies:
DOE Office of Energy Efficiency and Renewable Energy (EERE)
National Science Foundation (NSF)
European Commission (Horizon Europe Program)
World Economic Forum (WEF)
Rationale: These entities fund cutting-edge research and development, promoting the advancement of safe AI technologies through cross-sector collaboration and global benchmarking.
Summary Table
| Domain | Recommended Bodies | Justification |
|---|---|---|
| Cybersecurity | DOE CESER, CISA, FERC, NERC | Secure AI-enabled grid and OT systems |
| AI Governance | NIST, IEEE, ISO/IEC JTC 1/SC 42 | Ensure model transparency, trustworthiness |
| Automation Safety | OSHA, IEC 61508, NREL | Maintain operational safety in hybrid AI-human environments |
| Grid and Market Integration | FERC, ACER, IEA | Prevent market manipulation and grid instability |
| Research & Innovation | DOE EERE, NSF, Horizon Europe, WEF | Accelerate safe AI innovation in energy systems |
Recommendations
Implement robust cybersecurity protocols, especially for AI components in operational technology (OT) environments. [1][2][3]
Enforce model validation and explainability standards to ensure AI decisions can be trusted and audited. [4][6][8]
Incorporate human-in-the-loop mechanisms where feasible, particularly for safety-critical decisions. [5][9]
Monitor AI systems for concept drift and degradation in data quality over time. [4][11]
Stay aligned with emerging standards for trustworthy AI (e.g., NIST AI Risk Management Framework, ISO/IEC 24029-1). [4][8]
Conclusion
As LDES technologies evolve alongside the accelerating integration of AI and automation, their safe deployment will hinge on a proactive and collaborative regulatory approach. This white paper has identified key risk areas from cybersecurity and model reliability to workforce readiness and compliance and mapped them to appropriate governing bodies. By embedding accountability, transparency, and robustness into AI systems used in energy storage, we can ensure that these innovations enhance rather than jeopardize grid security and resilience. The future of energy demands both innovation and vigilance; by following the outlined recommendations, industry and policymakers can work together to achieve a secure, intelligent, and sustainable energy infrastructure.
References
CISA. (n.d.). Cybersecurity and Infrastructure Security Agency. https://www.cisa.gov
DOE CESER. (n.d.). Office of Cybersecurity, Energy Security, and Emergency Response. https://www.energy.gov/ceser
FERC. (n.d.). Federal Energy Regulatory Commission. https://www.ferc.gov
NIST. (2023). AI Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology. https://www.nist.gov/itl/ai-risk-management-framework
International Electrotechnical Commission (IEC). (2010). IEC 61508 - Functional safety of electrical/electronic/programmable electronic safety-related systems.
IEEE. (2021). Ethically Aligned Design. https://ethicsinaction.ieee.org
NERC. (n.d.). Critical Infrastructure Protection (CIP) Standards. https://www.nerc.com
ISO/IEC JTC 1/SC 42. (n.d.). Artificial Intelligence Standards. https://www.iso.org/committee/6794475.html
NREL. (n.d.). National Renewable Energy Laboratory. https://www.nrel.gov
OSHA. (n.d.). Occupational Safety and Health Administration. https://www.osha.gov
WEF. (2022). Global Future Council on Artificial Intelligence for Humanity. World Economic Forum. https://www.weforum.org
