Generator interconnection automation using multi-agent collaboration

The clean energy transition faces a significant challenge that threatens to impede progress toward a sustainable future. Generator interconnection (GI) is the process of connecting new power plants to the electrical grid. This process has become a major problem across the United States and other regions of the world. It is creating significant delays for renewable energy projects. Many new solar and wind facilities cannot be built on schedule because of these interconnection bottlenecks.

This pressing issue has caught the attention of regulatory authorities, notably highlighted in a recent letter from Federal Energy Regulatory Commission (FERC) Commissioner Rosner to the Independent System Operators (ISOs) and Regional Transmission Organizations (RTOs). It stated, “…my priority is ensuring the reliable, affordable, and abundant energy that American families depend on to prosper and American businesses require to grow, including to power the artificial intelligence revolution and re-shore advanced manufacturing all across our country. … Recent experience has demonstrated that improving the speed and efficiency of the electric grid interconnection process is key to achieving these shared goals, and this has become a national imperative.”

The complexity of the GI process cannot be understated. It begins with the initial application intake, where project developers submit detailed technical specifications and requirements. This is followed by an intricate model building and validation phase, where engineers must ensure the proposed generation facility can safely integrate with the existing grid infrastructure. The process then moves through comprehensive interconnection studies, detailed cost allocation analyses, and finally, the negotiation and execution of interconnection agreements.

What makes this process particularly challenging is the complex coordination required between multiple stakeholders. Application reviewers must thoroughly examine submission details, GI engineers need to conduct complex technical studies, and legal teams must carefully craft and review agreements. The current manual approach to managing these interactions has led to processing times that often stretch into years, inconsistent application of technical standards, and significantly increased costs for project developers. These challenges collectively create a substantial barrier to the rapid deployment of clean energy resources that our changing climate demands.

We will illuminate a solution that uses agentic AI through Amazon Bedrock Agents to automate the entire interconnection process. We will cover from the initial application to the final agreement signing. The solution manages the whole process through AI-powered automation. This evolution represents a leap forward in making the entire interconnection process more efficient and scalable.

Amazon Bedrock Agents and multi-agent collaboration

Amazon Bedrock emerges as a transformative solution through its sophisticated generative AI agent feature. This technology represents a fundamental shift in how we approach complex workflow automation. Amazon Bedrock Agents function as autonomous intelligent task performers, capable of understanding and executing multifaceted processes with remarkable precision and efficiency. Amazon Bedrock Agents can each be specialized.

Amazon Bedrock multi-agent collaboration is the ability to create a collaborative environment where multiple specialized agents work in concert through a main supervising agent. Each specialized agent can be tailored to handle specific aspects of the interconnection process, bringing an expertise to their designated tasks. These agents can then work collaboratively, accelerating the processing of applications, while maintaining high accuracy through their focused domain expertise.

The solution being put forth here uses Amazon Bedrock multi-agent collaboration, by having a supervisor agent coordinate several sub-agents. Each sub-agent will have its own role, or task, within the workflow to help automate the GI process.

The solution’s architecture confirms secure access to company data and maintains a clear chain-of-thought trace for all responses made. This transparency is crucial for maintaining regulatory compliance and building trust with stakeholders. As business needs evolve and application volumes grow, the loosely coupled, serverless, multi-agent framework can seamlessly scale. It can accommodate increased demand or emerging requirements without compromising performance or accuracy.

The agent collaboration process in this solution is visualized in Figure 1.

Figure 1. Multi-agent collaboration workflow

Supervisor agent

The sophisticated collaboration between a supervisor agent and multiple specialized task agents has each playing a distinct, yet complementary, role in the interconnection process. The supervisor agent, which we will call the Interconnection Manager, acts as an intelligent orchestrator that maintains a comprehensive view of the entire process. Think of it as an experienced project manager who understands the complex dependencies between different stages of interconnection. It can prioritize tasks effectively, and knows when to engage specific expertise.

The Interconnection Manager first understands the query and breaks it down into actionable tasks. It then delegates tasks to collaborator agents, monitors their execution, and handles exceptions. The Interconnection Manager also verifies that all actions align with established interconnection procedures and compliance requirements.

Collaborator agents

Working under the supervision of the Interconnection Manager are several specialized task agents, also known as collaborator agents or sub-agents, each designed to excel in specific aspects of the interconnection process.

Our solution has four different collaborator agents, which are the:

• Application Reviewer agent
• Engineering Study Assistant agent
• Agreement Creator agent
• Live Support agent

The Application Reviewer agent brings precision to the initial stages. It performs detailed document analysis, validates technical specifications, and confirms all prerequisites are met before an application proceeds. It employs advanced text processing capabilities of underlying foundation models to extract and validate critical information from complex technical documents. This significantly reduces the time traditionally spent on manual review.

The Engineering Study Assistant agent provides technical analysis automation by adding an intelligence. It can analyze complex phenomena (such as transient stability), comprehend the implications of the study’s results, and generate clear, actionable reports—tasks that typically require significant engineering expertise.

The Agreement Creator agent transforms the traditionally laborious, iterative process of agreement generation and negotiation. It maintains a deep understanding of standard agreement templates, cost allocation methodologies, and project-specific requirements. This enables the agent to generate tailored interconnection agreements that account for all technical findings and cost implications.

The Live Support agent manages stakeholder communications and confirms all parties remain informed and engaged throughout the process. It works in close coordination with the Agreement Creator agent.

This multi-agent architecture has the ability to handle complex workflows through parallel processing, while maintaining coherent process flow. The Interconnection Manager verifies that all collaborative agents work in harmony, exchanging information effectively and maintaining process integrity.

For instance, when the Application Reviewer agent completes the initial assessment, the Interconnection Manager seamlessly hands off relevant technical specifications to the Engineering Study Assistant agent. Until the study result is retrieved, the Interconnection Manager provides the Agreement Creator agent with information needed for preliminary agreement preparation. This coordinated approach significantly reduces process handoff delays and eliminates information silos that often plague traditional interconnection processes.

Solution architecture

The solution’s architecture is built around a hierarchical structure that mirrors the complexity of the interconnection process itself. At its core sits the Interconnection Manager, serving as the supervisor agent orchestrating task routes. It delegates specific tasks to the corresponding collaborator agents (specialized task agents) through right-scoped system prompts and user prompts.

Figure 2. Solution architecture

Following is a brief explanation of the key functions of the solution architecture workflow:

1. End users connect to the application through the globally distributed network of Amazon CloudFront.
2. The Amazon CloudFront distribution is configured to serve static website content (HTML/CSS/JS) from an Amazon Simple Storage Service (Amazon S3) origin bucket configured for static website hosting.
3. Configure CloudFront distribution to use an AWS Regional AWS WAF web access control list (web ACL) for protecting against malicious traffic.
4. SSL/TLS encryption is managed through AWS Certificate Manager, with CloudFront enforcing HTTPS-only connections to confirm data privacy during transit.
5. User authentication and authorization is handled through the secure identity management service of Amazon Cognito.
6. Near real-time data synchronization between the frontend and backend is achieved through the GraphQL API capabilities of AWS AppSync.
7. A dedicated AWS Lambda function invokes Amazon Bedrock Agents and other generative AI services, handling frontend requests efficiently.
8. User interaction history is stored in Amazon DynamoDB, providing consistent single-digit millisecond latency.
9. An Amazon Bedrock supervisor agent serves as the central orchestrator, managing and coordinating tasks across the system.
10. Multiple specialized Amazon Bedrock sub-agents handle specific tasks, with the supervisor agent intelligently routing requests based on user requirements.
11. These sub-agents are empowered with access to AWS Lambda functions, Amazon Bedrock Knowledge Bases, and data from DynamoDB, providing rich context for task execution.
12. AWS Step Functions orchestrate the complex generator interconnection studies by coordinating compute services (such as AWS Lambda, AWS Fargate, and AWS Parallel Computing Service). Users can start these workflows through an Amazon Bedrock Agent.

Code snippet for multi-agent definition

You can create these agents using Amazon Bedrock Agents APIs along with the helper classes provided in the multi-agent collaboration workshop. The following code demonstrates how to define both the supervisor agent and collaboration agents, and establish the connections between them using Python.

Create Supervisor Agent
In your Jupyter notebook or Python script, enter in the following code block to create your agent:

agents = AgentsForAmazonBedrock()
supervisor_agent = agents.create_agent(
    supervisor_agent_name,
    """
        You are an Generator Interconnection (GI) Process Management bot.
        You can help users with various GI steps such as application    review, launch study and document creation, etc.""",
    """
        You are an Generator Interconnection (GI) Process Management assistant. 
        You can review an GI application bundle.
        You can extract information from the submitted GI applicaton form.
        You can also fire a GI study, list past runs, and retrieve study report.
        You can also draft an GI agreement and advise revisions.
        You can also provide live support to send an email with study report attached and identified issues summarized.
        Resist the temptation to ask the user for input. Only do so after you have exhausted available actions. 
        Never ask the user for information that you already can retrieve yourself through available actions. 
    """,
    agent_foundation_model,
    agent_collaboration='SUPERVISOR_ROUTER',
    verbose=True
)

Collaborator Agents
In your Jupyter notebook or Python script, enter in the following code block to create your collaborator agents:

sub_agents_list = [
    {
        'sub_agent_alias_arn': app_review_agent_alias_arn,
        'sub_agent_instruction': """Delegate application bundle review tasks to the App Review Agent, ensuring adherence to its specific protocols and capabilities.""",
        'sub_agent_association_name': 'AppReviewCoordinationAgent',
        'relay_conversation_history': 'TO_COLLABORATOR'
    },
    {
        'sub_agent_alias_arn': study_helper_agent_alias_arn,
        'sub_agent_instruction': """Delegate study-related tasks to the Study Helper Agent, ensuring adherence to its specific protocols and capabilities.""",
        'sub_agent_association_name': 'StudyCoordinationAgent',
        'relay_conversation_history': 'TO_COLLABORATOR'
    },
    {
        'sub_agent_alias_arn': agreement_agent_alias_arn,
        'sub_agent_instruction': """Delegate agreement creation tasks to the Agreement Creation Agent, ensuring adherence to its specific protocols and capabilities.""",
        'sub_agent_association_name': 'AgreementCoordinationAgent',
        'relay_conversation_history': 'TO_COLLABORATOR'
    }
    # - you can add more sub-agents here
]

supervisor_agent_id = supervisor_agent[0]
supervisor_agent_alias_id, supervisor_agent_alias_arn = agents.associate_sub_agents(
    supervisor_agent_id, sub_agents_list
)

One-stop-shop for generator interconnection automation

The solution implements a streamlined one-stop-shop approach to generator interconnection automation, breaking down the traditionally complex process into intuitive, automated stages. This user-friendly interface begins with the selection of roles, representing a variety of stakeholders in the interconnection process: Applicant, Reviewer, Studier and Agreement Creator. The roles also correspond to the right-scoped task agents, working behind the scenes for each of these roles.

Figure 3. Select roles to assume in the interconnection process

In this demonstration, we use the interconnection request application form from California ISO as the template. We filled ours in with mock data, creating various application portfolios, including complete dataset, incomplete dataset, and error dataset. For actual implementation, customers will need to submit their interconnection request with their specific data.

Figure 4. Submit interconnection requests to the applicant portal

When project developers submit their applications by uploading their interconnection request documents to the solution, the Application Reviewer agent automatically validates the submission. It verifies the request’s completeness by checking whether required fields in the document have been provided with values. It also validates the proposed Point of Interconnection (POI) against the list provided by the ISOs/RTOs. This initial automation eliminates the few weeks typically spent in preliminary application reviews and back-and-forth communications.

Figure 5. Portal for reviewers to work with interconnection requests

Once an application passes the preliminary validations, the agent then intelligently extracts critical project information from the submitted documents, structuring it into a standardized format (for example, JSON, CSV or XML) for further processing. Key details such as request date, fuel type, POI, desired net megawatt capacity, project location in terms of GPS coordinates, and proposed in-service date are automatically parsed and validated. This structured approach provides consistency and eliminates human error in data interpretation.

Through the use of the Application Reviewer agent, reviewers are empowered by the agent to perform more advanced tasks. For instance, interpreting transient stability assessment results by utilizing the visual perception, or reasoning and summarization capability of the underlying foundation model.

The agent can be invoked to analyze stability plots and identify critical characteristics (such as oscillation amplitude, duration, frequency and decay rate). An example of generator stability plots and single line diagram at the point of interconnection is shown in Figure 6. With a zero-shot prompt, the agent can assess the generator’s dynamic performance from the grid stability perspective. It can also suggest specific mitigation measures such as Power System Stabilizer (PSS) tuning or the installation of Flexible AC Transmission System (FACTS) devices. A user prompt and the agent response to the prompt are shown in Figure 7. It should be noted that such advanced analyses can be done without the need to finetune or retrain the foundation model.

Figure 6. Generator dynamic performance at the point of interconnection

Figure 7. Agent summary for the dynamic performance assessment

In accordance with AWS Responsible AI principles, this solution incorporates human oversight as a critical control point. After the agents complete their review process, human reviewers maintain final decision-making authority. Reviewers can examine the results and make informed decisions to either approve or reject applications.

Once the human reviewer approves an application, it will be automatically moved over to the S3 bucket of the Study Assistant agent to begin the start of a study cycle.

Figure 8. Human oversight for interconnection request approval

A particularly innovative aspect of the solution is its handling of technical assessments, also known as interconnection study. The Study Assistant agent has the ability to launch a run for a series of engineering analyses assessing a project’s potential impact on the grid. It can also look up study status, identify root cause for failure, retrieve study report from completed runs, and list runs with filters applied through a Text-to-SQL conversion.

When starting a new run, a workflow that mimics the realistic interconnection study is triggered in AWS Step Functions. As revealed in Accelerating generator interconnection study with serverless workflow and elastic HPC, the workflow can be empowered by HPC to accelerate complicated engineering study tasks. The workflow shown within this demonstration is pre-defined, however, its definition can be dynamic and even generated by the Study Assistant agent based on a user’s prompt. This adds flexibility to the interconnection study process when certain requesters withdraw and a restudy is required.

Figure 9. Portal for engineers to work with interconnection studies

Figure 10. Interconnection study workflow launched by the Study Assistant agent

The solution demonstrates the ability to handle complex document generation and stakeholder interactions in the agreement negotiation phase. The Agreement Creator agent automatically incorporates cost allocation details into standardized templates, while maintaining the flexibility to address project-specific requirements. The agent, which merges the live support functionality in the implementation, can also manage the communication workflow through composing and sending emails. It can send agreements as attachments, track responses, and manage revisions.

Figure 11. Portal for working with interconnection agreements

This complete automation transforms what was a fragmented, multi-month process (involving numerous departments and stakeholders) into a streamlined, transparent workflow that can be monitored and managed through a consolidated interface. The solution maintains clear visibility into each stage of the process. Stakeholders can track progress in near real-time and receive immediate updates on their interconnection requests.

Expected return on investment

The implementation of this agentic AI solution can facilitate transformative returns that extend far beyond only labor savings. Thanks to the streamlined process and elimination of unnecessary back and forth iterations, organizations implementing this solution can see their average interconnection processing time cut by up to 300%. This may be achieved while simultaneously handling 2-3 times their current application volume.

The reduction in manual labor requirements decreases operational costs and also provides skilled personnel a way to focus on more complex decision-making tasks. However, human oversight is still embedded as a core component in the transformed process. It is used to retain ultimate control and responsibility for the final outcomes, avoiding significant potential for misuse or unintended consequences.

Figure 12. Expected solution outcome—speedier interconnection process

The qualitative improvements are equally impressive. The consistency and transparency brought by automated processing build greater trust with project developers and regulatory bodies alike. The standardization of workflows confirms that every application receives the same thorough treatment, while real-time status tracking keeps all stakeholders informed throughout the process.

Perhaps most importantly, this solution directly addresses the urgent call from FERC for interconnection automation. By removing the bottleneck in generator interconnection, we’re improving a business process and accelerating the transition to clean energy at a time when it’s most critical. The scalable framework provided by the multi-agent capabilities of Amazon Bedrock can facilitate the demand for renewable energy connections as it grows. Utilizing this solution utilities will be well-equipped to meet that demand efficiently and effectively.

For new deployments that require enterprise-grade agent scalability, observability and security, Amazon Bedrock AgentCore provides a full-featured framework to securely build, deploy, and scale AI agents in production environments. It has a suite of modular, pre-built solutions that include built-in monitoring, traceability, authentication, and access controls. It addresses the complex infrastructure challenges of moving AI agents from prototypes to production deployments at scale.

Conclusion

We demonstrated that the multi-agent capabilities of Amazon Bedrock can address complex industry workflows that call for task collaboration (such as generator interconnection automation). This feature is ideal for fast integration with applications which have adopted Amazon Bedrock (either direct invocation of foundation models or Amazon Bedrock Agents).

What this solution leads to is a brighter picture for more dynamic, responsive, and capable interconnection process that serves as an enabler, rather than a barrier, to a clean energy future. Through the intelligent application of AI technology, we’re solving today’s challenges and building the foundation for tomorrow’s energy landscape.

Contact us if you are interested in learning more about the solution discussed or alternative implementation using Amazon Bedrock AgentCore.

Further reading

• What are foundation models
• Supported foundation models in Amazon Bedrock
• Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
• Agentic AI solutions and development tools on AWS
• Interconnection 101