Agent-Based Electricity Ecosystem Simulation via Causal Diagrams
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Solution Overview
Problem
Conventional systems for simulating the electricity value ecosystem fail to model agent behavior and interactions effectively, leading to difficulty in decision-making due to complexity and uncertainty, and do not fully leverage available data, resulting in suboptimal use of technology investments.
Innovation Solution
A processor-implemented method and system using agent-based modeling to generate a causal diagram of the electricity value ecosystem, identifying and interconnecting agents, determining processes and models based on dependencies, and refining the diagram with constraints like geography and market to provide detailed outputs capturing cascading effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional simulation tools are used to support decision making, then the system can handle basic simulation needs, but they fail to model agents own behaviour and its response to other agents based on interaction
Solution Approach 1:
The system segments the electricity value ecosystem into discrete autonomous agents (generators, system operators, market operators, retailers, consumers) that can be independently modeled and simulated. Each agent has its own behavior rules and decision-making logic, allowing the system to capture complex interactions without requiring a monolithic simulation model.
Solution Approach 2:
The simulation system implements dynamic agent behavior where agents adapt their actions based on interactions with other agents and changing market conditions. The model transitions from static representations to dynamic simulations where agent decisions evolve over time based on feedback from the ecosystem, enabling realistic behavior modeling.
2Loss of information
If traditional simulation systems are used, then implementation is simpler, but they do not fully leverage volumes of data generated due to high usage of technology
Solution Approach 1:
The system implements comprehensive feedback loops where data generated by agent actions and market operations is continuously collected, analyzed, and fed back into the simulation model. This feedback mechanism enables the system to learn from generated data, refine agent behaviors, and improve simulation accuracy, fully leveraging the volumes of data produced by the electricity ecosystem.
3Adaptability or versatility
If new assets and technology are added to the electricity value ecosystem, then the system becomes more capable, but it becomes very difficult to make decisions with growing uncertainty and complexity
Solution Approach 1:
The agent-based simulation model serves as an intermediary between the complex electricity ecosystem and decision-makers. Instead of directly analyzing complex real-world systems, users interact with the simulated agent ecosystem that processes complexity internally and presents simplified, actionable insights, making decision-making easier despite ecosystem growth.
Solution Approach 2:
The system performs preliminary simulations and scenario analyses before actual decision-making occurs. By pre-testing different strategies and outcomes in the virtual agent ecosystem, the system prepares decision-makers with advance insights, reducing uncertainty and complexity when real decisions need to be made.
4Ease of manufacture
If stakeholders run planning and simulation models in silos, then each model can be managed independently, but they do not unlock full potential of their investments in technologies
Solution Approach 1:
The system merges previously siloed planning and simulation models into a unified agent-based ecosystem where generators, system operators, market operators, retailers, and consumers interact through common market mechanisms and physical constraints. This integration allows stakeholders to unlock synergies and fully leverage their technology investments by capturing interdependencies that exist in the real ecosystem.
Data Source
AI summary
This disclosure relates to methods and systems for simulation of electricity value ecosystem using agent based modeling approach. State-of-the-art methods utilize simulation tools to support decision making that do not model agents own behaviour and its response to other agents based on an interaction, thereby unable to analyse complex interactions in the electricity value ecosystem. The present disclosure provides a generalized integrated simulation platform which provides dynamic configurability to simulate a plurality of user requirements associated with the electricity value eco-system using a causal diagram which is further used to identify a plurality of agents. Further, a plurality of a plurality of models and processes for the plurality of agents are determined or generated based on their availability in a repository. The causal diagram is refined in accordance with one or more constraints which supports in making a better and informed decision considering changing dynamics of the plurality of agents.