# FIDES: Ensuring a reliable supply round the clock **Introduction** Reliability analysis in modern power systems requires the ability to simulate large numbers of possible system states to account for uncertainty in component outages, repair processes, network behavior and different generation and demand situations. Traditional methods often suffer from long computation times and lack the flexibility to capture realistic probability distributions of the most relevant events. FIDES (Fast Integrated Distributional Evaluation Simulator) addresses these challenges through a high-performance Monte Carlo simulation engine enhanced with advanced variance reduction techniques, and a proprietary acceleration framework. FIDES enables planners and reliability engineers to evaluate system adequacy, risk metrics, and operational resilience under a wide range of conditions at extremely high speed. --- **1. Monte Carlo Engine with Advanced Statistical Techniques** FIDES performs high-resolution Monte Carlo simulations, capturing the stochastic behavior of system components. Key features include: * **Latin Hypercube Sampling (LHS):** To improve convergence and reduce the number of required simulation runs, FIDES implements LHS, a variance reduction technique that ensures more representative sampling of the input probability space. * **Custom Probability Distributions:** Users can define non-standard distributions for both failure events and repair times. This allows modeling of: * Weather related outages * Component-specific profiles * Time-dependent maintenance schedules --- **2. Thermal Generator Constraints** A distinguishing feature of FIDES is its ability to model the complex operational characteristics of thermal generation units. This includes: * **Temperature-based Start-up Costs:** Accurately reflects the impact of ambient and idle temperature on fuel and wear costs. * **Non-linear Heat Rates:** Models fuel efficiency as a function of load, capturing realistic operating cost profiles. * **Ramping Constraints:** Enforces limits on how quickly units can change output, critical for dynamic system response. * **Minimum Online and Offline Durations:** Ensures physical feasibility by modeling minimum up/down time constraints. These constraints are especially important in systems with high renewable penetration, where flexibility and fast response from conventional generators play a crucial role. Accurately capturing these behaviors allows FIDES to: * Improve the fidelity of dispatch and restoration simulations. * Provide more accurate estimates of capacity contribution and reserve requirements. * Identify operational bottlenecks and reliability vulnerabilities related to thermal fleet inflexibility. By including these operational nuances, FIDES enables more robust planning and helps stakeholders evaluate both reliability and economic performance under uncertainty. --- **3. Fast and Scalable Computation** Reliability assessments often involve simulating thousands to millions of system states. FIDES achieves high performance through: * **Distributed Incremental Acceleration (DIA):** A proprietary parallel simulation framework that distributes computational workload across CPU cores or clusters. DIA reduces runtime by around 95%, enabling: * Execution of large Monte Carlo experiments in minutes rather than hours * Real-time or near-real-time feedback in operational contexts * **Modular architecture:** Simulation tasks are decomposed into manageable sub-processes, allowing easy integration with contingency analysis tools and optimization layers. * **Cloud-native deployment:** Scalability is supported via cloud infrastructure, offering elastic resource allocation for massive simulation campaigns. FIDES also supports on-premise deployment for integration with internal tools and secure data environments. --- **4. Applications in Power System Reliability** FIDES supports a wide range of use cases, including: * **Generation and Resource Adequacy Studies:** Evaluate LOLP (Loss of Load Probability), EENS (Expected Energy Not Served), and capacity contribution of VREs under uncertainty. * **Transmission Reliability Evaluation:** Assess the probability of overloads, cascading failures, and N-1/N-k contingencies under stochastic component behavior. * **Repair and Restoration Planning:** Simulate restoration paths and outage durations considering probabilistic repair timelines and access constraints. * **Risk-Informed Planning:** Identify critical assets and prioritize reinforcements based on probabilistic impact metrics. * **High Renewable Penetration Scenarios:** Analyze system behavior under deep decarbonization, including the adequacy of backup generation, flexibility, and grid reinforcement needs. --- **5. Integration and Customization** FIDES can operate standalone or be embedded in broader planning and operational workflows: * **Data-driven inputs:** Compatible with historical outage records, condition-based monitoring data, and synthetic failure models. * **Flexible output metrics:** Configurable to report reliability indices, distribution percentiles, risk maps, or scenario visualizations. * **Interoperability:** APIs and modular data formats allow integration with power flow solvers, SCADA data, and market analysis platforms. --- **Conclusion** FIDES combines statistical rigor with computational efficiency to deliver fast, detailed, and customizable reliability assessments. Its support for advanced Monte Carlo techniques—including Latin Hypercube Sampling, custom distributions, and parallel acceleration—makes it suitable for both long-term planning and operational risk management. With its detailed representation of thermal unit constraints and scalability via the Distributed Incremental Acceleration engine, FIDES offers a robust solution for reliability analysis in today's evolving power systems.