---
title: 'PPSim: Process Power Simulator'
url: https://www.emergentmind.com/topics/process-power-simulator-ppsim
type: topic
---

# PPSim: Process Power Simulator

The Process Power Simulator (PPSim) is a versatile simulation framework for modeling electrical power systems, process-level equipment, and their interactions under dynamic scenarios. PPSim has been applied both in industrial-scale electrical network studies and in fine-grained, real-time HPC power modeling, supporting component-level parametrization, high-resolution control strategies, and integration of physical and cyber-physical processes. Its use spans from offshore energy hubs to supercomputing centers, offering a modular simulation approach combining dynamic systems equations, controller logic, and granular scenario management [2409.17368], [2601.15040].

## 1. Architectural Overview

PPSim comprises three principal software layers:

- **Component-Model Library:** A repository of parametrized dynamic models (`voltage-source converter`, `synchronous-machine`, `thyristor rectifier`, `transformer`, `static load`) that adhere to IEC-style dynamic equations. Each model exposes configuration via built-in parameters such as inertia constants, droop gains, and machine impedances. This library is designed for extensibility and modularity, facilitating reuse across diverse system scenarios [2601.15040].

- **Control-Logic Engine:** A modular, PLC-style interpreter that dispatches high-level set-points (e.g., active/reactive power, SoC targets, and equipment state transitions) to each component. The engine includes secondary frequency control, SoC loops, and plant-level coordination, directly supporting operator-in-the-loop studies with sub-second granularity.

- **Scenario Manager:** A dynamic scheduling and scripting layer enabling minute-to-year-long simulations, injection of contingencies (N-1 trips, wind time series), real-time data logging, and post-processing for key system metrics (e.g., frequency, SoC, capacity factor). All modules communicate over an internal bus with a nominal 1 ms resolution, supporting high-fidelity system emulation [2601.15040].

## 2. Component and Process Modeling

PPSim enables process-level and system-level modeling with rigorous mathematical formulations:

- **Wind Turbine Model:** Conforms to IEC 61400-27-1 (Type-4) and models aerodynamic conversion, rotor speed dynamics, pitch control, and converter regulation. For turbine $i$, the aerodynamic power is calculated as 
$$ P_{\mathrm{aero}}(t) = \frac{1}{2}\rho A C_p(\lambda(t),\beta(t)) v_i(t)^3, $$
where $\rho$ is air density, $A$ rotor area, $C_p$ the power coefficient, $\lambda$ the tip-speed ratio, and $\beta$ the pitch angle. Rotor inertia and pitch actuator dynamics are explicitly modeled [2601.15040].

- **Electrolyzer Model:** Each train contains two 2.5 MW stacks on a 12-pulse rectifier. The stack's I–V characteristic is $V_{\mathrm{cell}}(I) = E_{\mathrm{rev}} + r_\Omega I$ and electrical losses, ramp-rate limitations ($|\dot P_{\mathrm{ely}}(t)| \leq R_{\mathrm{ely}}$), and master–slave/synchronized control are included.

- **BESS Model:** Includes SoC dynamics, grid-forming virtual synchronous machine (VSM) swing equations, and frequency/voltage droop control. SoC evolution follows:
$$ \frac{d\,\mathrm{SoC}}{dt} = -\frac{P_{\mathrm{BESS}}(t)}{E_{\mathrm{rated}}\,\eta_{c/d}} $$
with $\eta_{c/d}$ round-trip efficiency and $E_{\mathrm{rated}}$ capacity [2601.15040].

- **Process-Level Power:** In HPC scenarios, PPSim can embed regression-based process power models derived from instruction mix and utilization features, as described in EfiMon [2409.17368].

## 3. Data Acquisition and Feature Engineering

PPSim's methodology for process power estimation (notably for supercomputing contexts) is based on noninvasive, real-time data collection:

- **Observers:** Modules such as ProcStatObserver, PerfAnnotateObserver, RAPLMeterObserver, and IPMIMeterObserver gather per-process CPU utilization, instruction histograms (across eight instruction families), RAM usage, and system/socket/PSU-level power [2409.17368].

- **Temporal and Spatial Resolution:** Sampling intervals ($\Delta t$) range from 100 ms to 1 s. Observers align all readings on a common timescale and spatially tag process-level vs. socket-level metrics.

- **Feature Construction:** PPSim constructs normalized instruction histograms $h_k^{(p)}(t)$ and CPU utilization $w_p(t)$ for each process $p$, aligned to a grid and optionally smoothed for robustness against measurement noise [2409.17368].

## 4. Mathematical Formulation of Process Power Models

At process level, PPSim applies an additive regression model decomposing system power $P_{\mathrm{system}}(t)$ into static and dynamic components:

- **System-level decomposition:** 
$$ P_{\mathrm{system}}(t) = P_{\mathrm{static}} + \sum_{p=1}^{N_p} P_p(t) $$
- **Per-process dynamic power:** 
$$ P_p(t) = \sum_{k=1}^8 \gamma_k \cdot \sigma_k( h_k^{(p)}(t), w_p(t), N_c ) $$
where $k$ indexes instruction families, $h_k$ the normalized retired instruction count, $w_p$ the process-level CPU utilization, $N_c$ the total number of cores, $\gamma_k$ weights fitted with Non-Negative Least Squares, and $\sigma_k$ is the shape function defined as:
  - For vector arithmetic/memory: $\sigma_k = h_k \cdot \ln(N_c w_p + 1)$
  - For others: $\sigma_k = N_c h_k w_p$

Parameter fitting is performed separately per hardware architecture, using K-fold cross-validation, resulting in 2-5% typical prediction error of per-process power, and a maximum deviation of 4.4% on AMD and 2.2% on Intel in shared environments [2409.17368].

## 5. Simulation Workflows and Application Domains

The simulation workflow in PPSim comprises stepwise scenario execution, control-loop updating, and event-driven recalculation of power/process states:

- **Simulation timestep:** Matches data acquisition rates, typically 250 ms to 1 s.
- **Inputs/outputs:** Per-process instruction mix and CPU utilization drive per-tick power prediction; component models receive control commands from the scenario manager/control-logic engine.
- **Event handling:** Change-driven update logic reduces computational overhead; for 1000 processes at 500 ms, less than 1 ms CPU overhead on a modern core [2409.17368].

Application examples include:

- **CleanOFF Offshore Hub:** PPSim was used for real-time simulation of a wind/green hydrogen platform, supporting design optimization of BESS sizing, analysis of wind farm turbulence, and the impact of electrolyzer ramp speed and coordination. Fast-ramp + synchronised electrolyzer control reduced the required BESS power rating by up to 70% compared to slow-ramp sequential strategies [2601.15040].
- **HPC Power Accounting:** PPSim enables granular attribution of power to individual processes, supporting power-aware scheduling, what-if analysis, and node-subsystem accounting beyond the granularity of node-level metering [2409.17368].

## 6. Validation, Limitations, and Extensibility

### Validation

- **Testbeds:** Validation includes both industrial electrical network hardware (e.g., 2×AMD EPYC/Intel Xeon nodes for HPC; scaled 66 kV main bus for CleanOFF) and process benchmarks (dgemm, daxpy, stream, synthetic workloads).
- **Benchmarks:** Validation spans isolated and co-executed process scenarios, control strategy impact assessment, and contingency injection (wind-turbine or load trips).
- **Performance:** Predictive accuracy for process-level models is within a 2.2–4.4% envelope; system-level dynamic response fidelity is matched to empirical wind- and load-time series [2409.17368], [2601.15040].

### Limitations

- **Linearity:** Current models assume linear, additive contributions of process power; contention effects are not modeled.
- **Static power constancy:** $P_{\mathrm{static}}$ is held fixed over time/workload, and thermal/fan dynamics are omitted.
- **Feature sufficiency:** Only instruction mix and CPU utilization are modeled explicitly; memory bandwidth, RAM power, and DVFS behaviors are not parameterized in current public implementations.

### Extensibility

- **Hardware support:** Model APIs are compatible with new analytic $\sigma_k$ functions, extended instruction sets (e.g., GPU via NVMLObserver), heterogeneous hardware, and dynamic fan/thermal modeling.
- **Machine learning:** Potential exists to replace linear NNLS with random forests, neural nets, or nonparametric regressors, presuming access to sufficient training data.
- **Scenario expansion:** The scenario manager allows for long-term stochastic analysis (year-scale Monte Carlo), system upscaling, and integration with external process simulators for hydrogen, fuel cell, and thermal loads [2409.17368], [2601.15040].

## 7. Parameter Tables and Boundary Conditions

Selected parameters and modeling assumptions as used in published studies:

| Subsystem   | Key Model Parameters/Assumptions                                   | Source        |
|-------------|---------------------------------------------------------------------|---------------|
| Wind Farm   | 8 × 8 MW, Type-4 turbines, spacing 5$D$, Kaimal spectrum, $L$=340m | [2601.15040]  |
| Electrolyzer| 7 × 5 MW trains, ramp: 11 s/706 s, sequence vs. sync control       | [2601.15040]  |
| BESS        | Power: 10 or 30 MW, Energy: 10 MWh, VSM grid-forming control       | [2601.15040]  |
| HPC Process | $\gamma_k$ (per architecture), $\beta_0$, 8-feature instruction mix| [2409.17368]  |

Boundary conditions include fixed network voltage at 66 kV, process configurations per experimental run, and system states reset via the scenario manager. Evaluation in both isolated and shared-core environments ensures model applicability across use cases.

## References

- "EfiMon: A Process Analyser for Granular Power Consumption Prediction" [2409.17368]
- "Electrical Design of a Clean Offshore Heat and Power (CleanOFF) Hub" [2601.15040]

Source: https://www.emergentmind.com/topics/process-power-simulator-ppsim