Bus

A bus (also called node or busbar) is a fundamental element in the network where multiple components can be connected. Buses serve as connection points and voltage reference points in the electrical network.

Documentation References: 📘 pandapower Bus 📗 OpenDSS Documentation

Parameters:

Line

Lines represent transmission or distribution lines that connect buses in the network. They are characterized by their resistance, reactance, and capacitance per unit length.

Documentation References: 📘 pandapower Line 📗 OpenDSS Line

Parameters:

Load

Loads represent power consumption at a bus. They can be defined as constant power, constant current, or constant impedance loads.

Documentation References: 📘 pandapower Load 📗 OpenDSS Load

Parameters:

Motor

Motor elements represent electric motors in the network. Motors have dynamic characteristics that affect short-circuit calculations and stability studies.

Documentation References: 📘 pandapower Motor 📗 OpenDSS Motor

Parameters:

Asymmetric Load

Asymmetric loads allow modeling of unbalanced three-phase loads where each phase can have different power consumption. This is particularly useful for distribution network analysis.

Documentation References: 📘 pandapower Asymmetric Load 📗 OpenDSS Load

Parameters:

Static Generator

Static generators represent generators with static behavior, such as wind turbines, photovoltaic systems, or small generation units that don't have significant dynamic characteristics.

Documentation References: 📘 pandapower Static Generator 📗 OpenDSS Generator

Parameters:

Asymmetric Static Generator

Asymmetric static generators allow modeling of unbalanced generation where each phase can produce different power levels.

Documentation References: 📘 pandapower Asymmetric Static Generator 📗 OpenDSS Generator

Parameters:

External Grid

The external grid represents the connection to a higher voltage level or an infinite bus. It serves as the slack bus in power flow calculations and provides the voltage reference.

Documentation References: 📘 pandapower External Grid 📗 OpenDSS Vsource

Parameters:

Transformer

Transformers connect different voltage levels in the network. They can be equipped with tap changers for voltage regulation.

Documentation References: 📘 pandapower Transformer 📗 OpenDSS Transformer

Parameters:

Three Winding Transformer

Three winding transformers have three separate windings, typically connecting three different voltage levels at one location.

Documentation References: 📘 pandapower Three Winding Transformer 📗 OpenDSS Transformer

Parameters:

Generator

Generators represent synchronous generators with dynamic behavior. They are typically used for large power plants and have voltage control capabilities.

Documentation References: 📘 pandapower Generator 📗 OpenDSS Generator

Parameters:

Shunt

Shunt elements represent devices connected in parallel to the bus, such as capacitor banks, reactors, or filters used for reactive power compensation.

Documentation References: 📘 pandapower Shunt 📗 OpenDSS Reactor

Parameters:

Impedance

Impedance elements represent arbitrary impedances connecting two buses. They are useful for modeling special connections or simplified network equivalents.

Documentation References: 📘 pandapower Impedance 📗 OpenDSS Documentation

Parameters:

Ward

Ward equivalents represent a reduced network with a constant impedance and constant power component. They are used for network reduction.

Documentation References: 📘 pandapower Ward 📗 OpenDSS Documentation

Parameters:

Extended Ward

Extended Ward equivalents are an extension of Ward equivalents with an additional internal bus and impedance, allowing for more accurate representation of reduced networks.

Documentation References: 📘 pandapower Extended Ward 📗 OpenDSS Documentation

Parameters:

DC Line

DC lines represent high-voltage direct current (HVDC) connections between AC systems. They provide controlled power transfer and can connect asynchronous networks.

Documentation References: 📘 pandapower DC Line OpenDSS: Not Available

Parameters:

Storage

Storage elements represent battery energy storage systems (BESS) or other energy storage technologies that can both consume and generate power.

Documentation References: 📘 pandapower Storage 📗 OpenDSS Storage

Parameters:

Static Var Compensator (SVC)

Static Var Compensators are FACTS devices used for dynamic voltage support and reactive power control. They can rapidly adjust reactive power output.

Documentation References: 📘 pandapower SVC 📗 OpenDSS Documentation

Parameters:

Thyristor-Controlled Series Capacitor (TCSC)

TCSCs are FACTS devices that provide controllable series compensation to regulate power flow and improve transmission capacity.

Documentation References: 📘 pandapower TCSC 📗 OpenDSS Documentation

Parameters:

Static Synchronous Compensator (SSC / STATCOM)

Static Synchronous Compensators (STATCOM) are advanced FACTS devices that use voltage source converters to provide dynamic reactive power support. They offer superior voltage control compared to traditional SVCs.

Documentation References: 📘 pandapower SSC 📗 OpenDSS Documentation

Parameters:

DC Bus

DC buses represent connection points in DC networks, similar to AC buses but for direct current systems. They serve as nodes where DC components like DC loads, DC sources, and DC lines can be connected.

Documentation References: 📘 pandapower DC Bus OpenDSS: Not Available

Parameters:

Load DC

DC loads represent power consumption in DC networks. They consume active power from the DC system.

Documentation References: 📘 pandapower Load DC OpenDSS: Not Available

Parameters:

Source DC

DC sources represent voltage sources in DC networks, similar to external grids in AC systems. They provide voltage reference and power injection into the DC network.

Documentation References: 📘 pandapower Source DC OpenDSS: Not Available

Parameters:

Switch

Switches represent circuit breakers, load break switches, or disconnectors that can connect or disconnect lines or transformers. They are essential for network topology control and protection.

Documentation References: 📘 pandapower Switch 📗 OpenDSS Switch

Parameters:

Voltage Source Converter (VSC)

Voltage Source Converters are power electronic devices that connect AC and DC networks. They enable bidirectional power flow and provide voltage control capabilities, making them essential for HVDC systems and renewable energy integration.

Documentation References: 📘 pandapower VSC OpenDSS: Not Available

Parameters:

Back-to-Back Voltage Source Converter (B2B VSC)

Back-to-Back Voltage Source Converters connect two AC networks through a common DC link. They enable asynchronous interconnection and provide independent control of active and reactive power flow between the two AC systems.

Documentation References: 📘 pandapower B2B VSC OpenDSS: Not Available

Parameters:

Load Flow (Power Flow)

The Load Flow (Power Flow) calculation is the most fundamental analysis operation in power system engineering. It determines the steady-state operating conditions of an electrical network, including voltages, currents, and power flows throughout the system.

How It Works

Electrisim supports load flow calculations using two simulation engines:

1. Pandapower Engine: Full-featured power flow solver with multiple algorithms including Newton-Raphson, Iwamoto, Backward Forward Sweep, Gauss-Seidel, FDBX, and FDXB.
2. OpenDSS Engine: Alternative power flow solver with support for snapshot, daily, and dutycycle simulation modes.

The calculation iteratively solves the power balance equations at each bus until convergence is achieved. The Newton-Raphson method is the default algorithm, providing fast and reliable convergence for most networks.

Input Parameters (Pandapower)

Input Parameters (OpenDSS)

Output Results

The load flow results include comprehensive information about network state:

Controller Results: When "Include Controllers" is enabled, a Tap Changer Control Results dialog displays controlled transformer tap positions, voltage targets, and any limit warnings.

Algorithm Details

Newton-Raphson Method (Default): This is the most widely used power flow algorithm, providing fast convergence for well-conditioned networks. It linearizes the power balance equations using the Jacobian matrix and iteratively solves for voltage corrections.

Other Algorithms:

• Iwamoto: Enhanced Newton-Raphson with improved convergence for ill-conditioned systems
• Backward Forward Sweep: Suitable for radial distribution networks
• Gauss-Seidel: Simpler iterative method, slower convergence but more robust for some cases
• FDBX/FDXB: Fast decoupled variants for transmission networks

Limitations and Considerations

Technical References

Based on: pandapower Power Flow Documentation and OpenDSS.

Algorithm Type: Iterative solution of power balance equations using various numerical methods.

Documentation References: 📘 pandapower Power Flow 📘 pandapower runpp()

Contingency Analysis

Contingency Analysis evaluates the robustness of a power system by simulating the impact of equipment outages (N-1, N-2, or N-K contingencies). This analysis identifies potential system vulnerabilities and ensures that the network remains stable and within operating limits even when critical elements are removed.

How It Works

The contingency analysis performs the following steps:

1. Base Case (N-0): First runs a load flow for the intact network to establish baseline conditions
2. Contingency Definition: Identifies elements to analyze (lines, transformers, generators) based on user selection
3. N-1/N-2/N-K Analysis: For each contingency, the selected element(s) are switched off and a new load flow is performed
4. Limit Checking: Checks for voltage violations and thermal overloads in each contingency case
5. Results Aggregation: Summarizes minimum and maximum values across all contingencies

Input Parameters

Output Results

The contingency analysis provides comprehensive results for each contingency case and aggregated statistics:

Algorithm Details

The contingency analysis uses the pandapower run_contingency() function, which:

• Performs sequential power flow calculations for each defined contingency
• Aggregates results to find minimum and maximum values for each monitored variable
• Identifies which contingency causes each maximum/minimum condition
• Checks compliance with user-defined voltage and thermal limits
• Provides detailed diagnostics for limit violations

Limitations and Considerations

Technical References

Based on: pandapower Contingency Analysis Documentation.

Algorithm Type: Sequential power flow calculations with limit checking and result aggregation.

Documentation References: 📘 pandapower Contingency Analysis 📘 run_contingency()

Optimal Power Flow (OPF)

Optimal Power Flow (OPF) determines the optimal dispatch of generation resources to minimize operating costs while satisfying all network constraints, including voltage limits, thermal limits, and generator capabilities. Unlike standard load flow, OPF optimizes an objective function subject to equality and inequality constraints.

How It Works

Electrisim supports two types of OPF calculations:

1. AC Optimal Power Flow (AC OPF): Full nonlinear optimization considering active and reactive power, voltage magnitudes and angles. More accurate but computationally intensive.
2. DC Optimal Power Flow (DC OPF): Linearized approximation that considers only active power and voltage angles. Faster but less accurate, suitable for transmission planning.

The optimization problem is solved using mathematical programming techniques (typically Interior Point Method or Sequential Quadratic Programming) to find the optimal generator dispatch that minimizes costs while respecting all operational constraints.

Input Parameters

Objective Function

The default objective function minimizes total generation cost, which is typically defined as:

Minimize: Σ (Cost_i × P_gen,i) for all generators i

Where Cost_i is the cost coefficient for generator i and P_gen,i is the active power output. Cost functions can be linear, quadratic, or piecewise linear.

Constraints

The OPF solution must satisfy:

• Power Balance: Total generation equals total load plus losses
• Generator Limits: P_min ≤ P_gen ≤ P_max, Q_min ≤ Q_gen ≤ Q_max
• Voltage Limits: V_min ≤ V ≤ V_max at all buses
• Thermal Limits: Loading ≤ 100% for lines and transformers
• Reactive Power Limits: Generator reactive power capability constraints

Output Results

The OPF results include optimized dispatch and system state:

Algorithm Details

AC OPF: Solves the full nonlinear optimization problem using Interior Point Method (IPM) or Sequential Quadratic Programming (SQP). Provides accurate results but requires significant computation time for large networks.

DC OPF: Uses a linearized power flow model (ignoring losses and reactive power). Much faster than AC OPF but less accurate. Suitable for transmission planning where reactive power is less critical.

Solver: PYPOWER, which is based on MATPOWER and uses MATLAB optimization solvers (converted to Python).

Limitations and Considerations

Technical References

Based on: pandapower Optimal Power Flow Documentation and PYPOWER.

Algorithm Type: Nonlinear/Linear optimization with Interior Point Method or Sequential Quadratic Programming.

Documentation References: 📘 pandapower Optimal Power Flow 📘 PYPOWER OPF

Short Circuit

Short Circuit analysis calculates fault currents that occur when an electrical fault (short circuit) is applied to the network. This analysis is essential for sizing protective devices, determining interrupting ratings, and ensuring equipment can withstand fault conditions. The calculations follow international standards (DIN/IEC EN 60909).

How It Works

The short circuit calculation uses the superposition principle and symmetrical components to determine fault currents:

1. Network Modeling: The network is represented using positive, negative, and zero sequence impedances
2. Fault Application: A fault impedance is applied at the specified fault location
3. Equivalent Circuit: The Thévenin equivalent circuit is calculated from the fault location
4. Fault Current Calculation: Fault currents are calculated based on fault type (3-phase, 2-phase, 1-phase)
5. Current Distribution: Currents are distributed throughout the network to determine branch contributions

Fault Types

Electrisim supports three types of short circuit faults:

• Three-Phase Fault (3ph): Symmetrical fault affecting all three phases. Typically produces the highest fault current. Used for symmetrical interrupting capacity rating.
• Two-Phase Fault (2ph): Fault between two phases. Produces lower fault current than three-phase. Used for phase-to-phase protection.
• Single-Phase Fault (1ph): Fault between one phase and neutral/ground. Includes zero sequence impedance. Critical for ground fault protection and neutral sizing.

Input Parameters

Calculation Cases

According to DIN/IEC EN 60909, different calculation cases are used:

• Maximum Case (Ik"): Highest fault current (typically for breaker sizing). Assumes maximum generation, minimum impedances, voltage = 1.1 pu (c_max).
• Minimum Case (Ik'): Lowest fault current (typically for protection coordination). Assumes minimum generation, maximum impedances, voltage = 0.9 pu (c_min).

Output Results

The short circuit analysis provides detailed fault current information:

Algorithm Details

The short circuit calculation follows DIN/IEC EN 60909 standard:

• Voltage Factor (c): c_max = 1.1 for maximum case, c_min = 0.9 for minimum case (low voltage)
• Impedance Correction: Generator and transformer impedances are corrected according to the standard
• Far-from-Generator Approximation: Used when fault is far from generators (symmetrical current decays quickly)
• Near-to-Generator: Special handling when fault is close to generators (asynchronous contribution)
• Solution Method: Matrix inversion or LU factorization of network admittance matrix

Limitations and Considerations

Short Circuit based on OpenDSS

In addition to the Pandapower (IEC 60909) short circuit analysis, Electrisim offers short circuit calculation based on OpenDSS. In the Short Circuit dialog, select the OpenDSS tab to run a conventional fault study using the Open-Source Distribution System Simulator.

OpenDSS fault study (Solve Mode=FaultStudy) computes for every bus:

• Initial symmetrical short-circuit current (I''_k, ikss) in kA
• Peak short-circuit current (i_p, ip) in kA
• Thermal short-circuit current (I_th, ith) in kA
• Positive-sequence short-circuit resistance (R_k, rk_ohm) and reactance (X_k, xk_ohm) in ohms

Fault type (e.g. 3-phase) and frequency can be set in the OpenDSS tab. Results are shown on the diagram at each busbar. This option uses the same network model as OpenDSS load flow (buses, lines, loads, generators, external grid).

OpenDSS references: Fault Studies, Fault Study Mode, Bus.Isc().

Technical References

Based on: pandapower Short Circuit Documentation and DIN/IEC EN 60909 standard (Pandapower); OpenDSS fault study (OpenDSS).

Algorithm Type: Pandapower: symmetrical components method with impedance-based fault calculation following international standards. OpenDSS: conventional fault study with Thévenin equivalent and open-circuit voltage per bus.

Documentation References: 📘 pandapower Short Circuit 📘 Short Circuit Calculation 📗 OpenDSS Fault Studies

Standards: IEC 60909: Short-circuit currents in three-phase a.c. systems (Pandapower).

Battery Sizing for Busbar Requirements

The Battery Sizing for Busbar Requirements functionality allows you to calculate the required Battery Energy Storage System (BESS) power output to achieve specific active power (P) and reactive power (Q) targets at a Point of Coupling (POC) busbar.

How It Works

The sizing algorithm uses an iterative control loop approach based on pandapower's control framework:

1. Initial Guess: The algorithm starts with an initial estimate of BESS power based on the target P and Q values, accounting for expected losses (typically 5% overhead).
2. Iterative Control Loop: A proportional controller adjusts BESS active and reactive power iteratively until the target P/Q at the POC is achieved within the specified tolerance.
3. Power Flow Analysis: Each iteration runs a Newton-Raphson power flow to evaluate the current network state and calculate the error between target and achieved P/Q values.
4. Convergence Check: The algorithm converges when both P and Q errors are below the specified tolerance, or reaches the maximum number of iterations.

Input Parameters

Multiple Scenarios Mode

When selecting "Multiple Scenarios" mode, you can evaluate five different operational scenarios simultaneously. Each scenario has editable target P and Q values:

All scenario values are editable before running the calculation. The results show the required BESS power for each scenario separately, allowing you to determine the maximum BESS capacity needed across all scenarios.

Output Results

For each scenario (or single target), the results include:

Visualization (Multiple Scenarios Only)

When running multiple scenarios, the results include two visualization plots:

1. POC P–Q (Target vs Achieved) Plot: Shows a scatter plot comparing target and achieved P/Q values at the Point of Coupling. Blue circles represent target values, green circles represent achieved values. Dashed lines connect target-achieved pairs for each scenario.
2. Required BESS P–Q Plot: Shows a scatter plot of the required BESS active and reactive power for each scenario. Green circles represent the calculated BESS requirements.

Both plots include grid lines, axis labels, and scenario name annotations for easy identification.

Algorithm Details

The sizing algorithm implements a proportional controller with the following characteristics:

• Initial Guess: initial_p = -target_p_mw × 1.05 and initial_q = -target_q_mvar × 1.05. The negative sign accounts for the fact that BESS must discharge (negative P from BESS perspective) to supply positive power at POC, and the 1.05 factor accounts for losses.
• Control Loop: Uses proportional control with damping factor of 0.5 to prevent oscillations:
  • delta_p = -kp_p × error_p × damping
  • delta_q = -kp_q × error_q × damping
• Sign Convention:
  • At POC: Positive P = consumption from grid, Negative P = generation to grid
  • For BESS: Negative P = discharging (generation), Positive P = charging (consumption)
• Convergence: Algorithm converges when |error_p| < tolerance AND |error_q| < tolerance

Limitations and Considerations

Technical References

Based on: pandapower control framework with custom BESS control controller implementation.

Algorithm Type: Iterative proportional control with Newton-Raphson power flow solver.

Documentation References: 📘 pandapower Control Documentation 📘 pandapower Storage Element