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Pandapower—An Open-Source Python Tool for Convenient Modeling, Analysis, and Optimization of Electric Power Systems
Abstract
Pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of power systems. It is a full fledged power system analysis tool that provides power flow, optimal power flow, state estimation, topological graph searches and short circuit calculations according to IEC 60909. The pandapower network model is based on electric elements, which are defined by nameplate parameters and internally processed with equivalent circuit models. The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters. The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries. pandapower has been successfully applied in several grid studies and validated with real grid data.
... • rettij: Utilizes Kubernetes orchestration, allowing the use of configuration files to define components and network connections in virtual smart grids via Docker images. • Mosaik and Panda Power: Add power simulation capabilities, enabling realistic power grid simulations [11]. Based on this co-simulation environment, we developed an attacker container that autonomously performs specified cyberattacks. ...
... This file describes power generators, transformers, buses, and power lines. The framework extracts detailed information about current usages and loads across all components [11]. ...
The transition to smart grids has increased the vulnerability of electrical power systems to advanced cyber threats. To safeguard these systems, comprehensive security measures-including preventive, detective, and reactive strategies-are necessary. As part of the critical infrastructure, securing these systems is a major research focus, particularly against cyberattacks. Many methods are developed to detect anomalies and intrusions and assess the damage potential of attacks. However, these methods require large amounts of data, which are often limited or private due to security concerns. We propose a co-simulation framework that employs an autonomous agent to execute modular cyberattacks within a configurable environment, enabling reproducible and adaptable data generation. The impact of virtual attacks is compared to those in a physical lab targeting real smart grids. We also investigate the use of large language models for automating attack generation, though current models on consumer hardware are unreliable. Our approach offers a flexible, versatile source for data generation, aiding in faster prototyping and reducing development resources and time.
... In this approach the worst cases needed as a basis for grid planning are determined using time series simulations. Control strategies can be integrated into those time series simulations by means of pandapower power flow controllers, which allow the modeling of any type of control behavior [5]. The big strength of this methodology is enabling the evaluation of the effects of any control strategy on the grid and the required reinforcement and extension measures. ...
... The methods needed to perform this case study are implemented using Python and the open source library pandapower, which is developed by the Fraunhofer Institute for Energy Economics and Energy System Technology and the Department of Energy Management and Power System Operation at the University of Kassel. pandapower can be used for power system modeling, analysis and optimization [5]. ...
In this publication, we introduce a methodology for power system planning that enables large-scale analyses with the consideration of control strategies for electric vehicle charging. This methodology is developed within the research project Ladeinfrastruktur 2.0. A part of the scope of this project is deriving recommendations for a cost-optimized integration of charging infrastructure into the electric distribution system [1]. In [2] we introduced a time-series-based planning approach which can consider different types of control strategies and allows detailed analyses of their influence on selected grids as well as required grid reinforcement and extension measures. We further extended this planning approach by incorporating a simultaneity-factor-based method for determining worst case grid situations without the need for time series simulations, which enables large-scale grid analyses and grid planning studies. This facilitates more general conclusions regarding the effect of control strategies for electric vehicle charging on the need for grid reinforcement and extension measures. To illustrate the functionality of this new methodology, a case study with a large number of real German low voltage grids is performed. The case study highlights the feasibility of large-scale studies with the presented methodology. It also shows how the modeled grid-friendly control strategy for electric vehicle charging contributes to the mitigation of grid violations and therefore the reduction of required grid reinforcement and extension measures, while the considered market-oriented approaches have the opposite effect. 1 Introduction The number of electric vehicles (EVs) has been increasing rapidly over the last years [3] leading to challenges in the electric distribution system caused by the increasing power demand [4]. The research project Ladeinfrastruktur 2.0 tackles these challenges by providing a holistic investigation of the integration of electromobility, while focus-ing on optimizing the operation and rollout of charging infrastructure in distribution grids [1]. Control strategies for EV charging can contribute to reducing or delaying the need for costly grid reinforcement or extension measures. Therefore, simulation tools for the evaluation of control strategies and their effect on these grid reinforcement and extension demands are being developed within this project. In [2] we introduced a time-series-based approach for considering control strategies in grid planning. In this approach the worst cases needed as a basis for grid planning are determined using time series simulations. Control strategies can be integrated into those time series simulations by means of pandapower power flow controllers , which allow the modeling of any type of control behavior [5]. The big strength of this methodology is enabling the evaluation of the effects of any control strategy on the grid and the required reinforcement and extension measures. Its drawback is the amount of power flow calculations that are needed to determine the relevant worst case situations, which limits the simulation scope to case studies with a manageable number of grids and scenarios. Such small-scale case studies are a suitable way to analyze the effects of control strategies on grid reinforcement cost but a bigger simulation scope is needed for more general conclusions. Therefore, the existing methodology was enhanced to enable large-scale studies by determining worst case situations in a more efficient manner, which significantly reduces the number of required power flow calculations. This methodology will be described in section 3 after an overview of the state of the art regarding control strategies and grid planning with EVs in section 2. Afterwards, a case study will be presented in section 4 to highlight the behavior of the methodology. Finally, the conclusions are presented in section 5 and an outlook is given in section 6. 2 State of the Art In this section we give a short overview of the state of the art regarding control strategies and how EVs can be considered in grid planning. Based on that, the need for further developments regarding the integration of control strategies in grid planning is highlighted. 2.1 Control Strategies for EV Charging Currently, there is a spectrum of approaches regarding control strategies for EV charging with varying goals. On the one side of the spectrum, there are strategies that focus on grid-friendly EV charging based on power limits, which are expected to reduce the impact on the grid. This can be done by shifting charging processes to times when the overall grid load is expected to be low. In order to achieve this, a time-dependent power limit can be set by the grid operator as illustrated in [2] and [6]. This power limit is usually determined based on historic data regarding the load situation in the grid. On the other side of the spectrum, market-oriented approaches focus on economic goals and are usually based on tariff systems, which aim at incentivizing users to shift their loads, e.g. electric vehicle charging, to times when electricity prices are low. There
... By contrast, SSSA and TDS utilize dynamic models to evaluate the system's dynamic response and stability following perturbations. Static modelsbased simulators have been well-developed, like MATPOWER [5], Pandapower [6], PowerSimulations.jl [7], PyPSA [8], PowerModels [9], PowerModelsDistribution.jl [10], OpenDSS [11] and GridLAB-D [12]. ...
... c) Layer Segregation: Our simulation environment leverages the NetworkX [94] library to construct a layered graph structure. It starts at the process level, represented by a PandaPower [95] network. The simulation uses a versatile PandaPower network to represent various electrical grid models, customizable in nodes, voltage levels, and substations for diverse analyses. ...
The power grid is a critical infrastructure essential for public safety and welfare. As its reliance on digital technologies grows, so do its vulnerabilities to sophisticated cyber threats, which could severely disrupt operations. Effective protective measures, such as intrusion detection and decision support systems, are essential to mitigate these risks. Machine learning offers significant potential in this field, yet its effectiveness is constrained by the limited availability of high-quality data due to confidentiality and access restrictions. To address this, we introduce a simulation environment that replicates the power grid's infrastructure and communication dynamics. This environment enables the modeling of complex, multi-stage cyber attacks and defensive responses, using attack trees to outline attacker strategies and game-theoretic approaches to model defender actions. The framework generates diverse, realistic attack data to train machine learning algorithms for detecting and mitigating cyber threats. It also provides a controlled, flexible platform to evaluate emerging security technologies, including advanced decision support systems. The environment is modular and scalable, facilitating the integration of new scenarios without dependence on external components. It supports scenario generation, data modeling, mapping, power flow simulation, and communication traffic analysis in a cohesive chain, capturing all relevant data for cyber security investigations under consistent conditions. Detailed modeling of communication protocols and grid operations offers insights into attack propagation, while datasets undergo validation in laboratory settings to ensure real-world applicability. These datasets are leveraged to train machine learning models for intrusion detection, focusing on their ability to identify complex attack patterns within power grid operations.
... Different levels of abstraction can be modeled based on the use case, introducing variations in the simulation's detail. The co-simulation design integrates steady-state simulation of power grids using pandapower [20] for real-time performance in hardware-in-the-loop simulation scenarios. Emulation of communication networks is achieved using containernet [21] or rettij [22] with Docker container simulators. ...
As the integration of digital technologies and communication systems continues within distribution grids, new avenues emerge to tackle energy transition challenges. Nevertheless, this deeper technological immersion amplifies the necessity for resilience against threats, encompassing both systemic outages and targeted cyberattacks. To ensure the robustness and safeguarding of vital infrastructure, a thorough examination of potential smart grid vulnerabilities and subsequent countermeasure development is essential. This study delves into the potential of digital twins, replicating a smart grid's cyber-physical laboratory environment, thereby enabling focused cybersecurity assessments. Merging the nuances of communication network emulation and power network simulation, we introduce a flexible, comprehensive digital twin model equipped for hardware-in-the-loop evaluations. Through this innovative framework, we not only verify and refine security countermeasures but also underscore their role in maintaining grid stability and trustworthiness.
... Input features and output labels of the datasets are represented as x and y, respectively, where x includes ( p d i , q d i ) and y includes ( µ i , ω i ) obtained from the Newton-Raphson numerical method (NR) for all load buses i. The PandaPower Python package [39] is used to perform NR and generate the datasets. Note that PandaPower specifies the state variables of power systems, i.e., [δ v] T , that is, there is a need for converting the state variables to µ i = v i cosδ i and ω i = v i sinδ i to yield the output labels y = {( µ i , ω i ) : i = 1, 2, . . . ...
This study introduces PINN4PF, an end-to-end deep learning architecture for power flow (PF) analysis that effectively captures the nonlinear dynamics of large-scale modern power systems. The proposed neural network (NN) architecture consists of two important advancements in the training pipeline: (A) a double-head feed-forward NN that aligns with PF analysis, including an activation function that adjusts to active and reactive power consumption patterns, and (B) a physics-based loss function that partially incorporates power system topology information. The effectiveness of the proposed architecture is illustrated through 4-bus, 15-bus, 290-bus, and 2224-bus test systems and is evaluated against two baselines: a linear regression model (LR) and a black-box NN (MLP). The comparison is based on (i) generalization ability, (ii) robustness, (iii) impact of training dataset size on generalization ability, (iv) accuracy in approximating derived PF quantities (specifically line current, line active power, and line reactive power), and (v) scalability. Results demonstrate that PINN4PF outperforms both baselines across all test systems by up to two orders of magnitude not only in terms of direct criteria, e.g., generalization ability but also in terms of approximating derived physical quantities.
... This grid planning methodology is implemented using Python and the open source library pandapower. pandapower is a tool for power system modeling, analysis and optimization, developed by the Fraunhofer Institute for Energy Economics and Energy System Technology and the Department of Energy Management and Power System Operation at the University of Kassel [7]. Figure 1 shows the overview of the methodology. ...
In this publication, we introduce a methodology for power system planning that considers grid-friendly electric vehicle (EV) charging, which is developed within the research project "Ladeinfrastruktur 2.0" [1]. This publication shows how control strategies for EV charging can be integrated into probabilistic, time-series-based grid planning approaches to determine necessary grid reinforcement and extension measures. Since this method can be computationally expensive, the efficient integration of control strategies into this process is crucial. In order to compare practical and simulated findings, control strategies based on the field test project "E-Mobility-Allee" by the German distribution system operator (DSO) Netze BW [2] are applied in simulations. The methodology presented in this paper is developed for this purpose and applied in a case study with a real low voltage (LV) grid. Finally, conclusions regarding the field test and real-life applications are drawn based on the results of the case study, which indicate that the selected control strategies can lead to a reduction of necessary grid reinforcement and extension measures. 1 Introduction The increasing power demand caused by a growing number of EVs in the energy system can lead to grid congestions that require grid reinforcement, which may have a significant impact on the cost of EV integration. Determining and optimizing the overall cost of the integration of EVs into distribution grids is one of the main goals of the research project "Ladeinfrastruktur 2.0" [1]. In this process , it is important to consider the influence of control strategies for EVs, since they can have a significant impact on critical grid situations and the need for grid extension measures. The influence of such control strategies on real grid situations was demonstrated in the field test project "E-Mobility-Allee" by the German DSO Netze BW, where the effect of a large share of EVs in one street was investigated [2]. This practical analysis of preventative and cura-tive control approaches showed that critical grid situations caused by EV charging can be mitigated notably when applying such approaches [3], leading to a reduction of grid reinforcement measures. Additionally, grid operators can achieve an improved planning reliability regarding the power demand of EVs and their grid impact, when charging processes are controlled. To achieve universally valid statements regarding the effect of control strategies on grid extension measures, analyses involving a high number of probabilistic time-series-based simulations are needed. Therefore, a new grid planning methodology is developed within the project "Ladeinfrastruktur 2.0" and introduced in this publication. This methodology can take the seasonal behavior and dependency on the time of day for any consumer and/or producer into account. Thus, it also enables the consideration of control strategies for EV charging. This publication focuses on presenting the methodology regarding its ability to consider such control strategies. It is applied in a case study on a real LV grid provided by the Stadtwerke Wiesbaden Netz GmbH and the results are contrasted to the aforementioned field test. This publication is structured as follows: First, the state of the art regarding grid planning with EVs, control strategies and field tests as well as the derived research gap is introduced in section 2. Secondly, the developed methodology for considering control approaches for EVs in grid planning is presented in section 3. Subsequently, it is applied and evaluated in a case study in section 4. Finally, conclusions are drawn and an outlook is presented. 2 State of the Art In this section, the state of the art regarding the consideration of EVs in grid planning, control strategies and a related , recent field test is introduced. Based on that, the need for a methodology for considering control strategies for EV charging in grid planning is highlighted.
... These modules work with DA HPP optimization, RT HPP optimization, and adjustable HPP AGC. Efficient Python packages, including Gurobipy [47] for unit commitment (UC)/economic dispatch (ED) optimization, Pandapower [48] for alternating-current optimal power flow (ACOPF), and ANDES [45] for RT time domain simulation, are integrated within this framework. [51] considering HPP is as follows: ...
Hybrid power plants (HPPs) present a promising solution to address the significant challenges posed by the rapid integration of variable renewable energy sources (VREs) into power systems, particularly in maintaining power balance and frequency stability. Therefore, there is a pressing need for system operators and HPP owners to effectively manage both the energy and regulation services of HPPs within the current system operational framework. Existing studies on HPP modeling often separate dynamic control from steady-state scheduling and lack coordinated integration of self-scheduling of HPPs with the system-level scheduling, leading to over/under estimation of the flexibility of HPPs. To address this challenge, this paper presents a generic modeling framework for HPPs that integrates steady-state optimization with dynamic control across multiple timescales, enabling seamless HPP participation in day-ahead and real-time markets and real-time control. Additionally, the framework facilitates comprehensive economic and frequency performance evaluations. Case studies on a modified IEEE 39-bus system demonstrate the framework’s ability to ensure frequency performance with flexible HPP operation modes, align BESS state-of-charge (SOC) with dispatch targets, and optimize reliability and economic outcomes under various scenarios.
... IMPACT ON THE GRID For industrial applications, the IEEE recommends performing a balanced steady-state load flow analysis [29]. To this end, we used PandaPower [30] to simulate a 14-node mediumvoltage CIGRE distribution system. The load profiles from [24] associated with each node were chosen randomly, as shown in Table V. 3 20 17 7 8 5 14 9 12 18 1 2 16 10 To assess the changes in voltage behaviour due to the introduction of the HPS in the network, the voltages of buses 10, 9, and 8 (and the lines connected to them) are examined separately as direct neighbours of bus 11. ...
Industry plays a significant role in the energy transition due to its share of energy consumption. More complex energy systems are proposed to accelerate the energy transition, including coupling renewable energy sources and energy storage to supply part of the industrial loads locally. In this work, we used a multi-objective genetic algorithm to optimally size an industrial hybrid power system comprising a PV system, a battery energy storage system, and a diesel generator to minimise energy costs and overall equivalent CO 2 emissions. The results suggest that the system does not require high power and capacity components to minimise the energy cost and equivalent CO 2 emissions, highlighting the importance of the EMS strategy. In our case scenario, the optimal HPS reduced the emission cost by 46.7 % and the energy cost by 8.7 %. For the EMS, we proposed a rolling horizon average approach, which defines a setpoint for the power exchanged with the grid to minimise its change rate in time. The EMS dispatched the power to minimise the sudden changes in the demand from the network, with a power allocation priority order of PV, BESS, and generator. We also evaluated the effect of adding the optimally sized hybrid power system into a CIGRE medium-voltage distribution network, using a real industrial load profile for each node. The hybrid power system improved the voltage sag on the hybrid power energy system node and its neighbouring nodes.
... We now discuss the novel RL agent configuration used for efficient grid management in extreme conditions. The grid environment is supported by the Grid2Op framework with a pandapower [20] backend for power flow based on the Newton-Raphson method. This section provides key information on how our custom agent is trained and implemented for proficiency in contingency screening security assessments. ...
Reinforcement learning (RL) agents are powerful tools for managing power grids. They use large amounts of data to inform their actions and receive rewards or penalties as feedback to learn favorable responses for the system. Once trained, these agents can efficiently make decisions that would be too computationally complex for a human operator. This ability is especially valuable in decarbonizing power networks, where the demand for RL agents is increasing. These agents are well suited to control grid actions since the action space is constantly growing due to uncertainties in renewable generation, microgrid integration, and cybersecurity threats. To assess the efficacy of RL agents in response to an adverse grid event, we use the Grid2Op platform for agent training. We employ a proximal policy optimization (PPO) algorithm in conjunction with graph neural networks (GNNs). By simulating agents' responses to grid events, we assess their performance in avoiding grid failure for as long as possible. The performance of an agent is expressed concisely through its reward function, which helps the agent learn the most optimal ways to reconfigure a grid's topology amidst certain events. To model multi-actor scenarios that threaten modern power networks, particularly those resulting from cyberattacks, we integrate an opponent that acts iteratively against a given agent. This interplay between the RL agent and opponent is utilized in N-k contingency screening, providing a novel alternative to the traditional security assessment.
... The computational experiments for comparing the metaheuristic algorithms were conducted on an HP ZBook Fury laptop, equipped with an 11th Gen Intel ® Core TM i9-11950H processor and 32 GB of RAM. The algorithms were implemented using Python programming language, leveraging the Pandapower library [38] for power system simulations and optimization tasks. Each algorithm was coded using standard optimization frameworks and was tested across the IEEE 14-bus, 39-bus, and 118-bus systems, and the Reduced Nordic 44 model under various load conditions. ...
The increasing complexity of modern energy grids amplifies the importance of realistic power flow studies in power system analysis. This study implements a Multiple Slack Bus Operation (MSO) framework to enhance the realism and efficiency of optimal power flow (OPF) analysis. This paper introduces a comparative evaluation of three metaheuristic algorithms—Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Grey Wolf Optimization (GWO)—within the MSO framework. The algorithms are assessed based on their effectiveness in system loss minimization, line loading optimization, generator voltage angle adjustment, and generation distribution changes. Utilizing the Reduced Nordic 44 model and IEEE benchmark test systems at various load conditions, the findings reveal that the GWO algorithm, when integrated with the MSO framework, achieves the most significant reduction in total system losses. Specifically, the implementation of MSO alone reduced system losses by 5%, and its combination with GWO led to an additional 8.3% decrease. This study investigates the application of metaheuristic algorithms within a multiple slack bus context, highlighting their potential to enhance power network efficiency and suggesting broader applications for future power flow optimization strategies.
... Other work has generated power flow cases using random perturbations of the load at each node, as well as by introducing topology variations such as dropping lines or generators at random [32,33]. To further enrich these datasets, we plan to incorporate more grid topologies from PGLIB-OPF [34] and generate additional power flow cases using different solvers from the PandaPower library [35]. Particular attention should be given to cases that lead to low solution accuracy or convergence difficulties with iterative solvers. ...
Quasi-static time series (QSTS) simulations have great potential for evaluating the grid's ability to accommodate the large-scale integration of distributed energy resources. However, as grids expand and operate closer to their limits, iterative power flow solvers, central to QSTS simulations, become computationally prohibitive and face increasing convergence issues. Neural power flow solvers provide a promising alternative, speeding up power flow computations by 3 to 4 orders of magnitude, though they are costly to train. In this paper, we envision how recently introduced grid foundation models could improve the economic viability of neural power flow solvers. Conceptually, these models amortize training costs by serving as a foundation for a range of grid operation and planning tasks beyond power flow solving, with only minimal fine-tuning required. We call for collaboration between the AI and power grid communities to develop and open-source these models, enabling all operators, even those with limited resources, to benefit from AI without building solutions from scratch.
... In order to evaluate the benefit of smart plug measurements for grid state monitoring, an IEEE 37 bus system is simulated. We implement the grid simulation using pandapower, an open-source tool written in Python for modeling and analyzing power grids [48]. The smart plugs providing the measurements are also simulated. ...
Smart home power hardware makes it possible to collect a large number of measurements from the distribution grid with low latency. However, the measurements are imprecise, and not every node is instrumented. Therefore, the measured data must be corrected and augmented with pseudo-measurements to obtain an accurate and complete picture of the distribution grid. Hence, we present and evaluate a novel method for distribution grid monitoring. This method uses smart plugs as measuring devices and a feature propagation algorithm to generate pseudo-measurements for each grid node. The feature propagation algorithm exploits the homophily of buses in the distribution grid and diffuses known voltage values throughout the grid. This novel approach to deriving pseudo-measurement values is evaluated using a simulation of SimBench benchmark grids and the IEEE 37 bus system. In comparison to the established GINN algorithm, the presented approach generates more accurate voltage pseudo-measurements with less computational effort. This enables frequent updates of the distribution grid monitoring with low latency whenever a measurement occurs.
... All the data necessary for calculations on nodes, lines, consumption and generation schedules are tabular data that can be easily loaded into software by reading XLS files through the Pandas library of the Python programming language [13], [14]. Then, based on the uploaded data, a network model is created using the Pandapower library. ...
The paper describes the selection of the optimal topology for a distributed electric network with renewable energy sources. The Newton-Raphson method is used to calculate power flows, which is the basis of the Pandapower library. The library is a Python-based tool for modeling, analyzing, and optimizing electrical networks. It allows for the creation and modification of electrical schematics, calculation of power flows, determination of voltages and currents, as well as optimization of system performance. The result of the work is the determination of the optimal configuration in terms of minimizing electrical energy losses in network lines. The developed software allows for loading data on network nodes and lines, including active and reactive power consumption and generation, performing steady-state calculations with determination of losses in lines. Due to the automation of calculations, it is possible to perform calculations for all permissible network topologies and select the optimal one for the given conditions. The result of the work is the determination of the optimal configuration in terms of minimizing electrical energy losses in network lines.
... To best demonstrate the capabilities of the developed solution, we used a standard benchmark power network model (Fig. 2) that comprises models of conventional power plants and loads, distributed generators, PV and wind units, OLTC transformers, and circuit breakers. The model is implemented on pandapower (Thurner et al. 2018) using the SimBench power system library (particularly CIGRE Task Force C6.04.02 (CIGRE 2021)), which is based on the European configuration of the high and medium voltage distribution networks benchmark. This configuration was chosen due to the fact that it is complex enough to model and study several phenomena which could occur in a real power system (e.g. ...
Modeling of carbon emissions and related footprints ought to be an important element in the development of smart power grids and advanced power systems. However, the necessary emissions calculations including scenario assumptions and default emission factors are usually beyond the scope of existing software solutions and are completely left to the user. In this work, we presented an open-source Python-based component that combines an emission calculation model and a database of available emission factors for a variety of fuels and geographic locations. This component can be part of a co-simulation scenario for advanced power systems, and provides dynamic calculations of the carbon footprint of the entire scenario, coupled systems or their elements.
... In-fraRisk can simulate the interdependent effects of water-, power-, and transport networks. It uses Python packages, namely wntr for water networks (Klise et al., 2020), pandapower for power networks (Thurner et al., 2018), and a Python implementation of the static traffic assignment model f or transport networks (Boyles, Lownes, & Unnikrishnan, 2020). The module also considers major dependencies among these three infrastructure systems to allow for cascading failures. ...
... The lecture notes focus on the modeling of power system components with focus on modeling of components representing renewable energy generation, energy storage and related enabling technologies. The simulation testing shall be performed using Matpower [1] and Pandapower [2]. ...
These lecture notes provide a comprehensive guide on Grid Modeling of Renewable Energy, offering a foundational overview of power system network modeling, power flow, and load flow algorithms critical for electrical and renewable energy engineering. Key topics include steady-state, dynamic, and frequency domain models, with a particular focus on renewable energy integration, simulation techniques, and their effects on grid stability and power quality. Practical examples using Matpower and Pandapower tools are included to reinforce concepts, ensuring that students gain hands-on experience in modeling and analyzing modern energy systems under variable conditions.
... In this section, we discuss the comparison of Algorithms 1 and 2, labeled Tensor (Dense) and Tensor (Sparse), respectively, against current methods, such as SAM [11], NR with its sparse formulation in polar coordinates (NR (Sparse)) [21] and as implemented in the PandaPower package [22]; and the backward-forward sweep method (BFS) [23]. A more comprehensive comparison using different PF algorithms can be found in [11]. ...
In this paper, we present two multidimensional power flow formulations based on a fixed-point iteration (FPI) algorithm to efficiently solve hundreds of thousands of Power flows (PFs) in distribution systems. The presented algorithms are the base for a new TensorPowerFlow (TPF) tool and shine for their simplicity, benefiting from multicore Central processing unit (CPU) and Graphics processing unit (GPU) parallelization. We also focus on the mathematical convergence properties of the algorithm, showing that its unique solution is at the practical operational point. The proof is validated using numerical simulations showing the robustness of the FPI algorithm compared to the classical Newton-Raphson (NR) approach. In the case study, a benchmark with different PF solution methods is performed, showing that for applications requiring a yearly simulation at 1-minute resolution, the computation time is decreased by a factor of 164, compared to the NR in its sparse formulation. Finally, a set of applications is described, highlighting the potential of the proposed formulations over a wide range of analyses in distribution systems.
... Este análisis es estático y se centra en el estado estacionario del sistema, es decir, en condiciones de equilibrio de todas las variables eléctricas. Para este trabajo, empleamos PandaPower [19], una herramienta de código abierto en Python diseñada para el análisis de sistemas de potencia, que ofrece flexibilidad, facilidad de uso y una detallada biblioteca de modelos de componentes de redes eléctricas. ...
La distribución de energía eléctrica enfrenta desafíos globales, como la creciente demanda, la integración de generación distribuida, las pérdidas elevadas y la necesidad de mejorar la calidad del servicio. En particular, el desbalance de cargas, donde las cargas no están distribuidas uniformemente entre las fases de los circuitos, puede reducir la eficiencia, acortar la vida útil de los equipos y aumentar la susceptibilidad a interrupciones del servicio. Los métodos que implican mover cargas de una fase a otra pueden ser costosos, pero son efectivos cuando se dispone de medidores inteligentes y se llevan a cabo de manera eficiente. Este trabajo propone el uso de algoritmos genéticos para identificar de manera óptima las cargas a redistribuir, con el fin de mejorar tanto el balance de cargas como la calidad de la tensión en los nodos finales de la red, minimizando la cantidad de cambios necesarios. El algoritmo fue evaluado mediante simulaciones utilizando PandaPower, una herramienta de análisis de flujo de carga, modelando redes simples basadas en características reales del sistema eléctrico en Tucumán.
... We use a modified version of the radial IEEE 33-bus test feeder. We use data provided by pandapower (Thurner et al. 2018) and manually add DERs. Fig. 17 in Appendix D illustrates the system in detail. ...
The increasing demand for electricity and the aging infrastructure of power distribution systems have raised significant concerns about future system reliability. Failures in distribution systems, closely linked to system usage and environmental factors, are the primary contributors to electricity service interruptions. The integration of distributed energy resources (DER) presents an opportunity to enhance system reliability through optimized operations. This paper proposes a novel approach that explicitly incorporates both decision- and context-dependent reliability into the optimization of control setpoints for DERs in active distribution systems. The proposed model captures how operational decisions and ambient temperature impact the likelihood of component failures, enabling a balanced approach to cost efficiency and reliability. By leveraging a logistic function model for component failure rates and employing a sequential convex programming method, the model addresses the challenges of non-convex optimization under decision-dependent uncertainty. Numerical case study on a modified IEEE 33-bus test system demonstrates the effectiveness of the model in dynamically adjusting power flows and enhancing system robustness under varying environmental conditions and operational loads. The results highlight the potential of DERs to contribute to distribution system reliability by efficiently managing power flows and responding to fluctuating energy demands.
... In addition to those use case-specific implementation, we use general implementations for all use cases. These implementations are axillary functions for the preparation of time series data and grid topologies, data collection, and a power flow simulation based on PandaPower (Thurner, L. et al., 2018). ...
The increased integration of information and communications technology at the distribution grid level offers broader opportunities for active operational management concepts. At the same time, requirements for resilience against internal and external threats to the power supply, such as outages or cyberattacks, are increasing. The emerging threat landscape needs to be investigated to ensure the security of supply of future distribution grids. This extended abstract presents a co-simulation environment to study communication infrastructures for the resilient operation of distribution grids. For this purpose, a communication network emulation and a power grid simulation are combined in a common modular environment. This will provide the basis for cybersecurity investigations and testing of new active operation management concepts for smart grids. Exemplary laboratory tests and attack replications will be used to demonstrate the diverse use cases of our co-simulation approach.
... The grid is a PandaPower [23] network containing a representation of the physical equipment in an electrical grid, such as transformers or lines. If provided, communication network devices are mapped to components in the PandaPower grid based on the specifications. ...
Power system communication networks enable operators to remotely monitor and control field equipment. The sophistication of these networks is also increasing as operators continue the trend towards digitization, which is beneficial in integrating distributed energy resources. However, as the attack surface increases in size so too does the risk of cyberattacks. The topology, configuration and composition of communication networks is therefore confidential since this can provide information to attackers. As a result, the number of benchmarks available for research purposes is limited. A tool for procedurally generating communication network topologies is therefore proposed. While primarily intended as an enabler for public research into communication networks, this tool also allows general insights to be gained into the effect of communication network design on the vulnerability of networks to cyberattacks. The tool includes the ability to encapsulate network characteristics in JSON specification files, which is demonstrated with example Advanced Metering Infrastructure (AMI), Supervisory Control and Data Acquisition (SCADA) and Wide Area Monitoring (WAM) specification files. The SCADA network generation is then compared to a real-world case. Finally, the effect of network redundancy on the networks cyber resilience is investigated.
Quasi-static time series (QSTS) simulations have great potential for evaluating the grid’s ability to accommodate the large-scale integration of distributed energy resources. However, as grids expand and operate closer to their limits, iterative power flow solvers, central to QSTS simulations, become computationally prohibitive and face increasing convergence issues. Neural power flow solvers provide a promising alternative, speeding up power flow computations by 3 to 4 orders of magnitude, though they are costly to train. In this paper, we envision how recently introduced grid foundation models could improve the economic viability of neural power flow solvers. Conceptually, these models amortize training costs by serving as a foundation for a range of grid operation and planning tasks beyond power flow solving, with only minimal fine-tuning required. We call for collaboration between the AI and power grid communities to develop and open-source these models, enabling all operators, even those with limited resources, to benefit from AI without building solutions from scratch.
Accurate line-parameter identification is an important foundation for refined the regulation, protection, and control of distribution systems. Traditional identification models provide accurate modeling, while conventional identification approaches are hindered by the high complexity and low observability of power systems. In this article, a parameter identification method based on the deep deterministic policy gradient is proposed for medium voltage distribution systems. The proposed method starts with objective function constructing, followed by power flow analysis and parameter identification modeling, where the L2 normalization theory is introduced to improve the computation efficiency. On this basis, the parameter identification framework is constructed through designing the Markov decision process of a parameter and using a training mechanism. An adaptive parameter correction method is proposed to improve the accuracy and efficiency of a deep-reinforcement-learning-based agent. The performance of the proposed modal is tested on IEEE 14-node and IEEE 33-node medium-voltage distribution systems. Case simulation results demonstrate that the proposed modal exhibits superior computational capability, while achieving fewer errors compared to traditional methods.
Gaussian process (GP) is a powerful modeling method with applications in machine learning for various engineering and non-engineering fields. Despite numerous benefits of modeling using GPs, the computational complexity associated with GPs demanding immense resources make their practical usage highly challenging. In this article, we develop a quantum version of multi-output Gaussian Process (QGP) by implementing a well-known quantum algorithm called HHL, to perform the Kernel matrix inversion within the Gaussian Process. To reduce the large circuit depth of HHL a circuit optimization technique called Approximate Quantum Compiling (AQC) has been implemented. We further showcase the application of QGP for a real-world problem to estimate line parameters of an electrical grid. Using AQC, up to 13-qubit HHL circuit has been implemented for a 32x32 kernel matrix inversion on IBM Quantum hardware for demonstrating QGP based line parameter estimation experimentally. Finally, we compare its performance against noise-less quantum simulators and classical computation results.
Accurate line parameters are critical for and dispatch in distribution systems. External operating condition variations affect line parameters, reducing the accuracy of state estimation and power flow calculations. While many methods have been proposed and obtained results rather acceptable, there is room for improvement as they don’t fully consider line connections in known topologies. Furthermore, inaccuracies in measurement devices and data acquisition systems can introduce noise and outliers, impacting the reliability of parameter identification. To address these challenges, we propose a line parameter identification method based on Graph Attention Networks and Multi-gate Mixture-of-Experts. The topological structure of the power grid and the capabilities of modern data acquisition equipment are utilized to capture. We also introduce a multi-task learning framework to enable joint training of parameter identification across different branches, thereby enhancing computational efficiency and accuracy. Experiments show that the GAT-MMoE model outperforms traditional methods, with notable improvements in both accuracy and robustness.
This paper proposes a data-driven solution for Volt-VAR control problem in active distribution system. As distribution system models are always inaccurate and incomplete, it is quite difficult to solve the problem. To handle with this dilemma, this paper formulates the Volt-VAR control problem as a constrained Markov decision process (CMDP). By synergistically combining the augmented Lagrangian method and soft actor critic algorithm, a novel safe off-policy reinforcement learning (RL) approach is proposed in this paper to solve the CMDP. The actor network is updated in a policy gradient manner with the Lagrangian value function. A double-critics network is adopted to synchronously estimate the action-value function to avoid overestimation bias. The proposed algorithm does not require strong convexity guarantee of examined problems and is sample efficient. A two-stage strategy is adopted for offline training and online execution, so the accurate distribution system model is no longer needed. To achieve scalability, a centralized training distributed execution strategy is adopted for a multi-agent framework, which enables a decentralized Volt-VAR control for large-scale distribution system. Comprehensive numerical experiments with real-world electricity data demonstrate that our proposed algorithm can achieve high solution optimality and constraints compliance.
In this paper, latest results of the industrial project "Q-Study" are presented, which is carried out by "Fraunhofer IWES" together with the German distribution system operator "Bayernwerk Netz GmbH". The proposed project focuses on reactive power management in distribution systems using distributed generators and covers comprehensive research activities such as concept development, potential assessment, cost-benefit analysis and test in laboratory and in a real distribution grid. In addition, the applied real-time test-and simulation environment is also presented in detail, which allows the user to test an operative control approach in the smart grid domain by emulating a large power system with multiple voltage levels and substantial amounts of generators, storages and loads in real time. Reactive Power Control; Distribution system; Real-Time Simulations I. MOTIVATION Changing reactive power behavior of distribution systems (e.g., due to higher degrees of cabling and local reactive power provision through DGs) [1], together with the loss of generator-based reactive power sources at transmission system level could require the exploitation of novel reactive power sources by the transmission system operator (TSO) in the future [2] [3]. TSOs are therefore interested in using the aggregated reactive power capabilities of the downstream distribution system for their own voltage control purposes. The question is, how can distribution system operators (DSOs) utilize the reactive power control capabilities of their local reactive power sources (e.g., dispersed generators, capacitor stacks) in order to provide a certain amount of controlled reactive power at their interface to the transmission system level but still keeping its own grid in a safe operation mode? In order to answer this question, different research and industrial projects are carried out by Fraunhofer IWES together with German distribution and transmission system operators regarding reactive power coordination strategies in distribution networks. This paper presents the latest outcomes of the industrial project "Q-Study", which focuses on reactive power management and voltage limitation using distributed generators in the distribution network.
Python for Power System Analysis (PyPSA) is a free software toolbox for simulating and optimising modern electrical power systems over multiple periods. PyPSA includes models for conventional generators with unit commitment, variable renewable generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks. It is designed to be easily extensible and to scale well with large networks and long time series. In this paper the basic functionality of PyPSA is described, including the formulation of the full power flow equations and the multi-period optimisation of operation and investment with linear power flow equations. PyPSA is positioned in the existing free software landscape as a bridge between traditional power flow analysis tools for steady-state analysis and full multi-period energy system models. The functionality is demonstrated on two open datasets of the transmission system in Germany (based on SciGRID) and Europe (based on GridKit).
The global energy system is undergoing a major transition, and in energy planning and decision-making across governments, industry and academia, models play a crucial role. Because of their policy relevance and contested nature, the transparency and open availability of energy models and data are of particular importance. Here we provide a practical how-to guide based on the collective experience of members of the Open Energy Modelling Initiative (Openmod). We discuss key steps to consider when opening code and data, including determining intellectual property ownership, choosing a licence and appropriate modelling languages, distributing code and data, and providing support and building communities. After illustrating these decisions with examples and lessons learned from the community, we conclude that even though individual researchers' choices are important, institutional changes are still also necessary for more openness and transparency in energy research.
This paper describes a modelling approach suitable for assessments of future scenarios for renewable energy integration in large and interconnected power systems, based on sequential optimal power flow computations that take into account variability in power consumption, in renewable power production, energy storage, and flexible demand. The approach and the implementation as an open source Python package called Power Grid And Market Analysis is described in some detail. Particular emphasis is put on the modelling of energy storage systems, and the use of storage values as a means to define storage utilisation strategies. A case study representing a 2030 scenario for the Western Mediterranean region is then analysed using this approach. The main aim of this study is to assess the benefit for the system of adding flexibility in terms of storage associated with concentrated solar power or flexible demand. But other results are also presented, such as the resulting energy mix, generation costs, price variations, and grid congestion.
—In this paper we will discuss pandas, a Python library of rich data structures and tools for working with structured data sets common to statistics, finance, social sciences, and many other fields. The library provides integrated, intuitive routines for performing common data manipulations and analysis on such data sets. It aims to be the foundational layer for the future of statistical computing in Python. It serves as a strong complement to the existing scientific Python stack while implementing and improving upon the kinds of data manipulation tools found in other statistical programming languages such as R. In addition to detailing its design and features of pandas, we will discuss future avenues of work and growth opportunities for statistics and data analysis applications in the Python language.
This work presents a stochastic optimization framework for operations and planning of an electricity network as managed by an Independent System Operator. The objective is to maximize the total expected net benefits over the planning horizon, incorporating the costs and benefits of electricity consumption, generation, ancillary services, load-shedding, storage and load-shifting. The overall framework could be characterized as a secure, stochastic, combined unit commitment and AC optimal power flow problem, solving for an optimal state-dependent schedule over a pre-specified time horizon. Uncertainty is modeled to expose the scenarios that are critical for maintaining system security, while properly representing the stochastic cost. The optimal amount of locational reserves needed to cover a credible set of contingencies in each time period is determined, as well as load-following reserves required for ramping between time periods. The models for centrally-dispatched storage and time-flexible demands allow for optimal tradeoffs between arbitraging across time, mitigating uncertainty and covering contingencies. This paper details the proposed problem formulation and outlines potential approaches to solving it. An implementation based on a DC power flow model solves systems of modest size and can be used to demonstrate the value of the proposed stochastic framework.
NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility mades NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for eash exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdoes-Renyi, Small World, and Barabasi-Albert models, are included. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.
This paper presents the main concepts of Free and Open Source Software (FOSS) that are relevant for power system analysis, research and education. The main FOSS projects for power system analysis are briefly introduced and discussed. The paper also provides some outlines about the future of FOSS for power systems and the activities of the IEEE Task force on Open Source Software.
MATPOWER is an open-source Matlab-based power system simulation package that provides a high-level set of power flow, optimal power flow (OPF), and other tools targeted toward researchers, educators, and students. The OPF architecture is designed to be extensible, making it easy to add user-defined variables, costs, and constraints to the standard OPF problem. This paper presents the details of the network modeling and problem formulations used by MATPOWER, including its extensible OPF architecture. This structure is used internally to implement several extensions to the standard OPF problem, including piece-wise linear cost functions, dispatchable loads, generator capability curves, and branch angle difference limits. Simulation results are presented for a number of test cases comparing the performance of several available OPF solvers and demonstrating MATPOWER's ability to solve large-scale AC and DC OPF problems.
The deregulated electricity market calls for robust optimal power flow (OPF) tools that can provide a) deterministic convergence; b) accurate computation of nodal prices; c) support of both smooth and nonsmooth costing of a variety of resources and services, such as real energy, reactive energy, voltages support, etc.; d) full active and reactive power flow modeling of large-scale systems; and e) satisfactory worst-case performance that meets the real-time dispatching requirement. Most prior research on OPF has focused on performance issues in the context of regulated systems, without giving much emphasis to requirements a)-c). This paper discusses the computational challenges brought up by the deregulation and attempts to address them through the introduction of new OPF formulations and algorithms. Trust-region- based augmented Lagrangian method (TRALM), step-controlled primal-dual interior point method (SCIPM), and constrained cost variable (CCV) OPF formulation are proposed. The new formulations and algorithms, along with several existing ones, are tested and compared using large-scale power system models.
preprint @ https://arxiv.org/abs/1711.03331
There is an increasing interest in operating the power system close to its limits in order to avoid grid reinforcements. Distribution management requires the knowledge of grid state parameters, but outfitting grids with a large amount of measurements is costly. Therefore, we developed a new heuristic monitoring method (HMM) for balanced grids that relies only on few mandatory measurements and enables a fast way to monitor the grid for off-limit conditions. Due to a new formulation of the power flow equations, it has a low computational complexity for radial grids. The method analyzes the network topology; network buses are categorized and sorted into branches. Depending on the location of available voltage measurements, the bus powers of the corresponding branches are adjusted iteratively to better fit the measured voltage. To test the performance of our new algorithm, we design an evaluation process to compare our approach with the standard weighted least squares (WLS) state estimation (SE) method. Simulation results on artificial and real unmeshed distribution grids on the medium voltage (MV) level show very promising results, outperforming the WLS estimator even with a high amount of distributed generation (DG).
Network integration studies try to assess the impact of future developments, such as the increase of Renewable Energy Sources or the introduction of Smart Grid Technologies, on large-scale network areas. Goals can be to support strategic alignment in the regulatory framework or to adapt the network planning principles of Distribution System Operators. This study outlines an approach for the automated distribution system planning that can calculate network reconfiguration, reinforcement and extension plans in a fully automated fashion. This allows the estimation of the expected cost in massive probabilistic simulations of large numbers of real networks and constitutes a core component of a framework for large-scale network integration studies. Exemplary case study results are presented that were performed in cooperation with different major distribution system operators. The case studies cover the estimation of expected network reinforcement costs, technical and economical assessment of smart grid technologies and structural network optimisation.
Since failure of high- to medium voltage transformers affect a large number of consumers, they are usually built with a redundancy to guarantee service restoration in the case of a single contingency. The redundancy can be provided by a backup transformer or by transferring the load to a neighbouring substation in case of failure. While a load transfer allows for more efficient use of transformers in normal operation, it also requires the MV network to be dimensioned with redundant transmission capability for the contingency case. In this paper we present our approach to determine if it is profitable in the long term to remove a backup transformer in intrinsically safe substations and handle resupply with load transfer to neighbouring substations instead. To this end, we compare the cost of the necessary network expansion to the costs of a backup transformer. We introduce an iterated local search algorithm for the calculation of emergency switching plans as well as expansion of the MV network. The methodology is applied to a large real MV network group, where reinforcing the network to allow a load transfer is cost efficient compared to the existing backup transformer in three of four substations.
Dynamic, interpreted languages, like Python, are attractive for domain-experts and scientists experimenting with new ideas. However, the performance of the interpreter is often a barrier when scaling to larger data sets. This paper presents a just-in-time compiler for Python that focuses in scientific and array-oriented computing. Starting with the simple syntax of Python, Numba compiles a subset of the language into efficient machine code that is comparable in performance to a traditional compiled language. In addition, we share our experience in building a JIT compiler using LLVM[1].
It is widely accepted that the transition towards the widespread use of renewable and distributed energy resources (DER) is one of the key challenges in the 21st century. The success of this transition heavily relies on the availability of methods and techniques that enable the economic, robust, and environmentally responsible integration of DER. Industry, universities, and research institutes all over the world are actively engaged in developing these methods and techniques. What is missing, however, are test systems that facilitate the analysis and validation of the developed methods and techniques. This deficiency has been addressed by CIGRE Task Force (TF) C6.04.02. A common basis for testing has been developed and is presented in this report. Establishing a common basis for testing the integration of DER and Smart Grid technology is a significant challenge because distributed energy systems are diverse by themselves. TF C6.04.02 has taken care of this issue by developing a distinctive benchmark modeling methodology that covers the spectrum of DER integration issues in a mutually exclusive and collectively exhaustive manner. Following this principle, a comprehensive set of complementary reference systems has been developed to facilitate the analysis of DER integration at high voltage, medium voltage, and low voltage levels and at the desired degree of detail. For each of the benchmark networks, versions for North American style 60 Hz and European style 50 Hz were developed. It may be observed that many other parts of the world also use 50 Hz power systems and 60 Hz power systems. Users are encouraged to use these benchmarks with their own regional and national requirements in mind, and to adapt them to their best use based on sound engineering practices.
This paper presents a power system analysis tool, called DOME, entirely based on Python scripting language as well as on public domain efficient C and Fortran libraries. The objects of the paper are twofold. First, the paper discusses the features that makes the Python language an adequate tool for research, massive numerical simulations and education. Then the paper describes the architecture of the developed software tool and provides a variety of examples to show the advanced features and the performance of the developed tool.
Preface INTRODUCTION Operating States of a Power System Power System Security Analysis State Estimation Summary WEIGHTED LEAST SQUARES STATE ESTIMATION Introduction Component Modeling and Assumptions Building the Network Model Maximum Likelihood Estimation Measurement Model and Assumptions WLS State Estimation Algorithm Decoupled Formulation of the WLS State Estimation DC State Estimation Model Problems References ALTERNATIVE FORMULATIONS OF THE WLS STATE ESTIMATION Weaknesses of the Normal Equations Formulation Orthogonal Factorization Hybrid Method Method of Peters and Wilkinson Equality-Constrained WLS State Estimation Augmented Matrix Approach Blocked Formulation Comparison of Techniques Problems References NETWORK OBSERVABILITY ANALYSIS Networks and Graphs NetworkMatrices LoopEquations Methods of Observability Analysis Numerical Method Based on the Branch Variable Formulation Numerical Method Based on the Nodal Variable Formulation Topological Observability Analysis Method Determination of Critical Measurements Measurement Design Summary Problems References BAD DATA DETECTION AND IDENTIFICATION Properties of Measurement Residuals Classification of Measurements Bad Data Detection and IdentiRability Bad Data Detection Properties of Normalized Residuals Bad Data Identification Largest Normalized Residual Test Hypothesis Testing Identification (HTI) Summary Problems References ROBUST STATE ESTIMATION Introduction Robustness and Breakdown Points Outliers and Leverage Points M-Estimators Least Absolute Value (LAV) Estimation Discussion Problems References NETWORK PARAMETER ESTIMATION Introduction Influence of Parameter Errors on State Estimation Results Identification of Suspicious Parameters Classification of Parameter Estimation Methods Parameter Estimation Based on Residua! Sensitivity Analysis Parameter Estimation Based on State Vector Augmentation Parameter Estimation Based on Historical Series of Data Transformer Tap Estimation Observability of Network Parameters Discussion Problems References TOPOLOGY ERROR PROCESSING Introduction Types of Topology Errors Detection of Topology Errors Classification of Methods for Topology Error Analysis Preliminary Topology Validation Branch Status Errors Substation Configuration Errors Substation Graph and Reduced Model Implicit Substation Model: State and Status Estimation Observability Analysis Revisited Problems References STATE ESTIMATION USING AMPERE MEASUREMENTS Introduction Modeling of Ampere Measurements Difficulties in Using Ampere Measurements Inequality-Constrained State Estimation Heuristic Determination of F-# Solution Uniqueness Algorithmic Determination of Solution Uniqueness Identification of Nonuniquely Observable Branches Measurement Classification and Bad Data Identification Problems References Appendix A Review of Basic Statistics Appendix B Review of Sparse Linear Equation Solution References Index
GridLAB-D is a new power system modeling and simulation environment developed by the US Department of Energy. This paper describes its basic design concept, method of solution, and the initial suite of models that it supports.
An open-source distribution system simulator has been developed for distributed resource planning, harmonic studies, neutral-earth voltage studies, volt-var control studies, and other special applications. The software includes several means of interfacing user code, including compiled dynamic link library, COM automation, and text scripting. Co-simulation interfaces are under development for interfacing with proprietary vendor-supplied models, and communication system overlays. The simulator, called OpenDSS, has been used to conduct several smart grid research projects, including advanced automation, electric vehicle penetration, state estimation, and green circuits. The software architecture and solution methods are described, in the effort to foster more collaborative research.
In this paper, we present a new Matlab-based toolbox for power system analysis, called MatDyn. It is open-source software, and available for everyone to download. Its design philosophy is based on the well-known open-source Matlab toolbox MATPOWER, but its focus is transient stability analysis and time-domain simulation of power systems, instead of steady-state calculations. MatDyn's philosophy, design criteria, program structure, and implementation are discussed in detail. A trade-off is achieved between the flexibility of the program and readability of the code. MatDyn retains overall flexibility by, for instance, allowing user defined models, and custom integration methods. The software is validated by comparing its results with those obtained by the commercial grade power system analysis package, PSS/E. Despite the fact that MatDyn is fairly new, it has already been extensively used in research and education. This paper reports interesting results obtained with MatDyn in recent research that would be hard to obtain using commercial software.
SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
See http://www.scipy.org/ .
Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems. The latest release of matplotlib runs on all major operating systems, with binaries for Macintosh's OS X, Microsoft Windows, and the major Linux distributions. Matplotlib has a Matlab emulation environment called PyLab, which is a simple wrapper of the matplotlib API. Matplotlib provides access to basic GUI events such as button_press_event, mouse_motion_event and can also be registered with those events to receive callbacks. Event handling code written in matplotlib works across many different GUIs. It supports toolkits for domain specific plotting functionality that is either too big or too narrow in purpose for the main distribution. Matplotlib has three basic API classes, including, FigureCanvasBase, RendererBase and Artist.
A direct approach for unbalanced three-phase distribution load flow solutions is proposed in this paper. The special topological characteristics of distribution networks have been fully utilized to make the direct solution possible. Two developed matrices-the bus-injection to branch-current matrix and the branch-current to bus-voltage matrix-and a simple matrix multiplication are used to obtain load flow solutions. Due to the distinctive solution techniques of the proposed method, the time-consuming LU decomposition and forward/backward substitution of the Jacobian matrix or Y admittance matrix required in the traditional load flow methods are no longer necessary. Therefore, the proposed method is robust and time-efficient. Test results demonstrate the validity of the proposed method. The proposed method shows great potential to be used in distribution automation applications.
This paper describes the Power System Analysis Toolbox (PSAT), an open source Matlab and GNU/Octave-based software package for analysis and design of small to medium size electric power systems. PSAT includes power flow, continuation power flow, optimal power flow, small-signal stability analysis, and time-domain simulation, as well as several static and dynamic models, including nonconventional loads, synchronous and asynchronous machines, regulators, and FACTS. PSAT is also provided with a complete set of user-friendly graphical interfaces and a Simulink-based editor of one-line network diagrams. Basic features, algorithms, and a variety of case studies are presented in this paper to illustrate the capabilities of the presented tool and its suitability for educational and research purposes.
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