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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.
... According to this, a second set of solutions have been analysed. In [15], the authors introduced a co-simulation framework that uses PandaPower [16] and TRNSYS to simulate the electric grid and the buildings. The model used for buildings represents a single-family Spanish house typology. ...
... Starting from the models used by the DSO Agent we have the Grid Model block and the PF/OPF block as in Fig. 1. They both exploits the pandapower [16] python library. The former allows the representation of the power grid and all its components (e.g., lines, buses, transformers, loads, generators, storage), while the latter allows the calculation of Power Flow (PF) or Optimal Power Flow (OPF). ...
... It is worth noting that in the following formulation the exploitation of flexibility with respect to grid withdrawn is prioritized thanks to the realistic assumption of much lower prices. The used OPF formulation and solving method are taken from pandapower [16]. This model take into consideration: (i) AC load flow equations; (ii) branch constraints (maximum line loading); (iii) bus constraint (maximum and minimum voltages and angles requirements); (iv) transformer constraints (maximum loading); (v) operational constraints, for each load, generator and storage a maximum and minimum for active and reactive power can be specified (this is used to express the flexibility rather than operational limits); (vi) costs, the cost function to be minimized is expressed as a piece-wise linear function for all the element in the grid. ...
... Moreover, these software libraries are free, user-friendly, transparent, and validated by matching the results of reference calculations with established commercial software. The development of pandapower and pandapipes is continuously ongoing and documented [2]- [4]. The latest changes in these two Python libraries are described in the following sections. ...
... Similarly, element groups of different types can be used for simulations of virtual power plants (VPPs) and feeder analyses. To exemplify the new simple function of summing power values of all group members, a case of one VPP [2,3,4,5,9] and one grid area to be analyzed is considered. These two groups are applied to the example grid from the pandapower introduction article [2], see Fig. 5. Defining two generation units and two loads as VPP as well as all elements that are part of the lower feeder of the initial grid configuration, the information of the two groups "VPP 1" and "Area 1" is stored as a pandas DataFrame as shown in Table II. ...
... To exemplify the new simple function of summing power values of all group members, a case of one VPP [2,3,4,5,9] and one grid area to be analyzed is considered. These two groups are applied to the example grid from the pandapower introduction article [2], see Fig. 5. Defining two generation units and two loads as VPP as well as all elements that are part of the lower feeder of the initial grid configuration, the information of the two groups "VPP 1" and "Area 1" is stored as a pandas DataFrame as shown in Table II. Executing power flow calculations in a time series simulation using the switch and transformer tap position results of article [2], the group functionality allows accessing easily the sums of VPP 1 and Area 1, as illustrated in Fig. 6. ...
... The power values P are calculated using the steady-state solver pandapower based on the Newton-Raphson method (Thurner et al. 2018). In gas networks, the mass loss will be calculated similarly. ...
... The steady-state physical simulation is implemented using pandapipes and pandapower (Thurner et al. 2018;Lohmeier et al. 2020). These tools were chosen because they can calculate the steady-state variables necessary for calculating the network weights, and they further provide a possibility to implement coupling points utilizing the control loop. ...
The share and variants of coupling points (CPs) between different energy carrier networks (such as the gas or power grids) are increasing, which results in the necessity of the analysis of so-called multi-energy systems (MES). One approach is to consider the MES as a graph network, in which coupling points are modeled as edges with energy efficiency as weight. On such a network, local coalitions can be formed using multi-agent systems leading to a dynamic graph partitioning, which can be a prerequisite for the efficient decentralized system operation. However, the graph can not be considered static, as the energy units representing CPs can shut down, leading to network decoupling and affecting graph partitions. This paper aims to evaluate the effect of network adaptivity on the dynamics of an exemplary coalition formation approach from a complex network point of view using a case study of a benchmark power network extended to an MES. This study shows: first, the feasibility of complex network modeling of MES as a cyber-physical system; second, how the coalition formation system behaves, how the coupling points impact this system, and how these impact metrics relate to the CP node attributes.
... e., network constraints) at any moment. Therefore, a power flow function is defined as [48] shown in (11), which has been mentioned in ...
... To run the PF ( ) · function, pandapower.runpp module from the pandapower [48] package installed in the Python software is used. The pandapower package can be installed and used on every platform with an installation of Python 2.7 or higher. ...
This paper proposes a novel approach for the provision of non-frequency ancillary service (AS) by consumers connected to low-voltage distribution networks. The proposed approach considers an asymmetric pool-based local market for AS negotiation, allowing consumers to set a flexibility quantity and desired price to trade. A case study with 98 consumers illustrates the proposed market-based non-frequency AS provision approach. Also, three different strategies of consumers' participation are implemented and tested in a real low-voltage distribution network with radial topology. It is shown that consumers can make a profit from the sale of their flexibility while contributing to keeping the network power losses, voltage, and current within pre-defined limits. Ultimately, the results demonstrate the value of AS coming directly from end-users.
... WATTSON uses a steadystate simulation for the power grid, achieving scalability and meeting the real-time requirements induced by network emulation. Here, we utilize pandapower [101], a power flow solver supporting symmetric AC singleand three-phase systems. Consequently, changes in the configuration of the power grid, e.g., power adjustments, consumption changes, or topology changes resulting from opening or closing circuit breakers, trigger a simulation step that outputs the grid's power flow once a steady state is reached. ...
... To analyze WATTSON's accuracy, we replicate a physical low voltage distribution grid testbed operated at RWTH Aachen University [88], [104] shown in Fig. 2 within WATTSON and compare the behaviors of simulation and testbed. Hereby, we rely on the correct operation of both, Containernet [82] and pandapower [101], analyzing WATTSON's combined, i.e., overall accuracy. The testbed comprises a medium/low voltage substation at 630 kVA, two photovoltaic (PV) inverters (Inv.), a battery inverter, three resistive loads at 20 kW maximum power consumption, and a corresponding ICT network. ...
The increasing digitalization of power grids and especially the shift towards IP-based communication drastically increase the susceptibility to cyberattacks, potentially leading to blackouts and physical damage. Understanding the involved risks, the interplay of communication and physical assets, and the effects of cyberattacks are paramount for the uninterrupted operation of this critical infrastructure. However, as the impact of cyberattacks cannot be researched in real-world power grids, current efforts tend to focus on analyzing isolated aspects at small scales, often covering only either physical or communication assets. To fill this gap, we present WATTSON, a comprehensive research environment that facilitates reproducing, implementing, and analyzing cyberattacks against power grids and, in particular, their impact on both communication and physical processes. We validate WATTSON's accuracy against a physical testbed and show its scalability to realistic power grid sizes. We then perform authentic cyberattacks, such as Industroyer, within the environment and study their impact on the power grid's energy and communication side. Besides known vulnerabilities, our results reveal the ripple effects of susceptible communication on complex cyber-physical processes and thus lay the foundation for effective countermeasures.
... Input Data: The grid data and modeling are performed in the pandapower [28] format. The aim of the calculated characteristic is to address the time-varying grid condition, which is featured by the corresponding time series. ...
... Pandapower [28] is a program designed to automate analysis and optimization in power systems, utilizing a combination of the data analysis library pandas [35] and the power flow solver PYPOWER [36]. The pandapower library validated equivalent circuit models for lines, transformers, DER, generators, switches, etc., and provides the most commonly used static network analysis functions, including power flow calculation, times-series simulation, short-circuit analysis, state estimation, grid equivalent, etc. ...
The local reactive power control in distribution grids with a high penetration of distributed energy resources (DERs) is essential in future power system operation. Appropriate control characteristic curves for DERs support stable and efficient distribution grid operation. However, the current practice is to configure local controllers collectively with constant characteristic curves that may not be efficient for volatile grid conditions or the desired targets of grid operators. To address this issue, this paper proposes a time series optimization-based method to calculate control parameters, which enables each DER to be independently controlled by an exclusive characteristic curve for optimizing its reactive power provision. To realize time series optimizations, the open-source tools pandapower and PowerModels are interconnected functionally. Based on the optimization results, Q(V)- and Q(P)-characteristic curves can be individually calculated using the linear decision tree regression to support voltage stability and provide reactive power flexibility and potentially reduce grid losses and component loadings. In this paper, the newly calculated characteristic curves are applied in two representative case studies, and the results demonstrate that the proposed method outperforms the reference ones suggested by grid codes.
... In all scenarios, FSPs offer similar flexibility and the observable lines measure approximately similar values. The implementation in Python uses Pandapower's [16] version of the CIGRE medium voltage (MV) DN with solar photovoltaic and wind-distributed energy resources (DER). The network representation in Fig. 3, is modified from [17] to show the network's loads as obtained from Pandapower [16]. ...
... The implementation in Python uses Pandapower's [16] version of the CIGRE medium voltage (MV) DN with solar photovoltaic and wind-distributed energy resources (DER). The network representation in Fig. 3, is modified from [17] to show the network's loads as obtained from Pandapower [16]. The considered flexible devices are all the DER and the loads L1, L6, L9. ...
Power electronic interfaced devices progressively enable the increasing provision of flexible operational actions in distribution networks. The feasible flexibility these devices can effectively provide requires estimation and quantification so the network operators can plan operations close to real-time. Existing approaches estimating the distribution network flexibility require the full observability of the system, meaning topological and state knowledge. However, the assumption of full observability is unrealistic and represents a barrier to system operators' adaptation. This paper proposes a definition of the distribution network flexibility problem that considers the limited observability in real-time operation. A critical review and assessment of the most prominent approaches are done based on the proposed definition. This assessment showcases the limitations and benefits of existing approaches for estimating flexibility with low observability. A case study on the CIGRE MV distribution system highlights the drawbacks brought by low observability.
... Critically, the voltage v(t) is a function of the reactive power q(t), denoted as v(t) = v(q(t)), and this function is defined implicitly via the solution of a nonlinear algebraic equation system known as the powerflow equation (Low, 2014). In our experiment, we use PandaPower (Thurner et al., 2018) as the powerflow solver. The goal is to design a controller u(t) = π(v(t)) where the control action u(t) depends on the voltage v(t) such that in the close loop system, the voltage v(t) across all nodes will converge to the nominal value (which is 1.0). ...
... . For GridVoltage8, PPO, MAPPO, and LYPPO have almost identically bad rewards and tracking errors. This is because we use a professional solver PandaPower(Thurner et al., 2018) to simulate the underlying distribution grid. All three RL methods return controllers that quickly drive the system to an unsafe state, making the solver fail to solve the underlying model. ...
Developing stable controllers for large-scale networked dynamical systems is crucial but has long been challenging due to two key obstacles: certifiability and scalability. In this paper, we present a general framework to solve these challenges using compositional neural certificates based on ISS (Input-to-State Stability) Lyapunov functions. Specifically, we treat a large networked dynamical system as an interconnection of smaller subsystems and develop methods that can find each subsystem a decentralized controller and an ISS Lyapunov function; the latter can be collectively composed to prove the global stability of the system. To ensure the scalability of our approach, we develop generalizable and robust ISS Lyapunov functions where a single function can be used across different subsystems and the certificates we produced for small systems can be generalized to be used on large systems with similar structures. We encode both ISS Lyapunov functions and controllers as neural networks and propose a novel training methodology to handle the logic in ISS Lyapunov conditions that encodes the interconnection with neighboring subsystems. We demonstrate our approach in systems including Platoon, Drone formation control, and Power systems. Experimental results show that our framework can reduce the tracking error up to 75% compared with RL algorithms when applied to large-scale networked systems.
... The power values P are calculated using the steady-state solver pandapower based on the Newton-Raphson method [19]. In gas networks, the mass loss will be calcu-lated similarly. ...
... The multi-energy system uses a steady-state simulation using the Newton-Raphson method to solve the respective heat, power, and gas equations [19,20]. The coupling points are modeled as connected components in the respective networks. ...
The share and variants of coupling points (CPs) between different energy carrier networks (such as the gas or power grids) are increasing, which results in the necessity of the analysis of so-called multi-energy systems (MES). One approach is to consider the MES as a graph network, in which coupling points are modeled as edges with energy efficiency as weight. On such a network, local coalitions can be formed using multi-agent systems leading to a dynamic graph partitioning, which can be a prerequisite for the efficient decentralized system operation. However, the graph can not be considered static, as the energy units representing CPs can shut down, leading to network decoupling and affecting graph partitions. This paper aims to evaluate the effect of network adaptivity on the dynamics of an exemplary coalition formation approach from a complex network point of view using a case study of a benchmark power network extended to an MES. This study shows: first, the feasibility of complex network modeling of MES as a cyber-physical system; second, how the coalition formation system behaves, how the coupling points impact this system, and how these impact metrics relate to the CP node attributes.
... The data columns in Table 6 specify the parameters of a ZIP load model, similarly as the input data format used for instance in pandapower [9] : ...
... Circuit breakers (CBs) are located by the main feeder (MF) and the backup feeders (BF). The disconnectors by the backup feeders are normally open but can be closed in fault situations to reconfigure the grid to restore power supply to the parts of the grid that are isolated after sectioning to isolate the fault 9 . Switchgear data are given in the file CINELDI_MV_reference_system_switchgear.csv , and an extract illustrating the data format is given in Table 18 . ...
This article describes a reference data set for a representative Norwegian radial, medium voltage (MV) electric power distribution system operated at 22 kV. The data set is developed in the Norwegian research centre CINELDI and will in brief be referred to as the CINELDI MV reference system. Data for a real Norwegian distribution system were provided by a distribution grid company. The data have been anonymized and processed to obtain a simplified but still realistic grid model with 124 nodes. The first part of the data set describes the base version of the reference system that represents the present-day state of the grid, including information on topology, electrical parameters, and existing load points. The data set also comprises a load data set with load demand time series for a year with hourly resolution and scenarios for the possible long-term load development. These data describe an extended version of the reference system with information on possible new load points being added to the system in the future. A third part of the data set is data necessary for carrying out reliability of supply analyses for the system. The base version of the reference system described in this article can be extended to represent other types of distribution grids (e.g., with a ring topology). The reference grid can be used for assessing new methods and principles for distribution system operation and planning, including assessment of flexibility resources, active distribution grid measures, grid reinforcement planning, grid reinvestment planning, reliability of supply analysis, self-healing, etc.
... Kako bi se detektirani nedostatci nadomjestili, komercijalni alati se sve češće zamjenjuju javno dostupnim alatima temeljenima na programima otvorenog koda. Jedan od takvih alata je pandapower, programska biblioteka programskog jezika Python koja omogućuje razne analize kao što su proračun tokova snaga [9], proračun kratkog spoja [10], ali i brojne druge analize nakon nadogradnje predstavljene u radu [11]. Osim korištenja programskog jezika Python, određeni alati korišteni u analizi distribucijskih mreža temelje se na drugim programskim jezicima. ...
Kontinuiranim povećanjem udjela distribuiranih izvora energije, zadaća operatora distribucijskog sustava (ODS-a) zahtjevnija je nego prije. Integracija novih tehnologija onemogućuje korištenje tradicionalnih pristupa u planiranju i vođenju distribucijskih mreža. S obzirom na okolišne i ekonomske prednosti integracije distribuiranih izvora energije, od ključne je važnosti razviti metode kojima će se omogućiti analiza tehničkih prilika u distribucijskim mrežama s visokim udjelom niskougljičnih tehnologija. U ovom referatu prikazana su dva razvijena algoritma, algoritam optimalnih tokova snaga u trofaznim distribucijskim mrežama i algoritam harmoničkih tokova snaga u jednofaznim i trofaznim radijalnim distribucijskim mrežama. Za oba algoritma prikazani su matematički modeli kao i rezultati usporedbe s već razvijenim i korištenim simulacijskim alatima. Funkcionalnost razvijenih algoritama prikazana je na dva primjera u kojima se određuje potencijalna maksimalna proizvodnja fotonaponskih panela u niskonaponskim mrežama i analizira utjecaj distribuiranih izvora na harmoničke smetnje u mreži. Ključne riječi: distribucijske mreže, distribuirani izvori energije, harmonička analiza, optimalni tokovi snaga, trofazni elektroenergetski sustavi
With the continuous increase in the share of distributed energy resources, the task of the distribution system operator (DSO) is more demanding than before. The integration of new technologies makes it impossible to use traditional approaches in the planning and operation of distribution networks. Given the environmental and economic benefits of integrating distributed energy resources, it is crucial to develop methods that will enable the analysis of technical conditions in distribution networks with a high share of low-carbon technologies. This report presents two developed algorithms, the three-phase optimal power flow, and the single-phase and three-phase harmonic power flow algorithm. For both algorithms, mathematical models are presented, as well as a comparison of results with already developed and widely-used simulation tools. The functionality of the developed algorithms is shown in two examples in which the potential maximum production of photovoltaic panels in low-voltage networks is determined and the influence of distributed sources on harmonic interference in the network is analyzed.
... To validate the methods introduced in Section II, branches with different numbers K of segments are considered as single phase LV grids. Power-flow simulations are performed using the pandapower package in python for each individual time step in the data [10]. Profiles are randomly drawn from the household profiles provided in the IEEE European LV Test Feeder, in [11], with ∆t = 1 min and T = 1440. ...
This paper presents novel methods for parameter identification in electrical grids with small numbers of spatially distributed measuring devices, which is an issue for distribution system operators managing aged and not properly mapped underground Low Voltage (LV) grids, especially in Germany. For this purpose, the total impedance of individual branches of the overall system is estimated by measuring currents and voltages at a subset of all system nodes over time. It is shown that, under common assumptions for electrical distsribution systems, an estimate of the total impedance can be made using readily computable proxies. Different regression methods are then used and compared to estimate the total impedance of the respective branches, with varying weights of the input data. The results on realistic LV feeders with different branch lengths and number of unmeasured segments are discussed and multiple influencing factors are investigated through simulations. It is shown that estimates of the total impedances can be obtained with acceptable quality under realistic assumptions.
... Thus, T = 24 × 12 = 288. The simulator is built with Python [43] and the power flow is calculated using pandapower [44]. 2 http://dataminer2.pjm.com/ Moreover, to illustrate the convergence of Algorithm 1, we also add 4 more MGs to the distribution network at buses i ∈ {4, 17, 25, 28} and test the algorithm. ...
Coordinating the microgrids (MGs) in the distribution network is a critical task for the distribution system operator (DSO), which could be achieved by setting prices as incentive signals. The high uncertainty of loads and renewable resources motivates the DSO to adopt real-time prices. The MGs require reference price sequences for a long time horizon in advance to make generation plans. However, due to privacy concerns in practice, the MGs may not provide adequate information for the DSO to build a closed-form model. This causes challenges to the implementation of the conventional model-based methods. In this paper, the framework of the coordination system through real-time prices is proposed. In this bi-level framework, the DSO sets real-time reference price sequences as the incentive signals, based on which the MGs make the generation and charging plan. The model-free reinforcement learning (RL) is applied to optimize the pricing policy when the response behavior of the MGs is unknown to the DSO. To deal with the large action space of this problem, the reference policy is incorporated into the RL algorithm for efficiency improvement. The numerical result shows that the minimized cost obtained by the developed model-free RL algorithm is close to the model-based method while the private information is preserved.
... Power grid -Our reference simulator LightSim2Grid has comparable speed to the proprietary RTE solver Hades 2 on mentioned IEEE grid cases, and is faster than PandaPower [60]. It is faster than the physical simulator used in SimBench, [29] by at least a factor 30 (see [18]) and also faster than the one used in Power Grid Lib [30] by at least a factor 5 on the hardware setup described above. ...
Physical simulations are at the core of many critical industrial systems. However , today's physical simulators have some limitations such as computation time, dealing with missing or uncertain data, or even non-convergence for some feasible cases. Recently, the use of data-driven approaches to learn complex physical simulations has been considered as a promising approach to address those issues. However, this comes often at the cost of some accuracy which may hinder the industrial use. To drive this new research topic towards a better real-world applicability, we propose a new benchmark suite "Learning Industrial Physical Simulations"(LIPS) to meet the need of developing efficient, industrial application-oriented, augmented simulators. To define how to assess such benchmark performance, we propose a set of four generic categories of criteria. The proposed benchmark suite is a modular and configurable framework that can deal with different physical problems. To demonstrate this ability, we propose in this paper to investigate two distinct use-cases with different physical simulations, namely: the power grid and the pneumatic. For each use case, several benchmarks are described and assessed with existing models. None of the models perform well under all expected criteria, inviting the community to develop new industry-applicable solutions and possibly showcase their performance publicly upon online LIPS instance on Codabench.
... In addition, many studies deal with BSSs in terms of balancing electricity supply and demand at the household level or village/town level but do not determine the exact impact on LV grids [43]. In this paper, load flow calculations using the open-source grid simulation tool pandapower [40] are performed on a minute basis to determine line and local power transformer (LPT) loading and voltage band violations. ...
... In the second step, a power flow is performed to evaluate the impacts on the physical grid based on the market results from the first step. The load flow is performed using Pandapower software [36], [37], which is an open-source tool based on Python for power system studies. A 3-phase AC power flow is performed because the case study is unbalanced LVDN, and the focus of the study is to evaluate the phase unbalance in addition to components loadings and voltage at different nodes of the LVDN. ...
The wide spread of distributed energy resources (DERs) enabled the transformation of the passive consumer to an active prosumer. One of the promising approaches for optimal management of DERs and maximizing benefits for the community and prosumers is community energy trading (CET). CET gives the prosumers the flexibility and freedom to trade electricity within the neighborhood and maximize their economic benefits besides maximizing local consumption of renewable energy sources generation. Despite the economic benefits of CET for individuals and the whole community, it could cause impacts on the low voltage distribution network (LVDN). Therefore, there is a need for a comprehensive evaluation of the potential impacts of CET on LVDN. This study compared CET with the home energy management system (HEMS) regarding community operation costs and interaction with the retailer. Furthermore, this paper focused on assessing the impacts of CET between prosumers on the phase unbalance of LVDN. Moreover, the impacts on transformer loading, lines loading, and voltage deviations are assessed. Compared to the corresponding HEMS scenarios, the results demonstrate that CET reduces the community electricity cost by up to 31%. CET resulted in better self-consumption by reducing the exports to the retailer by 93% and better self-sufficiency by covering up to 54% of energy demand by community DERs. However, CET resulted in increasing the community peak demand, accordingly, higher impacts on the LVDN. The transformer is lightly loaded in all scenarios. CET resulted in limit violations in some lines, whereas most are lightly loaded. The voltage magnitude and voltage unbalance exceeded the acceptable limits at some nodes of the LVDN.
... ANALYSE uses the power grid simulator pandapower 4 to simulate a power grid with its topology and power flows [9]. To achieve more realistic behavior, we also added simulators for publicly available load profiles and a simulator for a photovoltaic (PV) model that takes real weather data as input. ...
The ongoing penetration of energy systems with information and communications technology (ICT) and the introduction of new markets increase the potential for malicious or profit-driven attacks that endanger system stability. To ensure security-of-supply, it is necessary to analyze such attacks and their underlying vulnerabilities, to develop countermeasures and improve system design. We propose ANALYSE, a machine-learning-based software suite to let learning agents autonomously find attacks in cyber-physical energy systems, consisting of the power system, ICT, and energy markets. ANALYSE is a modular, configurable, and self-documenting framework designed to find yet unknown attack types and to reproduce many known attack strategies in cyber-physical energy systems from the scientific literature.
... To validate the feasibility of network candidates and identify constraint violations, AC power flows based on the Newton Raphson method are executed. Further details on the implementation of the power flow solver in pandapower networks can be found in [40]. ...
A successful energy transition will be firmly based on the effective integration of distributed energy resources and on the integration of new flexibility providers into the energy system. To make this possible, a deep transformation in the design, operation and planning of power distribution systems is required. Currently, a lack of comprehensive planning tools capable of supporting operators in their investment plan options exists. As a result, reinforcements of conventional grid assets are common solutions put in place. This paper proposes a methodology to obtain cost-optimal distribution network expansion plans, by modelling a single-stage distribution network planning tool, using conventional assets as well as flexibility contracting from demand response. A Tabu Search metaheuristic has been implemented in order to solve the optimization problem. A case study based on a realistic large-scale city network model is presented, for a planning horizon of ten years with significant load growth due to electromobility penetration. Results show that, in the case study analysed, the use of load flexibility in combination with conventional reinforcements can reduce the total expansion network cost by about 7.5 %. Furthermore, a sensitivity analysis on the cost of flexibility contracting is undertaken. Remarkably, the methodology presented generalises to further alternative solutions by providing a straightforward financial benchmark between the latter and conventional grid expansion.
... PandaPower is an open-source tool [189]. ...
Cyber‐Physical Systems (CPSs) are becoming more automated and aimed to be as efficient as possible by enabling integration between their operations and Information Technology (IT) resources. In combination with production automation, these systems need to identify their assets and the correlation between them; any potential threats or failures alert the relevant user/department and suggest the appropriate remediation plan. Moreover, identifying critical assets in these systems is essential. With numerous research and technologies available, assessing IT assets nowadays can be straightforward to implement. However, there is one significant issue of evaluating operational technology critical assets since they have different characteristics, and traditional solutions cannot work efficiently. This study presents the necessary background to attain the appropriate approach for monitoring critical assets in CPSs' Situational Awareness (SA). Additionally, the study presents a broad survey supported by an in‐depth review of previous works in three important aspects. First, it reviews the applicability of possible techniques, tools and solutions that can be used to collect detailed information from such systems. Secondly, it covers studies that were implemented to evaluate the criticality of assets in CPSs, demonstrates requirements for critical asset identification, explores different risks and failure techniques utilised in these systems and delves into approaches to evaluate such methods in energy systems. Finally, this paper highlights and analyses SA gaps based on existing solutions, provides future directions and discusses open research issues.
... The comparison of the different control algorithms is conducted on a benchmark distribution grid, and using real load and PV generation data to obtain meaningful time-series results. The simulation framework is based on Python and the open-source package pandapower [10]. All code has been made available here [11]. ...
The rise in residential photovoltaics (PV) as well as other distributed energy sources poses unprecedented challenges for the operation of distribution grids. The high active power infeed of such sources during times of peak production is a stress test which distribution grids have usually not been exposed to in the past. When high amounts of active power are injected into the grid, the overall power flow is often limited because of voltages reaching their upper acceptable limits. Volt/VAr methods aim to raise this power flow limit by controlling the voltage using reactive power. This way, more active power can be transmitted safely without physically reinforcing the grid. In this paper, we use real consumption and generation data on a low-voltage CIGR\'E grid model and an experiment on a real distribution grid feeder to analyze how different Volt/VAr methods can virtually reinforce the distribution grid. We show that droop control and machine-learning-improved droop control virtually reinforce the grid but do not utilize the reactive power resources to their full extent. In contrast, methods which coordinate the usage of reactive power resources across the grid, such as \ac{OFO}, can reinforce the grid to its full potential. The simulation study performed on data of an entire year suggests that Online Feedback Optimization (OFO) can enable another 9\% of maximum active power injections. To achieve that, OFO only requires voltage magnitude measurements, minimal model knowledge and communication with the reactive power sources. A real-life experiment provides a demonstration of how OFO acts at the level of a single device, and proves the practical feasibility of the proposed approach.
... Alternatively, third-party R/S distribution models can be integrated within the iRe-CoDeS system-of-systems model of the built environment. Examples of such models are WNTR (Klise et al. [2017], Hansen [2022]) or HydrauSim (Soga et al. [2021]) for water distribution and pandapower (Thurner et al. [2018]) or PyPSA (Brown et al. [2018]) for electric power distribution. ...
As the built environment is expanding, aging, and becoming more interconnected, it is also becoming more vulnerable to extreme events. Unless we intervene, future disasters will likely set back the development of communities worldwide by years, if not decades, significantly impeding our efforts to develop sustainably. To prevent such grim future scenarios, civil engineering design philosophy is evolving from a building-level damage-based one to a region-level function-based design philosophy. Simultaneously, post-disaster functional recovery of the built environment is coming into research focus. The change in how we approach building and infrastructure system design is motivated by the recent institutional push toward improving their resilience. A resilient built environment can swiftly restore its functions following an extreme event, minimizing the disruption to the communities it serves. To implement measures that increase resilience effectively, we need novel tools that can identify factors crucial for the resilience of the built environment.
This dissertation proposes a novel framework for probabilistic resilience-based assessment and design of the built environment called iRe-CoDeS. The iRe-CoDeS framework views the built environment as an assembly of components, such as buildings, bridges, pipes, electric power plants, or power transmission lines, that interact by exchanging resources and services during post-disaster recovery in accordance with the operational principles of the systems they are a part of. The components’ supply, demand, and consumption of various resources are aggregated on the regional level and tracked over time following a disaster to assess the system’s functionality. Thus, the proposed resilience quantification framework is compositional. A resilient system is one that can meet the variable post-disaster demand of its users: the more unmet resource demand there is following a disaster, the less resilient the system providing the resource is. This dissertation shows how such an approach to resilience assessment can be consistently applied to various systems of the built environment that provide housing, infrastructure services and recovery resources. Furthermore, the proposed supply/demand method for simulating component interaction as an exchange of resources is used to capture post-disaster cascading failures among interdependent components, as well as the effects of recovery resource constraints. The outcome of an iRe-CoDeS built environment disaster resilience assessment are instantaneous and cumulative resilience metrics supporting risk-based evaluation of community resilience goals. Importantly, the proposed framework can be used to perform sensitivity studies to rank built environment's components based on their importance for system's resilience and to relate community's supply of recovery resources to its ability to restore its function after a disaster. Such information is the basis for selecting optimal community resilience improving measures. Finally, this dissertation concludes with a novel algorithm for the resilience-based design of the built environment. The algorithm is based on the well-established probabilistic performance-based engineering methodology, employs the iRe-CoDeS framework, and enshrines the principle of continuous assessment: making sure that community's built environment is disaster-resilient is a process that must account for the evolution and adaptation of the community over time.
The proposed iRe-CoDeS framework is extensible and modular. Resilience assessment approaches and system models developed by other researchers can be integrated with the iRe-CoDeS framework. This dissertation shows how third-party software for regional disaster loss assessment and advanced building recovery modelling are incorporated into the iRe-CoDeS framework to more accurately assess built environment's resilience.
The proposed iRe-CoDeS framework is illustrated in four Case Studies. A virtual community supported by three interdependent infrastructure systems exposed to seismic hazard is used to illustrate the iRe-CoDeS workflow, interdependency modelling and the effect of insufficient supply of recovery resources on community's resilience. Advanced recovery modelling capabilities of the framework are illustrated by assessing housing resilience of North-East San Francisco and the resilience of a five building virtual community characteristic of Los Angeles. Finally, the framework is validated by comparing the computed housing recovery trajectories with those observed after the 2010 Kraljevo, Serbia, earthquake.
... pandapower.org/) to represent and evaluate electrical grids' power flow. Pandapower is an open-source Python tool for power system modeling, analysis, and optimization, providing power flow, optimal power flow, state estimation, topological graph searches, and shortcircuit calculations according to IEC 60909 [41]. PFS encapsulates the pandapower library in a REST API for agent-based studies and easy integration with other systems and services, adding an upper layer for the automatic evaluation of a given electrical grid power flow. ...
The share of renewable generation is growing worldwide, increasing the complexity of the grids operation to maintain its stability and balance. This leads to an increased need for designing new electricity markets (EMs) suited to this new reality. Simulation tools are widely used to experiment and analyze the potential impacts of new solutions, such as novel EM designs and power flow analysis and validation. This work introduces two web services for EMs’ simulation and study, in addition to power flow evaluation and validation, namely the Electricity Market Service (EMS) and Power Flow Service (PFS). EMS enables the simulation of two auction-based algorithms and the execution of three wholesale EMs. PFS creates and evaluates electrical grids from the transmission to distribution grids. Being published as web services facilitates their integration with other services, systems, or software agents. Combining them allows for the simulation of EMs from wholesale to local markets and testing if the results are compatible with a specific grid. This article presents a detailed description of each service and a case study of an electricity trading community participating in the MIBEL day-ahead market through an aggregator to reduce their energy bills. The results demonstrate the accuracy and usefulness of the proposed services.
... The tool pandapower from [46] and [47] is a Python-based open-source power grid analysis tool. It is intended for the automated analysis of static or quasi-static power system states or the optimization of balanced power systems. ...
The decentralization of the energy system in Germany is leading to enormous investments in grid expansion, as the current regulation creates an obligation to expand the power grid to eliminate bottlenecks. Meanwhile, opportunities to leverage grid-friendly control of storage systems are neglected to alleviate the need for investment. For this reason, it is necessary to investigate intelligent alternatives to grid expansion, such as storage systems, to efficiently integrate distributed technologies into the power system and reduce the need for grid expansion. In this work, two representative configurations of a medium voltage grid in Germany are developed for the years 2022 and 2050, and different storage systems are compared economically with the grid expansion in a model-based simulation. Hydrogen storage and battery storage were chosen as storage systems. The results show that grid expansion is the least expensive option if only the grid expansion costs are included in the analysis. However, if additional uses for the storage systems are considered, the battery storage systems are more economical. While in the scenario for 2050 the grid expansion causes costs of approx. 56,000 EUR per year, revenues of at least 58,000 EUR per year can be achieved via the revenue opportunities of the battery storage, representing a 3.5% margin. Heat extraction, arbitrage trading, and avoidance of grid expansion in superimposed grid levels were integrated as additional revenue streams/sources. A robust data basis and cost degressions were assumed for the simulations to generate meaningful results. Overall, hydrogen storage systems are economically inferior to battery storage systems and grid expansion for this use case. The results demonstrate the complexity of analyzing the trade-offs in terms of storage as an alternative to grid expansion as well as the opportunities presented using battery storage instead.
... Here we will look at the family of quasi-newton methods that don't require the hessian information and instead build up an approximation over time. The "Broyden, Fletcher, Goldfarb, and Shanno", or [ ]One of the most popular quasi-Newton methods is a local search optimization algorithm 5,6 . ...
In this paper, we will discover the BFGS and compare the result of converging to minimize with the result of L — BFGS. And we discuss how to find an optimal solution of the objective function using the BFGS, and L — BFGS algorithms in Python lets get started.
... In this section, we select injected active power from these three POCs as dimensions, eventually targeting a 3D hosting capacity region. Pandapower toolkit is adopted for power flow calculation to check the feasibility of points [29]. All codes were run on a computer with an Intel i7-9750H processor running at 2.60 GHz using 15.8 GB of RAM, running Windows 10 Enterprise version. ...
p>The hosting capacity determines the grid ability to host integrated installations, where mutual-limitations among points of connection (POCs) are important and formulate a combinational feasible region. Intended to assess such interaction for region determination, this paper proposes a multidimensional hosting capacity region derivation scheme in a radial grid. As this scheme can be designed conservative, it earns more industrial acceptance from a risk-averse perspective. This multidimensional region not only exploits grid power delivery potential, but also benefits for making grid congestion management decisions. A simulative 10.5kV dutch grid case has been tested accordingly. Corresponding results revealed that the region conservative property is guaranteed. In the given case study, compared to the original hosting capacity concept, the region hypervolume gain ratio keeps higher than 1.94. With proper measure selection, the estimated region occupation ratio can keep up to 92.50% and the congestion management decision computation time can be reduced by 58.0% in respect to that in the monolithic model. Both the effectiveness of proposed scheme and the concept benefit in grid congestion management are successfully verified. </p
... In this section, we select injected active power from these three POCs as dimensions, eventually targeting a 3D hosting capacity region. Pandapower toolkit is adopted for power flow calculation to check the feasibility of points [29]. All codes were run on a computer with an Intel i7-9750H processor running at 2.60 GHz using 15.8 GB of RAM, running Windows 10 Enterprise version. ...
p>The hosting capacity determines the grid ability to host integrated installations, where mutual-limitations among points of connection (POCs) are important and formulate a combinational feasible region. Intended to assess such interaction for region determination, this paper proposes a multidimensional hosting capacity region derivation scheme in a radial grid. As this scheme can be designed conservative, it earns more industrial acceptance from a risk-averse perspective. This multidimensional region not only exploits grid power delivery potential, but also benefits for making grid congestion management decisions. A simulative 10.5kV dutch grid case has been tested accordingly. Corresponding results revealed that the region conservative property is guaranteed. In the given case study, compared to the original hosting capacity concept, the region hypervolume gain ratio keeps higher than 1.94. With proper measure selection, the estimated region occupation ratio can keep up to 92.50% and the congestion management decision computation time can be reduced by 58.0% in respect to that in the monolithic model. Both the effectiveness of proposed scheme and the concept benefit in grid congestion management are successfully verified. </p
... The nodes are represented by their indices and the branches are represented by the tuple of nodes on which they are incident. The naming convention of the buses in the test systems follow the PandaPower conventions [53]. ...
An increased energy demand, and environmental pressure to accommodate higher levels of renewable energy and flexible loads like electric vehicles have led to numerous smart transformations in the modern power systems. These transformations make the cyber-physical power system highly susceptible to cyber-adversaries targeting its numerous operations. In this work, a novel black box adversarial attack strategy is proposed targeting the AC state estimation operation of an unknown power system using historical data. Specifically, false data is injected into the measurements obtained from a small subset of the power system components which leads to significant deviations in the state estimates. Experiments carried out on the IEEE 39 bus and 118 bus test systems make it evident that the proposed strategy, called DeeBBAA, can evade numerous conventional and state-of-the-art attack detection mechanisms with very high probability.
... The power flow simulation has been implemented in Python via jupyter notebook with the open-source power flow library Pandapower [11][12][13]. For each scenario in this simulation, the net active and reactive power injection at each node is defined based on power consumptions and generations (i.e., P i = P Gen,i − P load,i and Q i = Q Gen,i − Q Load,i ∀i = 1 . . . ...
Electrification of final use sectors such as heating and mobility is often proposed as an effective pathway towards decarbonization of urban areas. In this context, power-driven heat pumps (HP) are usually strongly fostered as alternatives to fossil-burning boilers in municipal planning processes. In continental climates, this leads to substantially increased electricity demand in winter months that, in turn may lead to stress situations on local power distribution grids. Hence, in parallel to the massive implementation of electric HP, strategies must be put in place to ensure the grid stability and operational security, notably in terms of voltage levels, as well as transformer and line’s capacity limits. In this paper, three such strategies are highlighted within the specific situation of a mid-sized Swiss city, potentially representative of many continental, central Europe urban zones as a test-case. The hourly-based power flow simulations of the medium- and low-voltage distribution grids show the impact of various future scenarios, inspired from typical territorial energy planning processes, implying various degrees of heat pumps penetration. The first strategy relies on the implementation of decentralized combined heat and power (CHP) units, fed by the existing natural gas network and is shown to provide an effective pathway to accommodate heat pump electricity demand on urban power distribution grids. Two alternative solutions based on grid reinforcements and controlled usage of reactive power from photovoltaic (PV) inverters are additionally considered to ensure security constraints of grid operation and compared with the scenario relying on CHP deployment.
... Die Modellierung und Simulationen werden mittels Python 3.8 und dem Netzberechnungsprogramm pandapower durchgeführt [16]. ...
In der Arbeit wird ein mehrperiodisches AC Optimal Power-Flow Modell zur Dimensionierung der installierten Nennleistung von Photovoltaikanlagen für ein Niederspannungsnetz vor-gestellt. Das Modell soll die Einhaltung der technischen Planungskriterien nach DIN EN 50160 und VDE-AR-N 4105 gewährleisten und beruht auf quasi-stationären Zeitschritten. Dabei wird die installierte Nennleistung der Photovoltaikanlagen als globale Variable im Modell berücksichtigt. Zudem kann das Modell um stationäre Betriebspunkte erweitert werden. Diese werden häufig für die konventionelle Planung von Niederspannungsnetzen verwendet. Besonders für den Umbau bestehender Niederspannungsnetze zu gemeinschaftlichen Versorgungskonzepten könnte das Modell hilfreich sein, um eine Kombination von technisch-wirtschaftlicher und elektrotechnischer Analyse zu ermöglichen. Das entwickelte Modell wird für die Dimensionierung der installierten Photovoltaik-Leistung eines Niederspannungsnetzes angewendet. Das beispielhafte Bestandsnetz beinhaltet neben Haushaltslasten auch Elektromobilität und Wärmepumpen. Die Ergebnisse zeigen den Einfluss der Planungsgrundsätze auf die optimale Dimensionierung der Photovoltaik-Anlagenleistung. Insbesondere der Einfluss der langsamen Spannungsänderung von maximal 3 % wird herausgestellt. Dabei wird deutlich, dass das Kriterium für den Fall gleicher Anlagengröße und für den Fall reiner Wirkleistungseinspeisung der Erzeugungsanlagen in der Optimierung berücksichtigt werden muss. Ohne die Einbindung der konventionellen Netz-planung mit Betriebspunkten muss zudem darauf geachtet werden, dass die Maxima für Erzeugung und Verbrauch in den Zeitschritten enthalten sind. Vergleicht man die Ergebnisse so wird deutlich, dass durch Einbezug aller Planungskriterien die Ergebnisse von eingangs 364,45 kWp installierter Photovoltaik-Nennleistung im Quartier auf 233,74 kWp reduziert werden. Dies entspricht einer Reduktion von ca. 35 %. Speisen die Erzeugungsanlagen Blindleistung ein, spielt das Kriterium der langsamen Spannungsänderung eine untergeordnete Rolle.
... In our environment, we use the open-source tool pandapower 2 [19] for the OPF calculation and the SimBench 3 [20] benchmark system 1-HV-urban--0-sw with 372 buses and 42 generators as a power model. To generate realistic power system states, we use the associated full-year time-series data of the system. ...
Energy markets can provide incentives for undesired behavior of market participants. Multi-agent Reinforcement learning (MARL) is a promising new approach to determine the expected behavior of energy market participants. However, reinforcement learning requires many interactions with the system to converge, and the power system environment often consists of extensive computations, e.g., optimal power flow (OPF) calculation for market clearing. To tackle this complexity, we provide a model of the energy market to a basic MARL algorithm, in form of a learned OPF approximation and explicit market rules. The learned OPF surrogate model makes an explicit solving of the OPF completely unnecessary. Our experiments demonstrate that the model additionally reduces training time by about one order of magnitude, but at the cost of a slightly worse approximation of the Nash equilibrium. Potential applications of our method are market design, more realistic modeling of market participants, and analysis of manipulative behavior.
... Hence, the benchmark system permits many configurations to help validate the proposed adaptive protection method. We generate short circuit data using Pandapower [29]. Pandapower is an open-source software library for power system analysis and optimization in Python, which we use for the ease of integration with Python's data analysis and machine learning libraries. ...
Smart grids are critical cyber-physical systems that are vital to our energy future. Smart grids' fault resilience is dependent on the use of advanced protection systems that can reliably adapt to changing conditions within the grid. The vast amount of operational data generated and collected in smart grids can be used to develop these protection systems. However, given the safety-criticality of protection, the algorithms used to analyze this data must be stable, transparent, and easily interpretable to ensure the reliability of the protection decisions. Additionally, the protection decisions must be fast, selective, simple, and reliable. To address these challenges, this paper proposes a data-driven protection strategy, based on Gaussian Discriminant Analysis, for fault detection and isolation. This strategy minimizes the communication requirements for time-inverse relays, facilitates their coordination, and optimizes their settings. The interpretability of the protection decisions is a key focus of this paper. The method is demonstrated by showing how it can protect the medium-voltage CIGRE network as it transitions between islanded and grid-connected modes, and radial and mesh topologies.
... After the estimated impedances are obtained, we conduct a validation test by running the voltage controller over one day simulation horizon of 30 minutes time step and using determinstic PV and load profiles, as shown in Fig. 6. Moreover, we utilize an open-source software Pandapower [24] to emulate a real distribution grid and to generate emulated RMS voltage measurements at each 30 minutes over the considered simulation horizon. By doing this, it will allow us to compute the δ v that will be used to evalute the accuracy of the impedance estimation. ...
This paper deals with the impact of line impedances uncertainties on model-based voltage controllers for distribution networks in the context of secondary to tertiary control levels (i.e., 30 minutes control horizon). The study proposes two methodologies: i) centralized and ii) distributed approaches, to estimate grid impedances by relying on static historical measurement data and adjust the parameters of a model-based voltage controller. Furthermore, an online impedance tuning scheme is proposed to successively fine-tune the impedance estimation over successive control periods (along several days). The simulations results highlight the preciseness of the proposed methodologies, with both centralized and distributed able to estimate the grid impedances within an acceptable accuracy (between 4 % and 7 % of error). Moreover, the proposed tuning algorithm shows to be very effective, where the estimation error can be lowered under 1 %. Finally, robustness studies are performed to test the proposed methodologies in the presence of measurement noises. Through this study, the robustness of the proposed tuning scheme can be validated, in which the algorithm is able to correct massive impedance errors after three months of tuning rounds only.
... The problem is again solved via the same UPSO that was applied for the proposed decentralized EMS and with the same parameters as presented in Table 1. The centralized EMS includes the simultaneous operation scheduling of all prosumers' assets in the grid and the power flow analysis is performed in pandapower tool [45]. To incorporate the additional optimization variables of this approach, (12) is modified and presented in (15) and (16). ...
Here, an efficient Energy Management System (EMS) for residential installations is proposed. The EMS handles on site Photovoltaic (PV) power production along with Battery Energy Storage System (BESS) and performs load shifting under a Demand Side Management (DSM) scheme that considers user comfort. The goal of the proposed methodology is the minimization of power exchange between the prosumer and the grid. In this way, the power flow across the Distribution Network (DN) is decreased, thus reducing power losses, and improving the voltage profile along the DN. Moreover, the EMS increases the self-sufficiency of the residence. The minimization problem is solved via a metaheuristic algorithm, namely Unified Particle Swarm Optimization (UPSO) utilizing real consumption patterns for residential appliances. The latter adds complexity to the problem but offers accurate modelling of the power demand and improves the performance of the EMS scheme. The proposed EMS is locally implemented as a plug-and-play scheme and benefits both the prosumer and the DN under low computational burden, proving to be efficient. Simulation results for a case study verify the efficiency of the proposed EMS and the comparison with other similar methods proves its superior performance.
... The first option is using synthetic data by modeling the power grid. This tool uses pandapower [7] Python library for simulating IEEE9 and IEEE39-bus power grids. The second option is to use data from the real power grid provided by the system operator. ...
The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power grids. In recent years, integrating a significant amount of renewables into a power grid causes high fluctuations and thus brings a lot of uncertainty to power grid operations. This fact makes the conventional model-based CC-OPF problem non-convex and computationally complex to solve. The developed tool presents a novel data-driven approach based on the GP regression model for solving the CC-OPF problem with a trade-off between complexity and accuracy. The proposed approach and developed software can help system operators to effectively perform ED optimization in the presence of large uncertainties in the power grid.
... As emphasized in the introduction, it is particularly important that solutions can be implemented in practice. To ensure the feasibility of solutions in practice, we simulate all solutions using the current flow simulation library pandapower, which is described by Thurner et al. [54]. During simulation, we consider the failure scenarios to verify the reliability of the solution: we successively deactivate the distribution network's transformers to simulate their failure. ...
The increased use of renewable energies promotes decarbonization and
raises the load on power distribution networks, forcing responsible distribution
network operators to re-evaluate and re-design their networks.
Infrastructure planners employ a rolling-horizon planning procedure with
frequent recalculations to face informational uncertainty, which require
solving multiple scenarios. Keeping complexity manageable is particularly
challenging as distribution network areas may span multiple cities
and counties. In this study, we focus on infrastructural decomposition,
where the distribution network is decomposed into multiple parts
and planning problems, which are then optimized separately. However,
infrastructure planners lack the knowledge of how they should design a
scenario analysis for a subnetwork to account for informational uncertainties
subject to limited planning time and computing resources. Based
on empirical requirements from literature and discussions with experts,
we present a novel mixed integer linear optimization model that allows
to use exact solution approaches for realistic large-scale distribution
networks. Our approach considers the primary and secondary distribution
network in an integrated way and designs a flexible topology for
high reliability power distribution. We perform extensive computational
experiments and a sensitivity analysis to determine correlations between
the values of model parameters and computation times required to solve the resulting model instances to optimality. The results of the sensitivity
analysis indicate that the combination of the number of buses,
lines and the considered action scope have a considerable influence on
the solving time. In contrast, a higher number of available transformers
led to a better solvability of the model. From these computational
insights, we derive implications for infrastructure planners who wish to
perform scenario analysis for planning their power distribution networks.
Local reactive power control in distribution grids with a high penetration of distributed energy resources (DERs) will be essential in future power system operation. Appropriate control characteristic curves for DERs support stable and efficient distribution grid operation. However, the current practice is to configure local controllers collectively with constant characteristic curves that may not be efficient for volatile grid conditions or the desired targets of grid operators. To address this issue, this paper proposes a time series optimization-based method to calculate control parameters, which enables each DER to be independently controlled by an exclusive characteristic curve for optimizing its reactive power provision. To realize time series reactive power optimizations, the open-source tools pandapower and PowerModels are interconnected functionally. Based on the optimization results, Q(V)- and Q(P)-characteristic curves can be individually calculated using linear decision tree regression to support voltage stability, provide reactive power flexibility and potentially reduce grid losses and component loadings. In this paper, the newly calculated characteristic curves are applied in two representative case studies, and the results demonstrate that the proposed method outperforms the reference methods suggested by grid codes.
Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual static VAR compensator (SVC) and provide voltage stability by utilizing advanced electric vehicle (EV) chargers that are capable of four-quadrant operations to provide reactive power management. Finally, managed charging can benefit EVAs themselves by minimizing power factor penalties in their electricity bills. In this paper, we propose a novel EV charging scheduling algorithm based on a hierarchical distributed optimization framework that minimizes peak load and provides reactive power compensation for the DSO by collaboration with EVAs that manage both the active and the reactive charging and discharging power of participating EVs. Utilizing the alternative direction method of multipliers (ADMM), the proposed distributed optimization approach scales well with increased EV charging infrastructure by balancing active and reactive power while decreasing computational burden. In our proposed hierarchical approach, each EVA schedules the active and reactive EV charging and discharging power for 1) reactive power compensation in order to minimize power factor penalty and electricity cost accrued by the EVA, 2) satisfaction of each EV’s energy demand at minimal charging cost, and 3) peak shaving and load management for the DSO. When compared with an uncoordinated charging model, the efficacy of this proposed model is successfully demonstrated through a 300% decreased peak EV load for the DSO, 28% lower electricity costs for EV users, and 98.55% smaller power factor penalty, along with 17.58% lower overall electricity costs, for EVAs. The performance of our approach is validated in a case study with 50 EVs at multiple EVAs in an IEEE 13-bus test case and compared the results with uncoordinated EV charging.
This work develops an exposure-based optimal power flow model (OPF) that accounts for fine particulate matter (PM2.5) exposure from electricity generation unit (EGU) emissions. Advancing health-based dispatch models to an OPF with transmission constraints and reactive power flow is an essential development given its utility for short- and long-term planning by system operators. The model enables the assessment of the exposure mitigation potential and the feasibility of intervention strategies while still prioritizing system costs and network stability. A representation of the Illinois power grid is developed to demonstrate how the model can inform decision making. Three scenarios minimizing dispatch costs and/or exposure damages are simulated. Other interventions assessed include adopting best-available EGU emission control technologies, having higher renewable generation, and relocating high-polluting EGUs. Neglecting transmission constraints fails to account for 4% of exposure damages ($60 M/y) and dispatch costs ($240 M/y). Accounting for exposure in the OPF reduces damages by 70%, a reduction on the order of that achieved by high renewable integration. About 80% of all exposure is attributed to EGUs fulfilling only 25% of electricity demand. Siting these EGUs in low-exposure zones avoids 43% of all exposure. Operation and cost advantages inherent to each strategy beyond exposure reduction suggest their collective adoption for maximum benefits.
The daily production and life of human beings are inseparable from electricity. As the power supplier, electric power enterprises provide power demand for the majority of users. In the new era, electric power enterprises are also facing market-oriented reform. The focus of reform is electric power marketing. The formulation of electric power marketing strategy needs scientific decision-making analysis as guidance. With the increase of power demand, the scale of power grid is also expanding continuously. With the introduction of new energy equipment, the power system is becoming more and more complex, and it is difficult for relevant staff to effectively monitor and analyze the system. Combined with the above situation, this paper combined Bayesian network to build a power marketing decision analysis system, and combined Bayesian algorithm to test the power marketing real-time cost control system. The experimental results showed that the average judgment accuracy was 91.90 %, and the average warning time was 0.39 s. From the above data, it can be seen that this algorithm can play a good optimization effect on the performance of the system. In this paper, the elasticity test of the power system was also carried out from the aspect of wind speed, and the results showed that the maximum elasticity value can reach 0.94. It can be seen that the elasticity effect of the power system is good as a whole under different wind speeds.
The power system network is a very complex and exorbitant entity. Moreover, the condition becomes more complex once a distributed generation, renewable energy sources, energy storage devices are added to the grid/microgrid. For adding any source in the grid, proper analysis for load flow, short circuit studies, transient studies, etc., is required to understand the performance of different components. It is dangerous to directly load the network to check the performance of the system, hence, an alternative mechanism is adopted wherein the real-time scenario is modeled virtually using some simulation tools, and thereafter, different scenarios can be analyzed for different cases. Simulation using simulation tools is a well-known technique to assess the performance of the system in a virtual environment. Simulation tools artificially create models, and it helps in analyzing the performance of the system over multiple scenarios. Hence, an effort has been made in this paper in compiling the non-exhaustive list of simulation software package to tackle microgrid capabilities, wherein microgrid is comprised of distributed generation and renewable energy sources. Also, a detailed review has been done to discuss the features and shortcomings of different simulation software packages. After a detailed review of simulation software packages, it has been found that OpenDSS works well for distribution systems or microgrids and works efficiently not only for balanced systems but also for unbalanced systems. Therefore, a tutorial has been presented to model microgrids with the help of OpenDSS. Apart from this, an example using IEEE 13 node feeder is discussed with distributed generation and renewable energy sources to showcase the performance of OpenDSS.
In smart grids, two-way communication between end-users and the grid allows frequent data exchange, which on one hand enhances users’ experience, while on the other hand increase security and privacy risks. In this paper, we propose an efficient system to address security and privacy problems, in contrast to the data aggregation schemes with high cryptographic overheads. In the proposed system, users are grouped into local communities and trust-based blockchains are formed in each community to manage smart grid transactions, such as reporting aggregated meter reading, in a light-weight fashion. We show that the proposed system can meet the key security objectives with a detailed analysis. Also, experiments demonstrated that the proposed system is efficient and can provide satisfactory user experience, and the trust value design can easily distinguish benign users and bad actors.
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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