Detailed Power System Model Research Articles

PyPSA-Earth. A new global open energy system optimization model demonstrated in Africa

Macro-energy system modelling is used by decision-makers to steer the global energy transition towards an affordable, sustainable and reliable future. Closed-source models are the current standard for most policy and industry decisions. However, open models have proven to be competitive alternatives that promote science, robust technical analysis, collaboration and transparent policy decision-making. Yet, two issues slow the adoption: open models are often designed with particular geographic scope in mind, thus hindering synergies from collaborating, or are based on low spatially resolved data, limiting their use. Here we introduce PyPSA-Earth, an open-source global energy system model with data in high spatial and temporal resolution. It enables large-scale collaboration by providing a tool that can model the world’s energy system or any subset of it. The model is suitable for operational as well as combined generation, storage and transmission expansion studies. In this study, the novel power system capabilities of PyPSA-Earth are highlighted and demonstrated. The model provides two main features: (1) customizable data extraction and preparation with global coverage and (2) a PyPSA energy modelling framework integration. The data includes electricity demand, generation and medium to high-voltage networks from open sources, yet additional data can be further integrated. A broad range of clustering and grid meshing strategies help adapt the model to computational and practical needs. Data validation for the entire African continent is performed and the optimization features are tested with a 2060 net-zero planning study for Nigeria. The demonstration shows that the presented developments can build a highly detailed power system model for energy planning studies to support policy and technical decision-making. We anticipate that PyPSA-Earth can represent an open reference model for system planning, and we welcome joining forces to address the challenges of the energy transition together.

The Social Efficiency of Electricity Transition Policies Based on Renewables. Which Ways of Improvement?

Climate and energy policies use to be embedded in joint packages with seeming coherent goals on which the electricity sector is targeted. However, the complexity of power systems is rarely fully apprehended while setting up such packages, particularly when technical externalities from variable renewable energies (VRE) become widespread and different sources of flexibility need to be considered. We use a detailed power system model subject to a combination of RE goals and CO2 caps to seize their interplays and propose a methodology to rank the resulting equilibriums in terms of environmental effectiveness and economic efficiency. We show that: only modest levels of VRE develop without subsidies regardless the level of the CO2 cap; technical externalities create trade-offs between VRE penetration and environmental effectiveness; new flexibility technologies may correct or exacerbate these externalities, impacting effectiveness, costs, and coherence of such packages, requiring a sensitive target hierarchization and fine-tuning of such instruments.

Generation scheduling in power systems with high penetration of renewable energy

Share of renewable energy sources increased rapidly over last two decades primary as wind and solar power plants. Their increase was driven by governmental subsidies and priority access and dispatch regarding conventional units. Wind and solar power plants are inflexible sources because their generation depends on exterior, weather conditions and they cannot be controlled as conventional units. This paper will define term power system flexibility and provide an insight into flexibility of conventional and modern power systems. Detailed mathematical model of power system and all its components has been created and explained. Modeling has been executed as mixed integer linear program using Fico Xpress optimization suite. Using those models, flexibility analyses of power systems with different renewable energy sources share has been conducted

Harmonic Performance Analysis of Static Var Compensator Connected to the Power Transmission Network

The static var compensator (SVC) is a device which is designed to compensate reactive power, increase voltage stability and to reduce voltage fluctuations. Thyristor controlled reactors (TCRs) are composed of reactors in series with bidirectional pair of thyristors. Current through reactors can be continuously controlled by changing the firing angle of thyristor valves, thus
 the inductive power can be easily controlled. Typical applications of TCRs in AC systems are voltage stabilization and temporary overvoltage reduction, stability improvement, damping of power oscillations and load balancing. In this paper, harmonic performance analysis of SVC equipped with TCRs is presented. SVCs utilizing TCRs generate harmonic currents and therefore it is necessary to determine the effect of harmonics generated by the SVC on the power system and its elements. This includes interaction of the SVC with the system, the SVC performance under balanced and unbalanced operating conditions and finally, evaluation of countermeasures such as installation of harmonic filters. In order to carry out these analysis, it is necessary to determine harmonic characteristics of the network at the point of SVC connection, existing levels of harmonics, and to know appropriate standards regarding acceptable harmonic levels in the power system. Since harmonic distortions in the system are caused by the interaction between SVC and the system, all system contingencies which may affect system’s frequency response should be evaluated. Detailed power system model should be considered to make sure that parallel resonance points of system do not directly coincide with characteristic harmonics from the SVC. Harmonics generated by SVCs are largely dependent on the operating point within the SVC characteristic. A conservative approach is to use the maximum values of harmonics generated within the spectrum irrespective of the operating point. The results of harmonic performance analysis are important for appropriate design of SVC. Harmonic performance analysis related to SVC application which are presented in this paper include the determination of: frequency response of the transmission network impedance required for the specification and design of filters; the effects of SVC generated harmonics on the power system; the overall filter requirements and countermeasures to reduce harmonics to acceptable levels.

A review of publicly available data sources for models to study renewables integration in China's power system

The ongoing transformation of the world's energy system requires detailed power-system models that help plan a cost-effective and reliable integration of variable renewables and demand-side resources. The quality and depth of the results of these models depend on the existence of trustworthy, complete, and high-resolution data on extant electric power assets and the demand they serve, wind and solar resources, and projections on costs and performance of technologies that could be developed during the next three decades. This paper assesses the quality of China's power system's publicly available data compared to the U.S. It concludes that despite growing use of power system models to inform and analyze Chinese energy policy, the availability of necessary data is still a significant barrier that severely limits the transparency, replicability, relevance, and usefulness of their results.

Assessing global climate change mitigation scenarios from a power system perspective using a novel multi-model framework

There is a debate regarding the suitability of global integrated assessment models (IAMs) for long-term planning exercises of the global energy system. This study informs this debate from a power system perspective and proposes a methodological framework for soft-linking of global IAMs with detailed global power system models. With the proposed open-source framework, the scenario results from IAMs can be fed into a power system model to assess given scenarios with enhanced modelling resolution. Results from these simulations can be redirected to the IAM through iterative bi-directional soft-linking. A proof of concept application is presented by linking global IAM MESSAGEix-GLOBIOM with global power system model PLEXOS-World. Among others, the results suggest that the assumption of unconstrained electricity flows inside large regional copperplates without internal network constraints causes an overestimation of the potential of variable renewables within MESSAGEix-GLOBIOM. We propose areas for informed improvements in MESSAGEix-GLOBIOM and for IAMs in general.

Development and Application of Convolutional Neural Network for the Recognition of Objects in the Scheme of Electric Grid

One of the current design problems in the electric power industry is the labor intensity of synthesizing mathematical models to calculate the electrical modes of the network. It takes a lot of time to compose a detailed model of a large power system based on circuit diagram data. To simplify, speed up, and automate the data input, a convolutional neural network is proposed. In this paper the definition of convolutional neural network is given, its elements are described, the architecture of neural network is developed, the accuracy of its work on the schematic diagrams of various power systems is 0.84.

Hierarchical Coordinated Fast Frequency Control Using Inverter-Based Resources

The share of inverter-connected renewable energy resources (RESs) is increasing in the grid, with these resources partially displacing conventional synchronous generators. This has resulted in increased variability of active power supply, reduced overall inertia, and increased spatial heterogeneity of inertia, leading to faster system frequency dynamics along with larger and more frequent frequency control events. These effects are expected to become increasingly more important in power system control in next-generation grids, which may conceivably be made up entirely of RESs. To mitigate these challenges, a fast, area-based hierarchical control strategy is proposed. This scheme partitions the power system into small areas, estimates local power imbalances, and corrects them by utilizing local inverter-based resources (IBRs). In cases where sufficient resources are not available locally, power is preferentially sourced from electrically close neighbours using an iterative distributed optimization scheme which preserves information privacy between areas. The proposed frequency control architecture can be retrofit onto existing control systems, and allows for flexibility in the amount of model information available to the designer. The control strategy is validated on two detailed multi-area power system models. Simulation results show that the strategy provides fast and localized frequency control.

Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management

The heterogeneous and interoperable nature of the cyber-physical system (CPS) has enabled the smart grid (SG) to operate near the stability limits with an inconsiderable accuracy margin. This has imposed the need for more intelligent, predictive, fast, and accurate algorithms that are able to operate the grid autonomously to avoid cascading failures and/or blackouts. In this paper, a new comprehensive identification system is proposed that employs various machine learning architectures for classifying stability records in smart grid networks. Specifically, seven machine learning architectures are investigated, including optimizable support vector machine (SVM), decision trees classifier (DTC), logistic regression classifier (LRC), naïve Bayes classifier (NBC), linear discriminant classifier (LDC), k-nearest neighbor (kNN), and ensemble boosted classifier (EBC). The developed models are evaluated and contrasted in terms of various performance evaluation metrics such as accuracy, precision, recall, harmonic mean, prediction overhead, and others. Moreover, the system performance was evaluated on a recent and significant dataset for smart grid network stability (SGN_Stab2018), scoring a high identification accuracy (99.90%) with low identification overhead (4.17 μSec) for the optimizable SVM architecture. We also provide an in-depth description of our implementation in conjunction with an extensive experimental evaluation as well as a comparison with state-of-the-art models. The comparison outcomes obtained indicate that the optimized model provides a compact and efficient model that can successfully and accurately predict the voltage stability margin (VSM) considering different operating conditions, employing the fewest possible input features. Eventually, the results revealed the competency and superiority of the proposed optimized model over the other available models. The technique also speeds up the training process by reducing the number of simulations on a detailed power system model around operating points where correct predictions are made.

Novel approach for relays tuning using detailed mathematical model of electric power system

The correct operation of relay protection (RP) devices largely determines the security of electric power systems (EPS). The key point that in turn determines the behavior of protection in various emergency modes is a determination of their settings. The existing methodologies and tools often do not allow to provide RP settings adequate to the practical conditions of their functioning. This statement is confirmed by the statistics of faults in EPSs. The main reason for this issue is the inability to reliably reproduce transients in EPS via the software programs used in practice to calculate the RP settings. The Hybrid Real-Time Power System Simulator (HRTSim), developed by the authors, allows to reliably reproduce the entire range of normal and emergency modes and processes in EPS of any scale, topology and configuration by using detailed three-phase models of all power system components. Taking into account this possibility and features of the HRTSim, the task of detailed modeling of RP devices, including instrument current and voltage transformers, becomes relevant. The implemented special tools for RP modeling and the HRTSim in general allow to develop a new methodology for RP tuning. At the same time, the typical elements – relays – can be identified for each type of RP that determine their operation algorithm. This paper presents an analysis of the main relays of the most modern microprocessor-based RP devices and proposed principles of their tuning. The developed approach allows to adapt the RP settings to the practical conditions of their functioning in EPSs and to minimize the probability of RP maloperation.

Measurement-Based Fast Coordinated Voltage Control for Transmission Grids

The increasing integration of utility-scale renewable energy sources (RESs) brings emerging challenges to the classical problems of voltage control in transmission grids, including increased potential for voltage violations. To address this challenge, we present a novel measurement-based coordinated voltage control scheme that can enable fast participation of RESs. In this approach, RESs are coordinated with the traditional voltage control devices such as synchronous generators (SGs) and static var compensators (SVCs) to maintain all bus voltages within operational limits while respecting device power limits. The control scheme allows different priorities to be assigned to different control resources, and ensures that both voltage and reactive power constraints are met in steady-state whenever it is possible to do so. The controller design requires only an approximate model of the steady-state relationships between voltage and reactive power in the system, and in online operation, processes voltage and reactive power measurements to produce set-point updates for RESs, SVCs, and SGs; this feedback provides robustness against both model uncertainty and unmeasured disturbances. The feasibility and effectiveness of the controller is demonstrated via simulation case studies on a detailed power system model.

System Redundancy Enhancement of Secondary Frequency Control Under Latency Attacks

Latency Attacks become emerging new types of cyber attacks with widely applied wireless wide-area network and Internet of Things (IoTs) technologies. The power system can suffer from several even tens of seconds of malicious time delay instead of normal latency of mere several milliseconds. Consequently, this exorbitant time delay or latency attack will significantly influence the system dynamics and stability. In this paper, from the perspective of parameter tuning, we study how to enhance the system redundancy of a secondary frequency control system with a high-order complex power system model under latency attacks. The tuning aims to achieve the maximal stability region under latency attacks and is performed with the aid of exhaustive and heuristic search methods. By testing the tuning method using a more practical and detailed power system model, it shows that the stability of the tuned system is greatly enhanced under latency attacks.

Power Flow Approach for Modeling Shipboard Power System in Presence of Energy Storage and Energy Management Systems

Recent improvements in shipboard systems focus on the use of new technological systems and the adoption of innovative operating strategies for both increasing energy efficiency and reduce gas emissions. In such a context, this article studies a ship power system, where an energy storage system and a tailored energy management system are applied. It focuses on a detailed model of the on-board power system and points out a scenario of analysis larger than those usually adopted in the literature. The model is carried out with reference to DC, AC and hybrid AC/DC power grid architectures, by means of a power flow approach. The strategy of the energy management system optimally controls the on-board power system integrated with a battery storage device. The strategy aims to minimizes fuel consumption of the prime movers, while satisfying time-varying load demand under different operating scenarios. The procedure, tested on actual operative conditions of a supply vessel, shows feasibility and effectiveness of the power flow approach, giving evidence to the reduction of fuel consumption, while the correct operation of the power system is guaranteed.

Impact of wind capacity share, allocation of inertia and grid configuration on transient RoCoF: The case of the Croatian power system

Increased wind energy penetration influences the power system dynamic response to transient disturbances. Replacement of conventional production units with converter-connected wind turbines reduces natural power system inertia contained in rotational masses of synchronously connected turbine-generator units, therefore creating low-inertia power systems. Such a transition has an adverse effect on system resilience to disturbances and on the capability to maintain stable operation. This research examines the impact of high regional wind power production on system transient stability in the case of island operation of the Croatian power system. The system is divided into four geographical areas modeled as four centers of inertia with aggregated parameters. The study investigated initial transient RoCoF values in different areas for current and future wind capacity share scenarios, loading data, and primary frequency regulation settings. The modeling and scenario analysis have been performed on a detailed phasor power system model in the MATLAB/Simulink environment.

Eco‐driving control for hybrid electric trams on a signalised route

This study proposes an integrated eco-driving controller, in which speed profile and power split optimisation are simultaneously treated. A speed profile controller is designed based on a multi-phase dynamic programming (DP) algorithm, which is utilised to optimise speed trajectories with the detailed model of the traction power system, vehicle dynamics system, and signal constraints at intersections. The optimised speed and demand power profiles are designed as the reference of the power split optimal controller. A DP algorithm is presented to determine voltage trajectories of supercapacitors with power plant voltage limitations and dynamic power losses. The performance of the proposed integrated method is evaluated by numerical simulation based on an actual route of Chengdu Tramway Line 2. The energy reduction can be achieved by the speed profiles controller, compared to the eco-cruising benchmark strategies. Energy efficiency can be further improved by the integrated eco-driving controller. Moreover, the influences of green signal time windows and trip time on the shape of the optimised solution are investigated. For a given journey, the trip time with minimum energy consumption can be obtained. The integrated eco-driving control method can be implemented by driver assistant systems or automatic train operation systems.

Zero-Sum Game Based Cooperative Control for Onboard Pulsed Power Load Accommodation

In this paper, the noncooperative control problem of onboard pulsed power load is formulated as a two-player zero-sum game. One player is the optimal controller which is designed to optimize a predefined cost function. The other player is the disturbance that represents the overall damping effect of the system, including that of unmodeled system dynamics. Neuro-dynamic programming based control design is developed to solve the nonlinear optimal control problem under disturbance. The neural network based control algorithm can achieve the near-optimal control without the acknowledgment of system dynamics. In addition, the control design can relax the requirements for initial admissible control conditions and predetermined control references. The effectiveness of the proposed algorithm is demonstrated through the real-time simulations on both simplified and detailed shipboard power system models.

Analysis of lithium-combustion power systems for extreme environment spacecraft

The longest duration mission on the Venus surface was Venera 13 at just over 2 h. This time constraint was due to limited battery power life and craft thermal management challenges. A lithium combustion based power system has been proposed to increase landed mission durations for Venus and other extreme environment targets. This paper presents a new detailed thermodynamic and heat transfer model of a conceptual lithium combustion power system. Findings are applied to specify engineering requirements for potential missions. Results indicate that a lithium combustion power system using the in-situ carbon dioxide atmosphere as an oxidizer could power a Venus lander for up five days (24 h, Earth day) with 185 kg of fuel, delivering 14 kWth thermal energy continuously. Even greater durations are possible if lower power missions are considered. The potential performances of a Li-CO2 powered Stirling engine and sulfur-sodium batteries were compared. It was found that sulfur-sodium batteries would require about 1.75–2.5 times more mass to provide 1 kW of power output for mission durations of five to ten days, respectively. A lithium combustion power system with a sulfur-hexafluoride oxidizer could power a Europa lander at 94W with a Stirling engine for up to twenty days with 43 kg of reactants mass. Lithium-combustion activated Stirling engines and TEG arrays were compared with batteries to meet this power and mission duration requirement. It was found that batteries would require less mass than either lithium-fueled system. However, for mission durations longer than twenty-six days the Stirling engine power system may require less total mass than batteries. Future work will include laboratory-based experimental studies to validate results and improve heat transfer closure models.

Voltage Stability Prediction Using Active Machine Learning

An active machine learning technique for monitoring the voltage stability in transmission systems is presented. It has been shown that machine learning algorithms may be used to supplement the traditional simulation approach, but they suffer from the difficulties of online machine learning model update and offline training data preparation. We propose an active learning solution to enhance existing machine learning applications by actively interacting with the online prediction and offline training process. The technique identifies operating points where machine learning predictions based on power system measurements contradict with actual system conditions. By creating the training set around the identified operating points, it is possible to improve the capability of machine learning tools to predict future power system states. The technique also accelerates the offline training process by reducing the amount of simulations on a detailed power system model around operating points where correct predictions are made. Experiments show a significant advantage in relation to the training time, prediction time, and number of measurements that need to be queried to achieve high prediction accuracy.

Guest Editorial Distributed Control and Efficient Optimization Methods for Smart Grid

Smart grid proactively uses the state-of-the-art technologies in communications, computing, and control to improve efficiency, reliability, sustainability, and stability of the electrical grid. In particular, distribution networks are expected to undergo dramatic changes by incorporating a large number of sensors and thousands of controllable devices such as distributed generators, batteries and flexible loads. To be able to efficiently operate such complex large-scale systems, new sets of control and optimization tools should be developed. On a slow time scale, optimization theory plays a major role in solving various large-scale decision-making problems for future power transmission and distribution systems. One major challenge is the design of computational methods for handling fairly detailed power system models that often include continuous and discrete nonlinearities. It is also important to develop distributed computation techniques to shift the computation from a centralized platform to many computing devices.

Assessing the potential role of concentrated solar power (CSP) for the northeast power system of Brazil using a detailed power system model

One of the technologies that stand out as an alternative to provide additional flexibility to power systems with large penetration of variable renewable energy (VRE), especially for regions with high direct normal irradiation (DNI), is concentrated solar power (CSP) plants coupled to thermal energy storage (TES) and back-up (BUS) systems. Brazil can develop this technology domestically, especially in its Northeast region, where most of VRE capacity is being deployed and where lies most of the CSP potential of the country. This work applies the Capacity Expansion Model REMix-CEM, which allows considering dispatch constraints of thermal power plants in long-term capacity expansion optimization. REMix-CEM calculates the optimal CSP plant configuration and its dispatch strategy from a central planning perspective. Results showed that the hybridization of CSP plants with jurema-preta biomass (CSP-BIO) becomes a least-cost option for Brazil by 2040. CSP-BIO contributes to the Northeast power system by regularizing the energy imbalance that results from the large-scale VRE expansion along with conventional inflexible power plants. CSP-BIO plants are able to increase frequency response and operational reserve services and can provide the required additional flexibility that the Northeast power system of Brazil will require into the future.