The Nordic Power System is undergoing significant transformation and development in the coming years. A driving factor for this is the increased dependency on renewable energy sources and high voltage direct current transmission (HVDC). Various stability challenges due to the dynamics of reactive power - voltage (Q-V) balancing and active power - frequency (P-f) balancing seep into the system with such rise of non-synchronous generation (NSG). This paper investigates the projected impact of NSGs on the P-f dynamic performance of Nordic power system in 2030. As NSGs are systems with low inertia, power discrepancies lead to larger oscillations, higher Rate of Change of Frequency (RoCoF) and bigger maximum frequency deviation (MFD) values. To address these issues, Emulated Inertia (EI) control for wind turbines and Synthetic Inertia (SI) control for VSC-HVDC links are proposed in this paper. The simulation is carried out using DIgSILENT PowerFactory 2022 SP1 simulation software. The results obtained confirm the Effectiveness of the proposed EI and SI methods to enhance frequency response and mitigate deviations.

Low-inertia power systems require more innovative operation, control, and protection strategies to maintain the operation secure and reliable. One of the challenges related to these systems is the need for knowledge of the inertia level (kinetic energy stored in the rotating masses) in real time. This paper proposes a framework for training and deploying machine learning methods for real-time power systems’ kinetic energy forecasting. Linear Regression and Long Short-Term Memory methods are implemented in the Python interpreter of the Typhoon HIL 404 real-time simulator for forecasting the kinetic energy of the Nordic Power System in real-time. This paper provides implementation details together with possible future expansions of the framework. Simulation results show that the trained models can predict the kinetic energy in a forecasting horizon of four hours with a Mean Absolute Error lower than other methods currently available in the literature.

Distribution grids are subject to a drastic evolution in their operating conditions due to the high integration of renewable energy resources (RES) and their ability to regulate voltage. To cope with this issue, modern solar photovoltaic (PV) systems are equipped with smart inverters enabled with communication capabilities that allow the coordinated operation to offer services such as controlling voltage by appropriately setting the reactive power production. This paper proposes a co-simulation framework for smart converter reactive power control in active distribution grids. The proposed framework is used to appropriately control smart inverters installed in PV systems to inject/withdrew reactive power ensuring voltage control at the time that minimises active power losses of the active distribution grid (ADG). The proposed approach has been tested in a modified version of the Kumamoto distribution system. The suitability of the proposed framework has been demonstrated.

Future distribution networks (DN) are subject to rapid load changes and high penetration of variable distributed energy resources (DER). Due to this, the DN operators face several operational challenges, especially voltage violations. Optimal power flow (OPF)-based reactive power control (RPC) from the smart converter (SC) is one of the viable solutions to address such violations. However, sufficient communication and monitoring infrastructures are not available for OPF-based RPC. With the development of the latest information communication technology in SC, cyber-physical co-simulation (CPCS) has been extensively used for real-time monitoring and control. Moreover, deploying OPF-based RPC using CPCS considering the controller design of SC for a realistic DN is still a big challenge. Hence, this paper aims to mitigate voltage violations by using OPF-based RPC in a real-time CPCS framework with multiple SCs in a realistic DN. The OPF-based RPC is achieved by performing the CPCS framework developed in this study. The CIGRE medium-voltage DN is considered as a test system. Real-time optimization and signal processing are achieved by Python-based programs using a model-based toolchain of a real-time DN solver and simulator. Real-time simulation studies showed that the proposed method is capable of handling uncertain voltage violations in real time.

With the rise of the integration of renewable energy sources, the operating characteristics of existing electric power distribution systems are evolving and changing. As a result, the digitalisation of the distribution network is gaining attention for effective real-time monitoring and control. Cyber-Physical cosimulation is one of the options for implementing and testing novel concepts and ideas before actual implementation on the distribution network. Therefore, this paper presents some experiences on the cyber-physical testbed in the distribution network. Moreover, the methodology, possible challenges and mitigation techniques are also presented for a cyber-physical cosimulation testbed of optimal reactive power control in smarter distribution network (SDN). The cyber-physical co-simulation testbed is analysed using a Typhoon HIL 604 and OpenDSS on a CIGRE MV distribution.

The increasing integration of distributed energy resources such as photovoltaic (PV) systems into distribution networks introduces intermittent and variable power, leading to high voltage fluctuations. High PV integration can also result in increased terminal voltage of the network during periods of high PV generation and low load consumption. These problems can be solved by optimal utilization of the reactive power capability of a smart inverter. However, solving the optimization problem using a detailed mathematical model of the distribution network may be time-consuming. Due to this, the optimization process may not be fast enough to incorporate this rapid fluctuation when implemented in real-time optimization. To address these issues, this paper proposes a co-simulation-based optimization approach for optimal reactive power control in smart inverters. By utilizing co-simulation, the need for detailed mathematical modeling of the power flow equation of the distribution network in the optimization model is eliminated, thereby enabling faster optimization. This paper compares three optimization algorithms (improved harmony search, simplicial homology global optimization, and differential evolution) using models developed in OpenDSS and DigSilent PowerFactory. The results demonstrate the suitability of the proposed co-simulation-based optimization for obtaining optimal setpoints for reactive power control, minimizing total power loss in distribution networks with high PV integration. This research paper contributes to efficient and practical solutions for modeling optimal control problems in future distribution networks.

The amount of power electronic interfaced generation (PEIG) is significantly proliferating in modern cyber-physical energy systems (CPESs). The limited capabilities (e.g. inertia, over-current) of PEIG, together with their location and technology-specific designed control systems, alter the dynamic properties of different types of stability phenomena, e.g. sub-synchronous oscillations (SSOs). A poorly damped SSO can emerge, within a sub-second time scale, through conflicting inter-actions between the controls of PEIG and the dynamic response of the surrounding electrical network. This paper focuses on the modelling and assessment of such interactions, with emphasis on the integration of large-size full converter (a.k.a. type-4) based wind power plants (WPPs). By combining different analysis tools, the implemented model supports sensitivity assessment of the occurrence and observability of a poorly damped SSO. State-space model based eigenanalysis is iteratively used to ascertain damping variability of a dominant SSO, excited by inappropriate controller settings of the WPP. Power spectral density (PSD) analysis is used to qualitatively estimate the degree of observability of the poorly damped SSO across different buses of a CEPS. Numerical tests are performed on a modified version of the IEEE-39 bus system by using DIgSILENT PowerFactory 2022 SP1. Suggestions are provided for the deployment of data generated from phasor measurement units (PMUs) in the monitoring and wide-area damping control of critical SSOs.

Reaching net-zero emissions within the proposed time requires an enormous effort from the energy sector, and it is even more challenging for the electricity infrastructure. This article offers a modified version of the IEEE 39-bus system specifically created to allow zonal day-ahead market (ZDAM) simulations. The system representation is based on the original version of the IEEE 39-bus system but considers the integration of renewable energy resources (RES) in the generation mix: solar and wind. Hourly time series are used to define load profiles and wind and solar power generation. The zonal dayahead energy market information has been created by solving the optimisation problem. Numerical results of the proposed power test system are provided for the yearly ZDAM and steady-state performance, in N and N-l conditions, respectively, through Pyomo and DIgSILENT PowerFactory features.

Future carbon-neutral power systems impose many challenges; one is the urgent need for a simulation platform that allows replicating the complex systems’ actual dynamic performance. This paper shows the results of implementing a cyber-physical testbed co-simulation in real-time to analyse the system frequency response considering primary frequency control and emergency frequency control: under-frequency load-shedding (UFLS) protection schemes. The proposed testbed uses a physical layer of two real-time simulators from different vendors in a closed loop, Opal-RT OP4510 and Typhoon HIL 604, being the first simulator for test system modelling and the remainder used to implement the UFLS protection scheme. Two connections of the real-time simulators are considered: physical connection using wires to exchange analogue signals and cybernetic digital communication using ANSI C37.118 communication protocol. The cybernetic layer of the testbed models a test system, controls the real-time simulation, and implements digital communication between the simulators. A modified version of the P.M. Anderson 9-bus systems is used for testing purposes, including phasor measurement units (PMUs). Results of the real-time simulation show the appropriate performance of the proposed testbed.

Modernization of power systems leads to more power electronic interfaced units in the generation, demand, and transmission. Examples are remotely installed renewable energy sources, loads with constant power, or high voltage direct current (HVDC) corridors. These changes significantly affect the frequency stability margins of the system and thus special control techniques should be applied in the converters of the new installed units so as to shoulder the frequency regulation in case of commonly occurred active power imbalances. The response of such units has to be cooperative in order to avoid problems such as insufficient reactions or overshoots. In this chapter, a coordinative tuning approach of the active power gradient control scheme applied to the controllers of modular multilevel converter (MMC)-based HVDC links and proton exchange membrane electrolyzers with the provision of fast frequency support in a multiarea hybrid HVDC-HVAC power system with responsive demand units is proposed. This tuning uses an optimization approach based on mean variance mapping optimization and is able to minimize the frequency excursions in all interconnected areas participating in the frequency regulation even without communication between the system nodes. This technique has shown great results in terms of quality and convergence rate within a short number of fitness evaluations achieving a set of frequency responses within acceptable limits set by operators even in case of the loss of the largest generating unit in the weakest system area. It has also revealed the applicability of such a method in more complex systems and the necessity for sophisticated tuning methods according to the application needs and the system characteristics.

Distantly-located offshore energy hubs need to be connected to the shore via High Voltage Direct Current (HVDC) links to allow for an efficient bulk power exchange. A bipolar configuration of the HVDC link is suitable for a point-to-point connection, as it provides redundancy, and, therefore, a larger reliability, e.g. half of the rated transfer capacity can still be transferred via one of the poles in case of a fault occurring on the other pole. Nevertheless, a control strategy of the converters that can effectively enable a situation-dependent power routing between the two poles constitutes a research challenge. In this paper, two control strategies are proposed for the offshore Modular Multi-level Converters (MMCs) of a bipolar HVDC link connecting a 2 GW offshore hub to the shore. The strategies, based on DC current and DC voltage measurements, respectively, enable to track and adjust the amount of power flowing through each pole of the link. Real-time digital simulations show that both strategies can effectively route the power exchanges through the bipolar HVDC link, e.g. operation under balanced or unbalanced conditions. The strategy based on DC current seems more suitable to manage the dynamic performance of the HVDC link.

This paper is a step forward in the effort to create an appropriate open-access library of benchmark test systems for offline and real-time simulations. The library will consist of 13 test systems for steady-state and dynamic power system analysis considering different relevant features (topology, control mode and element details). In this scientific paper, the authors selected one of the test systems, the 5-bus test transmission system, in meshed topology. Details of its implementation using several power system analysis platforms for offline and online/real-time simulation. Offline steady-state performance results are presented using PowerWorld, DIgSILENT PowerFactory and IPSA. Plots of the main electromechanical variables of the time-domain response considering a generator outage are presented using offline simulations from PowerFactory and real-time simulation ePHASORSIM. Simulation results show very minimal discrepancies between the results considering different platforms.

This paper concerns with the determination of a suitable level of overplanting for photovoltaic systems. For this purpose, six futuristic operational scenarios for the Dutch electrical power system are generated for year 2050. A synthetic model is developed by using DIgSILENT Power Factory 2022 SP3 to investigate the steady-state systemic performance in each operational scenario, taking into account three cases with different levels of overplanting. Power flow calculations are conducted to reflect on the resulting voltage profiles and active power losses as well as on the implications on the required network upgrades (e.g. addition of lines, transformers, and reactive power compensation devices).

Future carbon-neutral power systems impose many challenges; one of them is an advanced simulation platform that allows replicating the complex systems' actual dynamic performance. DIgEnSys-Lab is working toward proposing solutions to the most critical challenges to carbon-neutral energy systems. This paper shows preliminary results of implementing a cyber-physical testbed co-simulation real-time dedicated to analysing system frequency response. The testbed is a cyber-physical system where the physical layer is represented by two real-time simulators in a closed loop: Typhoon HIL 604 and Opal-RT OP4510. The cybernetic layer is used to model a test system and to control the real-time simulation. This paper shows the results of the system frequency response of a modified version of the P.M Anderson 9-bus system using phasor measurement units. Simulation results show the appropriate performance of the proposed testbed.

Electrical power systems are continuously upgrading into networks with a higher degree of automation capable of identifying and reacting to different events that may trigger undesirable situations. In power systems with decreasing inertia and damping levels, poorly damped oscillations with sustained or growing amplitudes following a disturbance may eventually lead to instability and provoke a major event such as a blackout. Additionally, with the increasing and considerable share of renewable power generation, unprecedented operational challenges shall be considered when proposing protection schemes against unstable electro-mechanical (e.g. ringdown) oscillations. In an emergency situation, islanding operations enable splitting a power network into separate smaller networks to prevent a total blackout. Due to such changes, identifying the underlying types of oscillatory coherency and the islanding protocols are necessary for a continuously updating process to be incorporated into the existing power system monitoring and control tasks. This paper examines the existing evaluation methods and the islanding protocols as well as proposes an updated operational guideline based on the latest data-analytic technologies.

An N-1 contingency can negatively affect the reliability and security of electrical power systems. A single transmission line outage can cause overload on the local healthy transmission systems, and actions are required to alleviate the overload. Traditionally the system operator uses two actions depending on the operating local area power balance: load shedding or power plant curtailment; both have consequences. This paper proposes the use of a Battery Energy Storage System (BESS) enabled with an intelligent overload avoiding control. The control is illustrated in a test system, and numerical simulations has demonstrated the suitability of the proposed approach.

The increasing penetration of renewable energy resources (RES) in transmission system operating conditions require a suitable test system and a dataset to cope with current issues. RES penetration remarkably affects day-ahead market outcomes regarding zonal prices and dispatched generation levels. For this purpose, zonal day-ahead energy market models in the presence of RES in the generation mix need to be implemented. In this paper, the IEEE 39-bus system has been suitably modified to include solar and wind generation in the traditional generation mix. Hourly time series are used to define load profiles and wind and solar power generation. The zonal day-ahead market (ZDAM) resolution is simulated by solving a Linear Programming optimization problem employing Pyomo. Furthermore, steady-state nodal analysis is carried out using DIgSILENT PowerFactory, performed over a year horizon.

The operation schedule of the power generation units in electrical power systems is determined by the optimisation problem known as unit commitment (UC), aiming at minimising the total cost considering the generation constraints. To obtain a feasible solution from the network perspective, the security-constrained UC (SCUC) problem has been defined to embed the network constraints in the optimisation problem as well. Also, the higher penetration of renewable energy sources (RES) has increased the difficulty of UC problem, mainly due to the uncertainty and the high variability of RES. This paper proposed a SCUC with economic dispatch (SCUCED) optimisation developed in two stages. The first one is the solution of a merit-order based zonal day-ahead market (ZDAM) optimisation to define a preliminary generation schedule. In the second stage, the SCUCED is solved based on AC load flow routines and sensitivity factors to embed the full network representation. The approach is applied to a modified version of the IEEE 39-bus test system.

Existing electric power distribution systems are evolving and changing as a result of the high renewable energy sources integration. Hence, future smart distribution networks will involve various technical challenges; one of them is real-time monitoring and controlling the network to operate it effectively and efficiently. This paper develops and analyzes a cyber-physical co-simulation testbed for real-time reactive power control in the smart distribution network. The testbed is a two-layer system, with Typhoon HIL 604 representing the physical layer and the other layer as a cybernetic layer. The cybernetic layer is used to model a test system and control reactive power from smart inverters in real-time. The implementation of real-time reactive power control of smart inverters on a CIGRE MV distribution network is shown in this study. The proposed testbed's usefulness in real-time reactive power control is demonstrated through simulation results.

The computational burden and the time required to train a deep reinforcement learning (DRL) can be appreciable, especially for the particular case of a DRL control used for frequency control of multi-electrical energy storage (MEESS). This paper presents an assessment of four training configurations of the actor and critic network to determine the configuration training that produces the lower computational time, considering the specific case of frequency control of MEESS. The training configuration cases are defined considering two processing units: CPU and GPU and are evaluated considering serial and parallel computing using MATLAB® 2020b Parallel Computing Toolbox. The agent used for this assessment is the Deep Deterministic Policy Gradient (DDPG) agent. The environment represents the dynamic model to provide enhanced frequency response to the power system by controlling the state of charge of energy storage systems. Simulation results demonstrated that the best configuration to reduce the computational time is training both actor and critic network on CPU using parallel computing.