Looking to the future, there are several challenges that electricity networks will face: prosperity, global sustainable growth and security. The electricity industry situation is complex because resources worldwide are becoming scarce and the need for sustainable growth is important. Dealing with this challenge led to the foundation of micro-grids. The classical 'micro-grid' defines a mix of distributed energy resources (DER) capable of providing sufficient and continuous energy to a significant portion of the internal demand. Transmission system friendly micro-grids is a novel concept to encourage the power electronic converters to provide grid services and functionalities in line with new standards such as IEEE 1547- 2018. This paper presents a method for reducing losses by injecting reactive power from the grid-connected DER. The methodology is developed in Python and the system is modelled in DIgSILENT PowerFactory. The paper includes numerical results of the proposed methodology to show the suitability of the proposed concept.

The increase in renewable energy sources in addition to the decrease in conventional synchronous generators is leading to significant challenges for the power system operators to maintain generation load balance and to manage the system's decreasing inertia. Proton Exchange Membrane (PEM) fuel cells are characterised by high current density and fast power injection, which makes them ideal for frequency containment. This paper presents a generic model for PEM fuel cells developed in PowerFactory for frequency stability studies and provides an evaluation of its performance in a reduced-size dynamic model of the North Netherlands high voltage transmission network. The results show that the PEM fuel cell provides improved frequency response within the containment period when compared with synchronous generators for the same amount of support reserve.

The increased adoption of renewable energy generation is reducing the inertial response of the Great Britain (GB) power system, which translates into larger frequency variations in both transient and pseudo-steady-state operation. To help mitigate this, National Grid, the transmission system operator in GB, has designed a control scheme called enhanced frequency response (EFR) specifically aimed at energy storage systems (ESSs). This study proposes a control system that enables the provision of EFR services from a multi-electrical ESS and at the same time allows the management of the state of charge (SOC) of each ESS. The proposed control system uses a Fuzzy Logic Controller to maintain the SOC as near as possible to the desired SOC of each ESS while providing EFR. The performance of the proposed controller is validated in transient and steadystate domains. Simulation results highlight the benefits of managing the SOC of the energy storage assets with the proposed controller. These benefits include a reduced rate of change of frequency and frequency nadir following a loss of generation as well as an increase in the service performance measure which renders into increased economic benefits for the service provider.

Future power system has several challenges. One of them is the major changes to the way of supply and use energy; building a smarter grid lies at the heart of these changes. The ability to accommodate significant volumes of decentralized and highly variable renewable generation requires that the network infrastructure must be upgraded to enable smart operation. The reliable and sophisticated solutions to the foreseen issues of the future networks are creating dynamically intelligent application/solutions to be deployed during the incremental process of building the smarter grid. The smart grid needs more powerful computing platforms (centralized and dispersed) to handle large-scale data analytic tasks and supports complicated real-time applications. The implementation of highly realistic real-time, massive, online, multi-time frame simulations is required. The objective of this chapter is to present a general introduction to the DIgSILENT PowerFactory, the most important aspects of advanced smart grid functionalities, including special aspects of modelling as well as simulation and analysis, e.g. wide area monitoring, visualization, and control; dynamic capability rating, real-time load measurement and management, interfaces and co-simulation for modelling and simulation of hybrid systems. The chapter presents a very well-documented smart grid functionality, and limited cases are explained: Virtual Control Commissioning: connection to SCADA system via OPC protocol, direct connection to Modbus TCP devices, GIS integration with PowerFactory using API. The explained cases allow showing the full potential of PowerFactory connectivity to fulfil the growing requirements of the smart grids planning and operation.

The growth of vehicle electrification is driven by the desire to reduce environmental pollution, and it is fueled by advancements in battery technology. If left unmanaged, electric vehicle (EV) charging will increase peak demand and put a strain on the electricity networks. However, if properly managed, EVs can provide useful services to the power system operator such as fast active-power injection which serves to improve the system frequency response (SFR) after a disturbance. The objective of this paper is to assess the impact that clusters of EVs, connected to frequency-responsive charging stations, have on the provision of SFR after a loss of generation event. The assessment considers EV charging demand in Great Britain (GB) for the year 2025 considering three different daily charging patterns. A generic model for the EV clusters is developed which includes the effects of measurement delays and control charger time response. The model and scenarios are integrated into a single-Area model representative of the GB power system and the minimum expected values for the system's inertia in the year 2025 are used. The results obtained highlight the benefits on the SFR of utilizing EVs as a dynamic energy storage system for different types of charging and the impact of the measurement delay on the dynamics of the response.

Load-flow analysis is an effective tool that is commonly used to capture the power system operational performance and its state at a certain point in time. Power grid operators use load-flow extensively on a daily basis to plan for day-ahead and dispatch scheduling among many other purposes. Also, it used to plan any grid expansion, alter or modernization. However, due to the deterministic nature and its applicability for only one set of operational data at a certain period, deterministic load-flow reduces the chances for predicting the uncertainty in power system. Researchers usually create a data model using probabilistic analyses techniques to produce a stochastic model that mimics the realistic system data. Combining this model with Monte Carlo methodology leads to form a probabilistic load-flow tool that is more powerful and potent to carry on many uncertainty tasks and other aspects of power system assessment. This chapter presents the DIgSILENT PowerFactory script language (DPL) implementation of a DPL script to perform probabilistic power flow (PLF) using Monte Carlo simulations (MCS) to consider the variability of the stochastic variables in the power system during the assessment of the steady-state performance. The developed PLF script takes input data from an external Microsoft Excel file, and then, the DPL can carry on a probabilistic load-flow and export the results using a Microsoft Excel file. The suitability of the implemented DPL is illustrated using the classical IEEE 14 buses.

The growing share of power electronic/converter interfaced generation is decreasing the total system rotational inertia. The reduced rotational inertia in the power system has important impact on the electro-mechanical processes, the power angle and frequency evolution time are quicker resulting in faster transient processes. Rapid response of the protection systems shall be applied, in order to clear the faults in the power systems. This paper aims to identify the roots/mechanism of critical clearing time reduction/deterioration by determining/analyzing the trajectories of the critical clearing time (CCT) in low rotational inertia power systems. In this paper, the equal area criterion (EAC) is used for analysis purposes. Theoretical and practical findings demonstrate the increase of the rotational inertia increases the CCT.

This paper introduces the use of the Swarm Variant of the Mean-Variance Mapping Optimization (MVMO-S) to solving the multi-scenario problem of the optimal placement and coordinated tuning of power system damping controllers (POCDCs). The proposed solution is tested using the classical IEEE 39-bus test system, New England test system. This papers includes performance comparisons with other emerging metaheuristic optimization: comprehensive learning particle swarm optimization (CLPSO), genetic algorithm with multi-parent crossover (GA-MPC), differential evolution DE algorithm with adaptive crossover operator, linearized biogeography-based optimization with re-initialization (LBBO), and covariance matrix adaptation evolution strategy (CMA-ES). Numerical results illustrates the feasibility and effectiveness of the proposed approach.

The grounding system is extremely important, as it affects the performance of the MTDC system virtually in any possible mode: normal (asymmetrical operation) and abnormal operation (faults), steady-state and dynamic. The objective of this paper is to introduce a simple approach to assess the steady-state post-contingency of multi-Terminal HVDC System and uses it order to illustrate the effects of grounding configurations on steady-state post-contingency performance. A 3-terminal HVDC system is used to formulate the main theoretical framework for performance prediction on post-contingency steady-state of MTDC system as well as for demonstrative purposes.