Power-to-X (PtX) technologies are accelerating the energy transition. Increasingly, these technologies are also being leveraged as flexible energy resources to support the electrical grid. PtX models are often represented using a constant efficiency term as a linear relation between the power input and energy output. However, the operational performance of any PtX device such as an electrolyser or an electric heat pump can depend on factors such as operational temperature. In this paper, we have developed and analyzed two levels of model fidelity of the most widely assessed PtX technologies: electrolyser and heat pump systems. We assess the impact of detailed models on operation of PtX within simulation-based energy system analysis. Our results show that for electrolyser systems, the efficiency errors can be almost 0.6%. With heat pump systems, the difference in COP can be as high as 1.4.@en

A numerical model of a power system can be used to get accurate insights into the impact of policies and investment decisions regarding the transformation of the energy system, while also helping in identifying bottlenecks in implementing decisions. Spatial aggregation, especially for generation and load, must be carefully approached to obtain such a valid model of a power system. The two main contributions of this paper are introducing a valid model of the Dutch high-voltage power system based on open data and open-source software, and proposing a method for spatially aggregating generation and load capacities to high-voltage nodes of the power system. The representative model will enable interdisciplinary research on policy-making and investment decisions specific to the Netherlands.@en

The transition to sustainable energy iswell underway and is introducing changes on both sides of the electricity balance scale – generation and demand. On the generation side, emissions-free renewable generation resources such as wind and solar are replacing the pollution-emitting thermal power plants. On the demand side, energy sectors traditionally dependent on fossil fuels such as heat, mobility, gas, chemicals, and others are being coupled to electricity using Power to X (or P2X) technologies. These developments are introducing changes to the planning and operation of the electricity grid. Large-scale power generation from renewables and an increased demand for power resulting from the electrification of energy sectors in a grid with limited capacity is causing congestion challenges. Increased penetration of renewables is also driving demand for power system services that can complement the uncertainty and intermittency associated with renewable power generation. Until sufficient capacity is installed, mitigating these challenges requires the grid, especially its participants, to be flexible....@en

The traditional methodology for conducting technical assessments of multi-energy systems involved using domain-specific modeling tools to focus on the energy sector of interest, while making simplifying assumptions about any coupled energy sector. This was acceptable since the interactions between energy domains were minimal. However, with the expectation of an increased adoption of energy conversion technologies (such as power to X (P2X) systems: power to heat, power to gas, etc.) in the future, and consequently higher interaction between various energy sectors and stronger dependence on one another, there is a need to update the current method for conducting technical assessments. This means taking into account not only the energy sector of interest, but also any dependent energy sectors, and the associated energy transformers (P2X). In this paper, we propose a co-simulation based approach to conduct simulation-based technical assessments of multi-energy systems, which allows us to couple domain specific modeling tools. We re-introduce the tool ENERGYSIM to conduct the multi-energy system co-simulations. We motivate the importance of the proposed tool and compare it with other available tools. We highlight its main functionalities, and using a study case, we show how a multi-stakeholder, multi-energy system co-simulation can be set up and assessed.

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This chapter presents a general overview of the experience learned with the use of DIgSILENT PowerFactory in the design of theoretical lectures and practical sessions of a power system dynamics course at postgraduate level. This chapter focuses on the experiences acquired in the course that is part of the MSc program in Electrical Engineering of TU Delft, Department of Electrical Sustainable Energy. The discussion provided in this chapter focuses on power systems application with special focus on (i) Steady-state, Dynamic, (ii) Voltage Stability and (iii) rotor angle stability. The main objective of using PowerFactory at MSc level is to expose the postgraduate students to real-life application, however, without lack of generalisation this chapter is dedicated to the is to expose to the application above by using a very well-known two area-four machine test power system (2A4G), it gives students insights and experience with cases closer to actual power systems. Results of this pedagogical experience demonstrate the importance of incorporating appropriate power system simulations tools in the postgraduate level.

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Flexibility will be required as more renewable generation is integrated in the power system. Therefore, entities such as Flexibility Service Providers (FSPs) have emerged who can control a diverse set of resources in their portfolio to provide requested flexibility. An accurate and reliable assessment of the amount of dispatchable flexibility in FSP's portfolio considering demand response activation, ramp rate, and post-activation rebound effects, etc. is important. In case FSP portfolio cannot fulfill the flexibility request, it must be known beforehand to avoid imbalance penalties. In this paper, we propose three flexibility quantification metrics: Expected Unserved Flexible Energy (EUFE), Expected Duration of Insufficient Flexibility (EDIF), and Expected Flexibility Index (EFI). These metrics are calculated using scenario-based simulations. The three metrics provide the FSP with quantifiable as well as graphical information on the magnitude and the duration of insufficient flexibility well ahead of gate closure time. This helps the FSP to design appropriate demand response programs (DRPs) and formulate their operational policy. These metrics account for uncertainty in forecasts of flexibility requests by evaluating multiple scenarios by conducting an operational simulation. With a representative case study, it is shown how an FSP can use the metrics to design DRPs, and when needed, schedule intraday market participation.@en

Coping with severe active power imbalances constitutes a challenging task in low inertia multi-energy systems. The phase out of the majority of conventional power plants with large size synchronous generators entails that power electronic interfaced devices should take over the primary task of active power balance-frequency control. In view of this, the active power gradient (APG) control of these devices should be carefully designed to ensure effectiveness and to prevent collateral effects on other stability phenomena. This paper presents two novel formulations for the optimal tuning of the parameters of the APG controllers. Both formulations are defined as a constrained single objective optimization problem. The goal is to optimally manage the APG controllers to quickly bound the instantaneous frequency deviations excited by a large active power imbalance. The first formulation concerns with the minimization of the instantaneous variation of the kinetic energy of the synchronous areas of an interconnected system, whereas the second formulation concerns with the minimization of the spatial displacements between the dynamic trajectories of the time frequency responses in different synchronous areas. The formulations are solved by using a new proposed variant of the mean-variance mapping optimization algorithm (MVMO), and their conceptual implications are illustrated based on numerical simulations performed on a small-size multi-energy system.@en

To counter the inherent intermittent and unpre dictable power generation from large amounts of wind and solar, fast-acting resources are required, one of the options being sector coupling via power to gas devices. Industrial Power to Gas (IPtG) resources, such as an electrolyzer, represent an attractive solution to satisfy the rising energy flexibility needs of renewable-rich power systems. Since these electrolyzers can be asked to respond quickly following steep power ramps of renewables, it is imperative to understand their capabilities and limitations in fulfilling such requirements. The contribution of this paper is twofold. First, we introduce a detailed model of a Proton Exchange Membrane (PEM) electrolyzer suitable for power system flexibility studies. Second, using this model we assess large scale electrolyzer as a flexibility service provider (FSP) to the grid. To evaluate electrolyzer performance, we construct the V-I characteristic curve before and after simulating each test case to derive insights on the influence of time and dynamic operation on the electrolyzer system.

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Power system dynamic studies rely on model-based simulations. With increasing penetration of DG, modeling of a network that is not only geographically diverse but is also technologically mixed entails great efforts and accuracy for such studies. Despite the availability of highly powerful simulation tools and increased processing power, performing time domain simulations on such interconnected and complex system remains a great hurdle. Reducing the complexity of the model using aggregation can help with these problems. The purpose of this chapter is twofold. First, we go through the concept of network aggregation and introduce a white-box DE suitable for system studies with high penetration of distributed PV. Next, we introduce a sophisticated approach for solving the nonlinear optimization problem of parameter identification of the developed DE. This approach is based on a new optimization technique called mean-variance mapping optimization (MVMO). The parameters of the DE are identified by measuring the response of DE to certain disturbances and comparing them with reference signal sets. By using the proposed approach, we determine the parameters of our suggested DE that entails high accuracy. The accuracy of the DE is measured by calculating the root mean square error (RMSE) value. The time required to simulate faults and perform time domain simulations using a DE is considerably less than for a full-scale model. This has significance for system planners and researchers who want to analyze the effect of increasing penetration of DE into distribution grid on the system as a whole with regard to strategy development@en

High penetration of power electronic interfaced generation, like wind power, has an essential impact on the inertia of the interconnected power system. It can pose a significant threat to the frequency stability. This paper introduces the notion of the key performance indicator (KPI) and illustrates its application on large scale power systems, including Fast Frequency Response (FFR) and a high share of wind power, to measure the possible distance to the frequency stability limit. The proposed KPI estimates the change of frequency performance (e,g., ROCOF, NADIR) in the frequency containment period. The effect of FFR is analyzed by introducing a droop based controller for wind power plants. The FFR controller responds to a drop in grid frequency with a temporary increase of the wind active power. The proposed KPI maps a change in key system variables (e.g., system kinetic energy, aggregated generation output) onto the change of frequency performance. A comprehensive analysis using DIgSILENT, Matlab, and Python is performed for GB reduced size system. According to the obtained results, the FFR capability of wind generator leads to improvements of NADIR especially in cases with high penetration levels of wind power. The proposed KPI is a valuable tool for the frequency stability assessment in power system planning studies. It can be determined based on off-line simulations, and it can assist the system operators for frequency stability assessment in intra-day operational planning.@en

The value-proposition for Microgrids depends largely on the need and application. A Microgrid can be described as a group of interconnected loads and distributed energy resources (DER) inside some distinct electrical boundaries that act as a single controllable unit relating to the grid. It should be capable of safe transitioning from the grid and reconnection back to the grid; with the voltages, frequency, etc. of all devices still operating within their allowed limits. Although system resilience and continuous supply is one of the main proponents for the establishment of microgrids, it could also be used to electrify an off grid or remote area, far from the grid. The mode of operation as well as generation types and sources, together with the current protection devices and schemes, will determine the extent of power system studies that will be conducted when planning and designing a Microgrid. This paper highlights the typical power system studies that needs to be considered, when designing aa Microgrid.

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Cossembler (co-simulation assembler) is a rapid prototyping tool for co-simulation. The tool is created to expedite the process of co-simulation development for power and energy system studies targeting user groups of power engineers, energy consultants and grid operators. Instead of focusing on message encoding, transportation and synchronization, as many other co-simulation tools do, Cossembler emphasizes application-level functionalities which are of interest to the intended users (such as power flow studies, stability simulations, market simulations, etc.). Cossembler is a block modeling tool whose blocks reflect these main functionalities in power and energy sector. In this paper, we show the main characteristics of Cossembler architecture, discuss some of its advantages and disadvantages, and finally, show examples of its use.@en

The evolved smart grid has become a cyber physical energy system that could be exposed to a massive amount of cyber threats. Vulnerabilities within the cyber part can be used to launch multiple types of attacks that corrupt the physical system. The complexity of cyber physical energy system, the existing of different kinds of attacks, require an appropriate tool to aid in modeling and simulation for cyber security analysis. In this paper, we introduce a modeling language - Modelica to the security community of cyber physical system. We show the capability of Modelica in modeling complex systems and attacks by building up a power grid model with frequency control loop (i.e., automatic generation control), as well as data integrity attack and data availability attack models. The simulation results show how different types of attacks or even combined attacks can affect the system frequency stability.

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Energy flexibility is key to integrating more renewables into the grid. An essential contributor to enabling energy flexibility is P2X technologies such a Power2Heat, Power2Gas, among others. To evaluate the flexibility available from these resources and the impact they can have on the electrical grid, complex simulations need to be set up that may not always be possible using traditional simulation tools, given the multi-domain nature of such systems. Hence, the need for intelligent simulation techniques arises. This paper introduces a co-simulation tool, FMUWorld, to overcome simulation problems for complex energy systems. We use a multi-energy co-simulation and an energy flexibility analysis as use cases to explore the capabilities of the proposed tool. The ease-of-use offered by FMUWorld is shown to allow users to focus more on analysis of the system, such as parameter sensitivity, system optimisation, etc., rather than co-simulation setup. The paper highlights the key features and functionalities of FMUWorld that make it a novel tool for cosimulation of multi-energy systems.

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Improved modeling and simulation of power and energy systems has become increasingly important in the face of energy transition. The main challenge is to capture the complexity that heterogeneity of technologies and uncertainty of renewable resources bring along. One approach to improve simulation modeling capabilities, that relies on reusing existing expertise and legacy tools, is a so-called combined simulation (co-simulation). In this approach, well-established tools are combined together resulting in simulation environments with greater capabilities. In this paper, we introduce a new cosimulation
rapid prototyping tool called Cossembler (which stands for Co-simulation assembler), whose main benefits are high usability and a variety of potential application domains that could be addressed by it. The paper further presents two use cases which illustrate Cossembler capabilities.@en

This paper introduces an approach for determining the parameters of aggregated dynamic equivalents for active distribution system from a reference set of signals associated with various disturbances using mean-variance mapping optimization (MVMO) algorithm. As the penetration of renewable energy sources in the LV and MV network increases, it has become extremely important that we have models that mimic actual system response with least computational overhead for bulk transmission system studies. Full scale detailed, complex, interconnected models are most accurate but carry significant simulation time, making analysis unfeasible. Dynamic equivalents (DE) are simplified representations of larger models that mimic the dynamic response of those models. They can be used to replace the neighbor area in a study while the area of focus is modeled with great detail. This reduces the computational burden. Our test system is a small part of the German MV/LV network whose dynamic equivalent is identified and compared. The dynamic equivalent is a Western Electric Coordination Council (WECC) developed distributed PV model (PVD1). This identification is done with the use of MVMO algorithm that utilizes a single parent crossover and a unique mapping function. The results show an almost identical response with good normalized root mean square error (NRMSE) between the detailed and aggregated model. The MVMO shows fast convergence and accurate results.@en