The inertia reduction suffered by worldwide power grids, along with the upcoming necessity of providing frequency regulation with renewable sources, motivates the present work. This paper focuses on developing a control architecture aimed to perform frequency regulation with renewable hybrid power plants comprised of a wind farm, solar photovoltaic, and a battery storage system. The proposed control architecture considers the latest regulations and recommendations published by ENTSO-E when implementing the first two stages of frequency control, namely the fast frequency response and the frequency containment reserve. Additionally, special attention is paid to the coordination among sub-plants inside the hybrid plant and also between different plants in the grid. The system’s performance is tested after the sudden disconnection of a large generation unit (N-1 contingency rules). Thus, the outcome of this study is a control strategy that enables a hybrid power plant to provide frequency support in a system with reduced inertia, a large share of renewable energy, and power electronics-interfaced generation. Finally, it is worth mentioning that the model has been developed in discrete time, using relevant sampling times according to industrial practice.

Since the dawn of humanity, people have contemplated the sky exploring the firmament. However, it was not until the twentieth century that humans were able to leave Earth and visit other celestial objects. In fact, nowadays, rovers roam Mars on a daily basis pushing the limits of science in a seemingly routine fashion. It is just a matter of time before humanity sets foot on the red planet with the aim of establishing a permanent colony. Such a complex endeavour demands continuous research, simulation, and planning. Consequently, this paper is aimed at starting a proper discussion about the configuration and design of a suitable power system for said Martian outpost. An initial literature review leads to the definition of a reference colony and its growing stages, which is followed by a revision of available energy-related technologies leading to a concrete design of a suitable electrical network. Lastly, the proposed hybrid power system is evaluated in terms of its reliability during the long-term operation under the extreme environmental conditions of Mars. The reference colony starts as an unmanned mission, as robots will prepare the selected location for the first human inhabitants. Later, it suffers several upgrades in size reaching a permanent population of 100 people. Therefore, a holistic approach is needed when designing the power system in order to ensure the continuous supply of the colony. Finally, the selected topology of the colony’s power system is presented.

Machine Learning (ML)-based methods have been identified as capable of providing up to one day ahead Photovoltaic (PV) power forecasts. In this research, we introduce a generic physical model of a PV system into ML predictors to forecast from one to three days ahead. The only requirement is a basic dataset including power, wind speed and air temperature measurements. Then, these are recombined into physics informed metrics able to capture the operational point of the PV. In this way, the models learn about the physical relationships of the different features, effectively easing training. In order to generalise the results, we also present a methodology evaluating this physics informed approach. We present a study-case of a PV system in Denmark to validate our claims by extensively evaluating five different ML methods: Random Forest, Support Vector Machine, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and a hybrid CNN–LSTM. The results show consistently how the best predictors use the proposed physics-informed features disregarding the particular ML-method, and forecasting horizon. However, also, how there is a threshold regarding the number of previous samples to be included that appears as a convex function.

No abstract

Generation uncertainty is an obvious challenge posed by renewable energy sources such as wind power with effects spawning from stability threats, to economic losses. Datadriven forecasting methods draw increasing attention due to the amount of data available, flexibility and cost-effectiveness among other factors. However, there are concerns regarding effective feature selection and tuning of these models since common naive approaches focus on Pearson or Shapley. This papers uses the development of an active power forecaster for a wind turbine to conduct a thorough sensitivity analysis addressing how different sampling rates, machine learning (ML) methods, features and hyperparameters influence accuracy. Which is computed with the Root-Mean Squared Error and compared against Persistence. The selected ML-methods are Random Forest and Long-Short Term Memory Artificial Neural Networks. The forecasters are multi-horizon & multi-output model targeting 1 minute, 1 hour, 5 hours and 2 days ahead by using sampling rates of 1 second, 1 minute, 5 minutes and 1 hour respectively. The results show which method is more suitable for which horizon and provides insight into which features reduce RMSE of the best performers, whose average is 10, 13, 17 and 25 % for each horizon respectively. The conclusions of the sensitivity analysis can be applied for regions with highly volatile weather, such as coastal areas.

No abstract

Fully renewable, isolated power systems have gained relevance given the global agenda related to the energy transition. Thus raising the amount and diversity of the performed gridrelated research. However, the existing generic reference systems are usually aimed to a particular type of study and don't capture the influence of technologies and methods used to accommodate renewables such as power electronics, energy storage, demand response, etc. In addition, the majority of studies are focused on the micro-grid perspective. When analyzing grids, size does matter, and yet, there is no benchmark available suitable for validating both static and dynamic studies in the dozens to hundreds of MW range. Therefore, there is a need for a reference system capturing the behaviour of modern, mid & large size isolated power systems ranging from 20 to 100 % renewable energy penetration, accommodating a very diverse technological mix. The purpose of this work is to fill these gaps, presenting a benchmark suitable for studies in mid to large size power system using real data from existing isolated grids. The network of two islands from Cape Verde is used as inspiration for the models due to the relevance of their layout and configuration, but also the country's renewable penetration targets. All the data has been provided by Electra and Cabeólica, the local System Operator and largest renewable utility of the country respectively. The data is Open-Access, accessible in an online repository [1], conveniently prepared and presented in different tables and files covering a range of traditional and modern studies such as: power flow, energy management, control, stability, reliability, resiliency etc.

Reference systems are key enabling platforms facilitating the evaluation and comparison of different methods and technologies prior to prototyping and field deployment. In the context of the energy transition, where the number and diversity of the grid-related research is ever expanding, we propose a reference system based on two islands of Cape Verde. These isolated power systems capture the behaviour of modern, mid & large size grids ranging from 20 to 100 % renewable energy penetration, accommodating a very diverse technological mix. The topology is based on the real transmission system as of 2021, considers sector coupling and the role of power electronicsinterfaced units. The Open-Access data was provided by the local system operator and the largest renewable utility of the country. The main objective is to enable off-the-shelf usage by minimizing the amount customization required for any possible study. In that way, it is suitable for a range of traditional and modern studies such as: power flow, energy management, control, stability, reliability, resiliency etc. We showcase the usefulness of this reference system with four short studies regarding grid strength, frequency stability, optimal sizing & placement of battery systems and synthetic inertia provision.