Abstract
The replacement of fossil fuel power plants by variable renewable energy sources is reducing the flexibility of the energy system, which puts at risk its security. Exploiting the flexibility of distributed multi-energy resources through aggregators presents a solution for this problem. In this context, this paper presents a new hierarchical model predictive control framework to assist multi-energy aggregators in the network-secure delivery of multi-energy services traded in electricity, natural gas, green hydrogen, and carbon markets. This work builds upon and complements a previous work from the same authors related to bidding strategies for day-ahead markets - it closes the cycle of aggregators' participation in multi-energy markets, i.e., day-ahead bidding and real-time activation of flexibility services. This new model predictive control framework uses the alternating direction method of multipliers on a rolling horizon to negotiate the network-secure delivery of multi-energy services between aggregators and distribution system operators of electricity, gas, and heat networks. We used the new model predictive control framework to conduct two studies. In the first study, we found that considering multi-energy network constraints at both day-ahead and real-time optimization stages produces the most cost-effective and reliable solution to aggregators, outperforming state-of-the-art approaches in terms of cost and network security. In the second study, we found that the adoption of a green hydrogen policy by multi-energy aggregators can reduce their consumption of natural gas and respective CO2 emissions significantly if carbon and green hydrogen prices are competitive.& COPY; 2023 Elsevier Ltd. All rights reserved.

Abstract
The decarbonization of the economy and the increasing integration of renewable energy sources into the generation mix are bringing new challenges, requiring novel technological solutions in the topic of smart grids, which include smart transformers and energy storage systems. Additionally, power quality is a vital concern for the future smart grids; therefore, the continuous development of power electronics solutions to overcome power quality problems is of the utmost importance. In this context, this article proposes a novel three-phase multiobjective unified power quality conditioner (MO-UPQC), considering interfaces for solar PV panels and for energy storage in batteries. The MO-UPQC is capable of compensating power quality problems in the voltages (at the load side) and in the currents (at the power grid side), while it enables injecting power into the grid (from the PV panels or batteries) or charging the batteries (from the PV panels or from the grid). Experimental results were obtained with a three-phase four-wire laboratory prototype, demonstrating the feasibility and the large range of applications of the proposed MO-UPQC.

Abstract
In isolated power systems with very high instantaneous shares of renewables, additional inertia should be used as a complementary resource to battery energy storage systems (BESSs) for improving frequency stability, which can be provided by synchronous condensers (SCs) integrated into the system. Therefore, this paper presents a methodology to infer the system dynamic security, with respect to key frequency indicators, following critical disturbances. Of particular interest is the evidence that multiple short-circuit locations should be considered as reference disturbances regarding the frequency stability in isolated power grids with high shares of renewables. Thus, an artificial neural network (ANN) structure was developed, aiming to predict the network frequency nadir and Rate of Change of Frequency (RoCoF), considering a certain operating scenario and disturbances. For the operating conditions where the system frequency indicators are violated, a methodology is proposed based on a gradient descent technique, which quantifies the minimum amount of additional synchronous inertia (SCs which need to be dispatch) that moves the system towards its dynamic security region, exploiting the trained ANN, and computing the sensitivity of its outputs with respect to the input defining the SC inertia.

Abstract
There is growing recognition that decarbonisation of existing uses of electricity is only 'part of the story' and that closer attention needs to be given to demand for energy in heating or cooling and in transport, and to all the energy vectors and infrastructures that supply the end-use demand. In this respect, concepts such as 'multi-energy systems' (MES) have been put forward and are gaining increasing momentum, with the aim of identifying how multiple energy systems that have been traditionally operated, planned and regulated in independent silos can be integrated to improve their collective technical, economic, and environmental performance. This paper addresses the need for modelling of MES which is capable of assessing interactions between different sectors and the energy vectors they are concerned with, so as to bring out the benefits and potential unforeseen or undesired drawbacks arising from energy systems integration. Drivers for MES modelling and the needs of different users of models are discussed, along with some of the practicalities of such modelling, including the choices to be made in respect of spatial and temporal dimensions, what these models might be used to quantify, and how they may be framed mathematically. Examples of existing MES models and tools and their capabilities, as well as of studies in which such models have been used in the authors' own research, are provided to illustrate the general concepts discussed. Finally, challenges, opportunities and recommendations are summarised for the engagement of modellers in developing a new range of analytical capabilities that are needed to deal with the complexity of MES.