Abstract
This paper presents a new strategy for recharging electric vehicles in residential buildings. The proposed approach minimizes the difference between desired and final state of charge (SOC) by the end of the charging period, by adjusting the charging power for each vehicle in real-time. A non-linear optimization problem is formulated, considering the initial and final SOC, as well as available charging time, and total available power. Results were compared to a baseline and show that the proposed solution outperforms the currently most used nonoptimized method, particularly in high demand scenarios, where we achieve values of 9.3% of curtailed range when compared with the non-optimized methodology.

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
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
Electric power systems are currently experiencing a profound change, as increasing amounts of Renewable Energy Sources (RESs) displace conventional forms of generation. This development has gone hand-in-hand with an increasing share of distributed power generation being connected directly to the Distribution Network (DN), and the widespread of other types of Distributed Energy Resources (DERs), such as Energy Storage Sytems (ESSs), Electric Vehicles (EVs), and active (flexible) consumers. As these trends are expected to continue, this will require a profound revision of the way Transmission System Operators (TSOs) and Distribution System Operators (DSOs) interact with each other to fully benefit from the growing flexibility that is available at the DN level. In this work we propose a new tool for the coordinated operational planning of transmission and distribution systems, considering the existence of shared resources that can be simultaneously used by TSO and DSOs for the optimal operation of their networks. The tool uses advanced distributed optimization techniques, namely the Alternating Direction Method of Multipliers (ADMM) in order to maintain data privacy of the several agents involved in the optimization problem, and keep the tractability of the problem. The proposed tool is applied to modified IEEE test systems, and the results obtained highlight the benefits of the proposed coordination mechanism to solve problems occurring simultaneously at the transmission and DN-levels.

Abstract
This paper presents a general comparison between network-secure and network-free optimization frameworks to manage flexible multi-energy resources. Both frameworks were implemented in a test case that includes electricity, gas, and heat distribution networks. Several potential scenarios for the decarbonization of the multi-energy system were simulated. The economic, technical, and environmental impacts were compiled. The network-secure framework is highly recommended to avoid service disruptions due to network violations, but its implementation comes with a price - overall operational costs increase, sometimes substantially.

Abstract
This paper proposes a novel probabilistic model for quantifying the impact of demand flexibility (DF) on the long-term generation system adequacy via Sequential Monte Carlo Simulation (SMCS) method. Unlike load shedding, DF can be considered an important instrument to postpone bulk consumption from periods with limited reserves to periods with more generating capacity available, avoiding load shedding and increasing the integration of variable renewable generation, such as wind power. DF has been widely studied in terms of its contribution to the system's social welfare, resulting in numerous innovative approaches ranging from the flexibility modeling of individual electric loads to the definition of aggregation strategies for optimally deploying this lever in competitive markets. To add to the current state-of-the-art, a new model is proposed to quantify DF impact on the traditional reliability indices, such as the Loss of Load Expectation (LOLE) and the Expected Energy Not Supplied (EENS), enabling a new perspective for the DF value. Given the diverse mechanisms associated with DF of different consumer types, the model considers the uncertainties associated with the demand flexibility available in each hour of the year and with the rebound effect, i.e., the subsequent change of consumption patterns following a DF mobilization event. Case studies based on a configuration of the IEEE-RTS 79 test system with wind power demonstrate that the DF can substantially improve the reliability indices of the static and operational reserve while decreasing the curtailment of variable generation cause by unit scheduling priorities or by short-term generation/demand imbalances.