Abstract

Abstract
This paper proposes a centralized energy management system for low voltage (LV) distribution networks. The main contribution of this model is to manage the energy serving at the local energy communities in the presence of electric vehicle supply equipment (EVSE). Unlocking the demand response potential by the EVSE at the distribution network with the contribution of the active residential prosumers has been investigated in this study under different operational planning scenarios. The developed model is based on the multi-temporal optimal power flow (MTOPF) concept while the unbalanced nature of LV networks has been addressed using unbalanced power flow equations. The aggregator can effectively manage the optimal charging of electric vehicles (EVs) by home and public chargers available at the distribution network. Simulation results on a modified unbalanced LV network illustrate that the optimal operation of EVSE minimizes the electricity costs of end-users. The simulation results show that the operating costs and systems losses reduce by 9.22% and 43.45%, respectively. These results have been obtained considering the switching actions and 100% PV power generation index using the presented MV-LV coordinated operational model. Besides, the energy storage systems improve the peak-to-average (PAR) ratio by 9.87%.

Abstract
Portugal's electricity generation relies heavily on renewable sources, which accounted for over half of the country's production in recent years. The Portuguese government has set ambitious renewable energy targets for 2030. The R3EA project (https://r3ea.web.ua.pt/pt/projeto) evaluates the impact of new investments in solar and wind energy capacity in the Centro Region of Portugal, focusing on the costs and benefits of externalities. This study examines Portugal's electricity market outcomes in terms of prices, generation mix, and emissions for different wind and solar capacities, using the National Energy and Climate Plans (NECP) of Portugal and Spain as the reference scenario. The electricity markets of both countries are modelled together, reflecting the integrated Iberian market with significant interconnections. The NECP scenario results in lower market prices and emissions, but less significantly than scenarios with lower demand and higher renewable energy share. In all scenarios, increasing renewable energy sources drives market prices down from over 200/MWh in 2022 to under 100/MWh during peak hours in 2030. Demand is the main driver of emissions, as higher demand leads to more reliance on fossil fuel plants. Lower demand scenarios in 2030 show 20 % fewer CO2 emissions per TWh than higher demand ones.

Abstract

Abstract
Accurately imputing missing data is critical in time series analysis. The present work compares Foundation Model Chronos against Linear Interpolation, K-Nearest Neighbor Imputer, and Gaussian Mixture Model Imputer with three types of missing data patterns: random, short sequential chunks, and a long sequential chunk. These results confirm that for random missing values, KNN and interpolation yield the highest performance, while Chronos outperforms these on sequences. Indeed, however, for longer sequences of missing values, Chronos starts suffering from cascading errors which eventually allow the simpler imputation methods to outrank it. Another test with limited quantities of training data showed different tradeoffs for the different methods. Unlike KNN and interpolation, which smooth out the gaps, Chronos generates variable synthetic data. This can be beneficial in tasks which require control or simulation. The results highlight the strengths and weaknesses of the imputers and, therefore, offer practical insights into trade-offs between computational complexities, accuracy, and suitability for time series imputation scenarios.

Abstract
Grid scale Battery Energy Storage Systems (BESS) have a key role for future power systems operation and stability. However, cyclic degradation, intensified by multi-service operation, remains a major challenge, directly affecting battery lifespan and profitability. This study examines BESS participation in energy markets and in automatic frequency restoration reserve (aFRR) markets, assessing the impact of cyclic degradation costs on BESS planning and operation. The methodology involved modelling the daily dispatch of an 8.1 MW lithium-ion battery for participation in day-ahead, intraday and reserve markets, incorporating a degradation cost minimization model. The simulations were conducted using the historical data from Iberian electricity and Portuguese ancillary services market, such as energy prices, historical reserve requirements and AGC forecasts. The results show that reserve market participation is highly profitable and can be successfully complemented with day-ahead and intraday market participation. Also, incorporating cyclic degradation cost into planning extends BESS lifespan in all cases. However, this approach is beneficial only in arbitrage scenarios, while in reserve market participation, it reduces profits. The findings highlight the importance of balancing BESS degradation minimization with profitability, particularly in reserve market participation. Future research could apply this model to different battery technologies and real-world systems to validate the simulated results.