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The high penetration of Renewable Energy Sources (RES) and Electric Vehicles (EVs) into the grid introduces new challenges for Distribution Systems (DSs). The uncertainties related to these assets necessitate the development of real-time methodologies to optimize the operation of Low Voltage (LV) and Medium Voltage (MV) DSs. This paper aims to fill the gap in the literature by proposing a holistic real-time DS optimization model that considers the coupling of MV and LV DSs. Specifically, the methodology adopts a bottom-up three-layer approach. At the first layer an optimal EV Smart Charging Scheduling (SCS) methodology is applied for power losses minimization at the LV DSs, considering the characteristics of individual households (maximum rated power of the electrical installation, Photovoltaic generation, and load and EV charging demand). The second layer introduces a residential controller that fully exploits the flexibility of EVs, minimizing the impact of forecasting errors while satisfying limitations regarding households’ overloading protection. The third layer involves a real-time Network Reconfiguration (NR) methodology, considering real-time power transactions between MV and LV DSs, and determining the optimal topology through a cost-worth analysis of power loss reduction and switch operation costs. The overall design of the proposed methodology ensures broader adoption, repeatability, adaptability, and scalability across diverse DSs, including various types of LV DSs (residential, commercial, etc.) and different MV DS configurations. The proposed methodology can reduce DS power losses by up to 34.41% compared to the base scenario, which involves the operation of the DS without employing either EV SCS or NR.

Abstract The increasing penetration of photovoltaic (PV) systems into the electrical energy systems brings forward several technical and economic issues that mostly relate to their unpredictable nature. A promising solution to many of these is the implementation of robust PV generation forecasting models. In this paper a novel hybrid Ensemble Long Short‐Term Memory‐Feed Forward Neural Network (ELSTM‐FFNN) model is proposed, that is able to perform both very‐short and short‐term forecasting. The performance of the proposed model is compared with individual LSTM models, and its forecasting accuracy is assessed in two different forecasting horizons: (a) 15‐min ahead and (b) 1‐h ahead. Moreover, in order to fully examine the contribution of the utilized data to the performance of the model, several scenarios have been formulated for each forecasting horizon. The results indicate that the proposed ELSTM‐FFNN model can increase the forecasting accuracy in both horizons between 3–11.9% and 0.2–17.8%, respectively, considering the Mean Absolute Range Normalized Error (MARNE).

The topic of power loss reduction in distribution systems has gained significant attention over recent years. Despite the efforts of the European Union towards the minimization of power losses, the decarbonization of the transport sector has raised several concerns, since charging overlaps of Electric Vehicles (EVs) can cause extensive power losses and power quality issues. Considering these, the present paper proposes a two-stage EV charging planning and Network Reconfiguration (NR) methodology, addressing the problem of power loss minimization in both Low-Voltage (LV) and Medium-Voltage (MV) Distribution Networks (DNs), respectively. In the first stage, considering the key role of the aggregator, the EV charging planning is applied to LV DN. In the second stage, the NR technique is applied to the MV DN, by taking into account the hourly power demand of LV DNs as obtained by the aggregators. The proposed methodology has been applied on a benchmarked MV network for which each node is represented by a real LV network. The results indicate that the proposed methodology could yield up to a 63.64% power loss reduction, in respect to the base scenario, i.e., no charging planning and no NR are applied.