Energies

Accurate short-term station-level electric vehicle (EV) charging demand forecasting is important for efficient charging-station operation and local grid management. However, several challenges remain. First, charging demand exhibits overall demand evolution and rapid short-term variations that are difficult to model simultaneously. Second, demand varies across spatially distinct stations and exhibits recurring intraday and weekly patterns, requiring effective spatio-temporal representations to capture such characteristics. Existing methods commonly combine graph-based inter-station modeling with temporal modeling, but uneven station distributions complicate inter-station modeling and general temporal modeling may not fully capture recurring patterns. Third, electricity price and temperature provide complementary information, but their predictive relevance can differ across stations. To address these challenges, we propose a Spatio-Temporal Representation-Enhanced GRU (STR-GRU). The proposed model includes three core modules: LD-GRU, STRE, and AEFF. The Level-Difference Dual-Branch GRU (LD-GRU) separately encodes demand levels and first-order differences to model overall demand evolution and short-term variations. Spatio-Temporal Representation Enhancement (STRE) uses learnable station, time-of-day, and day-of-week embeddings to represent demand differences across spatially distinct stations and recurring temporal patterns without inter-station graph construction or information propagation. Adaptive Exogenous Feature Fusion (AEFF) adaptively fuses electricity price and temperature according to station-specific demand representations to account for differences in their predictive relevance. Experiments on six urban charging networks and three forecasting horizons demonstrate competitive forecasting performance and a favorable accuracy–efficiency trade-off.

This paper presents a theoretical and numerical extension of Budeanu’s power theory for three-phase three-wire systems supplied by symmetrical nonsinusoidal voltages and feeding unbalanced linear time-invariant loads. The proposed formulation combines the classical Budeanu framework with the Currents’ Physical Components (CPC) theory to provide a physical interpretation of Budeanu distortion power. Five mutually orthogonal components of the load current are distinguished, namely the active, scattered, Budeanu reactive, Budeanu-complemented reactive, and negative-sequence unbalanced currents. Consequently, Budeanu distortion power is shown to be an orthogonal combination of three powers (scattered, Budeanu-complemented reactive, and negative-sequence unbalanced) and not merely a residual obtained from the apparent, active, and reactive powers. The theoretical formulation is verified numerically using three illustrative delta-connected load configurations fed by a voltage composed of the 1st, 5th, and 7th harmonics. In all examples, the sum of the proposed components reproduces exactly the line currents calculated directly with Ohm’s law and Kirchhoff’s current law, and they distinguish the physical mechanisms responsible for each contribution to distortion power. The analysis is restricted to 3-p 3-w circuits fed by symmetrical nonsinusoidal voltages and loaded with linear time-invariant (LTI) elements, which are not sources of harmonics; consequently, it does not directly address asymmetrical supply conditions, four-wire systems, time-varying loads, or nonlinear loads with harmonic generation.

Hybrid renewable energy systems (HRESs) are increasingly used because they integrate diverse energy resources whose availability can complement one another over the day–night cycle. However, the high cost of system components, particularly batteries, and the dependence of energy generation on weather conditions can undermine their economic viability. Researchers, therefore, seek to optimize HRES configurations to achieve a balance between reliability and lifecycle cost by performing techno-economic studies using different techniques. One of the industries that started relying more on renewable energy is the mobile telecommunication industry, especially in the remote stations where the expansion of the grid is expensive and the renewable resources are abundant. This study introduces the scope of hybrid renewable energy system optimization and applies a study case to a remote telecommunication station where the existing energy system was installed without prior techno-economic assessment. Using mathematical models, single and multi-objective (lexicographic) optimizations are conducted using the particle swarm optimization algorithm and a deterministic exhaustive search numerical algorithm, to minimize the lifecycle cost of the system, maintain reliability for a full continuity of service, and reduce the dependency to the diesel generator to contribute to the decarbonization objective of the world. The computational results show a significant reduction in the cost of the system from USD 525,512.83 for the existing system to approximately USD 197,879.03 (reduction of 62.3% of the actual cost), and the operating hours of the diesel generator from 1401 h/year to 770 h/year. More details are presented in the Discussion section.