Powering Net-Zero 6G: Packetized Energy Management for Grid-Interactive Telecom Infrastructure

5G-Advanced Network Slicing for Smart Grid Communications: Intelligent Resource Scheduling Under Energy-Efficiency and Performance Trade-Offs

An interpretable intelligent scheduling framework, where intelligence refers to state-aware, service-aware, energy-aware, and resilience-aware adaptation rather than purely black-box learning, is presented, indicating that intelligent 5G-Advanced slice scheduling is a promising standards-aligned approach for service-differentiated and dependable smart-grid communications.

Sustainable Energy, Grids and Networks

Hierarchical PPO–TD3 Energy Management for Renewable-Rich Microgrids with Safety-Filtered Action Projection

For renewable-rich microgrids, effective energy management is essential to improve economic efficiency and renewable-energy utilization while maintaining supply adequacy and network security. However, online dispatch remains challenging because variable renewable generation and demand must be coordinated under battery operating limits, distribution-network voltage constraints, and limited forecast information. To address this challenge, this paper proposes a safety-filtered hierarchical PPO–TD3 framework for microgrid energy management. The upper PPO layer selects coarse battery operating modes for long-horizon economic scheduling, while the lower TD3 layer refines continuous battery-power commands for short-term regulation. Before execution, the fused command is projected through a model-based safety filter considering state-of-charge limits, supply-priority rules, and voltage screening under a 33-bus radial-network surrogate. Fixed-test evaluation over five random seeds and 62 chronologically held-out test days shows that the proposed controller reduces total operating cost from 1003.66 to 686.31 k c.u. (31.6%) and unserved energy from 1743.25 to 1052.14 kWh (39.6%) relative to MPC-Lite, while reducing average decision time from 180.21 to 43.55 ms per step. In a separate safety-filter ablation, the filter reduced total cost from 912.47 to 686.31 k c.u., the hard-voltage violation rate from 25.31% to 19.46%, and the executed state-of-charge violation rate from 0.40% to 0.00%. These results indicate that the proposed framework supports reliable and efficient renewable-rich microgrid operation, facilitating renewable-energy integration and enhancing operational resilience.

Scaling Vehicle-to-Grid Services in Europe: A Smart Grid Architecture Model

Vehicle-to-grid (V2G) services for electric vehicles (EVs) are still in a nascent stage of technological and policy readiness. Although considerable studies emphasize the advantages of integrating EVs as distributed energy resources (DERs) into the grid, there is a significant lack of mature V2G use cases to incentivize EV owners to engage as prosumers. By examining V2G business use cases, such as energy arbitrage and balancing services, a comprehensive analysis is proposed to identify business opportunities benefiting all involved parties from EVs at any grid connection point. This article addresses the business and logical models of, such use cases, tailored to the European energy market by employing harmonized roles from the European Network of Transmission System Operators for Electricity (ENTSO-E) to ensure international scalability. The analysis identifies the combination of direct and indirect V2G demand response balancing programs as the most beneficial solution for saving costs for congestion management and balancing operations while generating revenue for end-users and service providers. To manage cross-domain complexities, the selected and developed solution is presented as a smart grid architecture model (SGAM)-based computational independent model, and ultimately evaluated on country-specific energy markets. The adopted domain-specific language through the SGAM Toolbox aids in model design and provides a foundation for further exploration of its viability.

5G-Enabled Energy-Efficient Operation and Management of Renewable Power Generation

To handle the growing share of renewable energy resources in a modern smart grid, efficient operation, real-time monitoring and reliable communication support are necessary. This paper proposes an operation and management framework for energy generation from renewable energy sources with the aid of 5G technology that is energy efficient. The proposed model is based on the combination of solar PV, wind and hybrid solar PV, wind and battery generation units with a fifth generation (5G) communication layer for enhanced renewable dispatch, reduced battery curtailment, load balancing and communication reliability. The mathematical model includes the following variables: renewable uncertainty, load uncertainty, battery state of charge, grid support power, renewable utilization, curtailment ratio, energy efficiency, latency, packet error rate, signal-to-interference-plus-noise ratio and communication reliability. Using several case studies, a MATLAB-based simulation has been carried out for a solar PV–only system, a wind-only system, a hybrid PV–wind system, a PV–wind–battery system, and a proposed 5G-enabled PV–wind–battery system. The results demonstrate 94.1% renewable utilization, 5.9% curtailment ratio, 0.11 MW average load mismatch, 91.3% energy efficiency, 7.6ms average latency, 24.8 dB average SINR, and 0.94 communication reliability, respectively. The results show that the optimization of renewable generation, battery storage and 5G communication jointly has a positive effect on the efficient operating of renewable power generation systems.

A Bi-Level Operation Strategy for Home Energy Management System Integrating the Goals of Residential Users with Distribution Network Operator

With the advancement of net-zero targets and the large-scale deployment of distributed photovoltaic (PV) systems, battery energy storage systems (BESS), and electric vehicles (EVs) in residential sectors, home energy management systems (HEMS) have become central to improving end-use energy efficiency. However, reliance on synthetic data, simplified and homogeneous load modeling, and lack of coordination between dynamic pricing mechanisms and multi-device scheduling, make them practically inefficient. Furthermore, most studies address only unilateral user-side optimization while neglecting the distribution network operational constraints and omitting rigorous anti-arbitrage mechanisms to preclude speculative user behavior. To address these gaps, this paper proposes a data-driven coordinated scheduling framework for residential PV-BESS and multi-device systems formulated within bi-level game-theoretic architecture. The upper-level employs particle swarm optimization (PSO) to determine dynamic additional price signals for peak shaving and distribution network security, with explicit constraints on distribution transformer capacity, node voltage deviation, and load ramp rate adapted to three-user scenarios. The lower-level formulates a mixed-integer quadratic programming (MIQP) model to achieve multi-objective optimization of user electricity cost, thermal comfort, device usage preference, battery cycle degradation, and end-of-cycle energy balance. Simulation results for representative summer and winter days indicate that the proposed framework reduces user-side electricity cost by around 30%, elevates PV self-consumption rate to over 70%, and achieves about 20% peak load reduction with around 15% peak-valley difference narrowing on the grid side. All distribution network security constraints and anti-arbitrage rules are strictly satisfied. The framework effectively reconciles the objectives of both residential users and the distribution grid.