The rapid digitalization and decarbonization of electrical power systems have brought increased operational complexity and new occupational risk dynamics. This transition renders traditional compliance-based safety models inadequate for managing the emerging complexities of cyber–physical and socio-technical systems. This paper develops a conceptual socio-technical safety architecture for occupational risk management in electrical power systems, grounded in the concepts of systems innovation and socio-technical modeling. A structured narrative review of international standards, accident investigations, and emerging technologies is conducted to reinterpret hazards as interacting subsystems within a dynamic, adaptive framework. The proposed framework synthesizes technical safety controls, human reliability factors, and artificial intelligence-driven predictive maintenance within a single architecture, supported by dynamic feedback loops. The model addresses nonlinear risk propagation across smart grid applications, hydrogen systems, and battery energy storage systems. By transitioning from a reactive to a proactive, adaptive approach to safety governance, the architecture enhances the resilience of electrical power systems, reduces the potential for cascading failures, and aligns occupational safety with infrastructure modernization strategies for electrical power systems. The framework provides a conceptual basis for integrating technology innovation with occupational risk management across complex energy infrastructures undergoing digital transformation.
H. Smadi, S. Albatran, Yazan M. Alsmadi· Applied System Innovation· 0 citations
Smart charging of electric-vehicle (EV) fleets must balance energy cost, transformer/feeder power limits, user satisfaction, and the operational value of on-site resources such as rooftop PV and battery energy storage systems (BESS). This work presents a scenario-based model predictive control (SB-SMPC) framework for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) coordination that minimizes the net operating cost while satisfying the system constraints. The controller explicitly models stochasticity in base load, PV generation, and electricity prices via sampled scenarios, and it also integrates demand charge cost for distribution grid services. EV service quality is guaranteed through departure energy targets, connection-time policies, and a minimum state of charge (SoC) floor. BESS dynamics, round-trip efficiency, terminal SoC targets, and battery degradation costs are included to capture battery storage economics. This study compares V2G operations with and without BESS across daily horizons. Results show that SB-SMPC systematically limits transformer import, curtails PV only when economically justified, and shifts charging to low-price periods while meeting EV energy requirements; enabling V2G further reduces net costs when energy export cost and demand charges are favorable. Comparative results (with/without BESS) reveal that BESS helps to reduce net electricity cost around 4% and grid peaks around 10% as compared to without BESS installation. Imposing high demand charges further cuts the peaks about 11%. The sensitivity analysis further confirmed the robustness of the proposed framework under varying load, PV, and price conditions.
Obaid Ur Rehman, Noman Ahmad, A. Uppal et al.· IEEE Access· 0 citations
