This paper proposes an optimal photovoltaic (PV) and battery storage system sizing procedure for a small-industrial building under uncertainty, using real high-resolution electricity consumption and PV production data. The techno-economic optimization framework considers discounted cash flow metrics such as Net Present Value (NPV), Internal Rate of Return (IRR), payback period, and annual energy savings to assess how different scenarios and combinations affect electricity cost and energy self-sufficiency. Focusing on a small-industrial scale case study under the net-billing scheme, it is demonstrated that larger PV systems can deliver substantial annual savings and investment value, especially when electricity prices are high and export compensation is low. Battery storage increases self-consumption and operational flexibility but maximizes NPV only when a substantial scale PV system is installed. Sensitivity analysis shows that optimal capacity choices are highly dependent on electricity price growth, discount rates, and export compensation. The results apply to firms, energy managers, and policy makers seeking to accelerate renewable adoption, enhance energy resilience, and improve their carbon footprint.

Cooperative, Connected, and Automated Mobility (CCAM) is increasingly dependent on fast, secure, and reliable Vehicle-to-Everything (V2X) communication as well Artificial Intelligence (AI) techniques to enable collective perception and maneuver coordination in autonomous driving. Despite intense research, two problems still challenge wide scale deployment: uncertainties mainly emanating from leveraging AI methodologies; and high-frequency wireless communications, particularly in the millimeter wave (mmWave) and terahertz (THz) bands, which are highly susceptible to environmental obstacles, leading to severe signal attenuation and communication delays. To mitigate the latter RIS (Reconfigurable Intelligent Surface) technology has recently received significant attention. However, the effect of RIS on collective perception and automated control maneuvers has been scarcely researched in literature. In this work we report on the development of a simulator which allows integration of RIS technology with CPM exchange for CCAM applications enabling the characterization of uncertainties and communication delays demonstrating the performance gains as a result of RIS integration. Moreover, the adversarial effects of the uncertainties and delays on the performance of the cooperative control schemes are demonstrated using a platoon merging maneuver example as our test case. The results highlight the need for adaptive controllers which take into consideration the observed uncertainties and the delays. The pursued analysis formalizes the intuitive approach of increasing the inter-vehicle spacing as well as decreasing the speed of response in case of increased uncertainties and delays. Simulations results demonstrate the effectiveness of the presented approach.

Islanded and weakly interconnected power systems face increasing operational challenges as the penetration of renewable energy grows, particularly in environments with limited flexibility and reserve support. This study investigates a residential energy community in Cyprus using real time prosumer data and proposes a hierarchical decentralized Energy Management System (EMS). The EMS was initially implemented as a rule-based Minimum Viable Prototype (MVP), enabling practical validation of system operation and data integrity under realistic conditions. Building on this foundation, the final EMS adopts a three-layer control architecture in which rule-based household storage operation and peer-to-peer energy trading are complemented by a community-level Model Predictive Control (MPC) strategy for shared battery management. The MPC leverages short-term net-energy forecasts to proactively schedule charging and discharging of the community battery with the objective of reducing grid energy imports and improving local renewable energy utilization, while respecting battery operational limits and regulatory constraints. Simulation results based on measured data demonstrate consistent improvements over the rule-based baseline, achieving up to 23.4% reduction in electricity cost, approximately 6–7% reduction in grid energy imports, and a 1–2% increase in community self-sufficiency. Although the reduction in imported energy is moderate, its cumulative impact over long-term operation leads to significant economic benefits under time-varying tariffs. These results demonstrate that even when MPC is applied exclusively at the community battery layer, coordinated system-level energy management can deliver stable and economically meaningful improvements under realistic operating conditions.