This paper presents the design of a predictive controller for a boost converter and validation through real-time simulation. First, the boost converter was mathematically modeled, and then the electronic components were designed to meet the operation requirements. Subsequently, a model-based predictive controller (MBPC) and a digital PI (Proportional–Integral) controller were designed, and their performance was compared using MATLAB/SIMULINK^®. The controls were further verified by implementing test benches based on an FPGA (Field-Programmable Gate Array) with an OPAL-RT real-time simulator, which included the RT-LAB and RT-eFPGAsim simulation packages. These tests were successfully carried out, and the methodology used for this design was validated. The results showed a better response obtained with MBPC, both in terms of stabilization time and lower overvoltage.

Hydropower-dependent electricity systems, such as Ecuador’s, face critical supply disruptions during droughts: a vulnerability exemplified by the 2024 power outages. This study assesses the technical, economic and environmental feasibility of a 200.84 MWp grid-connected solar photovoltaic (PV) plant proposed for the Pacific Refinery site in Manabi, Ecuador, as a strategy to diversify the energy matrix and reduce hydrological risk. Using site-specific solar resource data (4.65 kWh/m²/day) and PVSyst simulations, the plant achieves an annual energy production of 295 GWh with a performance ratio (PR) of 85.3%. A discounted cash flow analysis over 25 years, assuming a 7% discount rate and an electricity price of 60 USD/MWh, yields a net present value (NPV) of 104.9 MUSD, an internal rate of return (IRR) of 62.2%, and a levelized cost of energy (LCOE) of 14.5 USD/MWh, well below current industrial tariffs in Ecuador. Sensitivity analysis confirms project viability under ±15% variations in investment cost, energy price, and solar resource. Over its lifetime, the plant avoids 1.83 Mt of CO₂ emissions, supporting national decarbonization goals. The results demonstrate that large-scale PV deployment in high-radiation, low-latitude regions can be highly profitable and contribute to energy sovereignty in hydropower-dependent systems. Furthermore, this study provides a replicable model for repurposing unused industrial land for renewable energy generation, offering actionable insights for policymakers and investors in developing economies.

In recent years, the rapid expansion of electric vehicle (EV) charging infrastructure and the increasing penetration of renewable energy sources require highly efficient and dynamically robust power electronic interfaces. In photovoltaic (PV)-assisted EV charging stations and DC microgrids, bidirectional DC-DC converters (BDCs) are essential for managing power flow between PV arrays, battery energy storage systems, and the DC bus supplying EV chargers. This paper presents a novel voltage and current control design for a BDC operating in a PV-powered DC microgrid oriented to EV charging applications. Following a detailed mathematical model of the converter, a digital current controller and a predictive voltage regulator were developed using Model-Based Predictive Control (MBPC). The proposed cascade control structure enables accurate DC bus voltage regulation and seamless bidirectional power flow under dynamic load variations representative of EV charging and discharging scenarios. The control scheme was evaluated in MATLAB/SIMULINK^® and experimentally validated through Field-Programmable Gate Array (FPGA)-based test benches using an OPAL-RT real-time (RT) simulator, integrating the RT-LAB and RT-eFPGAsim environments. The predictive controller achieved precise regulation in both buck and boost modes, reaching efficiencies of 97.07% and 98.57%, respectively. The results demonstrate that integrating MBPC with RT validation provides high performance, fast dynamic response, and computational efficiency, making the proposed approach suitable for renewable-integrated EV charging stations and next-generation DC microgrid-based mobility systems.