2 research outputs found
An Adaptive Multi-Objective Decision System With Reinforcement Learning for PV-ESS Sizing and Operation for Grid-Connected Industrial PV-ESS Systems in Distribution Networks
No full textThe integration of photovoltaic (PV) systems with energy storage systems (ESS) introduces complex design and operational challenges due to the stochastic nature of solar generation and the dynamic behavior of energy markets. This paper presents a reinforcement learning -enhanced Decision Support System (DSS). The system jointly addresses the optimal sizing and real-time control of PV-ESS configurations. The proposed framework combines a multi-objective optimization core with a policy-gradient RL agent. This agent jointly optimizes operational decisions and dynamically adjusts the weights in the multi-objective function—based on real-time feedback and system state—to reflect the relative importance of competing objectives such as cost, emissions, curtailment and battery degradation. These adjustments reflect evolving environmental, market and load conditions. Historical and real-time data from utility-scale PV parks and industrial consumers inform the learning process, ensuring policy relevance and robustness. The system is designed for grid-connected industrial sites embedded in distribution networks, where node-level optimization can indirectly support peak reduction, load smoothing and demand-side flexibility at the network level. The system utilizes a policy-gradient reinforcement learning agent with adaptive weight tuning to balance conflicting objectives. Simulation results over a two-year horizon demonstrate that the proposed RL-DSS reduces average energy costs by up to 35%, increases PV self-consumption above 70% and lowers curtailed energy below 8%, outperforming static and rule-based strategies. The results validate the potential of RL-based decision systems to enable scalable, flexible and economically viable energy management under real-world uncertainty. Future work includes integration with probabilistic forecasting models and distributed multi-agent coordination, where each industrial node may act as an autonomous RL agent contributing to cooperative grid-level objectives
Energy yield estimation of on-vehicle photovoltaic systems in urban environments
No full textGreenhouse gases from the propulsion systems of road transportations constitute a significant obstacle to achieve the Paris Agreement objectives. Nowadays, the substitution of conventional internal combustion engines with electric motors, along with electrochemical storage systems are the leading efforts to reduce the use of fossil fuels in road transportations. However, their limited driving range and the long charging times are the main technical factors that hinder the development of electromobility. Thus, energy harvesters and regeneration systems are increasingly incorporated in road vehicles, in order to increase their driving range. In this context, Vehicle Integrated and Applied Photovoltaics (VIAPVs) constitute an attractive prospect. The electricity yield for VIAPVs depends strongly on the route, the shadings due to the urban environment, the applied Maximum Power Point (MPPT) algorithm and the traffic conditions. In this paper, four commonly used commercial MPPT algorithms are experimentally evaluated, regarding their ability to extract the maximum available power simulating realistic city routes. The results show notable discrepancies in the performance of the studied algorithms, between terrestrial and VIAPV applications, highlighting the impact of poor MPPT performance in terms of power generation in moving vehicles
