相关论文: Adaptive Gradient Descent MPPT Algorithm With Comp…

Determination of the device performance parameters of perovskite solar cells is far from trivial as transient effects may cause large discrepancies in current-voltage measurements as a function of scan rate and pre-conditioning. Maximum…

Power-to-gas (P2G) can be employed to balance renewable generation because of its feasibility to operate at fluctuating loading power. The fluctuating operation of low-temperature P2G loads can be achieved by controlling the electrolysis…

This paper presents novel extremum seeking (ES) strategies for maximum power point tracking (MPPT) in photovoltaic (PV) systems that ensure unbiased convergence and prescribed-time performance. Conventional ES methods suffer from…

Micro-inverter technologies are becoming increasingly popular as a choice of grid connection for small-scale photovoltaic systems. Efficiently harvesting the maximum energy from a photovoltaic system reduces the Levelized cost for solar…

In this paper we propose a parallel coordinate descent algorithm for solving smooth convex optimization problems with separable constraints that may arise e.g. in distributed model predictive control (MPC) for linear network systems. Our…

Shading faults remain one of the most critical challenges affecting photovoltaic (PV) system efficiency, as they not only reduce power generation but also disturb maximum power point tracking (MPPT). To address this issue, this study…

The use of charge controllers in photovoltaic generation systems increases the energy harvested significantly. The most efficient type of charge controller is the Maximum Power Point Tracking (MPPT) controller. The commercially available…

A distributed model predictive control (DMPC) approach based on distributed optimization is applied to the power reference tracking problem of a hydro power valley (HPV) system. The applied optimization algorithm is based on accelerated…

Agrivoltaic systems--photovoltaic (PV) panels installed above agricultural land--have emerged as a promising dual-use solution to address competing land demands for food and energy production. In this paper, we propose a model predictive…

We consider decentralized machine learning over a network where the training data is distributed across $n$ agents, each of which can compute stochastic model updates on their local data. The agent's common goal is to find a model that…

In this paper, the behavior of a photovoltaic (PV) system, with maximum power point tracking (MPPT) connected to three-phased grid has been investigated. A voltage source inverter (VSI) has been used to connect the photovoltaic system to…

This paper presents an efficient suboptimal model predictive control (MPC) algorithm for nonlinear switched systems subject to minimum dwell time constraints (MTC). While MTC are required for most physical systems due to stability, power…

The Projected Gradient Descent (PGD) algorithm is a widely used and efficient first-order method for solving constrained optimization problems due to its simplicity and scalability in large design spaces. Building on recent advancements in…

We develop an implementable stochastic proximal point (SPP) method for a class of weakly convex, composite optimization problems. The proposed stochastic proximal point algorithm incorporates a variance reduction mechanism and the resulting…

In this paper we extend the adaptive gradient descent (AdaGrad) algorithm to the optimal distributed control of parabolic partial differential equations with uncertain parameters. This stochastic optimization method achieves an improved…

Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science. A popular approach in practice is to factorize the matrix into two compact low-rank factors, and…

The uncertainties associated with technology- and geography-specific degradation rates make it difficult to calculate the levelized cost of energy (LCOE), and thus the economic viability of solar energy. In this regard, millions of fielded…

Photovoltaic Power Ramp-Rate Control (PRRC) constitutes a key ancillary service for future power systems. Although its implementation through the installation of storage systems or irradiance sensors has been widely investigated, fewer…

The Model Predictive Control (MPC) approach is used in this paper to control the voltage profiles in MV networks with distributed generation. The proposed algorithm lies at the intermediate level of a three-layer hierarchical structure. At…

Robotic systems must be able to quickly and robustly make decisions when operating in uncertain and dynamic environments. While Reinforcement Learning (RL) can be used to compute optimal policies with little prior knowledge about the…