where, R i is the input impedance, V i is the input voltage, I i is the input current, the external load resistance is R L, the voltage at both ends of the external load is V 0, the current flowing through the external load is I 0 and D is the duty cycle. According to Equation (4), at a constant load impedance, the system can be adjusted to reach the …
where, R i is the input impedance, V i is the input voltage, I i is the input current, the external load resistance is R L, the voltage at both ends of the external load is V 0, the current flowing through the external load is I 0 and D is the duty cycle. According to Equation (4), at a constant load impedance, the system can be adjusted to reach the …
The inherent randomness, fluctuation, and intermittence of photovoltaic power generation make it difficult to track the scheduling plan. To improve the ability to …
The rapid development of wind and solar power, with their randomness and uncertainty, reduces system stability. Optimizing schedules of complementary systems can help promote the accommodation of wind and solar power. However, it is challenging to formulate appropriate schedules for the multi-energy complementary systems under the uncertainty …
The inherent randomness, fluctuation, and intermittence of photovoltaic power generation make it difficult to track the scheduling plan. To improve the ability to track the photovoltaic plan to a greater extent, a real-time charge and discharge power control method based on deep reinforcement learning is proposed. Firstly, the …
This work focuses on applying deep reinforcement learning (RL) to the EV charging control problem with the objectives to increase photovoltaic self-consumption and EV state of charge at departure. Particularly, we propose mathematical formulations of environments with discrete, continuous, and parametrized action spaces and respective deep RL …
A new model-based reinforcement learning method was proposed. • A dual objective optimization reward function was designed. • The measured dataset of an existing building was applied. • Comparative analysis of four advanced reinforcement learning algorithms
Uncertain power generation, consumption, and non-linearity of the system make it challenging for the controllers and converters to maintain constant voltage in conditions in the normal operation ...
The most studied methods for directly converting solar energy are solar photovoltaic (PV), concentrated solar energy, and thermoelectric power generation [12]. Solar PV energy has attracted wide attention in the past few decades because of various advantages [ 13 ].
reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation | In ... our improved Reinforcement Learning method with Double Deep Q -learning approach ...
DOI: 10.1016/J.APENERGY.2021.117504 Corpus ID: 238669356 Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation @article{Dorokhova2021DeepRL, title={Deep reinforcement learning control of …
Automatic generation control (AGC) (see Fig. 1) performs a vital role [5] in control and operations of modern interconnected electrical power system by providing the balance in between the connected load plus losses …
In this research, the UC procedure with the BESS reinforcement is proposed to evaluate the maximum PV integration of the existing Java Bali Power …
To evaluate the optimal battery size of the proposed grid-tied solar PV battery-based system under the TOU pricing strategy, parameters such as system''s …
The output characteristic of photovoltaic array is nonlinear. Therefore, it needs to use the maximum power point tracking algorithm. The conventional perturbation and observation method is succinct and has high tracking efficiency. However, the excellent steady accuracy and dynamic property is difficult to achieve synchronously due to adopting fixed …
This paper proposes a hybrid prediction model of photovoltaic power based on 3DCNN + CLSTM. The overall conclusions of this paper are as follows: (1) In terms of speed and convergence, the prediction time of the hybrid model is …
In this research, an energy management algorithm based on reinforcement learning was proposed for a grid-tied solar PV-battery microgrid supplying power to a commercial load. The novelty of the proposed work is mainly computational energy …
batch reinforcement learning to implement a microgrid EMS that optimizes battery schedules. Charge and discharge efficiency of the battery and the microgrid nonlinearity
learning based optimal energy management for photovoltaic and battery energy storage ... Time series forecasting of solar power generation for large-scale photovoltaic plants . Renew. Energy 150 ...
Rain flow counting method is a commonly used battery life evaluation method [17], [18]. The rain flow counting method is also called the "tower top method", which is consistent with the stress–strain hysteresis loop of …
The paper [10] presents a novel adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO)-based hybrid maximum power point tracking (MPPT) method for efficient photovoltaic power generation. The proposed method eliminates oscillations
PV arrays combined with battery systems are a model for strengthening the resilience of distributed power generation to reduce power interruptions of critical facilities [1,2]. Generally, resilience implies the ability of a system to withstand or quickly return to normal condition after the occurrence of an event that disrupts its state [3].
Detection of abnormal photovoltaic (PV) system operation is essential to ensure safe and uninterrupted performance. In this study, the authors present a data-driven two-stage method for PV fault detection and diagnosis (FDD). We exploit an inherent characteristic of PV systems, i.e., voltage and current changes at maximum power point …
1.1. Capacity of solar power generation Although the use of renewable energy globally has noticeably increased, the unpredictability of these resources has put enormous pressure on large-scale power generation projects in the national grids. In this context, Al-Maamary et al. (2017) reviewed the challenges in the renewable energy sector …
The objective of the study is to jointly propose a design of the PV and battery components, as well as a control strategy of the described energy system in order to minimize the total …
This application aims to examine the variations in grid-point voltage during transient processes. The DC side of the virtual synchronous generator is selected as a …
Research on distributed photovoltaic power prediction method based on reinforcement learning Chengjing Wang 1, Haitao Zhang 1 and Zhenxing Chen 1 Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series, Volume 2728, 2023 2nd International Conference on Energy and Electrical Power Systems (ICEEPS …
DOI: 10.1016/j.aej.2021.12.012 Corpus ID: 245228548 Reliability-driven distribution power network dynamic reconfiguration in presence of distributed generation by the deep reinforcement learning method With the large-scale access of …
Our paper evaluates the potential of reducing investment needs through decentralized photovoltaic electricity generation and battery energy storage systems. We use power-flow analyses on representative grid models to test rural, urban, and suburban grids'' resilience to higher electric vehicle penetration.