Photovoltaic Power Prediction for Solar Car Park Lighting Office Energy Management

Zdiri

et al. 2022

IJEER

Self Cite

With the ever-increasing prevalent power crisis and pollution of the environment, solar power, has attracted greater attention as a new and clean energy source. It provides an alternative solution for isolated sites with an unavailable grid connection. However, it is not without any drawbacks, mainly its intermittent nature, related primarily owing to its reliance on meteorological variables such as the temperature outside and the amount of sunlight. In effect, the PV systems that produced electrical energy could well display an electricity excess or deficit at the loads level, likely to result in system service discontinuity. In this respect, the present paper is designed to provide an intelligent management strategy to PV station owners with a dump load. It can involve serving two customers simultaneously according to the following scenarios: the PV production installation of the customer1 is greater than their required load; however, the customer2's neighboring station does not have enough power to cover its electrical load. This case brings electrical energy from the initial station to make up for the shortfall, and vice versa. Lithium-ion batteries step in the case when the essential electrical power cannot be delivered either by the local station or the neighboring one or to keep the accumulated power excess. If one of the stations (1 or 2) detects a power surplus and the batteries are completely charged, the generated power excess must be redirected to a secondary load, commonly known as the dump load. Relying on the artificial neural network controller, the suggested exchange control is used for two independent PV-battery stations with dump load. The MATLAB/Simulink attained simulation turns out to demonstrate the advanced controller’s noticeable performance and effectiveness in managing the standalone PV system’s operability in terms of continuous electrical energy delivery flow to the resistive load while reducing power waste and increasing the lithium-ion battery lifespan.

“…𝐼 𝑝𝑣,𝑛𝑒𝑤 = 𝐼 𝑝𝑣 + ∆𝐼 𝑝𝑣 (7) The PV generator is constructed by linking solar PV modules in series (NSS) and parallel (NPP), as presented by [22][23]:…”

Section: Pv Panelmentioning

confidence: 99%

An Advanced Artificial Neural Network Energy Management in Standalone PV Systems

Alzaroog

Zdiri

et al. 2022

IJEER

Self Cite

“…𝑉 𝑝𝑣,𝑛𝑒𝑤 = 𝑉 𝑝𝑣 + ∆𝑉 𝑝𝑣 (11) 𝐼 𝑝𝑣,𝑛𝑒𝑤 = 𝐼 𝑝𝑣 + ∆𝐼 𝑝𝑣 (12) The PV station is built by connecting PV cells in series (Ns) and parallel (Np). The resulting PV power can be computed using equation (13) [19][20].…”

Section: Photovoltaic Arraymentioning

confidence: 99%

“…The battery is in the charged mode if the battery current is a negative value. The OCV for the charge mode can be defined using equation (20). To keep the battery from overcharging and discharging, two limits of the state of charge were defined: 𝑆𝑂𝐶 𝑚𝑖𝑛 = 15 % and 𝑆𝑂𝐶 𝑚𝑎𝑥 = 85 %.…”

Section: 𝑂𝐶𝑉 Denotes the Open-circuit Voltage (𝑉)mentioning

confidence: 99%

Fuzzy Logic Energy Management between Stand-alone PV Systems

Zdiri

2021

IJRER

Because of its environmental benefits, PV technology is widely used in several applications. It presents an alternative solution for isolated sites that does not dispose a grid connection. However, it has drawbacks, its intermittent character, due to its dependency on meteorological parameters such as the outside temperature and solar irradiation. The energy produced by the PV systems can have an excess or deficit of electricity at the loads, which leads to unused energy or a service discontinuity. This paper presents an intelligent strategy management between photovoltaic station owners. It can be involved as follow between two customers: the photovoltaic installation production of the first customer is higher than its load requirement; however, the neighboring station has a lack of energy to cover its load. In this case, it brings energy from station 1 to cover the insufficiency and vice versa. The batteries intervene neither, if the required energy cannot be provided by the local and the neighbored stations, nor, to store the excess power. The dual standalone photovoltaic system was created using MATLAB Simulink environment. Basing on the fuzzy logic controller, the proposed management exchange algorithm is applied for a two stand-alone PV-battery stations. The Mamdani is one of the widely known fuzzy inference system. It ensures continuous energy delivery to the load while minimizing wasted power. The simulation results demonstrate the previously mentioned controller performances and effectiveness between standalone PV systems.

Journal of Renewable and Sustainable Energy

Application of machine learning methods in photovoltaic output power prediction: A review

Zhang

2022

As the proportion of photovoltaic (PV) power generation rapidly increases, accurate PV output power prediction becomes more crucial to energy efficiency and renewable energy production. There are numerous approaches for PV output power prediction. Many researchers have previously summarized PV output power prediction from different angles. However, there are relatively few studies that use machine learning methods as a means to conduct a separate review of PV output power prediction. This review classifies machine learning methods from different perspectives and provides a systematic and critical review of machine learning methods for recent PV output power applications in terms of the temporal and spatial scales of prediction and finds that the artificial neural network and support vector machine are used much more frequently than other methods. In addition, this study examines the differences between the output power prediction of individual PV plants and regional PV stations and the benefits of regional PV plant prediction, while this paper presents some performance evaluation matrices commonly used for PV output power prediction. In addition, to further improve the accuracy of machine learning methods for PV output power prediction, some researchers suggest preprocessing the input data of the prediction models or considering hybrid machine learning methods. Furthermore, the potential advantages of machine model optimization for prediction performance improvement are discussed and explored in detail.