A simple experimental prediction model of photovoltaic power for DC microgrid

Houssamo, Issam; Wang, B.; Sechilariu, Manuela; Locment, Fabrice; Friedrich, Guy

doi:10.1109/isie.2012.6237160

Cited by 10 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to describe this limit, a load The microgrid power management and control are based on weather and load consumption short-term forecasting data. Using a PV sources model [29] and a load consumption model, they estimate PV sources power and load power. Based on these two prediction results, the microgrid optimizes the power flow while satisfying constraints.…”

Section: Microgrid Control Strategymentioning

confidence: 99%

See 1 more Smart Citation

Photovoltaic Electricity for Sustainable Building. Efficiency and Energy Cost Reduction for Isolated DC Microgrid

2015

View full text Add to dashboard Cite

Abstract:In the context of sustainable buildings, this paper investigates power flow management for an isolated DC microgrid and focuses on efficiency and energy cost reduction by optimal scheduling. Aiming at high efficiency, the local produced power has to be used where, when, and how it is generated. Thus, based on photovoltaic sources, storage, and a biofuel generator, the proposed DC microgrid is coupled with the DC distribution network of the building. The DC bus distribution maximizes the efficiency of the overall production-consumption system by avoiding some energy conversion losses and absence of reactive power. The isolated DC microgrid aims to minimize the total energy cost and thus, based on forecasting data, a cost function is formulated. Using a mixed integer linear programming optimization, the optimal power flow scheduling is obtained which leads to an optimization-based strategy for real-time power balancing. Three experimental tests, operated under different meteorological conditions, validate the feasibility of the proposed control and demonstrate the problem formulation of minimizing total energy cost.

show abstract

Section: Microgrid Control Strategymentioning

confidence: 99%

“…A few minutes ahead the test, based on solar irradiation hourly forecast data and PV source model [29], the PV sources power prediction is calculated and also corrected to correspond to the PV panels tilt. The PV sources power uncertainty is shown in Figure 7.…”

Section: Testmentioning

confidence: 99%

Photovoltaic Electricity for Sustainable Building. Efficiency and Energy Cost Reduction for Isolated DC Microgrid

2015

View full text Add to dashboard Cite

show abstract

“…It calculates possible PV power and load power evolutions for the next day. Based on solar irradiance and temperature forecasting, and on PV model, built with parameters identification or PV solar irradiance mapping [51,69], the PV predictable power p PV _prediction could be calculated with as less error as possible.…”

Section: Prediction Layermentioning

confidence: 99%

Optimal Energy Management

Mahmoud

AL-Sunni

2015

Power Systems

View full text Add to dashboard Cite

The increasing incidence of distributed generation (DG) and active distributed networks, changing regularities, and needs to improve the power system reliability and clean power support is providing development of a new power system perception commonly referred to as the smart grid (SG). In this regard, the microgrid (MG) can be considered as one of the most promising concepts. A MG is essentially an active distribution network defined as an integrated power delivery system. A MG consists of a low-voltage (LV) network composed of loads, renewable energy sources (RESs), and DG units operating as a single controllable load connected to the main grid. Compared to the conventional power plants, a MG is characterized by specific operation and constraints depending on several critical stochastic parameters. So far, MGs have been mostly established as test-bed platforms in some developed countries such as Japan, Canada, and the U.S. In fact, such concept cannot be widely implemented in practice without a prior successful establishment of an energy manager to achieve optimal and reliable control of the MG for a given site to minimize its operation cost while consolidating its reliability and environment-friendly features [1, 5-7, 12, 17, 23-27, 31].As an essential element in the new era of smart power, several approaches have been reported in the literature in relation to MG intelligent energy management applicable within the SG system [22,56]. A fuel consumption minimization approach has been proposed in [47] based on power sharing approach, in which the optimized cost function includes a penalty function for heat generation excess. The authors promoted the importance of a MG communication infrastructure allowing the coordination with RES forecasting as a part of the central control. However, the proposed approach does not take into account the prediction of RE power generation and the management of storage devices. In [76], mesh-adaptive direct-search-based optimization algorithm is applied for MG online energy management; however, this approach does not provide information regarding the forecasting of the RESs or load demand (LD). Moreover, the MG energy management problem has been reduced to

show abstract

“…Recently, renewable energy projects are expanding in order to face the continual increase of power demand and the environmental problems resulting from the use of fossil fuel sources [1]. Among all renewable energy sources, solar power is thought of as the optimal power source [2].…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of PV behaviour under harsh weather aspects

Khuffash¹,

Lamont

Abdel-Magid

2015

2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

View full text Add to dashboard Cite

The recent energy demand and environmental problems has led to the expansion of renewable energy use in power generation utilities worldwide. Many projects are being planned in order to implement solar power especially PV generated power into utility grids. However, since PV panels are highly affected by the surrounding environment, it is difficult to predict their output power. Although the manufacturer supply performance data with the panel, those data are taken under Standard Test Condition (STC), while in practical implementation the panel is rarely operated under those conditions. Hence, in this paper the effect of different weather aspects on the performance of the PV panel are studied statistically. Ninety four measured points are collected under practical desert environment in order to perform this study. Minitab software was used in order to conduct the regression analysis on the collected measurements. The results show that the weather aspects under study have a significant effect on the performance of the panel. In addition, it shows that humidity have a cooling effect on the panel which enhances its performance.

show abstract

A simple experimental prediction model of photovoltaic power for DC microgrid

Cited by 10 publications

References 9 publications

Photovoltaic Electricity for Sustainable Building. Efficiency and Energy Cost Reduction for Isolated DC Microgrid

Photovoltaic Electricity for Sustainable Building. Efficiency and Energy Cost Reduction for Isolated DC Microgrid

Optimal Energy Management

Statistical analysis of PV behaviour under harsh weather aspects

Contact Info

Product

Resources

About