This study provides several models for accurately computing the annual optimum tilt angle for fixed solar photovoltaic arrays or solar collectors, in any location of the world. The optimum tilt angle that maximizes the annual energy yield can therefore be easily calculated in the absence of meteorological data and simulation software tools. The proposed models are calculated using global horizontal radiation data collected from 2551 sites across the world. In the process, well-established submodels have been selected to estimate the hourly irradiance on any possible inclined surface, and its corresponding annual energy yield. After selecting the optimum angle for each location, through a regression analysis, a mathematical model that calculates annual optimum angles as a function of latitude has been developed. Furthermore, regression techniques such as neural networks and decision trees have been compared with the polynomial models. Finally, the results are compared to those obtained from high-quality 1-min measured irradiance data obtained at 52 research-class stations from the World Radiation Monitoring Center–Baseline Surface Radiation Network, providing a remarkably high number of validation data points. The results are analyzed, validated, and compared with previous research proposals proving the good performance of the proposed models.

This manuscript proposes a series of global models to estimate optimum annual tilt angle (βopt) as a function of local variables (latitude, diffuse fraction and albedo) based on the hourly irradiance data of 14,468 sites spread across the globe from the One Building database. As a result, these models can be used for any location in the absence of local meteorological data. First, a polynomial regression model, applicable worldwide, is proposed to estimate βopt as a function of latitude. This model fits the global data considered with a 2% RMSE error. Average energy losses are estimated to be 1% for a 10° variation from βopt. A variation of 40° with respect to βopt, implies a 12–18% energy loss depending on latitude. In addition, if only latitude is considered to estimate βopt, different expressions should be used for latitudes >50° depending on the hemisphere. These variations are a result of the influence of diffuse irradiance on βopt, due to the fact that sites with higher amounts of diffuse irradiance have a lower βopt. Secondly, a polynomial surface regression model to estimate βopt as a function of latitude and the annual diffuse fraction is proposed improving the results, reaching a 0.7% RMSE error. Thirdly, a simplified polynomial surface regression model to estimate βopt as a function of latitude and albedo (without the influence of the diffuse fraction) is proposed, and finally a model gathering all three variables under study (latitude, annual diffuse fraction and albedo) to calculate the optimum tilt angle is presented.

Distributed generation is increasingly being integrated into distribution networks worldwide, presenting new challenges for network operators and planners. In particular, the introduction of photovoltaic (PV) generation at the low voltage (LV) level has highlighted the ongoing need for more extensive and detailed modeling to quantify the full extent and nature of potential impacts. While a number of approaches have been developed to address the size of this problem, the most accurate and comprehensive approach is to carry out simulations for the entire network across multiple scenarios. However, this task is computationally complex and requires significant amounts of data. To address this challenge, this paper presents a simplified and computationally efficient methodology based around a two-bus equivalent model, which may be used to estimate the maximum voltage in an LV area due to PV generation over time. The developed model is validated against a full three-phase power flow approach for a real-world distribution network comprising 10 213 LV network areas. Furthermore, to highlight its utility, the model is used in a case study examining the effectiveness of reactive power injection for mitigating overvoltage due to PV generation.

Residential rooftop-mounted solar photovoltaic (PV) panels are being installed at an increasing rate, both in New Zealand and globally. There have been concerns over possible issues such as overvoltage and overcurrent. These PV systems are mostly connected at low voltage (LV). This study presents a case study of simulating the entire LV network from a single utility, comprising 10,558 11 kV-415 V transformers and their associated distribution feeders. These results are also presented by network type. Various solar PV penetration levels are added to the model and the power-flow results are presented. From these results, possible maximum limits of solar PV penetration are investigated and measures to alleviate overvoltage problems are simulated. The effect of using PV inverters with voltage regulation is simulated. Results show that some minor overvoltage problems can be expected in the future, particularly in urban areas. However, in most cases the overvoltage would not be much higher than the statutory limit of 1.06 p.u.

Photovoltaic (PV) generation systems are increasingly being integrated into distribution networks, presenting new challenges for network operators and planners. To fully quantify the impact of this distributed generation, power systems and control engineers are often required to model a large number of networks across multiple scenarios of PIT uptake and allocation. However, due to the complexity of these network models and simulation methods, and the associated computational constraints, very simplified models are often adopted for solar irradiance, and PV module and inverter output power. These ad hoc models often utilise simple linear relationships, and are not validated against real data or more detailed models. The proposed model (SoL) is composed of pre-existing equations selected from the current state of the art models, as well as newly formulated equations. Each submodel is validated against pre-existing models using data from a number of meteorological sites around the world, showing good performance with reduced computational complexity. The resulting model is subsequently shown to be suitable for running large scale quasi-steady state power flow simulations and Monte Carlo analyses as required for grid integration studies. (C) 2015 Elsevier Ltd. All rights reserved.