Motivated by concerns about the environment and energy shortages, considerable progress has recently been made in the development of photovoltaic (PV) and other forms of distributed generation. These developments have contributed greatly to awareness of the importance of renewable energy and governmental policies to revise energy priorities to ensure the adoption and significant growth of renewable energy. Safely and reliably interconnecting various PV generators is a major challenge in the development of modern power systems and the interconnection of PV may have effects that require close attention. Standards or guidelines for grid-connected PV generation systems considerably affect PV development. This investigation reviews and compares standards and guidelines for distributed generation, and especially for PV integration. Pertinent standards and guidelines that ensure the successful operation of PV systems are presented. This investigation serves as a reference for improving standards for grid-connected PV generation systems.

As renewable power generation becomes more prevalent, the problem of frequency stability has become a particular concern of transmission system operators (TSOs), especially those of small power transmission systems. Traditional wind generation systems do not provide frequency regulation because they are decoupled from the power grid. Therefore, as conventional thermal generators are replaced by wind generators, the issue of frequency regulation for wind generation systems has become increasingly important. To release the kinetic energy stored in the rotating mass, inertia and droop control loops can be added into the controller of a wind turbine (WT). This work proposes an advanced control strategy with the time-varying gains of two control loops. In the proposed strategy, the gains are determined based on the desired frequency-response time. Moreover, the initial gain of the control loop is determined based on the wind speed, considering the operating condition of each WT in a wind farm (WF). The effectiveness of the proposed method is verified using an actual power system, revealing that it can be used to improve frequency regulation in a power grid.

During the past one to two decades, the probabilistic forecasting of a wind-power generation has been regarded as a necessary input to decisions made for the purpose of reliable and economic power systems operations, especially since the penetration of the renewable energy has begun to grow rapidly. Probabilistic forecasting differs from traditional deterministic forecasting in which it takes uncertainty into account. This paper proposes a modified nonparametric method for constructing reliable prediction intervals (PIs). The lower upper bound estimation (LUBE) method is adapted to construct PIs for the wind-power generation, based on the ensemble wind-speed data from the numerical weather prediction system of the Central Weather Bureau of Taiwan. The charged system search (CSS) is used to adjust parameters in LUBE. The performance of the proposed method is examined using datasets from several wind farms in Taiwan. Simulation results demonstrate that the quality of PIs output by the proposed model significantly exceeded that of those constructed using the persistence model with a 1-h-ahead time horizon.

In recent years, the integration of wind power generation facilities, and especially offshore wind power generation facilities, into power grids has increased rapidly. Therefore, the grid codes concerning wind power integration have become a major factor in ensuring power system reliability. This work compares grid codes about wind power integration around the world. The grid codes of Denmark, Ireland, the UK, Germany, Spain, China, the US, Canada, and other countries are considered. The most important of these grid codes concern reactive power, frequency regulation, fault ride-through, and power quality. Several grid codes also address communication, ramp rate, and the offshore wind power plants (WPP). This work provides information on the future of grid code requirements for offshore wind power integration, which helps the system operators ensure the safe operation of a power system with a high penetration of wind power generation.

This paper presents two probabilistic approaches based on bootstrap method and quantile regression (QR) method to estimate the uncertainty associated with solar photovoltaic (PV) power point forecasts. Solar PV output power forecasts are obtained using a hybrid intelligent model, which is composed of a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on radial basis function neural network (RBFNN) that is optimized by particle swarm optimization (PSO) algorithm. The point forecast capability of the proposed hybrid WT+RBFNN+PSO intelligent model is examined and compared with other hybrid models as well as individual SCM. The performance of the proposed bootstrap method in the form of probabilistic forecasts is compared with the QR method by generating different prediction intervals (PIs). Numerical tests using real data demonstrate that the point forecasts obtained from the proposed hybrid intelligent model can be effectively used to quantify PV power uncertainty. The performance of these two uncertainty quantification methods is assessed through reliability.

Solar power has rapidly become an increasingly important energy source in many countries over recent years; however, the intermittent nature of photovoltaic (PV) power generation has a significant impact on existing power systems. To reduce this uncertainty and maintain system security, precise solar power forecasting methods are required. This study summarizes and compares various PV power forecasting approaches, including time-series statistical methods, physical methods, ensemble methods, and machine and deep learning methods, the last of which there is a particular focus. In addition, various optimization algorithms for model parameters are summarized, the crucial factors that influence PV power forecasts are investigated, and input selection for PV power generation forecasting models are discussed. Probabilistic forecasting is expected to play a key role in the PV power forecasting required to meet the challenges faced by modern grid systems, and so this study provides a comparative analysis of existing deterministic and probabilistic forecasting models. Additionally, the importance of data processing techniques that enhance forecasting performance are highlighted. In comparison with the extant literature, this paper addresses more of the issues concerning the application of deep and machine learning to PV power forecasting. Based on the survey results, a complete and comprehensive solar power forecasting process must include data processing and feature extraction capabilities, a powerful deep learning structure for training, and a method to evaluate the uncertainty in its predictions.