A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contains wind turbines, photovoltaic panels, and concentrated solar renewable energy technologies with storage capabilities. The fully operational Kuwait Renewable Energy Prediction System (KREPS) employs artificial intelligence (AI) in multiple portions of the forecasting structure and processes, both for short-range forecasting (i.e., the next six hours) as well as for forecasts several days out. These AI methods work synergistically with the dynamical/physical models employed. This paper briefly describes the methodology used for each of the AI methods, how they are blended, and provides a preliminary assessment of their relative value to the prediction system. Each operational AI component adds value to the system. KREPS is an example of a fully integrated state-of-the-science forecasting system for renewable energy.

The penetration of renewable energy sources, particularly wind energy, into power systems has been rapidly increasing in recent years. This type of energy sources is relatively variable and unpredictable. Wind could, for example, be available at low-load periods and it may not be committed to supply power at peak-load hours, which results in a mismatch between generation and load. One of the actions that can be taken to support wind integration is the use of Energy Storage Systems (ESSs). In this paper, we formulate an AC Optimal Power Flow (OPF) problem with Battery Energy Storages (BESs) and then perform tests on IEEE 14-bus case study with different locations and numbers of BESs to discuss impacts of BES location on system operation. NOMENCLATURE 0 , 1 , 2 Cost coefficients of generating units at bus i , ℎ Cost coefficients for charging and discharging power of BES at bus j ℎ, Real charging power of BES at bus i in hour t , Real discharging power of BES at bus i in hour t ℎ, Charging efficiency of BES at bus i , Discharging efficiency of BES at bus i Energy of BES at bus i in hour t −1 Energy of BES at bus i in hour t-1 , Real generation power at bus i in hour t , Load real power at bus i in hour t , Reactive generation power at bus i in hour t ,

In this paper, we propose a new scheme for probabilistic power flow in networks with renewable power generation by making use of a data clustering technique. The proposed clustering technique is based on the combination of Principal Component Analysis and Differential Evolution clustering algorithm to deal with input random variables in probabilistic power flow. Extensive testing on the modified IEEE-118 bus test system shows good performance of the proposed approach in terms of significant reduction of computation time compared to the traditional Monte Carlo simulation, while maintaining an appropriate level of accuracy.

The increasing penetration of renewable energy sources has introduced great uncertainties and challenges into computation and analysis of electric power systems. To deal with uncertainties, probabilistic approaches need to be used. In this paper, we propose a new framework for probabilistic assessment of power systems taking into account uncertainties from input random variables such as load demands and renewable energy sources. It is based on the cumulant-based Probabilistic Power Flow (PPF) in combination with an improved clustering technique. The improved clustering technique is also developed in this study by making use of Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) to reduce the range of variation in the input data, thus increasing the accuracy of the traditional cumulant-based PPF (TCPPF) method. In addition, thanks to adopting PCA for dimensionality reduction, the improved clustering technique can be effectively dealt with a large number of input random variables so that the proposed framework for probabilistic assessment can be applied for large power systems. The IEEE-118 bus test system is modified by adding five wind and eight solar photovoltaic power plants to examine the proposed method. Uncertainties from input random variables are represented by appropriate probabilistic models. Extensive testing on the test system indicates good performance of the proposed approach in comparison to the traditional cumulant-based PPF and Monte Carlo simulation. The IEEE-118 bus test system is modified by adding five wind and eight solar photovoltaic power plants to examine the proposed method. Extensive testing on the test system, using Matlab (R2015a) on an Intel Core i5 CPU 2.53 GHz/4.00 GB RAM PC, indicates good performance of the proposed approach (PPPF) in comparison to the TCPPF and Monte Carlo simulation (MCS): In terms of computation time, PPPF needs 4.54 seconds, while TCPPF and MCS require 2.63 and 251 seconds, respectively; ARMS errors are calculated for methods using benchmark MCS and their values clearly demonstrate the higher accuracy of PPPF in estimating probability distributions compared to TCPPF, i.e., the maximum (Max) and mean (Mean) values of ARMS errors of all output random variables are: ARMSPPPFmax = 0.11%, ARMSTCPPFmax = 0.55%, and ARMSPPPFmean = 0.06%, ARMSTCPPFmean  = 0.35%.