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.

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.

Increasing demand for electricity and the modernization of power systems within competitive markets has induced power systems to operate close to their stability limits. Therefore, the continuous monitoring and control of power systems through voltage stability indices is urgently needed. This is the first-ever effort to examine more than 40 voltage stability indices based on their formulation, application, performance, and assessment measures. These indices are sorted based on a logical and chronological order considering the most recent indices to be applied worldwide. However, the generalizability of these indices in terms of multivariable objectives is limited. Despite its limitation, this study systematically reviews available indices in the literature within the past three decades to compile an integrated knowledge base with an up-to-date exposition. This is followed by a comparative analysis in terms of their similarity, functionality, applicability, formulation, merit, demerit, and overall performance. Also, a broad categorization of voltage stability indices is addressed. This study serves as an exhaustive roadmap of the issue and can be counted as a reference for planning and operation in the context of voltage stability for students, researchers, scholars, and practitioners.

Electric vehicle charging station have become an urgent need in many communities around the world, due to the increase of using electric vehicles over conventional vehicles. In addition, establishment of charging stations, and the grid impact of household photovoltaic power generation would reduce the feed-in tariff. These two factors are considered to propose setting up charging stations at convenience stores, which would enable the electric energy to be shared between locations. Charging stations could collect excess photovoltaic energy from homes and market it to electric vehicles. This article examines vehicle travel time, basic household energy demand, and the electricity consumption status of Okinawa city as a whole to model the operation of an electric vehicle charging station for a year. The entire program is optimized using MATLAB mixed integer linear programming (MILP) toolbox. The findings demonstrate that a profit could be achieved under the principle of ensuring the charging station’s stable service. Household photovoltaic power generation and electric vehicles are highly dependent on energy sharing between regions. The convenience store charging station service strategy suggested gives a solution to the future issues.

In this paper, the concept of end-user driven micro grid is introduced and an economic dispatch framework is proposed for optimal power and battery scheduling of such a micro grid. The main advantage is that unlike other centralized approaches reported in the literature, the proposed architecture provides end-user flexibility, optimally manages the battery minimizing the power required from the grid and at the same time ensures grid level reliability. The proposed optimal scheduling is first validated on a grid connected micro grid including a photovoltaic (PV) farm and a battery, considering the grid as a dispatchable power source. Then the architecture is compared with one of the existing centralized optimization approaches. To demonstrate the applicability and scalability, the architecture is further evaluated on a modified IEEE 33 bus distribution feeder including multiple micro grids suitable for real-time implementation. The test results indicate that the approach optimal, scalable, feasible, improves feeder reliability, and provide maximum utilization of the PV arrays.

Numerous articles have been written regarding the potential affects that electric vehicle (EV) charging stations can have on power distribution systems. With the advent to adopt a dynamic wireless power transfer (DWPT) roadway charging infrastructure, there lies a potential for hazardous effects to occur. Therefore, this paper presents a novel methodology to analyze the sustainability and the potential effects that a DWPT roadway can have on electric power distribution grid. Historical traffic flow (HTF) data is converted using a Monte Carlo method to develop an EV traffic flow (EV-TF). Then, a full DWPT simulation of a 1-mile and 10-mile roadway is performed to determine the power fluctuations that can occur as different densities of EVs traverse the roadway. This method creates simulations using an IEEE 33-Bus power distribution system to evaluate if a DWPT roadway for in-motion EV charging causes voltage magnitudes to fluctuate beyond operational standards.