An Improved Model of Total Supply Capability for Distribution Systems
The Total Supply Capability (TSC) model is newly proposed to evaluate the maximum load a distribution system can supply under the N-1 security guideline. This TSC model and a linear programming algorithm can calculate the accurate TSC of a distribution system; but the results of transformer loading rates calculated by this model are usually unbalanced, which is unacceptable in the practical operation. In order to solve this problem, this paper discusses the solution properties of the TSC model first. It is found that the transformer loading rates vector is not unique under the same TSC level; and only one of the optimal solution can be obtained from the linear programming software such as lingo. Then, an improved TSC model is proposed, which takes variance of loading rates and heavy-load areas into account. This new model is divided into two parts, the first part is the TSC calculation process, and the second part is the most balanced loading rate calculation process. Finally, a practical case is given to illustrate the effect of the improved model. It has shown that the loading rates calculated through this method are more balanced than the original one under the same TSC. The method can be useful tools to help system planners to get the best configuration of transformers in distribution systems.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.
Low-cost data acquisition systems for photovoltaic system monitoring and usage statistics
This paper presents the design of a low-cost data acquisition system for monitoring a photovoltaic system’s electrical quantities, battery temperatures, and state of charge of the battery. The electrical quantities are the voltages and currents of the solar panels, the battery, and the system loads. The system uses an Atmega328p microcontroller to acquire data from the photovoltaic system’s charge controller. It also records individual load information using current sensing resistors along with a voltage amplification circuit and an analog to digital converter. The system is used in conjunction with a wall power data acquisition system for the recording of regional power outages. Both data acquisition systems record data in micro SD cards. The data has been successfully acquired from both systems and has been used to monitor the status of the PV system and the local power grid. As more data is gathered it can be used for the maintenance and improvement of the photovoltaic system through analysis of the photovoltaic system’s parameters and usage statistics.