A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features

Abstract

Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.

Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems

“…Therefore, it is vital to predict the CBL accurately [23]. However, in a residential distribution network, the distributed PV owned by customers generally is located behind-the-meter (BTM) [24], which measures the net load and denotes actual electricity load minus PV power generation. Hence, the distributed PV generation, such as rooftop PV, makes it difficult to predict CBL with net load [25] due to the volatility of PV and load.…”

“…In recent years, there has been a large amount of literature on BTM PV, which involves BTM PV detection and capacity estimation [24,26], and BTM PV output power prediction [27,28], etc. In the meantime, it has been a heated research topic for PV-load decoupling and CBL forecasting.…”

“…be a set of timestamp, then the aggregated net load on time t day d can be formulated as follows: Figure 1 gives an example for the composition of net load. Considering that the roof solar PV systems are usually invisible for the aggregator, and in most cases, the small scale distributed PV systems (less than 10 kW) are not set up with a separated PV output power meter [24], it is a big challenge to obtain the pure PV output power without the relevant monitoring information. Therefore, the purpose of the study is to acquire the decomposed PV output power by historical load data, solar irradiation data, and temperature data.…”

Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting

“…Among the previous studies, solar irradiance forecasting approaches can be generally divided into several categories: statistical approaches, physical approaches and machine learning approaches and ensemble approaches. In physical approaches, three kinds of basic methods are NWP forecasting model [16], Total Sky Imagery (TSI) [17] and cloud moving based satellite imagery models, which can also help to estimate the output power of distributed PV system [18]. These kinds of physical based forecasting models require additional information about the sky image.…”

Convergence Analysis of Capacities for Photovoltaics and Energy Storage System Considering Energy Self-Sufficiency Rates and Load Patterns of Rural Areas

“…Once the PV module and inverter were selected, then, with the simulations from the SolarPro Program and/or modeling method [21], the average power generation per day could be calculated with different environmental conditions. By combining these data with the application and correlation coefficient between the PVs and ESSs, the optimized capacities of the PVs and ESSs can be derived [22]. Figure 4 shows the procedure for optimizing the capacity of the ESS, using Equations (1)-(5), by considering the load pattern of a rural area.…”