A Probabilistic-based PV and Energy Storage Sizing Tool for Residential Loads Xiangqi Zhu, Jiahong Yan, and Ning Lu Objective: Technical Approach: Accomplishments: Next Steps: Potential Impact: This work presents a probabilistic-based sizing tool for residential home owners, load serving entities, and utilities to select energy storage (ES) and photovoltaic (PV) based on historical load characteristics and load management options. Inputs: historical load profiles and solar radiation data. Outputs: home energy consumptions, monthly energy payment, self-consumed solar and backfeeding energy, etc. Demand-side Energy Management (DSM) is also involved. Data Description Various patterns of house load models and solar power output models could be extracted from this data set. Modeling and Sizing Methodologies target load PV P P P = − target max_d target max_c _ _d max_d target max_d _d max_ target max_c _c , ( )&E ( 1) , & ( 1) () , & ( 1) 0 Lmt c ES Lmt ES Lmt ES c ES Lmt P P P P E t E P P P E t E P t P P P E t E otherwise > >− > − > > − > = − > − < (t) ( 1) () ES ES ES E E t P t T • = − − target P load P PV P () ES E t () ES P t max_ c P max_ d P max_ c E max_ d E :Target battery power output, kW; : Load power consumption, kW : PV power output, kW; : Power output of the battery, kW : Battery energy level at t, kWh; :charging and discharging power limits, kW :charging and discharging energy limit, kWh Battery Control Logic Battery Sizing Methodology Step 1: Create seasonal load groups. Winter load group November, December, January, February, March Summer load group April, May, June, July, August, September, October TABLE I. Load Categorization Step 2: Generate the net load ensembles. For each load group, an ensemble of the net load scenarios is generated by subtracting corresponding all possible solar profiles from each load profile ( as shown in Fig.5). int int int 151 96 151 96 151 96 w ergroup w ergroup w ergroup netload load solar P P P × × × = − 214 96 214 96 214 96 summergroup summergroup summergroup netload load solar P P P × × × = − Step 3: Calculate the statistics of the backfeeding power. When the battery power or energy limit is reached, the excess solar power will be backfed to the main grid. After modeling the battery operation for all scenarios, we obtain a probability distribution of the daily reverse power for each 15-min interval that can help the user to evaluate the occurrence of reverse events. As shown in Fig. 6, the black line represents the 80% probability line for a 5kW PV operating in conjunction with a 1kW/1kWh battery. Fig.1. Fig.2. Fig.3. Fig.4. Fig.5. Fig.6. Sizing PV and ESD at the Home Level As shown in Fig. 7, Reverse power is not an issue in the winter load group but can cause problems in the summer load group This shows that it is not wise to just use one size of ES for the whole year because of the seasonal load differences. Also, the users can run different ES and PV combinations to find the optimal one that meets the power limits of backfeeding. Fig.7. Sizing PV and ESD at the Transformer Level Assume that a transformer supplies three homes, each of which has a 5-kW rooftop PV. As shown in Fig.8, installing a 2kW/2kWh battery at the transformer level will receive the same result as installing one 1kW/1kWh battery at each home. Fig.8. Next Step: Sizing PV and ESD at the Feeder Level Fig.9 shows the reverse power for penetration level of 50%, 70% and 90% respectively, also shows the trend of the maximum reverse power on the 80%-line of various penetration levels. To give a comparison, the red line in the trend figure is obtained using a 10kW/3kWh ESD at the feeder head. In the future, sizing analysis involving DSM will be studied in detail, to see to what extend DSM will benefit the residential loads with renewable energy. This method can give the users a clear comparison of the tradeoffs among different PV and ES options and assist them make more informed decisions. Fig.9.