Impact of distributed generation of solar photovoltaic (PV) generation on the Massachusetts transmission system

Abstract

After reaching 250 megawatt direct current (MW dc) of solar photovoltaic (PV) generation installed in Massachusetts (MA) in 2013, four years ahead of schedule, Governor Deval Patrick in May of 2013 announced an increase in the MA solar PV goal to 1,600 MW by 2020 ([13]). However, integration of such significant quantities of solar PV into the electric power system is potentially going to require changes to the transmission system planning and operations to ensure continued reliability of operation ([14]). The objective of this project is to predict the distribution of solar PV in MA and to develop a simulation framework to analyze the impact of solar generation on the electric power system. To accomplish this objective, we first developed a prediction model for solar PV aggregate and spatial long term distribution. We collected solar PV installation data and electricity consumption data for 2004 to 2014 for each ZIP code in MA. Additional information such as population, land availability, average solar radiance, number of households, and other demographic data per ZIP code was also added to improve the accuracy of the model. For example, ZIP codes with higher solar radiance are more likely to have solar PV installations. By utilizing machine learning methods, we developed a model that incorporates demographic factors and applies a logistic growth model to forecast the capacity of solar PV generation per ZIP code. Next we developed an electrically equivalent model to represent the predicted addition of solar PV on the transmission system. Using this model, we analyzed the impact of solar PV installations on steady-state voltage of the interconnected electric transmission system. We used Siemens PTI's PSS/E software for transmission network modeling and analysis. Additionally, we conducted a sensitivity analysis on scenarios such as peak and light electricity consumption period, different locations of solar PV, and voltage control methods to identify potential reliability concerns. Furthermore, we tested the system reliability in the event of outages of key transmission lines, using N-1 contingency analysis. The analysis identified that the voltage deviation on transmission system because of adding 1,600 MW dc of distributed solar PV is within +/- 5% range. Based on the analysis performed in this thesis, we conclude that the current MA transmission system can operate reliably after the addition of the expected 1,600 MW dc of solar PV. As National Grid acquires information on solar installations, new data will improve the ability and accuracy of the prediction model to predict solar PV capacity and location more accurately. The simulation framework developed in this thesis can be utilized to rerun the analysis to test the robustness of the electric transmission system at a future date.