Design And Simulation Of A Real-time Price Demand Response Program For Electricity Subject To A Capacity Constraint For The Philadelphia Navy Yard
Open Access
- Author:
- Cortes, Mercedes H
- Graduate Program:
- Energy and Mineral Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- October 24, 2012
- Committee Members:
- Andrew Nathan Kleit, Thesis Advisor/Co-Advisor
Seth Adam Blumsack, Thesis Advisor/Co-Advisor
Anastasia Shcherbakova, Thesis Advisor/Co-Advisor - Keywords:
- demand response
real-time pricing
electricity
philadelphia navy yard - Abstract:
- This thesis is based on data from the Philadelphia Navy Yard, which has its own transmission and distribution electric micro-grid and is home for commercial and industrial customers of different sizes. Additionally, the Navy Yard is a non-regulated entity in terms of electricity pricing, while being managed by a non-profit organization. These features make it a good place for experimentation with the demand for electricity. Currently, there are two electricity-related problems observed at the Navy Yard. The first is an inefficient pricing structure, since customers pay a fixed monthly rate, based on an average of the marginal cost of producing electricity. The second problem is insufficient capacity to supply electricity to the demand. In particular, one of the two substations that deliver electricity to tenants is currently congested several hours of the year. The congested substation is referred to as SS93 and it has a capacity limit of 15MW, while the other substation is SS664 with a capacity of 19MW. In addition, new developments are planned to be built and incorporated to the Navy Yard’s electric grid, increasing the risk of system congestion if no investments are made in capacity expansion, since projected peak demand is estimated to triple current levels. Based on the characteristics of the problem presented above, the main goal of this research project is to simulate the application of a real-time pricing program (RTP) for electricity in the Navy Yard, subject to a capacity constraint, based on the PJM Interconnection’s wholesale market locational marginal prices (LMP). This real-time pricing program will have an added component, which is revenue collection at times when congestion occurs to further invest it in new capacity. If the revenue collected is enough to build sufficient substation capacity to cope with the increasing demand, this might avoid future congestion problems of the electric system and eliminate reliability issues associated with the electricity supply within the Navy Yard’s electric grid. The simulation of the two-stage real-time pricing program determined that the revenue collected would be plenty to expand the substations’ capacity. Specifically, in the case of SS664 the simulation of the two-stage RTP program showed that over $12 million in revenues could be collected in 15 years, which would allow to build over 43MW of additional capacity. This would leave SS664 with an excess capacity for additional demand increments, let it be from existing or new tenants. Similarly, the simulation of the two-stage RTP program on SS93 data showed that over $3.1 million could be collected in revenues, which could be invested in over 11.3MW of new capacity, also leaving some room for additional demand growth. This way the system would be able to cope with the increasing demand, even if the customers do not respond substantially to changes in prices, while leaving room for further demand growth in all cases. Finally, the environmental impact of the program is estimated, as well as the profitability for a customer to invest in a natural gas generator to be used at times of high electricity prices. Results show that the two-stage RTP program would have a negative impact on the environment, since CO2, SO2 and NOx emissions are increased as a consequence from the program application. Also, it would not be profitable to invest in an on-site generator, if the average of the natural gas price is below $5.22 per thousand cubic feet.