TY - GEN
T1 - An MPEC for electricity retail alternatives of plug-in electric vehicle (PEV) aggregators
AU - Momber, Ilan
AU - Wogrin, Sonja
AU - Gómez, Tomás
PY - 2014/2/10
Y1 - 2014/2/10
N2 - Coordinated charging schedules of plug-in electric vehicles (PEVs) by an aggregator agent may lead to increased system efficiency in allocating resources in generation, transmission and distribution. To achieve optimal charging schedules, many studies have assumed that the PEV aggregator can exercise full direct load control. This paper proposes a mathematical program with equilibrium constraints for the PEV aggregator's decision making in different electricity markets, using indirect load control by determining optimal retail prices for the PEV. This permits the final customers to decide on their charging schedule by decentralized profit optimization. These decisions respect a potential discomfort that may arise when PEV users have to deviate from their preferred charging schedule as well as include the option of using alternative sources of energy. In a small case study of 3 vehicle clusters and 6 time periods the model's functionality is highlighted. Results indicate that under reasonable competition on the retail market, the PEV aggregator's profitability depends on providing the right price signals to the final customers, such that the most efficient charging schedule response is achieved.
AB - Coordinated charging schedules of plug-in electric vehicles (PEVs) by an aggregator agent may lead to increased system efficiency in allocating resources in generation, transmission and distribution. To achieve optimal charging schedules, many studies have assumed that the PEV aggregator can exercise full direct load control. This paper proposes a mathematical program with equilibrium constraints for the PEV aggregator's decision making in different electricity markets, using indirect load control by determining optimal retail prices for the PEV. This permits the final customers to decide on their charging schedule by decentralized profit optimization. These decisions respect a potential discomfort that may arise when PEV users have to deviate from their preferred charging schedule as well as include the option of using alternative sources of energy. In a small case study of 3 vehicle clusters and 6 time periods the model's functionality is highlighted. Results indicate that under reasonable competition on the retail market, the PEV aggregator's profitability depends on providing the right price signals to the final customers, such that the most efficient charging schedule response is achieved.
KW - Bi-Level Optimization
KW - Optimal PEV Charging Schedules
KW - Plug-in Electric Vehicle (PEV) Aggregator
KW - Retail Tariffs for Electricity
UR - http://www.scopus.com/inward/record.url?scp=84946691664&partnerID=8YFLogxK
U2 - 10.1109/PSCC.2014.7038400
DO - 10.1109/PSCC.2014.7038400
M3 - Conference paper
AN - SCOPUS:84946691664
T3 - Proceedings - 2014 Power Systems Computation Conference, PSCC 2014
BT - Proceedings - 2014 Power Systems Computation Conference, PSCC 2014
PB - Institute of Electrical and Electronics Engineers
T2 - 2014 Power Systems Computation Conference, PSCC 2014
Y2 - 18 August 2014 through 22 August 2014
ER -