Gene

Pricing strategies

Engineering

Environmental economics

Microeconomics

Materials science

Dynamic pricing

Layer (electronics)

Accounting

Economics

Biochemistry

Computer science

Demand response

Operations research

Nanotechnology

Electricity price

Automotive engineering

Transcription factor

Electricity market

Revenue

Repressor

Chemistry

Operator (biology)

Electricity

Electrical engineering

Energy

Pilot projects in power networks conducted across continents have established the benefits of dynamic pricing by inducing increased demand response. However, a key hurdle in the growth of demand response is the lack of widespread availability of advanced metering infrastructure, which has stymied the adoption of dynamic pricing. We believe that this hurdle will be partially addressed by the growth of electric vehicles (EVs), as smart and connected EV parking lots will be a provider of demand response. We develop a two-layer optimization model that simultaneously determines dynamic pricing policy for the system operator and demand response strategies for the EV parking lots. The model minimizes the cost to consumers, while ensuring the system operator's revenue neutral status and addressing real-time price uncertainties. A variant of the 5-bus PJM network is used to demonstrate model implementation. Numerical results show that for a low to moderate price spike scenario, dynamic pricing with demand response from EVs alone can lower the daily average consumer cost of 1.42% compared to the cost of flat pricing. A cost reduction of 6.5% is achieved when price spikes are relatively high. Computational challenges of implementing our model for real networks are discussed in the concluding remarks.

A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes