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A Distributed Demand Response Algorithm and Its Applications to PHEV Charging in Smart Grid. Zhong Fan IEEE Trans. on Smart Grid.

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A distributed demand response algorithm and its applications to phev charging in smart grid

A Distributed Demand Response Algorithm and Its Applications to PHEV Charging in Smart Grid

Zhong Fan

IEEE Trans. on Smart Grid.

Z. Fan. A Distributed Demand Response Algorithm and Its Applications to PHEV Charging in Smart Grid. IEEE Trans. on Smart Gird, vol. 3, num. 3, pp. 1280-1290, 2012.


Contents
Contents to PHEV Charging in Smart Grid

  • Demand Response Model

  • Distributed PHEV Charging

  • Leveraging Networking Concepts into Smart Grid Load Leveling


I demand response dr in smart grid
I - Demand to PHEV Charging in Smart GridResponse (DR) in Smart Grid

  • Demand Response (DR): a mechanism for achieving energy efficiency through managing customer consumption of electricity in response to supply conditions.

  • Ex. Reducing customer demand at critical times (or in response to market price)

  • Advanced communication will enhance the DR capability (E.g., real-time pricing).

  • PHEVs require enhanced demand response mechanism.


Dr model congestion pricing
DR Model – Congestion Pricing to PHEV Charging in Smart Grid

  • Fully distributed system (only price is known)

  • A principle of congestion control in IP networks – Proportionally Fair Pricing (PFP)

    • Each user declares a price he is willing to pay per unit time.

    • The network resource (bandwidth) is shared in proportion to the prices paid by the users.

    • If each user chooses the price that maximizes his utility, then the total utility of the network is maximized [1].

[1] F. Kelly, A. Maulloo, and D. Tan, “Rate control for communication networks: Shadow prices, proportional fairness and stability,” J. Oper. Res. Soc., vol. 49, no. 3, pp. 237–252, 1998.


Dr model and user adaption 1
DR Model and User Adaption (1) to PHEV Charging in Smart Grid

  • A discrete time slot system

    • N users

    • demand of user i at slot n

    • user i’s willingness to pay (WTP) parameter

    • Price of energy in slot n:

    • Utility function of user i:

    • The users choose demand to maximize:


Dr model and user adaption 2
DR Model and User Adaption (2) to PHEV Charging in Smart Grid

  • User adaption: user i adapts its demand according to:

  • The convergence of the adaption:

    • The error of demand estimate:

: optimal demand

: equilibrium price


Dr model numerical results 1
DR Model – Numerical Results (1) to PHEV Charging in Smart Grid

Basic simulation

The effect of gamma


Dr model numerical results 2
DR Model – Numerical Results (2) to PHEV Charging in Smart Grid

Heterogeneous initial demands

and adaption rates

Heterogeneous initial demands


Dr model numerical results 3
DR Model – Numerical Results (3) to PHEV Charging in Smart Grid


Ii distributed phev charging
II - Distributed to PHEV Charging in Smart GridPHEV Charging

  • Price function:

  • User adaption:

  • Charging dynamics:

  • Difference:

    Finish service (Charging done, y=1)


Differential qos
Differential to PHEV Charging in Smart GridQoS?

  • Total charging cost for PHEV i:

  • If we assume the price remains constant (p)

  • Equilibrium price:


Differential qos1
Differential to PHEV Charging in Smart GridQoS?

  • Several observation

    • WTPs affect the price of energy.

    • WTPs decide the charging time of individual PHEVs

    • PHEVs with same total charging demand and different WTPs will have almost same total charging cost.

      • After some PHEVs finish charging, the price will go down, which results in slight differences of the charging cost between PHEVs with different WTPs.


Simulation results
Simulation Results to PHEV Charging in Smart Grid

  • Basic simulation

  • Differential QoS and total cost of charging

  • Impact of WTPs on system performance

  • Maximum charging rate

  • Different number of PHEVs


Basic simulation
Basic simulation to PHEV Charging in Smart Grid


Differential qos and total cost of charging
Differential to PHEV Charging in Smart GridQoS and total cost of charging

Total charging cost:

PHEV1 only 7% less

than PHEV50


Impact of wtps on system performance
Impact of WTPs on system performance to PHEV Charging in Smart Grid


Maximum charging rate
Maximum charging rate to PHEV Charging in Smart Grid


Maximum charging rate1
Maximum charging rate to PHEV Charging in Smart Grid


Different number of phevs
Different number of PHEVs to PHEV Charging in Smart Grid


Some future work
Some Future Work to PHEV Charging in Smart Grid

  • How should PHEVs adapt their WTPs according to the price policy and their own charging preference?

  • In-depth analysis of how maximum charging rate impacts the performance.

  • Game theoretical analysis of the proposed demand response model (the social optimum is a Nash bargaining solution[1])

  • The impact of PHEVs as energy storage on the SG.

  • The introduction of energy service company (like charging station) will bring about new problems of optimization, security and social-economic interactions[2].

[2] C.Wang and M. de Groot, “Managing end-user preferences in the smart grid,” in Proc. 1st Int. Conf. Energy-Efficient Comput. Network. (ACM e-Energy), 2010.


Iii incorporating networking ideas and methods into the research of sg
III - Incorporating Networking Ideas and Methods into the Research of SG

  • Load leveling as a resource usage optimization problem

  • Resource allocation ideas from networking to the smart grid.

    • Load admission control

    • OFDMA allocation

    • Cooperative energy trading

S. Gormus, P. Kulkarni, and Z. Fan, “The power of networking: How networking can help power management,” in Proc. 1st IEEE Int. Conf. Smart Grid Commun., 2010.


Load leveling as a resource usage optimization problem
Load Research of SGLeveling as a Resource Usage Optimization Problem

  • Resource allocation:

  • Optimization goals

    • Environmental impact – load will be shifted to when the renewable resources have higher general mix.

    • Cheapest resource available – load will be shifted to the off-peak time when the price is low.

  • When outage?

    • Hierarchical priority manner

    • Low priority appliances of low priority customer should be black out first.


Load admission control
Load Admission Control Research of SG

  • Like “call admission control”

  • Customers send request before accessing SG to the Power Management System (PMS)

    • Granted

    • Rejected

    • If the request with high priority


Ofdma allocation
OFDMA Research of SGAllocation

  • OFDMA: deciding which frequencies to allocate at what times to users

  • Resource allocation in SG: what loads to allocate at what times to which users to optimize resource utilization and hence improve energy efficiency.

  • Learn from the OFDMA with the allocation methods


Cooperative energy trading
Cooperative energy trading Research of SG

  • Future smart grid: micro grids with local generation plants (solar, wind, etc.) and users while connected to the macro grid.

  • The idea here is a better utilization of the available power resources by cooperatively using available generation resources.

  • Similar to the cooperative communication philosophy where the nodes in a wireless network try to increase the throughput and network coverage by sharing available bandwidth and power resources cooperatively.


Thanks
Thanks! Research of SG


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