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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
  • Demand Response Model
  • Distributed PHEV Charging
  • Leveraging Networking Concepts into Smart Grid Load Leveling
i demand response dr in smart grid
I - Demand Response (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
  • 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)
  • 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)
  • 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)

Basic simulation

The effect of gamma

dr model numerical results 2
DR Model – Numerical Results (2)

Heterogeneous initial demands

and adaption rates

Heterogeneous initial demands

ii distributed phev charging
II - Distributed PHEV Charging
  • Price function:
  • User adaption:
  • Charging dynamics:
  • Difference:

Finish service (Charging done, y=1)

differential qos
Differential QoS?
  • Total charging cost for PHEV i:
  • If we assume the price remains constant (p)
  • Equilibrium price:
differential qos1
Differential QoS?
  • 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
  • Basic simulation
  • Differential QoS and total cost of charging
  • Impact of WTPs on system performance
  • Maximum charging rate
  • Different number of PHEVs
differential qos and total cost of charging
Differential QoS and total cost of charging

Total charging cost:

PHEV1 only 7% less

than PHEV50

some future work
Some Future Work
  • 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 Leveling 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
  • 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 Allocation
  • 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
  • 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.
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