DOI:10.1007/s40565-017-0368-y |
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Stochastic optimization for retailers with distributed windgeneration considering demand response |
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Net amount: 745 |
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Author:
Hessam GOLMOHAMADI1
, Reza KEYPOUR1
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Author Affiliation:
1. Faculty of Electrical and Computer Engineering, Semnan
University, Semnan, Iran
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Foundation: |
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Abstract: |
In this paper, a multi-stage stochastic model is
presented for a renewable distributed generation (RDG)-
owning retailer to determine the trading strategies existing
in a competitive electricity market. Uncertainties associated
with wholesale electricity market price, clients’ consumption
and power output of wind resources are
considered through auto regressive integrated moving
average (ARIMA) approach. In the proposed method, three
trading floors are addressed for the retailer to hedge against
the uncertainties. In the first stage, the retailer participates
in day-ahead market to supply the clients and in the second
stage, intraday market is addressed to allow the retailer to
modify the schedule of its clients’ consumption/RDG
production. Due to unfavorable uncertainties, especially in
renewable power production, real-time market is considered
in the third stage to diminish the uncertainty at power
delivery time. Cost function of wind resources considering
capital, operation and maintenance (O&M) cost is incorporated
in the objective function to increase the applicability
of the mechanism. The proposed approach is
formulated for risk-averse and risk-taker retailer through
conditional value at risk (CVaR) approach. In order to
study the impact of retail strategies on consumption pattern
and consumers’ electricity bills, time-of-use (TOU)
demand response programs are discussed in this paper.
Formulating the problem, the mixed integer non-linear
programming (MILNP) problem is transformed into mixed
integer linear programming (MILP) by jointly using
decomposition and disjunctive constraints. Finally, a case
study containing wind power resources, energy storage
system and retailer is considered to analyze the proficiency
of the proposed approach. |
Keywords: |
Renewable, Retailer, Stochastic, Uncertainty,
Demand response |
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Online Time:2018/07/20 |
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