Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization

Liu, Guodong; Xu, Yan; Tomsovic, Kevin

doi:10.1109/TSG.2015.2476669

Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization

Journal Article · Fri Jan 01 00:00:00 EST 2016 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/TSG.2015.2476669· OSTI ID:1265375

Liu, Guodong ^[1]; Xu, Yan ^[1]; Tomsovic, Kevin ^[2]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); University of Tennessee (UTK), Knoxville, TN (United States)

In this paper, we propose an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG and price responsive loads. The microgrid coordinates the energy consumption or production of its components and trades electricity in both the day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total cost of operation minus total benefit of demand. This formulation can be solved by mixed integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator, a battery and a responsive load show the advantage of stochastic optimization in addition to robust optimization.

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1265375

Journal Information:: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 1 Vol. 7; ISSN 1949-3053

Country of Publication:: United States

Language:: English

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29 ENERGY PLANNING, POLICY, AND ECONOMY
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Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization

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