Abstract-This paper proposes 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 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, and 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 photovoltaic panel, a fuel cell, a micro-turbine, a diesel generator, a battery, and a responsive load show the advantage of stochastic optimization, as well as robust optimization. Index Terms-Market bidding strategy, microgrid, mixedinteger linear programming (MILP), robust optimization, stochastic optimization, uncertainty. The main symbols used in this paper are defined below. Others will be defined as required in the text. Index of dispatchable generators, running from 1 to N G .
Ref. REF studies the problem of determining optimal bidding strategies in the day-ahead market of a hybrid microgrid consisting of renewable and nonrenewable energy resources, energy storage systems, diesel generators, and price responsive loads.
Bidding Strategy for Microgrid in Day-Ahead Market Based on Hybrid Stochastic/Robust Optimization
IEEE Transactions on Smart Grid
IEEE Transactions on Smart Grid
Economics Computer Science