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  1. Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets
Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets
704 viewed

Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets

Fazlalipour, P

Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets

Fazlalipour, P ; Sharif University of Technology | 2019

704 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.energy.2018.12.173
  3. Publisher: Elsevier Ltd , 2019
  4. Abstract:
  5. A comprehensive optimal bidding strategy model has been developed for renewable micro-grids to take part in day-ahead (energy and reserve) and real-time markets considering uncertainties. A two-stage stochastic programming method has been employed to integrate the uncertainties into the problem. Moreover, the Latin hypercube sampling method has been proposed to generate the wind speed, solar irradiance, and load realizations via Weibull, Beta, and normal probability density functions, respectively. In addition, a hybrid fast forward/backward scenario reduction technique has been applied to reduce the large number of scenarios. Furthermore, the risk of participation in the markets has been investigated by the use of “conditional value at risk” criteria, and the efficiency of the stochastic approach has been evaluated via “value of stochastic solution”. The case study micro-grid involves three wind turbines, two photovoltaics, two microturbines, two fuel cells, one energy storage system, and 100 kw volunteer loads for curtailment. The accurate modeling of the components and constraints has led to a mixed integer nonlinear programming problem which has a lot of binary variables. Lindogloabal/AlphaECP solvers in GAMS have been applied to guarantee the global solutions
  6. Keywords:
  7. Bidding strategy ; Conditional value at risk ; Scenario generation ; Scenario reduction ; Value of the stochastic solution ; Commerce ; Fuel cells ; Fuel storage ; Integer programming ; Nonlinear programming ; Probability density function ; Risk perception ; Stochastic programming ; Value engineering ; Weibull distribution ; Wind ; Wind turbines ; Conditional Value-at-Risk ; Scenario reductions ; Stochastic solution ; Uncertainty ; Stochastic systems ; Alternative energy ; Detection method ; Energy storage ; Fuel cell ; Modeling ; Sampling ; Scenario analysis ; Stochasticity ; Uncertainty analysis ; Wind turbine
  8. Source: Energy ; Volume 171 , 2019 , Pages 689-700 ; 03605442 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0360544218325465