Abstract:
This paper proposes a novel stochastic decision-making tool for enabling large customers to strategically schedule their onsite solar power generations (OSG) while optimi...Show MoreMetadata
Abstract:
This paper proposes a novel stochastic decision-making tool for enabling large customers to strategically schedule their onsite solar power generations (OSG) while optimizing the utilization of external electricity sources (bilateral contracts and the energy market). The proposed model, which is formulated as a scenario-based two-stage stochastic mixed-integer programming problem, minimizes the total expected energy procurement cost by co-optimizing the contribution of energy resources in supplying the customer's demand. The first stage decisions optimize the utilization of bilateral contracts, and the second stage decisions optimize schedules to purchase power from and selling excess OSG to forward energy market. The uncertainty of solar irradiance and the resulting solar power generation is characterized using the combination of a Fourier series and an autoregressive integrated moving average (ARIMA) model, which respectively characterize the periodic and stochastic components of solar irradiance. Further, the fast-forward scenario reduction technique is utilized to reduce the number of scenarios and keep the proposed stochastic optimization model computationally tractable. The simulation results and sensitivity analysis are conducted on a sample large customer, which demonstrate the effectiveness of the proposed method to utilize OSG by the customer taking into account the uncertainty of solar generation.
Published in: 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Date of Conference: 24-28 June 2018
Date Added to IEEE Xplore: 19 August 2018
ISBN Information: