Risk-averse TSO-DSOs coordinated distributed dispatching considering renewable energy and demand response uncertainties

Tao Jiang, Chenghao Wu, Rufeng Zhang, Xue Li and Fangxing Li

Applied Energy, 2022, vol. 327, issue C, No S0306261922012818

Abstract: The renewable energy generation (REG) integration has introduced a significant amount of uncertainties to power systems. This new trend requires power systems to have sufficient flexibility to mitigate the power imbalance between supply and demand. Demand response (DR), as a potential flexibility resource, has gained widespread attention in recent years. However, communication failures or customers’ unexpected behaviors may result in DR uncertainties, leading to operational risks. To cope with the impacts of DR and REG uncertainties, this work proposes a stochastic programming-based risk-averse optimal dispatch model to reduce both electricity purchasing cost and potential risk cost through transmission network (TN) and active distribution networks (ADNs) coordinations. The risk-averse distributed optimal dispatch problem is formulated as two sub optimization models. One conducts the transmission-level risk-averse generation dispatch, while the other represents the distribution-level ADN market clearings. Moreover, the conditional value at risk (CVaR) is used to measure the potential risk cost caused by DR and REG uncertainties. Finally, the optimization model is solved by a distributed solution algorithm, alternating direction method of multipliers (ADMM), to preserve data privacy between multiple stakeholders and reduce communication requirements. The performance of the proposed approach is evaluated in T30-D2 × 33 and T118-D85 + D2 × 69 test systems, the results validate its effectiveness.

Keywords: ADMM; Conditional value at risk; Stochastic programming; Demand response; Renewable energy generations; TSO-DSO coordination (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.apenergy.2022.120024

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