Robust inter-reliant resilience of cyber-physical smart grids

Nikoobakht, Ahmad; Aghaei, Jamshid

doi:10.1016/j.seta.2023.103449

Cited by 1 publication

(1 citation statement)

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“…However, it is difficult to cover all possible scenarios, especially extreme scenarios, to analyze operation risks for multi-energy complementary systems. In the case of the limited transmission capacity of hydropower and photovoltaic systems, extreme PV output scenarios may influence the operation of the combined power generation system, resulting in severe renewable energy generation curtailment [27,28].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Chance-Constrained Schedule Optimization of Cascaded Hydropower and Photovoltaic Complementary Generation Systems for Shaving Peak Loads

Li,

Wu,

Song

et al. 2023

Sustainability

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The coordinated scheduling of cascade hydropower with photovoltaic (PV) power stations can significantly improve the utilization rate of delivery transmission lines. However, the inherent uncertainty associated with photovoltaic (PV) forecasts challenges the reliable and economic operation of the complementary energy system. Against this background, in this paper, a day-ahead, chance-constrained scheduling for cascaded hydro–photovoltaic complementary generation systems (CHPSs) considering the transmission capacity is proposed. Firstly, the uncertainty of PV forecast errors is simulated by a probability density function fitted using kernel density estimation with historical sampling data. Then, a chance-constrained optimization model considering peak-shaving demands of the receiving-end power grid is developed to determine the day-ahead optimal schedules of CHPSs. Also, complex hydraulic coupling and unit operation constraints of cascade hydropower are considered in the proposed model. To deal with the nonlinear and stochastic constraints, an efficient linearization method is adopted to transform the proposed model into a mixed-integer linear programming (MILP) problem. Finally, the effectiveness and feasibility are verified by case studies. The results show that the day-ahead schedule optimized by the proposed method can fully balance peak-shaving and photovoltaic accommodation while considering photovoltaic output uncertainty.

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Section: Introductionmentioning

confidence: 99%