Profitability of battery storage in hybrid hydropower–solar photovoltaic plants

Fagerström, Jonathan; Das, Soumya; Klyve, Øyvind Sommer; Olkkonen, Ville; Marstein, Erik Stensrud

doi:10.1016/j.est.2023.109827

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Guo et al [16] proposed a novel short-term operation model for a cascade hydro-PV complementary system using the artificial intelligence method, in which hybrid time steps are considered. Fagerström et al [17] established an operation model for hydro-PV hybrid generation systems and investigated the potential of batteries to improve the cost-effectiveness of hydro-PV hybrid systems. The bundled transmission of hydro-wind-photovoltaic generation is inevitably constrained by the delivery transmission capacity.…”

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%