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The transition to low-carbon power systems necessitates cost-effective energy storage solutions. This study provides the first continental-scale assessment of micro-pumped hydro energy storage and proposes using agricultural reservoirs (farm dams) to significantly reduce construction costs. The continent of Australia is used as a representative case study for other arid and temperate regions internationally. From a new survey of its 1.7 million farm dams, we identified 30,295 promising pumped hydro sites in dam-to-dam and dam-to-river reservoir configurations. The average site had nearby reservoirs (132 m) with a high head height (32 m) and substantial discharge capacity (52 kWh). We then benchmarked a representative micro-pumped hydro site to a commercially available lithium-ion battery for a solar-powered irrigation system. Despite a low discharge efficiency (68%), pumped hydro storage was 30% less expensive (0.215 USD/kWh) for larger single-cycle loads (∼41 kWh/day) due to its high storage capacity. By capitalising on existing farm dams, micro-pumped hydro energy storage may support the uptake of reliable, low-carbon power systems in agricultural communities. • Agricultural reservoirs reduce micro-pumped hydro construction costs. • Identified 30,295 promising sites in arid and temperate climate zones. • Average system has 52 kWh capacity, reservoirs within 132 m and 32 m of head. • Estimated cost of 0.2 USD/kWh is comparable to home batteries at higher loads. • Micro-pumped hydro supports uptake of solar in agricultural regions. [ABSTRACT FROM AUTHOR]

This paper proposes a rule-based power management algorithm to enable the dispatch of a utility-scale PV power plant consisting of a hybrid energy storage system, in accordance with the Australian national electricity rules. This algorithm is purposely designed to regulate the instantaneous power of a PV plant with the same level of dispatchability as conventional power plants. Additionally, the proposed algorithm is robust under large forecasting errors (up to 60%) of solar irradiance and it can be easily implemented in practice. Specifically, this algorithm can be executed within seconds even when considering detailed market operation and model nonlinearities. A 30 MW PV plant, including a 7.5 MW/1.25 MWh vanadium redox battery and a 1.5 MW/0.25 MWh suppercapacitors bank, has been modeled using MATLAB/Simulink and PLECS software environment. The simulation results were based on various scenarios and confirmed the effectiveness of the proposed power management algorithm. It has been shown that even in the worst-case scenario of solar forecasting error of 60%, the proposed algorithm still managed to deliver over 94% of the available energy for a given solar irradiance profile of one month. [ABSTRACT FROM AUTHOR]