Value of deterministic day-ahead forecasts of PV generation in PV + Storage operation for the Australian electricity market

David, Mathieu; Boland, John; Cirocco, Luigi; Lauret, Philippe; Voyant, Cyril

doi:10.1016/j.solener.2021.06.011

Cited by 33 publications

(12 citation statements)

References 44 publications

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“…It is important to offer the most efficient benchmark in order to be able to compare studies with each other and identify the most efficient models even if it comes down to a fine detail. It is important to mention that in the solar energy community, it is usual to consider that an improvement of 1% in the prediction is approximately equivalent to an increase of 2% in economical gain [12]. For very large installations the gain can quickly become substantial.…”

Section: Discussionmentioning

confidence: 99%

“…In order for this approach to take effect, most generally, several prerequisites must be evaluated carefully. Firstly, reference methods should be applicable in various independent and operational modes [12,13,14,15]. That is to say that the reference method should be "universal" in a sense that it does not depend on the type of available data.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarks for Solar Radiation Time Series Forecasting

Voyant,

Notton,

Duchaud

et al. 2022

Preprint

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Benchmarks for Solar Radiation Time Series Forecasting

Voyant,

Notton,

Duchaud

et al. 2022

Preprint

Self Cite

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“…In this article, instead of taking the point of view of a single VRE producer who wants to provide ancillary services, as in some of the aforementioned works, [7,15,40,41] we take the point of view of the TSO who capable of controlling a distributed fleet of flexible VRE plants for system balancing. Compared with the study, Black et al [32] evaluated the added value of storage in reducing thermoelectric reserves and improving the share of wind power, in this paper, we show how these reserves can be entirely replaced by flexible VRE systems.…”

Section: Novelty Of the Work Compared To The Literaturementioning

confidence: 99%

“…Pierro et al [ 14 ] have also demonstrated how it is possible to reduce the volumes and costs of the 2016 Italian imbalance by an average of 15% using an accurate “state‐of‐the‐art” photovoltaic power forecast. David et al [ 15 ] studied the value of solar energy forecasting for a PV hybrid system bidding in the Australian energy market, finding that a 1% improvement in accuracy leads to 2% additional revenue.…”

Section: Introductionmentioning

confidence: 99%

Ancillary Services via Flexible Photovoltaic/Wind Systems and “Implicit” Storage to Balance Demand and Supply

et al. 2022

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Achieving the European Union renewable energy source penetration 2030 targets requires an increase in flexible resources to compensate for the variability/intermittency of solar and wind generation to ensure system safety and balancing. Herein, two readily deployable flexibility solutions to balance demand/supply as an alternative to building additional thermoelectric reserves are proposed. How the transmission system OPERATOR can use the ancillary services provided by a flexible photovoltaic (PV) fleet (solar regulation) or PV/wind fleet (variable renewable energy (VRE) regulation) together with a suitable underforecast and proactive curtailment of variable renewable generation (aka, implicit storage) to reduce current and future Italian imbalances is shown. How these flexibility solutions can become even more effective when combined with a strengthening of the transmission grid is shown. The imbalance reduction achievable by 2030 through solar/VRE regulation strategies would be of the order of 20–50% with zonal balancing and 27–80% with nationwide balancing is found. Imbalance costs would remain comparable with the business‐as‐usual (thermal generation) costs. A proactive curtailment of 5–17% of the total VRE generation is the environmental cost of stabilizing the system using VRE plants, avoiding the construction of thermoelectric reserves.

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“…Second, a wide variety of studies, e.g., power system planning and operation [25][26][27][28] , energy scheduling [29][30][31] , and market operation and mechanism design studies [32][33] , must consider the intermittency and volatility of renewable energy resources via robust optimization [34][35] , stochastic programming [36][37] , and statistical analysis methods [38][39] . Third, the prediction error of renewable power determines the revenue risk of power generation companies, especially in markets with deviation punishment.…”

Section: Introductionmentioning

confidence: 99%

Inherent Spatiotemporal Uncertainty of Renewable Power in China

Wang

Chen

Tan

et al. 2022

Preprint

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Solar and wind resources are vital for the sustainable and cleaner transition of the energy supply. Although renewable energy potentials are assessed in the literature, few studies examine the statistical characteristics of the inherent uncertainties of renewable generation arising from natural randomness, which is inevitable in stochastic-aware research and applications. Here we develop a rule-of-thumb statistical learning model for wind and solar power prediction and generate an hourly and year-long dataset of prediction errors in 30 provinces of China. The results reveal diversified spatial and temporal distribution patterns of prediction errors, indicating that more than 70% of wind prediction errors and 50% of solar prediction errors arise from scenarios with high utilization rates. We discover that the first-order difference and peak ratio of generation series are two primary indicators explaining the distribution characteristics of prediction errors. Furthermore, the prediction errors could result in additional CO2 emissions from coal-fired thermal plants. We estimate that such emission would potentially reach 319.7 megatons in 2030, accounting for 7.7% of China’s power sector. Finally, improvements in investment incentives and interprovincial scheduling could be suggested.

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Value of deterministic day-ahead forecasts of PV generation in PV + Storage operation for the Australian electricity market

Cited by 33 publications

References 44 publications

Benchmarks for Solar Radiation Time Series Forecasting

Benchmarks for Solar Radiation Time Series Forecasting

Ancillary Services via Flexible Photovoltaic/Wind Systems and “Implicit” Storage to Balance Demand and Supply

Inherent Spatiotemporal Uncertainty of Renewable Power in China

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