Economies of Scope for Electricity Storage and Variable Renewables

Terca, Goncalo; Wozabal, David

doi:10.1109/tpwrs.2020.3022823

Cited by 7 publications

(2 citation statements)

References 62 publications

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“…Furthermore, in a setting were prices are noisy, market participants may be unsure about their informational content and consequently may be hesitant to trade at all. This could, for example, lead to situations where asset-backed market participants prefer to invest in physical storage capacity to internally hedge their real-time imbalances rather than sharing storage assets in a common market (e.g., Terça and Wozabal, 2021). These effects jointly decrease welfare in case price signals are not reliable.…”

Section: Numerical Resultsmentioning

confidence: 99%

Frequent Auctions for Intraday Electricity Markets

Graf¹,

Kuppelwieser²,

Wozabal³

2024

The Energy Journal

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Continuous trading is currently becoming the standard for intraday electricity markets. In this paper, we propose frequent auctions as a viable alternative. We argue that batching orders in auctions potentially leads to lower liquidity cost, more reliable, less noisy price signals, and allows for better alignment of market outcomes with the technical realities of the transmission grid. In an empirical study, we compare the German continuous intraday market with counterfactual outcomes from frequent auctions. We find that traded volumes tend to be higher for continuous trading; however, the auction market benefits from lower liquidity costs and less noisy price signals. Furthermore, we critically discuss the suitability of continuous trading in the presence of network constraints and technical restrictions of conventional units. Taken together these findings suggest that in sparsely traded intraday markets, pooling orders in frequent auctions may be beneficial.

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Section: Numerical Resultsmentioning

confidence: 99%

Frequent Auctions for Intraday Electricity Markets

Graf¹,

Kuppelwieser²,

Wozabal³

2024

The Energy Journal

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“…There are studies in the literature that question the economic viability of using ESS, especially in deregulated electricity markets [15,16], which is usually the result of using simple stochastic optimisation models. This argument can be contradicted in cases where more complex control algorithms based on artificial intelligence methods are used [17,18].…”

Section: State Of the Artmentioning

confidence: 99%

Archives of Electrical Engineering

2022

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In recent years, due to the increasing number of renewable energy sources, which are characterised by the stochastic nature of the generated power, interest in energy storage has increased. Commercial installations use simple deterministic methods with low economic efficiency. Hence, there is a need for intelligent algorithms that combine technical and economic aspects. Methods based on computational intelligence (CI) could be a solution. The paper presents an algorithm for optimising power flow in microgrids by using computational intelligence methods. This approach ensures technical and economic efficiency by combining multiple aspects in a single objective function with minimal numerical complexity. It is scalable to any industrial or residential microgrid system. The method uses load and generation forecasts at any time horizon and resolution and the actual specifications of the energy storage systems, ensuring that technological constraints are maintained. The paper presents selected calculation results for a typical residential microgrid supplied with a photovoltaic system. The results of the proposed algorithm are compared with the outcomes provided by a deterministic management system. The computational intelligence method allows the objective function to be adjusted to find the optimal balance of economic and technical effects. Initially, the authors tested the invented algorithm for technical effects, minimising the power exchanged with the distribution system. The application of the algorithm resulted in financial losses, €12.78 for the deterministic algorithm and €8.68 for the algorithm using computational intelligence. Thus, in the next step, a control favouring economic goals was checked using the CI algorithm. The case where charging the storage system from the grid was disabled resulted in a financial benefit of €10.02, whereas when the storage system was allowed to charge from the grid, €437.69. Despite the financial benefits, the application of the algorithm resulted in up to 1560 discharge cycles. Thus,

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