Evolutionary Battery Scheduling Optimization Under Variable Electricity Prices in Micro-Grids with Renewable Generation

Author(s):  
R. Mallol-Poyato ◽  
S. Salcedo-Sanz ◽  
S. Jiménez-Fernández ◽  
P. Díaz-Villar
2021 ◽  
Author(s):  
Heikki Peura ◽  
Derek W. Bunn

Increasing variable renewable power generation (e.g., wind) is expected to reduce wholesale electricity prices by virtue of its low marginal production cost. This merit-order effect of renewables displacing incumbent conventional (e.g., gas) generation forms the theoretical underpinning for investment decisions and policy in the power industry. This paper uses a game-theoretic market model to investigate how intermittently available wind generation affects electricity prices in the presence of forward markets, which are widely used by power companies to hedge against revenue variability ahead of near-real-time spot trading. We find that in addition to the established merit-order effect, renewable generation affects power prices through forward-market hedging. This forward effect reinforces the merit-order effect in reducing prices for moderate amounts of wind generation capacity but mitigates or even reverses it for higher capacities. For moderate wind capacity, uncertainty over its output increases hedging, and these higher forward sales lead to lower prices. For higher capacities, however, wind variability conversely causes power producers to behave less aggressively in forward trading for fear of unfavorable spot-market positions. The lower sales counteract the merit-order effect, and prices may then paradoxically increase with wind capacity despite its lower production cost. We confirm the potential for such reversals in a numerical study, suggesting new empirical questions while providing potential explanations for previously contradictory observed effects of market fundamentals. We conclude that considering the conventional merit-order effect alone is insufficient for evaluating the price impacts of variable renewable generation in the presence of forward markets. This paper was accepted by Vishal Gaur, operations management.


2013 ◽  
Vol 6 (4) ◽  
pp. 695-706 ◽  
Author(s):  
Carlos Álvarez-Bel ◽  
Guillermo Escrivá-Escrivá ◽  
Manuel Alcázar-Ortega

2013 ◽  
Vol 40 ◽  
pp. S159-S171 ◽  
Author(s):  
Klaas Würzburg ◽  
Xavier Labandeira ◽  
Pedro Linares

2016 ◽  
Vol 20 (11) ◽  
pp. 4287-4300 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
C. Camacho-Gómez ◽  
R. Mallol-Poyato ◽  
S. Jiménez-Fernández ◽  
J. Del Ser

2021 ◽  
Author(s):  
William Seward ◽  
Weiqi Hua ◽  
Meysam Qadrdan

Traditionally, power system operation has relied on supply side flexibility from large fossil-based generation plants to managed swings in supply and/or demand. An increase in variable renewable generation has increased curtailment of renewable electricity and variations in electricity prices. Consumers can take advantage of volatile electricity prices and reduce their bills using electricity storage. With reduced fossil-based power generation, traditional methods for balancing supply and demand must change. Electricity storage offers an alternative to fossil-based flexibility, with an increase expected to support high levels of renewable generation. Electrochemical storage is a promising technology for local energy systems. In particular, lithium-ion batteries due to their high energy density and high efficiency. However, despite their 89% decrease in capital cost over the last 10 years, lithium-ion batteries are still relatively expensive. Local energy systems with battery storage can use their battery for different purposes such as maximising their self-consumption, minimising their operating cost through energy arbitrage which is storing energy when the electricity price is low and releasing the energy when the price increases, and increasing their revenue by providing flexibility services to the utility grid. Power rating and energy capacity are vitally important in the design of an electricity storage system. A case study is given for the purpose of providing a repeatable methodology for optimally sizing of a battery storage system for a local energy system. The methodology can be adapted to include any local energy system generation or demand profile.


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