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Energy storage in Madeira, Portugal: co-optimizing for arbitrage, self-sufficiency, peak shaving and energy backup

Authors: Hashmi, Md Umar; Pereira, Lucas; Bušić, Ana;

Energy storage in Madeira, Portugal: co-optimizing for arbitrage, self-sufficiency, peak shaving and energy backup

Abstract

International audience; Energy storage applications are explored from a prosumer (consumers with generation) perspective for the island of Madeira in Portugal. These applications could also be relevant to other power networks. We formulate a convex co-optimization problem for performing arbitrage under zero feed-in tariff, increasing self-sufficiency by increasing self-consumption of locally generated renewable energy, provide peak shaving and act as a backup power source during anticipated and scheduled power outages. Using real data from Madeira we perform short and long timescale simulations in order to select end-user contract which maximizes their gains considering storage degradation based on operational cycles. We observe energy storage ramping capability decides peak shaving potential, fast ramping batteries can significantly reduce peak demand charge. The numerical experiment indicates that storage providing backup does not significantly reduce gains performing arbitrage and peak demand shaving. Furthermore, we also use AutoRegressive Moving Average (ARMA) forecasting along with Model Predic-tive Control (MPC) for real-time implementation of the proposed optimization problem in the presence of uncertainty.

Country
France
Subjects by Vocabulary

Microsoft Academic Graph classification: Mathematical optimization Optimization problem Computer science Energy storage Peak demand Backup business.industry Renewable energy Model predictive control Peaking power plant Arbitrage business

Keywords

Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, FOS: Electrical engineering, electronic engineering, information engineering, [SPI.NRJ]Engineering Sciences [physics]/Electric power, [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [SPI.NRJ] Engineering Sciences [physics]/Electric power

20 references, page 1 of 2

[1] M. Papadopoulos, P. Malatestas, and N. Hatziargyriou, “Simulation and analysis of small and medium size power systems containing wind turbines,” IEEE Transactions on Power Systems, vol. 6, no. 4, pp. 1453-1458, 1991. [OpenAIRE]

[2] R. Calero and J. A. Carta, “Action plan for wind energy development in the canary islands,” Energy Policy, vol. 32, no. 10, pp. 1185-1197, 2004.

[3] C. Bueno and J. A. Carta, “Wind powered pumped hydro storage systems, a means of increasing the penetration of renewable energy in the canary islands,” Renewable and Sustainable Energy Reviews, vol. 10, no. 4, pp. 312-340, 2006.

[4] F. M. Vieira, P. S. Moura, and A. T. de Almeida, “Energy storage system for self-consumption of photovoltaic energy in residential zero energy buildings,” Renewable Energy, vol. 103, pp. 308-320, 2017.

[5] R. Luthander, J. Wid´en, D. Nilsson, and J. Palm, “Photovoltaic self-consumption in buildings: A review,” Applied Energy, vol. 142, pp. 80-94, 2015. [OpenAIRE]

[6] R. Bhandari and I. Stadler, “Grid parity analysis of solar photovoltaic systems in germany using experience curves,” Solar Energy, vol. 83, no. 9, pp. 1634-1644, 2009.

[7] J. Mosh¨ovel, K.-P. Kairies, D. Magnor, M. Leuthold, M. Bost, S. G¨ahrs, E. Szczechowicz, M. Cramer, and D. U. Sauer, “Analysis of the maximal possible grid relief from pv-peak-power impacts by using storage systems for increased self-consumption,” Applied Energy, vol. 137, pp. 567-575, 2015.

[8] C. A. Hill, M. C. Such, D. Chen, J. Gonzalez, and W. M. Grady, “Battery energy storage for enabling integration of distributed solar power generation,” IEEE Transactions on smart grid, vol. 3, no. 2, pp. 850-857, 2012.

[9] M. U. Hashmi, D. Muthirayan, and A. Buˇsi´c, “Effect of real-time electricity pricing on ancillary service requirements,” in ACM EEnergy, 2018, pp. 550-555.

[10] B. Cheng and W. Powell, “Co-optimizing battery storage for the frequency regulation and energy arbitrage using multi-scale dynamic programming,” IEEE Trans. on Smart Grid, 2016.

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    Top 10%
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    9
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Average
Average
Top 10%
Funded by
EC| SMILE
Project
SMILE
SMart IsLand Energy systems
  • Funder: European Commission (EC)
  • Project Code: 731249
  • Funding stream: H2020 | IA
Validated by funder
,
ANR| PARI
Project
PARI
Probabilistic Approach for Renewable Energy Integration: Virtual Storage from Flexible Loads
  • Funder: French National Research Agency (ANR) (ANR)
  • Project Code: ANR-16-CE05-0008
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