A multi-slack Optimization Model for Scheduling Energy Hubs in Smart Grids
Abstract
This paper provides a multi-slack optimization model in order to manage the operation of an energy hub in smart grids.This model is centralized on a multi-slack one in which the proposed slack variables are in line with actual energy providers.Both electrical and thermal loads are considered in this model. An external grid and boilers are respectively used for slackgeneration units for satisfying electrical and thermal loads. In order to reduce the penalty factors in the optimization model,we addressed fair and suitable slack variables in the optimization model. In a real power system, energy storage devicescould effect optimal operation in short-term planning. The main role of such devices in smart grids is to reduce the operatingcosts because of their state of charge (SOC) in peak, medium and base loads. Such devices could also handle load andgeneration uncertainties in the real world. In this model, we implement this feature to handle the uncertainties in the randomvariable generation sector of optimization algorithm. The proposed method could handle this challenge by discharging thestored energy if the slack unit is unable to satisfy the demanded load and vice versa. In order to evaluate the effectivenessof the proposed method, a benchmark is provided in this paper. The hourly electrical and thermal demands were extractedfrom DesignBuilder® for a commercial building. The simulation results show that the presented method is both satisfactoryand consistent with expectations.References
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in energy hub, in: Decision and Control and European Control
Conference (CDC-ECC), 2011 50th IEEE Conference on, IEEE, 2011,
pp. 4943–4948.
[2] F. Brahman, M. Honarmand, S. Jadid, Optimal electrical and thermal
energy management of a residential energy hub, integrating demand
response and energy storage system, Energy and Buildings 90 (2015)
65–75.
[3] M. Geidl, G. Koeppel, P. Favre-Perrod, B. Klockl, G. Andersson,
K. Frohlich, Energy hubs for the future, IEEE power and energy magazine
5 (1) (2007) 24–30.
[4] M. Geidl, Integrated modeling and optimization of multi-carrier energy
systems, Tech. rep., ETH Zurikh: Power Systems Laboratory (2007).
[5] A. Parisio, C. Del Vecchio, A. Vaccaro, A robust optimization approach
to energy hub management, International Journal of Electrical Power
& Energy Systems 42 (1) (2012) 98–104.
[6] G. Chicco, P. Mancarella, Distributed multi-generation: A comprehensive
view, Renewable and Sustainable Energy Reviews 13 (3) (2009)
535–551.
[7] G. Anders, A. Vaccaro, Innovations in power systems reliability,
Springer series in reliability engineering 16.
[8] T. Krause, G. Andersson, K. Frohlich, A. Vaccaro, Multiple-energy carriers:
modeling of production, delivery, and consumption, Proceedings
of the IEEE 99 (1) (2011) 15–27.
[9] M. Geidl, G. Andersson, Optimal coupling of energy infrastructures, in:
Power Tech, IEEE, Lausanne, 2007, pp. 1398–1403.
[10] A. Hajimiragha, C. Canizares, M. Fowler, M. Geidl, G. Andersson, Optimal
energy flow of integrated energy systems with hydrogen economy
considerations, in: Bulk Power System Dynamics and Control-VII. Revitalizing
Operational Reliability, 2007 iREP Symposium, IEEE, 2007,
pp. 1–11.
[11] L. Carradore, F. Bignucolo, Distributed multi-generation and application
of the energy hub concept in future networks, in: Universities Power
Engineering Conference, 2008, pp. 1–5.
[12] M. Schulze, L. Friedrich, M. Gautschi, Modeling and optimization of
renewables: applying the energy hub approach, in: IEEE international
conference on sustainable energy technologies, 2008, pp. 83–88.
[13] R. Evins, K. Orehounig, V. Dorer, J. Carmeliet, New formulations of
the ‘energy hub’model to address operational constraints, Energy 73
(2014) 387–398.
[14] M. La Scala, A. Vaccaro, A. Zobaa, A goal programming methodology
for multiobjective optimization of distributed energy hubs operation,
Applied Thermal Engineering 71 (2) (2014) 658–666.
[15] J. Carpentier, A. Merlin, Optimization methods in planning and operation,
International Journal of Electrical Power & Energy Systems 4 (1)
(1982) 11–18.
[16] F. Kienzle, P. Ahcin, G. Andersson, Valuing investments in multi-energy
conversion, storage, and demand-side management systems under
uncertainty, IEEE Transactions on sustainable energy 2 (2) (2011)
194–202.
[17] M. Rastegar, M. Fotuhi-Firuzabad, M. Lehtonen, Home load management
in a residential energy hub, Electric Power Systems Research
119 (2015) 322–328.
[18] M. Javadi, A. E. Nezhad, S. Sabramooz, Economic heat and power
dispatch in modern power system harmony search algorithm versus
analytical solution, Scientia Iranica 19 (6) (2012) 1820–1828.
[19] A. V. Borre, Definition of heat pumps and their use of renewable energy
sources, REHVA Journal (2011) 38–39.
[20] D. Mileni´c, P. Vasiljevi´c, A. Vranješ, Criteria for use of groundwater as
renewable energy source in geothermal heat pump systems for building
heating/cooling purposes, Energy and Buildings 42 (5) (2010) 649–
657.
[21] F. Madonna, F. Bazzocchi, Annual performances of reversible air-towater
heat pumps in small residential buildings, Energy and Buildings
65 (2013) 299–309.
[22] N. Zhu, P. Hu, L. Xu, Z. Jiang, F. Lei, Recent research and applications
of ground source heat pump integrated with thermal energy storage
systems: A review, Applied thermal engineering 71 (1) (2014) 142–
151.
[23] M. Moradi-Dalvand, B. Mohammadi-Ivatloo, M. F. Ghazvini, Short-term
scheduling of microgrid with renewable sources and combined heat
and power, Smart microgrids, new advances, Challenges and Opportunities
in the actual Power systems.
Published
2016-07-20
How to Cite
GERAMI MOGHADDAM, Iman; SANIEI, Mohsen; MASHHOUR, Elahe.
A multi-slack Optimization Model for Scheduling Energy Hubs in Smart Grids.
Journal of Power Technologies, [S.l.], v. 98, n. 3, p. 287–295, july 2016.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/828>. Date accessed: 25 apr. 2024.
Issue
Section
Energy Conversion and Storage
Keywords
: Energy Hub;Combined heat and power; Multi-Slack Optimization Model; State of Charge
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