Description

Energy is a resource that needs to be managed and decisions need to be made on production, storage, distribution, and consumption of energy. Determining how much to produce, where and when, and assigning resources to needs in the most efficient way is a problem that has been addressed in several fields. There are available tools that can be used to formulate and solve these kinds of problems. Using them in planning, operation, and control of energy systems requires starting with the basics of math programming techniques, addressing some standard optimization problems, and adapting the solutions to new particular situations of interest.
A first issue is revisiting the modelling concept. The model is a simplified representation of our reality. Complex multi-level problems may need different models and models valid at the operational level (operation and control) may not be useful at the tactical or strategic levels (scheduling and planning). Thus, when addressing optimization problems, detailed physical models based on differential equations will be replaced by algebraic equations expressing the basic relations between lumped parameters. The second issue is the choice of a problem-solving method. It is well known that all optimization methods have at least some limitations and there is no single method or algorithm that works best on all or even a broad class of problems. In order to choose the best method for a given problem, one must first understand the nature of the problem and the type of design space that is being searched..

Students attending this course will learn how to recognise and formulate different optimization problems in planning, operation and control of energy systems, and how to solve them using existing software and solvers such as MATLAB and GAMS. Different principal algorithms for linear, discrete, nonlinear and dynamic optimization are introduced and related methodologies together with underlying mathematical structures are described accordingly. Several illustrative examples and optimization

problems, ranging from the classical optimization problems to the recent MINLP models proposed for the optimization of integrated energy systems (such as residential AC/DC microgrids) will be introduced during supervised hand-on sessions and different tools (such as classic mathematical methods, heuristics and meta-heuristics) will be used for solving the cases. The choice of objective functions, representation of discrete decisions, using formulation tricks and checking the results will be also covered. Moreover, specific real applications of these methods and algorithms will be shown, not only focusing on the optimization by itself but also showing the techniques for interconnecting the computational system with the resources utilizing technologies such as the Internet of Things (IoT).

The course is intended for those students that, having a general knowledge in mathematics and simulation, have a very limited experience in math optimization and programming, and need to be introduced to these tools for energy systems optimization.

Prerequisites

Familiarity with basics of mathematical modelling, linear algebra, and probability and statistics. Skills regarding Matlab/Simulink is also needed.

Form of evaluation

The participants will be grouped and asked to teamwork on several case study scenarios and tasks proposed along the course.

Price

8000 DKK for the Industry and 6000 DKK for PhD students outside of Denmark (VAT-FREE Education)

The Danish universities have entered into an agreement that allows PhD students at a Danish university (except Copenhagen Business School) the opportunity to free of charge take a subject-specific course at another Danish university.
Read more here: https://phdcourses.dk/ 

Questions

hr@energy.aau.dk   

More information

https://www.energy.aau.dk/research/phd   

Course literature       

Papers:

1. Jaimes, Antonio López, Saúl Zapotecas Martınez, and Carlos A. Coello Coello. "An introduction to multiobjective optimization techniques." Optimization in Polymer Processing (2009): 29-57.
2. Talbi, El‐Ghazali, et al. "Multi‐objective optimization using metaheuristics: non‐standard algorithms." International Transactions in Operational Research 19.1-2 (2012): 283-305.
3. El-Shorbagy MA, Hassanien AE. Particle swarm optimization from theory to applications. International Journal of Rough Sets and Data Analysis (IJRSDA). 2018 Apr 1;5(2):1-24.
4. Reyes-Sierra M, Coello CC. Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International journal of computational intelligence research. 2006 Mar 22;2(3):287-308.
5. Khan AA, Naeem M, Iqbal M, Qaisar S, Anpalagan A. A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids. Renewable and Sustainable Energy Reviews. 2016 May 1;58:1664-83.
6. Zia MF, Elbouchikhi E, Benbouzid M. Microgrids energy management systems: A critical review on methods, solutions, and prospects. Applied energy. 2018 Jul 15;222:1033-55.
7. Olivares DE, Mehrizi-Sani A, Etemadi AH, Cañizares CA, Iravani R, Kazerani M, Hajimiragha AH, Gomis-Bellmunt O, Saeedifard M, Palma-Behnke R, Jiménez-Estévez GA. Trends in microgrid control. IEEE Transactions on smart grid. 2014 May 20;5(4):1905-19.
8. T. Li and M. Shahidehpour, “Price-Based unit commitment: a case of Lagrangian relaxation versus mixed integer programming,” IEEE Trans. Power Syst., vol. 20, no. 4, pp. 2015–2025, Nov. 2005.
9. J. M. Arroyo, A. J. Conejo, “Optimal response of a thermal unit to an electricity spot market”,  IEEE Trans. Power Syst., vol. 15, no. 3, pp. 1098–1104, Aug. 2000
10. Jalkanen JP, Johansson L, Kukkonen J, Brink A, Kalli J, Stipa T. Extension of an assessment model of ship traffic exhaust emissions for particulate matter and carbon monoxide. Atmospheric Chemistry and Physics. 2012 Mar 12;12(5):2641-59.
11. Bazmohammadi N, Karimpour A, Bazmohammadi S, Anvari-Moghaddam A, Guerrero JM. An efficient decision-making approach for optimal energy management of microgrids. In2019 IEEE Milan PowerTech 2019 Jun 23 (pp. 1-6). IEEE.
12. Raya-Armenta JM, Bazmohammadi N, Avina-Cervantes JG, Saez D, Vasquez JC, Guerrero JM. Energy management system optimization in islanded microgrids: An overview and future trends. Renewable and Sustainable Energy Reviews. 2021 Oct 1;149:111327.
13. Hu J, Shan Y, Guerrero JM, Ioinovici A, Chan KW, Rodriguez J. Model predictive control of microgrids–An overview. Renewable and Sustainable Energy Reviews. 2021 Feb;136:110422.
14. Farina M, Giulioni L, Magni L, Scattolini R. An approach to output-feedback MPC of stochastic linear discrete-time systems. Automatica. 2015 May 1;55:140-9.
15. Parisio, A.; Rikos, E.; Glielmo, L. Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study. J. Process. Control. 2016, 43, 24–37.
16. Olivares, D.E.; Lara, J.D.; Cañizares, C.A.; Kazerani, M. Stochastic-predictive energy management system for isolated microgrids. IEEE Trans. Smart Grid 2015, 6, 2681–2693.
17. Bazmohammadi N, Anvari-Moghaddam A, Tahsiri A, Madary A, Vasquez JC, Guerrero JM. Stochastic Predictive Energy Management of Multi-Microgrid Systems. Applied Sciences. 2020 Jan;10(14):4833.
18. Alessio A, Bemporad A. A survey on explicit model predictive control. InNonlinear model predictive control 2009 (pp. 345-369). Springer, Berlin, Heidelberg.
19. Bazmohammadi N, Tahsiri A, Anvari-Moghaddam A, Guerrero JM. Optimal operation management of a regional network of microgrids based on chance-constrained model predictive control. IET Generation, Transmission & Distribution. 2018 Jun 19;12(15):3772-9.
20. Wang X, Bazmohammadi N, Atkin J, Bozhko S, Vasquez JC, Guerrero JM. Operation Management of More-Electric Aircraft Using Two-stage Stochastic Model Predictive Control. In2022 Interdisciplinary Conference on Mechanics, Computers and Electrics (ICMECE) 2022 Oct 7. IEEE.
21. Wang X, Bazmohammadi N, Atkin J, Bozhko S, Guerrero JM. Chance-constrained model predictive control-based operation management of more-electric aircraft using energy storage systems under uncertainty. Journal of Energy Storage. 2022 Nov 25;55:105629.
22. Ciurans C, Bazmohammadi N, Poughon L, Vasquez JC, Dussap CG, Gòdia F, Guerrero JM. Hierarchically controlled ecological life support systems. Computers & Chemical Engineering. 2022 Jan 1;157:107625.

Books

1. Poler, Raúl, Josefa Mula, and Manuel Díaz-Madroñero. Operations research problems: statements and solutions. Springer Science & Business Media, 2013.
2. Boyd S, Boyd SP, Vandenberghe L. Convex optimization. Cambridge university press; 2004 Mar 8.
3. Applied Mathematical Programming by Bradley, Hax, and Magnanti (Addison-Wesley, 1977)
4. Soroudi A. Power system optimization modeling in GAMS. Switzerland: Springer; 2017 Aug 29.
5. Model Predictive Control, E. F. Camacho and C. Bordons, 2nd Edition