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Optimization techniques in hydropower
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Publisher: Editura Universitară
Author: Florica Popa, Eliza-Isabela Tica, Radu Popa
Edition: I
Pages: 270
Publisher year: 2024
ISBN: 978-606-28-1656-8
DOI: 10.5682/9786062816568
Product Code:
9786062816568
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The work is intended to be a tribute to the special professor and man who was Professor Radu Popa, who passed away far too early, and who for over 40 years cultivated aspects related to the optimization of hydropower facilities in the Department of Hydraulics, Hydraulic Machines and Environmental Engineering of the Faculty of Energy, National University of Science and Technology Politehnica Bucharest.
The book presents aspects related to the implementation of recent evolutionary algorithms for solving optimization problems in the hydropower field, namely: the firefly algorithm, the cuckoo search algorithm, the bat algorithm, the shark scent-based algorithm, the flower pollination-based algorithm.
The research included in the paper refers to some of the most important hydropower developments in our country: Vidraru, Dragan-Iad, Fântânele, Tarnița and the existing hydroelectric power plants, Mariselu and Tarnița, as well as to the analysis of the possibilities of optimal exploitation and economic performance of the extended ensemble with the Tarnița - Lapustesti pumped storage hydropower development.
The book presents aspects related to the implementation of recent evolutionary algorithms for solving optimization problems in the hydropower field, namely: the firefly algorithm, the cuckoo search algorithm, the bat algorithm, the shark scent-based algorithm, the flower pollination-based algorithm.
The research included in the paper refers to some of the most important hydropower developments in our country: Vidraru, Dragan-Iad, Fântânele, Tarnița and the existing hydroelectric power plants, Mariselu and Tarnița, as well as to the analysis of the possibilities of optimal exploitation and economic performance of the extended ensemble with the Tarnița - Lapustesti pumped storage hydropower development.
Foreword / Summary / 9
List of figures /13
List of tables / 17
Abbreviations used / 21
Notations of the main quantities used / 23
Words in English / 25
INTRODUCTION/ 27
1. GENERAL ASPECTS REGARDING OPTIMIZATION IN THE HYDROENERGY FIELD/ 31
1.1. Definition US Bureau of Reclamation/ 32
1.2. Deterministic, stochastic, fuzzy / 34
1.3. Example of a deterministic model for the optimal management of the exploitation of hydropower facilities / 37
2. ASPECTS REGARDING THE MATHEMATICAL SOLVING OF OPTIMIZATION PROBLEMS / 45
2.1. Methods of mathematical programming/ 46
2.1.1. Non-linear programming / 47
2.1.2. Linear programming / 49
2.1.3. Multi-objective programming / 50
2.1.4. Priority programming / 51
2.1.5. Programming in whole numbers / 52
2.1.6. Multilevel programming / 52
2.1.7. Dynamic programming / 53
2.2. Metaheuristic models / 53
2.3. Comparative characteristics of optimization methods / 57
3. LONG-TERM OPERATION, IN PROBABLE CONDITIONS, OF AN AHE WITH A LARGE LAKE AND A TOP FORCE / 61
3.1. General aspects regarding dynamic programming / 61
3.2. The use of dynamic stochastic programming in the operation of AHE / 67
3.2.1. Short review on stochastic programming / 67
3.2.2. PDS model for the long-term operation of a large lake with peak CHP / 70
3.2.3. Input data for the case study and results / 75
3.3. Operation simulation model based on PDS / 90 results
3.4. Synthetic generation of sets of average monthly tributary flows / 94
3.5. Validation of the simulation model of the operation of AHE Fantanele – Mariselu / 110
4. EVOLUTIONARY ALGORITHMS AND THEIR USE IN AN AHE OPERATION PROBLEM / 123
4.1. The Firefly Algorithm (ALic) / 124
4.2. The Cuckoo Algorithm (AC) / 129
4.3. The Bat Algorithm (ALil) / 135
4.4. Numerical application in hydropower (AHE Vidraru) / 142
4.5. Conclusions / 151
4.6. Optimizing the electricity production of a CHE
fed from a multi-use storage, using an algorithm based on shark smell (AMR) adapted (AHE Vidraru) / 152
4.6.1. Short review on the shark smell algorithm / 153
4.6.2. Case study / 155
4.6.3. Adapted AMR / 157
4.6.4. Results / 158
4.6.5. Comments and conclusions / 160
4.7. Optimization of AHE Dragan Iad operation using the flower pollination algorithm (APF) / 160
4.7.1. Short review of the APF / 160
4.7.2. Case study: AHE Dragan Iad / 163
4.7.3. Flower pollination algorithm (APF) / 164
4.7.4. Results and comments / 169
4.7.5. Conclusions / 171
5. GENERAL ASPECTS REGARDING HYDRO-ENERGY FACILITIES WITH PUMPED STORAGE / 173
5.1. The role of AHEAP and some statistical data / 173
5.2. AHEAP in Romania / 175
5.3. Aspects regarding AHEAP Tarnita – Lapustesti / 177
6. GENETIC ALGORITHM MODEL FOR OPTIMIZING THE EXPLOITATION OF AHE FANTANELE-TARNITA-LAPUSTESTI / 185
6.1. Formulation of the optimization model / 186
6.2. Genetic algorithm model for approaching the subject / 192
6.3. Input data for numerical simulations / 203
6.4. Estimation of the economic performance of AHE Fantanele-Tarnita Lapustesti, under the conditions of the average hydrological year / 208
6.5. Statistical analysis of the results of the optimization model / 224
7. THE ALGORITHM OF BEE MULTIPLICATION FOR OPTIMIZING THE EXPLOITATION OF AHE FANTANELE-TARNITA-LAPUSTESTI / 233
7.1. Short review of bee-inspired metaheuristic algorithms / 234
7.2. The bee breeding algorithm and the proposed version / 237
7.3. The application of HBMOA to the case of AHE Fantanele-Tarnita-Lapustesti / 244
BIBLIOGRAPHY/ 253
List of figures /13
List of tables / 17
Abbreviations used / 21
Notations of the main quantities used / 23
Words in English / 25
INTRODUCTION/ 27
1. GENERAL ASPECTS REGARDING OPTIMIZATION IN THE HYDROENERGY FIELD/ 31
1.1. Definition US Bureau of Reclamation/ 32
1.2. Deterministic, stochastic, fuzzy / 34
1.3. Example of a deterministic model for the optimal management of the exploitation of hydropower facilities / 37
2. ASPECTS REGARDING THE MATHEMATICAL SOLVING OF OPTIMIZATION PROBLEMS / 45
2.1. Methods of mathematical programming/ 46
2.1.1. Non-linear programming / 47
2.1.2. Linear programming / 49
2.1.3. Multi-objective programming / 50
2.1.4. Priority programming / 51
2.1.5. Programming in whole numbers / 52
2.1.6. Multilevel programming / 52
2.1.7. Dynamic programming / 53
2.2. Metaheuristic models / 53
2.3. Comparative characteristics of optimization methods / 57
3. LONG-TERM OPERATION, IN PROBABLE CONDITIONS, OF AN AHE WITH A LARGE LAKE AND A TOP FORCE / 61
3.1. General aspects regarding dynamic programming / 61
3.2. The use of dynamic stochastic programming in the operation of AHE / 67
3.2.1. Short review on stochastic programming / 67
3.2.2. PDS model for the long-term operation of a large lake with peak CHP / 70
3.2.3. Input data for the case study and results / 75
3.3. Operation simulation model based on PDS / 90 results
3.4. Synthetic generation of sets of average monthly tributary flows / 94
3.5. Validation of the simulation model of the operation of AHE Fantanele – Mariselu / 110
4. EVOLUTIONARY ALGORITHMS AND THEIR USE IN AN AHE OPERATION PROBLEM / 123
4.1. The Firefly Algorithm (ALic) / 124
4.2. The Cuckoo Algorithm (AC) / 129
4.3. The Bat Algorithm (ALil) / 135
4.4. Numerical application in hydropower (AHE Vidraru) / 142
4.5. Conclusions / 151
4.6. Optimizing the electricity production of a CHE
fed from a multi-use storage, using an algorithm based on shark smell (AMR) adapted (AHE Vidraru) / 152
4.6.1. Short review on the shark smell algorithm / 153
4.6.2. Case study / 155
4.6.3. Adapted AMR / 157
4.6.4. Results / 158
4.6.5. Comments and conclusions / 160
4.7. Optimization of AHE Dragan Iad operation using the flower pollination algorithm (APF) / 160
4.7.1. Short review of the APF / 160
4.7.2. Case study: AHE Dragan Iad / 163
4.7.3. Flower pollination algorithm (APF) / 164
4.7.4. Results and comments / 169
4.7.5. Conclusions / 171
5. GENERAL ASPECTS REGARDING HYDRO-ENERGY FACILITIES WITH PUMPED STORAGE / 173
5.1. The role of AHEAP and some statistical data / 173
5.2. AHEAP in Romania / 175
5.3. Aspects regarding AHEAP Tarnita – Lapustesti / 177
6. GENETIC ALGORITHM MODEL FOR OPTIMIZING THE EXPLOITATION OF AHE FANTANELE-TARNITA-LAPUSTESTI / 185
6.1. Formulation of the optimization model / 186
6.2. Genetic algorithm model for approaching the subject / 192
6.3. Input data for numerical simulations / 203
6.4. Estimation of the economic performance of AHE Fantanele-Tarnita Lapustesti, under the conditions of the average hydrological year / 208
6.5. Statistical analysis of the results of the optimization model / 224
7. THE ALGORITHM OF BEE MULTIPLICATION FOR OPTIMIZING THE EXPLOITATION OF AHE FANTANELE-TARNITA-LAPUSTESTI / 233
7.1. Short review of bee-inspired metaheuristic algorithms / 234
7.2. The bee breeding algorithm and the proposed version / 237
7.3. The application of HBMOA to the case of AHE Fantanele-Tarnita-Lapustesti / 244
BIBLIOGRAPHY/ 253
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