Nested algorithms for optimal reservoir operation and their embedding in a decision support platform

1 INTRODUCTION
1.1 Motivation
1.2 Problem description
1.2.1 Optimal reservoir operation
1.2.2 Development of a cloud decision support platform
1.3 Research objectives
1.4 Outline of the thesis

2 OPTIMAL RESERVOIR OPERATION: THE MAIN APPROACHES RELEVANT FOR THIS STUDY
2.1 Mathematical formulation of reservoir optimization problem
2.2 Dynamic programming
2.3 Stochastic dynamic programming
2.4 Reinforcement learning
2.5 Approaches to multi-objective optimization
2.5.1 Multi-objective optimization by a sequence of single-objective optimization searches
2.5.2 Multi-objective and multi-agent reinforcement learning
2.6 Conclusions

3 NESTED OPTIMIZATION ALGORITHMS
3.1 Nested dynamic programming (nDP) algorithm
3.2 Nested optimization algorithms
3.2.1 Linear formulation
3.2.2 Non-linear formulation
3.3 Nested stochastic dynamic programming (nSDP) algorithm
3.4 Nested reinforcement learning (nRL) algorithm
3.5 Multi-objective nested algorithms
3.6 Synthesis: methodology and experimental workflow
3.7 Conclusions

4 CASE STUDY: ZLETOVICA HYDRO SYSTEM OPTIMIZATION PROBLEM
4.1 General description
4.2 Zletovica river basin
4.3 Zletovica hydro system
4.4 Optimization problem formulation
4.4.1 Decision variables
4.4.2 Constraints
4.4.3 Aggregated objective function
4.4.4 Objectives weights magnitudes
4.5 Conclusions

5 ALGORITHMS IMPLEMENTATION ISSUES
5.1 nDP implementation
5.2 nSDP implementation
5.2.1 Implementation issues
5.2.2 Transition matrices
5.2.1 Optimal number of clusters
5.3 nRL implementation
5.3.1 nRL design and memory implications
5.3.2 nRL parameters
5.3.3 Agent starting state, action list and convergence criteria
5.4 Conclusions

6 EXPERIMENTS, RESULTS AND DISCUSSION
6.1 Experiments with nDP using monthly data
6.2 Comparison of nDP with other DP algorithms
6.2.1 nDP compared with a classical DP algorithm
6.2.2 nDP compared with an aggregated water demand DP algorithm
6.3 Experiments with nDP using weekly data
6.4 Experiments with nSDP and nRL using weekly data and their comparison to nDP
6.5 Identification of optimal solutions in multi-objective setting using MOnDP, MOnSDP and MOnRL
6.6 Conclusions

7 CLOUD DECISION SUPPORT PLATFORM
7.1 Background
7.2 Architecture and implementation
7.2.1 Data infrastructure web service
7.2.2 Web service for support of Water Resources Modelling
7.2.3 Web service for water resources optimization
7.2.4 Web service for user management
7.3 Results and tests
7.4 Discussion
7.5 Conclusion

8 CONCLUSIONS AND RECOMMENDATIONS
8.1 Summary
8.2 Conclusions
8.2.1 Conclusions concerning the algorithms
8.2.2 Conclusions concerning the decision support platform
8.3 Recommendations

Biography

Blagoj Delipetrev was born in 1980 in Shtip, Republic of Macedonia. He graduated from the Faculty of Electrical Engineering and Information Technologies, at University Ss. Cyril and Methodius in Skopje in 2003. Blagoj conducted his Master studies 2004-2007 at the same university, working on his thesis "Geo-model of the Republic of Macedonia," which focused on information systems technologies, Geographical Information Systems (GIS), Spatial Data Infrastructures (SDI), and their potential applications in Macedonia.

In January 2010 Blagoj started his PhD research at UNESCO-IHE. This publication presents his PhD thesis, entitled "Nested algorithms for optimal reservoir operation and their embedding in a decision support platform." It focusses on novel algorithms for optimal Reservoir Operation and development of cloud decision support systems.
Currently Blagoj is currently working as an assistant professor at Faculty of Computer Science, University Goce Delcev in Shtip, Republic of Macedonia.