Nested algorithms for optimal reservoir operation and their embedding in a decision support platform. (2020)

• Record Type: Book
• Further Information: Note: By Blagoj Delipetrev (Master of Science in Information Technology University "Ss Cyril and Methodius", Skopje).
• Authors: Delipetrev, Blagoj
• Contents: 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
• Publisher Details: Place of publication not identified : CRC Press
• Publication Date: 2020
• Extent: 1 online resource (142 pages)
• Subjects: 628.1/32; Reservoirs -- Mathematical models; Mathematical optimization; Mathematical optimization; Reservoirs -- Mathematical models
• Languages: English
• ISBNs: 9780429606038; 0429606036
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.