Kooperativer Bibliotheksverbund

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Umfang: 1 online resource (372 pages)

ISBN: 9780128241158 , 0128241152 , 9780128241141 , 0128241144

Anmerkung: Front cover -- Title -- Half title -- Copyright -- Contents -- Biography -- Chapter 1 Introduction of integrated energy systems -- 1.1 Introduction -- 1.2 Integrated energy system -- 1.2.1 Electricity sector -- 1.2.2 Heating and cooling sector -- 1.2.3 Natural gas sector -- 1.2.4 Transportation sector -- 1.2.5 Operation of integrated energy systems -- 1.3 Current status of integrated energy systems in China and Denmark -- 1.4 Recommendations for further development of integrated energy systems -- 1.5 Conclusion -- References -- Chapter 2 Mathematical model of multi-energy systems -- 2.1 Introduction -- 2.2 Modeling of coupling devices -- 2.2.1 CHP unit model -- 2.2.2 Heat pump model -- 2.2.3 Electric boiler model -- 2.2.4 Water pump model -- 2.3 Mathematical model of the district heating network -- 2.3.1 Hydraulic model -- 2.3.2 Thermal model -- 2.4 Mathematical model of the electric power network -- 2.5 Modeling of the natural gas system -- 2.5.1 Natural gas system overview -- 2.5.2 Mathematical model of the natural gas system -- 2.5.4 Linearization procedure -- 2.5.5 Natural gas system integration -- 2.5.6 Conclusion -- References -- Chapter 3 Uncertainty modeling -- 3.1 Introduction -- 3.2 Scenario generation with spatial-temporal correlations in SO -- 3.2.1 Temporal correlation modeling based on exponential function -- 3.2.2 Spatial correlation modeling based on GMM -- 3.2.3 Gibbs sampling -- 3.2.4 Procedure for scenario generation with spatial-temporal correlations -- 3.3 Partition-combine uncertainty set modeling in RO -- 3.3.1 Set partition and combination -- 3.3.2 Identification of inner subsets -- 3.4 Case study -- 3.4.1 Performance of the spatial-temporal correlated scenario set -- 3.4.2 Performance of the partition-combine uncertainty set -- 3.5 Conclusion -- References. , Chapter 4 Optimal operation of the multi-energy building complex -- 4.1 Introduction -- 4.2 Configuration of a BC -- 4.3 PMIB with the HVAC system -- 4.3.1 Thermal dynamics of a building -- 4.3.2 The prediction model of loads of an individual building -- 4.4 Formulation of the hierarchical method -- 4.4.1 Formulation of the control layer -- 4.4.2 Formulation of the scheduling layer -- 4.4.3 Simulation setup -- 4.5 Results and discussions -- 4.5.1 Case setting -- 4.5.2 Results and discussions -- 4.6 Conclusion -- References -- Chapter 5 MPC-based real-time dispatch of multi-energy building complex -- 5.1 Introduction -- 5.2 Configuration and modeling of the BC -- 5.2.1 Configuration of the BC -- 5.2.2 Building modeling -- 5.2.3 Mathematical models of DER -- 5.3 The multi-time scale and MPC-based scheduling method -- 5.3.1 Framework of the scheduling method -- 5.3.2 Formulations of the scheduling method -- 5.4 Results and discussions -- 5.4.1 5.4.1. Case setting -- 5.4.2 Day-ahead scheduling results -- 5.4.3 Real-time dispatch results -- 5.5 Discussions -- 5.6 Conclusion -- References -- Chapter 6 Adaptive robust energy and reserve co-optimization of an integrated electricity and heating system considering wind uncertainty -- 6.1 Introduction -- 6.2 Mathematical formulation of adaptive robust energy and reserve co-optimization for the IEHS -- 6.2.1 Objective function -- 6.2.2 Mathematical formulation of EPS and DHS operations in two stages -- 6.2.3 Robust compact formulation -- 6.3 Solution methodology -- 6.4 Simulation results -- 6.4.1 System configuration -- 6.4.2 Benchmark cases -- 6.4.3 Reserve sufficiency analysis in real-time regulation -- 6.4.4 Economic benefits of introducing DHS flexibility -- 6.5 Summary and conclusion -- References. , Chapter 7 Decentralized robust energy and reserve co-optimization for multiple integrated electricity and heating systems -- 7.1 Introduction -- 7.2 Structure and decentralized operation framework of multiple IEHSs -- 7.3 Mathematical formulation of decentralized robust energy and reserve co-optimization for multiple IEHSs -- 7.3.1 Objective function -- 7.3.2 Day-ahead operation constraints of IEHSs -- 7.3.3 Real-time regulation constraints of the EPS and DHS -- 7.4 Solution methodology -- 7.4.1 Compact formulation of decentralized two-stage robust optimization -- 7.4.2 Standard ADMM decentralized algorithm -- 7.4.3 Improved locally adaptive ADMM decentralized algorithm -- 7.4.4 C& -- CG algorithm for each IEHS -- 7.5 Simulation results -- 7.5.1 Validation of the proposed two-stage robust optimization -- 7.5.2 Comparison of different operation frameworks -- 7.5.3 Performance comparison of different ADMM algorithms -- 7.6 Conclusion -- References -- Chapter 8 Chance-constrained energy and multi-type reserves scheduling exploiting flexibility from combined power and heat units and heat pumps -- 8.1 Introduction -- 8.2 Framework of chance-constrained two-stage energy and multi-type reserves scheduling -- 8.3 Primary FRR and following reserve provision from CHP units and HPs -- 8.3.1 Flexibility model of extraction CHP units and HPs -- 8.3.2 Primary FRRs and following reserves provision from CHP units and HPs -- 8.4 Mathematical formulation of decentralized robust energy and reserve co-optimization for multiple IEHSs -- 8.4.1 Objective function -- 8.4.2 Day-ahead operation constraints of IEHSs under normal conditions -- 8.4.3 Contingency operation constraints of IEHSs following the outage of the largest generator -- 8.4.4 Real-time regulation constraints of IEHSs accommodating wind power forecast errors. , 8.5 Reformulation as a mixed-integer linear program -- 8.5.1 Linearization of constraint (8.7) -- 8.5.2 Convex approximation of chance constraint (8.34) based on CVaR -- 8.6 Simulation results -- 8.6.1 Test system -- 8.6.2 Impact of exploiting additional reserves from CHP units and HPs on scheduling results -- 8.6.3 Impact of chance constraints with adjustable risk levels on scheduling results -- 8.7 Conclusion -- References -- Chapter 9 Day-ahead stochastic optimal operation of the integrated electricity and heating system considering reserve of flexible devices -- 9.1 Introduction -- 9.2 Two-stage stochastic optimal dispatching scheme of the IEHS -- 9.2.1 Structure of the IEHS -- 9.2.2 Framework of the dispatching scheme -- 9.3 Reserve provision and heat regulation from condensing CHP units -- 9.4 Mathematical formulation of stochastic optimal operation of the IEHS -- 9.4.1 Objective function of the IEHS -- 9.4.2 Day-ahead operational constraints of the EPS -- 9.4.3 Day-ahead operational constraints of the DHS -- 9.4.4 Real-time operational constraints of the EPS -- 9.4.5 Real-time operational constraints of the DHS -- 9.4.6 Compact form of two-stage stochastic optimal operation -- 9.5 Case study -- 9.5.1 System description -- 9.5.2 Improved flexibility from condensing CHP units and flexible devices -- 9.6 Conclusion -- References -- Chapter 10 Two-stage stochastic optimal operation of integrated energy systems -- 10.1 Introduction -- 10.2 Background and DA scheduling -- 10.2.1 Background -- 10.2.2 Overview of two-stage stochastic programming approaches -- 10.3 Mathematical model of the IES for two-stage DA scheduling -- 10.3.1 Objective function -- 10.3.2 First-stage constraints -- 10.3.3 Second-stage constraints -- 10.4 Scenario generation and reduction method -- 10.4.1 Historical data of forecasted and measured wind power output. , 10.4.2 Constructing the data box using the historical data -- 10.4.3 Generating Gaussian random vector -- 10.4.4 Inverse transformation -- 10.5 Example of a case study -- 10.5.1 Benefits of the stochastic programming approach -- 10.5.2 Validation of the flexibility provision by synergy of multiple energy subsystems -- 10.5.3 Validation of the temporal correlation of the proposed scenario generation method -- 10.5.4 Summary of the results and discussion -- 10.6 Conclusion -- References -- Chapter 11 MPC-based real-time operation of integrated energy systems -- 11.1 Introduction -- 11.2 Background and RT scheduling -- 11.2.1 Background -- 11.2.2 Overview of traditional RT scheduling -- 11.3 MPC-based RT scheduling -- 11.3.1 MPC strategy -- 11.3.2 MPC-based RT scheduling for IESs -- 11.3.3 Principle and application of the OLM -- 11.4 Mathematical models of the IES for MPC-based RT scheduling -- 11.4.1 Objective function -- 11.4.2 Electric power system -- 11.4.3 District heating system -- 11.4.4 Natural gas system -- 11.4.5 Linking components -- 11.5 Solution process and case study -- 11.5.1 Solution process -- 11.5.2 Case study -- 11.6 Simulation results -- 11.6.1 Benefits of the OLM combined with MPC -- 11.6.2 Impact of the prediction horizon length on the computational efficiency -- 11.6.3 Validation of the MPC strategy -- 11.6.4 Summary of the results and discussion -- 11.7 Conclusion -- References -- Appendix A Basics of stochastic optimization -- A.1 Stochastic optimization fundamentals -- A.1.1 Random variables and probability distribution -- A.1.2 Multivariate probability distributions -- A.2 Scenario generation and reduction -- A.2.1 Scenario generation -- A.2.2 Scenario reduction -- A.3 General formulation of two-stage optimization -- References -- Appendix B Introduction to adaptive robust optimization -- B.1 Formulation of ARO with resource. , B.2 Solution methodology.

Sprache: Englisch