|
Preface |
6 |
|
|
Contents |
8 |
|
|
Part I Demand Response and Distribution Grids |
10 |
|
|
An Evolutionary Algorithm for the Optimization of Residential Energy Resources |
11 |
|
|
1 Introduction |
12 |
|
|
2 Methodology |
13 |
|
|
3 Simulation Results |
18 |
|
|
4 Conclusion |
23 |
|
|
References |
24 |
|
|
Comparison of Control Strategies for Electric Vehicles on a Low Voltage Level Electrical Distribution Grid |
25 |
|
|
1 Introduction |
26 |
|
|
2 Network Model and Load Profiles |
27 |
|
|
3 Control Strategies |
28 |
|
|
3.1 Direct Control with a Central Aggregator |
28 |
|
|
3.2 Indirect Control via a Static Time of Use (TOU) Tariff |
30 |
|
|
3.3 Autonomous Control Implementing Reactive Power to Voltage Control |
31 |
|
|
4 Results and Discussion |
32 |
|
|
4.1 Without Connected Plug-In Electric Vehicles (PEVs) |
33 |
|
|
4.2 Direct Control with Perfect Foresight |
34 |
|
|
4.3 Indirect Control via a Single Time of Use (TOU) Tariff |
34 |
|
|
4.4 Autonomous Control via Reactive Power to Voltage Control |
34 |
|
|
5 Conclusion |
35 |
|
|
References |
35 |
|
|
Part II Optimizing Transmission Grid Operation |
37 |
|
|
Optimal Storage Operation with Model Predictive Control in the German Transmission Grid |
38 |
|
|
1 Introduction |
39 |
|
|
2 Problem Formulation |
39 |
|
|
2.1 Time-Constrained Optimal Power Flow |
40 |
|
|
2.2 Model Constraints |
42 |
|
|
2.3 Solving the Problem |
42 |
|
|
3 The German Transmission Grid |
43 |
|
|
3.1 Costs |
43 |
|
|
4 Reference Solution |
46 |
|
|
4.1 Computation |
46 |
|
|
4.2 Results |
46 |
|
|
5 Model Predictive Control |
47 |
|
|
5.1 Computation |
47 |
|
|
5.2 Scenarios |
48 |
|
|
5.3 Results |
48 |
|
|
6 Critical Review |
49 |
|
|
7 Conclusion |
50 |
|
|
8 Outlook |
51 |
|
|
References |
51 |
|
|
Security-Constrained Optimization Framework for Large-Scale Power Systems Including Post-contingency Remedial Actions and Inter-temporal Constraints |
53 |
|
|
1 Introduction |
53 |
|
|
2 Modeling |
54 |
|
|
2.1 Area Definitions |
54 |
|
|
2.2 Power Flows |
54 |
|
|
2.3 Constraints |
54 |
|
|
2.4 Degrees of Freedom |
56 |
|
|
2.5 Objective Function |
58 |
|
|
3 Solution Methodology |
60 |
|
|
3.1 Successive Linear Algorithms for OPF Problems |
60 |
|
|
3.2 Linearization of Variable Quadripole Parameters |
61 |
|
|
3.3 Optimization Problem Speed-Up |
62 |
|
|
4 Exemplary Results |
66 |
|
|
5 Critical Review |
67 |
|
|
6 Conclusions |
68 |
|
|
References |
68 |
|
|
Part III Flexibility, Storage and Uncertainty Quantification |
70 |
|
|
Dispatch of Flexibility Options, Grid Infrastructure and Integration of Renewable Energies Within a Decentralized Electricity System |
71 |
|
|
1 Introduction |
72 |
|
|
2 Scenario Definition and Methodology of the Two Case Studies |
73 |
|
|
2.1 Consideration of Grid Restricitons in Electricity System Modeling |
73 |
|
|
2.2 Methodology of the Dispatch Model PowerFlex-Grid |
74 |
|
|
2.3 Description of the BMBF Project ``Transparency of Transmission Grid Planning'' and Scenario Definition |
75 |
|
|
2.4 Description of the BMWi Project ``D-Flex'' and Scenario Definition |
77 |
|
|
3 Results of the Two Case Studies |
81 |
|
|
3.1 Results of the BMBF Project ``Transparency of Transmission Grid Planning'' |
81 |
|
|
3.2 Results of the BMWi Project ``D-Flex'' |
86 |
|
|
4 Critical Review |
87 |
|
|
5 Conclusions |
88 |
|
|
6 Outlook |
89 |
|
|
References |
89 |
|
|
Dynamic Decision Making in Energy Systems with Storage and Renewable Energy Sources |
91 |
|
|
1 Introduction |
91 |
|
|
2 Problem Formulation |
93 |
|
|
2.1 State and Exogenous Information |
93 |
|
|
2.2 Decisions |
94 |
|
|
2.3 State Transition |
95 |
|
|
2.4 Objective |
96 |
|
|
3 Fundamental Classes of Policies |
96 |
|
|
4 The Competing Policies |
98 |
|
|
4.1 Policy Function Approximation |
98 |
|
|
4.2 Cost Function Approximation |
98 |
|
|
4.3 Value Function Approximation |
98 |
|
|
4.4 Lookahead Policy |
99 |
|
|
5 Selecting the Best Policy |
100 |
|
|
5.1 Problem Variations |
100 |
|
|
5.2 Computational Results |
101 |
|
|
6 Conclusions |
104 |
|
|
References |
105 |
|
|
Part IV Challenges in Microgrids |
106 |
|
|
An Optimal Investment Model for Battery Energy Storage Systems in Isolated Microgrids |
107 |
|
|
1 Introduction |
109 |
|
|
2 BESS Investment Framework |
110 |
|
|
2.1 Model I: Optimal BESS Investment |
112 |
|
|
2.2 Model II: Optimal Microgrid Operation |
118 |
|
|
3 Results and Analysis |
119 |
|
|
3.1 Microgrid Test System |
119 |
|
|
3.2 Optimal BESS Investment |
120 |
|
|
3.3 Optimal BESS and Microgrid Operations Schedule |
121 |
|
|
4 Conclusions |
122 |
|
|
References |
123 |
|
|
A Dynamic Programming Approach to Multi-period Planning of Isolated Microgrids |
124 |
|
|
1 Introduction |
125 |
|
|
2 State of the Art |
125 |
|
|
3 Problem Definition |
126 |
|
|
3.1 Graph Formulation of the Problem |
126 |
|
|
3.2 Objective Function and Constraints |
127 |
|
|
3.3 Input Data |
128 |
|
|
4 Problem Decomposition and Tool Structure |
129 |
|
|
4.1 Network Routing |
129 |
|
|
4.2 Network Sizing |
130 |
|
|
4.3 Constraints Verification and Transition Costs |
130 |
|
|
4.4 Investment Timing with Dynamic Programming |
131 |
|
|
4.5 Global Structure of the Planning Tool |
132 |
|
|
5 Case-Study |
132 |
|
|
6 Discussion |
136 |
|
|
7 Conclusion |
136 |
|
|
References |
137 |
|
|
Part V Renewable Energy and Power Grid Expansion Planning |
139 |
|
|
Curtailing Renewable Feed-In Peaks and Its Impact on Power Grid Extensions in Germany for the Year 2030 |
140 |
|
|
1 Introduction |
140 |
|
|
2 Input Data and Scenarios |
141 |
|
|
2.1 Scenarios of Different RES Integration |
141 |
|
|
2.2 Inputdata |
142 |
|
|
3 Optimal Transmission Extensions Using Benders Decomposition |
144 |
|
|
3.1 Sequential Transmission Capacity Increase |
144 |
|
|
3.2 Configuration of the Global Model |
144 |
|
|
3.3 Benders Decomposition (BD) |
147 |
|
|
3.4 Karush--Kuhn--Tucker-System (KKT-system) for Derivation of the Marginals |
149 |
|
|
3.5 Parallel SP for Determing the KKT-marginals |
150 |
|
|
3.6 Installation of New HVDC Lines |
152 |
|
|
4 Impact of Curtailing Different RES Technologies on Grid Extensions, Overall Costs and Curtailed Energy |
152 |
|
|
5 Critical Appraisal |
154 |
|
|
6 Conclusion |
155 |
|
|
7 Nomenclature |
156 |
|
|
References |
157 |
|
|
Simulation of Distribution Grid Expansion Costs and the Impact of Load Shifting |
159 |
|
|
1 Introduction |
159 |
|
|
2 Simulation of the Distribution Grid System in Baden-Württemberg |
160 |
|
|
2.1 Regions |
161 |
|
|
2.2 Grid Topology |
162 |
|
|
2.3 Electricity Demand and Generation |
163 |
|
|
2.4 Load Flow |
163 |
|
|
2.5 Line Extensions |
165 |
|
|
2.6 Use of Load Shifting to Prevent Line Extension |
166 |
|
|
3 Scenarios |
166 |
|
|
4 Required Line Extension |
166 |
|
|
5 Costs of the Grid Extension |
168 |
|
|
6 Critical Review |
168 |
|
|
7 Conclusion and Outlook |
170 |
|
|
References |
170 |
|
|
Part VI Data Provision for Power Grid Modeling |
172 |
|
|
Structure Analysis of the German Transmission Network Using the Open Source Model SciGRID |
173 |
|
|
1 Introduction |
174 |
|
|
2 Power Data in OSM and the SciGRID Model |
175 |
|
|
2.1 OpenStreetMap |
175 |
|
|
2.2 The SciGRID Model |
176 |
|
|
2.3 The German Transmission Network |
176 |
|
|
3 Structure of the German Transmission Grid |
177 |
|
|
3.1 Complexity Reduction |
179 |
|
|
3.2 Discussion |
181 |
|
|
4 Conclusions |
183 |
|
|
References |
183 |
|
|
Modeling of the Transmission Grid Using Geo Allocation and Generalized Processes |
185 |
|
|
1 Introduction |
185 |
|
|
2 Public Grid Data Sets |
186 |
|
|
2.1 Data Sets of Existing Transmission Lines |
187 |
|
|
2.2 Future Grid Projects |
188 |
|
|
2.3 Geo Allocation and Merging into Standardized Lists |
189 |
|
|
3 Process Model |
190 |
|
|
3.1 Definition of a Generalized Process |
190 |
|
|
3.2 General Approach |
191 |
|
|
3.3 Modification for the Integration of Grid Processes |
191 |
|
|
3.4 Assumptions for Grid Processes |
192 |
|
|
3.5 Implementation in a Database Environment |
193 |
|
|
4 Integration of Grid Data in Energy System Models |
193 |
|
|
4.1 Nodal Allocation of Load and Production Data |
194 |
|
|
4.2 Virtual Network in Peripheral Regions |
195 |
|
|
5 Resulting Transmission Grid Model |
197 |
|
|
6 Critical Review |
199 |
|
|
7 Conclusion and Outlook |
199 |
|
|
References |
200 |
|
|
Regionalizing Input Data for Generation and Transmission Expansion Planning Models |
201 |
|
|
1 Introduction |
202 |
|
|
2 Selected Approaches in Power Generation and Transmission Planning and Their Handling of Regionalized Input Data |
203 |
|
|
3 The Developed Approach for a Dynamic Assignment of Generation and Load Data to Varying Topologies of the Transmission Grid |
204 |
|
|
4 Case Study: Regionalization of Input Data for the Transmission Network Planning in Germany |
207 |
|
|
5 Conclusions and Outlook |
211 |
|
|
References |
212 |
|
|
Part VII Convex Versus Nonconvex Approaches for Power Flow Analysis |
214 |
|
|
Convexity/Nonconvexity Certificates for Power Flow Analysis |
215 |
|
|
1 Introduction |
215 |
|
|
2 Convexity of Quadratic Transformations |
216 |
|
|
3 Certificates of Convexity/Nonconvexity |
217 |
|
|
4 Numerical Results |
219 |
|
|
5 Conclusions and Future Work |
223 |
|
|
References |
223 |
|
|
A Convex Model for the Optimization of Distribution Systems with Distributed Generation |
225 |
|
|
1 Introduction |
225 |
|
|
2 Steady-State Representation of PDS |
227 |
|
|
2.1 Representation of Voltage Violation |
227 |
|
|
2.2 Representation of Power Losses |
228 |
|
|
2.3 Representation of Generators |
229 |
|
|
3 Optimization Model |
230 |
|
|
3.1 Constraints |
231 |
|
|
4 Tests and Results |
232 |
|
|
4.1 DG as a PV Bus |
234 |
|
|
4.2 DG as a PQ Bus |
235 |
|
|
4.3 Determination of the DG Operation Point |
236 |
|
|
5 Conclusions |
237 |
|
|
References |
239 |
|