Читать книгу Mathematical Programming for Power Systems Operation - Alejandro Garcés Ruiz - Страница 4
Contents
Оглавление1 Cover
7 1 Power systems operation1.1 Mathematical programming for power systems operation1.2 Continuous models1.2.1 Economic and environmental dispatch1.2.2 Hydrothermal dispatch1.2.3 Effect of the grid constraints1.2.4 Optimal power flow1.2.5 Hosting capacity1.2.6 Demand-side management1.2.7 Energy storage management1.2.8 State estimation and grid identification1.3 Binary problems in power systems operation1.3.1 Unit commitment1.3.2 Optimal placement of distributed generation and capacitors1.3.3 Primary feeder reconfiguration and topology identification1.3.4 Phase balancing1.4 Real-time implementation1.5 Using Python
8 Part I Mathematical programming2 A brief introduction to mathematical optimization2.1 About sets and functions2.2 Norms2.3 Global and local optimum2.4 Maximum and minimum values of continuous functions2.5 The gradient method2.6 Lagrange multipliers2.7 The Newton’s method2.8 Further readings2.9 Exercises3 Convex optimization3.1 Convex sets3.2 Convex functions3.3 Convex optimization problems3.4 Global optimum and uniqueness of the solution3.5 Duality3.6 Further readings3.7 Exercises4 Convex Programming in Python4.1 Python for convex optimization4.2 Linear programming4.3 Quadratic forms4.4 Semidefinite matrices4.5 Solving quadratic programming problems4.6 Complex variables4.7 What is inside the box?4.8 Mixed-integer programming problems4.9 Transforming MINLP into MILP4.10 Further readings4.11 Exercises5 Conic optimization5.1 Convex cones5.2 Second-order cone optimization5.2.1 Duality in SOC problems5.3 Semidefinite programming5.3.1 Trace, determinant, and the Shur complement5.3.2 Cone of semidefinite matrices5.3.3 Duality in SDP5.4 Semidefinite approximations5.5 Polynomial optimization5.6 Further readings5.7 Exercises6 Robust optimization6.1 Stochastic vs robust optimization6.1.1 Stochastic approach6.1.2 Robust approach6.2 Polyhedral uncertainty6.3 Linear problems with norm uncertainty6.4 Defining the uncertainty set6.5 Further readings6.6 Exercises
9 Part II Power systems operation7 Economic dispatch of thermal units7.1 Economic dispatch7.2 Environmental dispatch7.3 Effect of the grid7.4 Loss equation7.5 Further readings7.6 Exercises8 Unit commitment8.1 Problem definition8.2 Basic unit commitment model8.3 Additional constraints8.4 Effect of the grid8.5 Further readings8.6 Exercises9 Hydrothermal scheduling9.1 Short-term hydrothermal coordination9.2 Basic hydrothermal coordination9.3 Non-linear models9.4 Hydraulic chains9.5 Pumped hydroelectric storage9.6 Further readings9.7 Exercises10 Optimal power flow10.1 OPF in power distribution grids10.1.1 A brief review of power flow analysis10.2 Complex linearization10.2.1 Sequential linearization10.2.2 Exponential models of the load10.3 Second-order cone approximation10.4 Semidefinite approximation10.5 Further readings10.6 Exercises11 Active distribution networks11.1 Modern distribution networks11.2 Primary feeder reconfiguration11.3 Optimal placement of capacitors11.4 Optimal placement of distributed generation11.5 Hosting capacity of solar energy11.6 Harmonics and reactive power compensation11.7 Further readings11.8 Exercises12 State estimation and grid identification12.1 Measurement units12.2 State estimation12.3 Topology identification12.4 Ybus estimation12.5 Load model estimation12.6 Further readings12.7 Exercises13 Demand-side management13.1 Shifting loads13.2 Phase balancing13.3 Energy storage management13.4 Further readings13.5 Exercises
10 A The nodal admittance matrix
12 C Some Python examplesC.1 Basic PythonC.2 NumPyC.3 MatplotLibC.4 Pandas
13 Bibliography
14 Index
