{"product_id":"mathematical-programming-for-power-systems-operation-9781119747260","title":"Mathematical Programming for Power Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eExplore the theoretical foundations and real-world power system applications of convex programming\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn \u003ci\u003eMathematical Programming for Power System Operation with Applications in Python\u003c\/i\u003e, Professor Alejandro Garces delivers a comprehensive overview of power system operations models with a focus on convex optimization models and their implementation in Python. Divided into two parts, the book begins with a theoretical analysis of convex optimization models before moving on to related applications in power systems operations.\u003c\/p\u003e \u003cp\u003eThe author eschews concepts of topology and functional analysis found in more mathematically oriented books in favor of a more natural approach. Using this perspective, he presents recent applications of convex optimization in power system operations problems.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMathematical Programming for Power System Operation with Applications in Python\u003c\/i\u003e uses Python and CVXPY as tools to solve power system optimization problems and includes m\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAcknowledgment ix\u003c\/p\u003e \u003cp\u003eIntroduction xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Power systems operation \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Mathematical programming for power systems operation 1\u003c\/p\u003e \u003cp\u003e1.2 Continuous models 3\u003c\/p\u003e \u003cp\u003e1.2.1 Economic and environmental dispatch 3\u003c\/p\u003e \u003cp\u003e1.2.2 Hydrothermal dispatch 3\u003c\/p\u003e \u003cp\u003e1.2.3 Effect of the grid constraints 5\u003c\/p\u003e \u003cp\u003e1.2.4 Optimal power flow 5\u003c\/p\u003e \u003cp\u003e1.2.5 Hosting capacity 7\u003c\/p\u003e \u003cp\u003e1.2.6 Demand-side management 7\u003c\/p\u003e \u003cp\u003e1.2.7 Energy storage management 9\u003c\/p\u003e \u003cp\u003e1.2.8 State estimation and grid identification 9\u003c\/p\u003e \u003cp\u003e1.3 Binary problems in power systems operation 11\u003c\/p\u003e \u003cp\u003e1.3.1 Unit commitment 12\u003c\/p\u003e \u003cp\u003e1.3.2 Optimal placement of distributed generation and capacitors 12\u003c\/p\u003e \u003cp\u003e1.3.3 Primary feeder reconfiguration and topology identification 13\u003c\/p\u003e \u003cp\u003e1.3.4 Phase balancing 13\u003c\/p\u003e \u003cp\u003e1.4 Real-time implementation 14\u003c\/p\u003e \u003cp\u003e1.5 Using Python 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Mathematical programming \u003c\/b\u003e\u003cb\u003e17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 A brief introduction to mathematical optimization \u003c\/b\u003e\u003cb\u003e19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 About sets and functions 19\u003c\/p\u003e \u003cp\u003e2.2 Norms 22\u003c\/p\u003e \u003cp\u003e2.3 Global and local optimum 24\u003c\/p\u003e \u003cp\u003e2.4 Maximum and minimum values of continuous functions 25\u003c\/p\u003e \u003cp\u003e2.5 The gradient method 26\u003c\/p\u003e \u003cp\u003e2.6 Lagrange multipliers 32\u003c\/p\u003e \u003cp\u003e2.7 The Newton’s method 33\u003c\/p\u003e \u003cp\u003e2.8 Further readings 35\u003c\/p\u003e \u003cp\u003e2.9 Exercises 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Convex optimization \u003c\/b\u003e\u003cb\u003e39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Convex sets 39\u003c\/p\u003e \u003cp\u003e3.2 Convex functions 45\u003c\/p\u003e \u003cp\u003e3.3 Convex optimization problems 47\u003c\/p\u003e \u003cp\u003e3.4 Global optimum and uniqueness of the solution 50\u003c\/p\u003e \u003cp\u003e3.5 Duality 52\u003c\/p\u003e \u003cp\u003e3.6 Further readings 56\u003c\/p\u003e \u003cp\u003e3.7 Exercises 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Convex Programming in Python \u003c\/b\u003e\u003cb\u003e61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Python for convex optimization 61\u003c\/p\u003e \u003cp\u003e4.2 Linear programming 62\u003c\/p\u003e \u003cp\u003e4.3 Quadratic forms 67\u003c\/p\u003e \u003cp\u003e4.4 Semidefinite matrices 69\u003c\/p\u003e \u003cp\u003e4.5 Solving quadratic programming problems 71\u003c\/p\u003e \u003cp\u003e4.6 Complex variables 74\u003c\/p\u003e \u003cp\u003e4.7 What is inside the box? 75\u003c\/p\u003e \u003cp\u003e4.8 Mixed-integer programming problems 76\u003c\/p\u003e \u003cp\u003e4.9 Transforming MINLP into MILP 79\u003c\/p\u003e \u003cp\u003e4.10 Further readings 80\u003c\/p\u003e \u003cp\u003e4.11 Exercises 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Conic optimization \u003c\/b\u003e\u003cb\u003e85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Convex cones 85\u003c\/p\u003e \u003cp\u003e5.2 Second-order cone optimization 85\u003c\/p\u003e \u003cp\u003e5.2.1 Duality in SOC problems 90\u003c\/p\u003e \u003cp\u003e5.3 Semidefinite programming 92\u003c\/p\u003e \u003cp\u003e5.3.1 Trace, determinant, and the Shur complement 92\u003c\/p\u003e \u003cp\u003e5.3.2 Cone of semidefinite matrices 95\u003c\/p\u003e \u003cp\u003e5.3.3 Duality in SDP 97\u003c\/p\u003e \u003cp\u003e5.4 Semidefinite approximations 98\u003c\/p\u003e \u003cp\u003e5.5 Polynomial optimization 102\u003c\/p\u003e \u003cp\u003e5.6 Further readings 105\u003c\/p\u003e \u003cp\u003e5.7 Exercises 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Robust optimization \u003c\/b\u003e\u003cb\u003e109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Stochastic vs robust optimization 109\u003c\/p\u003e \u003cp\u003e6.1.1 Stochastic approach 110\u003c\/p\u003e \u003cp\u003e6.1.2 Robust approach 110\u003c\/p\u003e \u003cp\u003e6.2 Polyhedral uncertainty 111\u003c\/p\u003e \u003cp\u003e6.3 Linear problems with norm uncertainty 113\u003c\/p\u003e \u003cp\u003e6.4 Defining the uncertainty set 115\u003c\/p\u003e \u003cp\u003e6.5 Further readings 121\u003c\/p\u003e \u003cp\u003e6.6 Exercises 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Power systems operation \u003c\/b\u003e\u003cb\u003e125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Economic dispatch of thermal units \u003c\/b\u003e\u003cb\u003e127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Economic dispatch 127\u003c\/p\u003e \u003cp\u003e7.2 Environmental dispatch 133\u003c\/p\u003e \u003cp\u003e7.3 Effect of the grid 136\u003c\/p\u003e \u003cp\u003e7.4 Loss equation 140\u003c\/p\u003e \u003cp\u003e7.5 Further readings 143\u003c\/p\u003e \u003cp\u003e7.6 Exercises 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Unit commitment \u003c\/b\u003e\u003cb\u003e145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Problem definition 145\u003c\/p\u003e \u003cp\u003e8.2 Basic unit commitment model 146\u003c\/p\u003e \u003cp\u003e8.3 Additional constraints 150\u003c\/p\u003e \u003cp\u003e8.4 Effect of the grid 151\u003c\/p\u003e \u003cp\u003e8.5 Further readings 153\u003c\/p\u003e \u003cp\u003e8.6 Exercises 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Hydrothermal scheduling \u003c\/b\u003e\u003cb\u003e155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Short-term hydrothermal coordination 155\u003c\/p\u003e \u003cp\u003e9.2 Basic hydrothermal coordination 156\u003c\/p\u003e \u003cp\u003e9.3 Non-linear models 159\u003c\/p\u003e \u003cp\u003e9.4 Hydraulic chains 162\u003c\/p\u003e \u003cp\u003e9.5 Pumped hydroelectric storage 165\u003c\/p\u003e \u003cp\u003e9.6 Further readings 168\u003c\/p\u003e \u003cp\u003e9.7 Exercises 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Optimal power flow \u003c\/b\u003e\u003cb\u003e171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 OPF in power distribution grids 171\u003c\/p\u003e \u003cp\u003e10.1.1 A brief review of power flow analysis 173\u003c\/p\u003e \u003cp\u003e10.2 Complex linearization 177\u003c\/p\u003e \u003cp\u003e10.2.1 Sequential linearization 181\u003c\/p\u003e \u003cp\u003e10.2.2 Exponential models of the load 182\u003c\/p\u003e \u003cp\u003e10.3 Second-order cone approximation 184\u003c\/p\u003e \u003cp\u003e10.4 Semidefinite approximation 188\u003c\/p\u003e \u003cp\u003e10.5 Further readings 190\u003c\/p\u003e \u003cp\u003e10.6 Exercises 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Active distribution networks \u003c\/b\u003e\u003cb\u003e195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Modern distribution networks 195\u003c\/p\u003e \u003cp\u003e11.2 Primary feeder reconfiguration 196\u003c\/p\u003e \u003cp\u003e11.3 Optimal placement of capacitors 200\u003c\/p\u003e \u003cp\u003e11.4 Optimal placement of distributed generation 203\u003c\/p\u003e \u003cp\u003e11.5 Hosting capacity of solar energy 205\u003c\/p\u003e \u003cp\u003e11.6 Harmonics and reactive power compensation 208\u003c\/p\u003e \u003cp\u003e11.7 Further readings 212\u003c\/p\u003e \u003cp\u003e11.8 Exercises 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 State estimation and grid identification \u003c\/b\u003e\u003cb\u003e215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Measurement units 215\u003c\/p\u003e \u003cp\u003e12.2 State estimation 216\u003c\/p\u003e \u003cp\u003e12.3 Topology identification 221\u003c\/p\u003e \u003cp\u003e12.4 \u003cb\u003e\u003ci\u003eY\u003c\/i\u003e\u003c\/b\u003e\u003csub\u003ebus\u003c\/sub\u003e estimation 224\u003c\/p\u003e \u003cp\u003e12.5 Load model estimation 228\u003c\/p\u003e \u003cp\u003e12.6 Further readings 231\u003c\/p\u003e \u003cp\u003e12.7 Exercises 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Demand-side management \u003c\/b\u003e\u003cb\u003e235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Shifting loads 235\u003c\/p\u003e \u003cp\u003e13.2 Phase balancing 240\u003c\/p\u003e \u003cp\u003e13.3 Energy storage management 246\u003c\/p\u003e \u003cp\u003e13.4 Further readings 249\u003c\/p\u003e \u003cp\u003e13.5 Exercises 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA The nodal admittance matrix \u003c\/b\u003e\u003cb\u003e253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Complex linearization \u003c\/b\u003e\u003cb\u003e257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Some Python examples \u003c\/b\u003e\u003cb\u003e263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC.1 Basic Python 263\u003c\/p\u003e \u003cp\u003eC.2 NumPy 266\u003c\/p\u003e \u003cp\u003eC.3 MatplotLib 268\u003c\/p\u003e \u003cp\u003eC.4 Pandas 268\u003c\/p\u003e \u003cp\u003eBibliography 271\u003c\/p\u003e \u003cp\u003eIndex 281\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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