{"product_id":"mathematical-programming-for-power-systems-operation-isbn-9781119747260","title":"Mathematical Programming for Power Systems Operation","description":"\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 models that can be solved with the presented framework. The book also includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA thorough introduction to power system operation, including economic and environmental dispatch, optimal power flow, and hosting capacity\u003c\/li\u003e \u003cli\u003eComprehensive explorations of the mathematical background of power system operation, including quadratic forms and norms and the basic theory of optimization\u003c\/li\u003e \u003cli\u003ePractical discussions of convex functions and convex sets, including affine and linear spaces, politopes, balls, and ellipsoids\u003c\/li\u003e \u003cli\u003eIn-depth examinations of convex optimization, including global optimums, and first and second order conditions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e Perfect for undergraduate students with some knowledge in power systems analysis, generation, or distribution, \u003ci\u003eMathematical Programming for Power System Operation with Applications in Python\u003c\/i\u003e is also an ideal resource for graduate students and engineers practicing in the area of power system optimization.\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 \u003cp\u003e\u003cb\u003eAlejandro Garcés\u003c\/b\u003e, PhD, is a Professor at Universidad Tecnológica de Pereira in Colombia. Previously, he was a research fellow at the Norwegian University of Science and Technology in Trondheim-Norway, and an External Consultant for the Latin-American Organization of Energy and the Inter-American Development Bank. He is also Senior member of the IEEE, and Associate Editor of different IEEE and IET journals. In 2021 he was awarded with the Georg Forster Research fellow at the Alexander von Humboldt Foundation in Germany to continue his research in collaboration with TU-Dortmund.\u003c\/p\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 Systems Operation: From Theory to Applications in Python,\u003c\/i\u003e Professor Alejandro Garcés 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 models that can be solved with the presented framework. The book also includes: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA thorough introduction to power system operation, including economic and environmental dispatch, optimal power flow, and hosting capacity\u003c\/li\u003e \u003cli\u003eComprehensive explorations of the mathematical background of power system operation, including quadratic forms and norms and the basic theory of optimization\u003c\/li\u003e \u003cli\u003ePractical discussions of convex functions and convex sets, including affine and linear spaces, politopes, balls, and ellipsoids\u003c\/li\u003e \u003cli\u003eIn-depth examinations of convex optimization, including global optimums, and first and second order conditions\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for undergraduate students with some knowledge in power systems analysis, generation, or distribution, \u003ci\u003eMathematical Programming for Power System Operation with Applications in Python \u003c\/i\u003eis also an ideal resource for graduate students and engineers practicing in the area of power system optimization.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989587542245,"sku":"NP9781119747260","price":134.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119747260.jpg?v=1761784708","url":"https:\/\/k12savings.com\/es\/products\/mathematical-programming-for-power-systems-operation-isbn-9781119747260","provider":"K12savings","version":"1.0","type":"link"}