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|Title:||Transient stability constrained optimal power flow with renewable energy uncertainties||Authors:||Xia, Shiwei||Degree:||Ph.D.||Issue Date:||2015||Abstract:||Optimal Power Flow (OPF) is an essential and practical tool for power system planning and operating attributed to its ability of approaching the best economic operating point by optimally adjusting the controllable variables. However, OPF solutions without security constraints imposed would have little practical value when contingencies were encountered, especially for modern power systems with increasing load demand and decreasing stability margin. The transient stability constrained OPF (TSCOPF) capable of effectively reconciling both the economic and stability of power systems would be therefore imperative for power grid operation. Based on the foundations of pioneering research in TSCOPF, this thesis strives to further investigate this TSCOPF problem and its effective analytical and computational intelligence solution methods. TSCOPF is a semi-infinite optimization problem with finite number of controllable variables and infinite number of constraints, thus it is difficult to solve even for small power systems. Though promising results have been obtained in solving TSCOPF, a complete solution approach to effectively solve all types of TSCOPF problems, in particular extreme unstable and overstabilization cases, is still lacking. This thesis therefore develops an all-round analytical solution approach, in which the transient stability constraint for each contingency is incorporated into the OPF model as a single stability constraint derived from the minimum kinetic energy for normal unstable case or the minimum accelerating power distance for extreme unstable case using SIngle Machine Equivalent (SIME) theory with trajectory sensitivity strategy based on time domain simulation. The proposed constraint is robust and scalable for large power systems as well as applicable to multi-swing unstable, normal unstable and extreme unstable cases. In addition, this stability constraint is further refined to overcome the issue of over-stabilization by guiding the solution gradually across the stability boundary in the optimization process. As a whole, a complete solution method capable to solve all types of TSCOPF problems is established. With the fast development of electronic technology and the rapid expansion of power network, many complex dynamic components such as FACTS devices are now widely applied in power grids. Coupled with the extensive use of discrete control devices, such as transformer taps and capacitor banks for power system preventive control, and the physical operation limitations, such as prohibited operation zone (POZ) and valve point effects in thermal generators, the TSCOPF problem has become much more challenging and needs to be solved as a non-differentiable and discontinuous optimization problem. As a remedy, a general non-convex Mixed Integer Nonlinear Program (MINLP) TSCOPF model with consideration of discrete control variables, generation POZ and valve-point effects as well as applicable to all complex dynamic components is proposed and solved using an Enhanced Particle Swarm Optimization (EPSO) with dynamic adjusted inertia weight and shrinking Gaussian distribution disturbance. The effectiveness and efficiency of this MINLP-TSCOPF model and EPSO solution approach have been comprehensive evaluated using a well-established benchmarking mathematical function and two representative power systems with FACTS devices.
Since MINLP is a hard mathematical problem and TSCOPF with semi-infinite feature is tough to solve, the proposed MINLP-TSCOPF model would pose a huge challenge for any optimization methods. Though the proposed EPSO method is capable to search for effective solutions, further exploration for a better method with improved quality and consistency of MINLP-TSCOPF solutions is still needed. Inspired by the encouraging optimization capability of the Group Search Optimization (GSO) algorithm in many engineering problems, an enhanced version referred as improved GSO (IGSO) is developed with new features including backward searching strategy, Cauchy mutation and inheritance operator. Comparison study with seven representative artificial intelligence algorithms including EPSO on the WSCC 9-bus system, New England 39-bus system, and IEEE 145-bus system has confirmed the outperformance and superiority of the proposed IGSO method in solving this MINLP-TSCOPF problem. Over the years, TSCOPF model has been mostly handled as a deterministic optimization problem with pre-assumed conditions while uncertainties in real power grids, such as stochastic load injections, uncertain generations and protection device activation time, are seldom considered. Meanwhile, due to the worldwide growing concerns on the depletion of fossil resources and their environmental effects, recent installation surge of wind power generations has led to even higher level of uncertainties and higher risk to the safe operation of power systems. In the coming era of smart grid, a new generation of stochastic TSCOPF models considering economic, stability and uncertainty simultaneously will be essential and indispensable for power system preventive control. In this thesis, a novel probabilistic TSCOPF (P-TSCOPF) model is therefore proposed. In this model, not only the detailed wind generator model with rotor flux magnitude and angle control strategy but also uncertainties including probabilistic load injections, stochastic fault clearing time and multiple correlated uncertain wind generations will all be considered. While the correlated uncertainties are efficiently handled using the 2m+1 Point Estimated (PE) method with Cholesky decomposition, the proposed IGSO algorithm is further developed to form a new IGSO-PE solution approach to effectively solve this PTSCOPF problem. The validity of the proposed P-TSCOPF model and the capability of the proposed IGSO-PE solution method have been thoroughly tested on a modified New England 39-bus system with correlated uncertain wind generations and validated using the Monte Carlo (MC) simulations.
|Subjects:||Electric power system stability.
Electric power systems -- Control.
Hong Kong Polytechnic University -- Dissertations
|Pages:||xv, 162 leaves : illustrations (some color) ; 30 cm|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/8164
Citations as of May 15, 2022
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