Logarithmic barrier-augmented Lagrangian function to the optimal power flow problem

This paper presents a new approach to solve the Optimal Power Flow problem. This approach considers the application of logarithmic barrier method to voltage magnitude and tap-changing transformer variables and the other constraints are treated by augmented Lagrangian method. Numerical test results are presented, showing the effective performance of this algorithm. (C) 2005 Elsevier Ltd. All rights reserved.

Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General Algebraic Modeling Systems (GAMS). A Virtual Power Producer (VPP) can optimal operate the generation units, assured the good functioning of equipment, including the maintenance, operation cost and the generation measurement and control. A central control at system allows a VPP to manage the optimal generation and their load control. The application of methodology to a real case study in Budapest Tech, demonstrates the effectiveness of this method to solve the optimal isolated dispatch of the DC micro-grid renewable energy park. The problem has been converged in 0.09 s and 30 iterations.

Planejamento de reativos em sistemas de energia elétrica através de um algoritmo de Branch-and-Bound não linear

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Planejamento e despacho ótimo de reativos (VAr) em sistemas de energia elétrica multi-áreas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fluxo de potência ótimo reativo via método da função lagrangiana barreira modificada

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Primal-dual logarithmic barrier and augmented Lagrangian function to the loss minimization in power systems

This article presents a new approach to minimize the losses in electrical power systems. This approach considers the application of the primal-dual logarithmic barrier method to voltage magnitude and tap-changing transformer variables, and the other inequality constraints are treated by augmented Lagrangian method. The Lagrangian function aggregates all the constraints. The first-order necessary conditions are reached by Newton's method, and by updating the dual variables and penalty factors. Test results are presented to show the good performance of this approach.

Decomposição de Dantzig-Wolfe aplicada ao problema de planejamento de reativos em sistemas de potência multi-áreas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Valorização de serviços ancilares de reserva em geradores hidrelétricos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Optimal dispatch with reactive power compensation by Genetic Algorithm

This paper presents a Unit Commitment model with reactive power compensation that has been solved by Genetic Algorithm (GA) optimization techniques. The GA has been developed a computational tools programmed/coded in MATLAB. The main objective is to find the best generations scheduling whose active power losses are minimal and the reactive power to be compensated, subjected to the power system technical constraints. Those are: full AC power flow equations, active and reactive power generation constraints. All constraints that have been represented in the objective function are weighted with a penalty factors. The IEEE 14-bus system has been used as test case to demonstrate the effectiveness of the proposed algorithm. Results and conclusions are dully drawn.

Despacho ótimo de potências ativa e reativa de sistema elétricos multi-áreas considerando restrições físicas, econômicas e ambientais =: Envronmentally constrained active-reactive optimal power flow-a compromising strategy for economic-emission dispatch and a multi-area paradigm

Pós-graduação em Engenharia Elétrica - FEIS

Particle Swarm Optimization Based Reactive Power Dispatch for Power Networks with Distributed Generation

Reactive power is critical to the operation of the power networks on both safety aspects and economic aspects. Unreasonable distribution of the reactive power would severely affect the power quality of the power networks and increases the transmission loss. Currently, the most economical and practical approach to minimizing the real power loss remains using reactive power dispatch method. Reactive power dispatch problem is nonlinear and has both equality constraints and inequality constraints. In this thesis, PSO algorithm and MATPOWER 5.1 toolbox are applied to solve the reactive power dispatch problem. PSO is a global optimization technique that is equipped with excellent searching capability. The biggest advantage of PSO is that the efficiency of PSO is less sensitive to the complexity of the objective function. MATPOWER 5.1 is an open source MATLAB toolbox focusing on solving the power flow problems. The benefit of MATPOWER is that its code can be easily used and modified. The proposed method in this thesis minimizes the real power loss in a practical power system and determines the optimal placement of a new installed DG. IEEE 14 bus system is used to evaluate the performance. Test results show the effectiveness of the proposed method.

Optimal activation of tumor-reactive T cells by selected antigenic peptide analogues.

Many mechanisms have been proposed to explain why immune responses against human tumor antigens are generally ineffective. For example, tumor cells have been shown to develop active immune evasion mechanisms. Another possibility is that tumor antigens are unable to optimally stimulate tumor-specific T cells. In this study we have used HLA-A2/Melan-A peptide tetramers to directly isolate antigen-specific CD8(+) T cells from tumor-infiltrated lymph nodes. This allowed us to quantify the activation requirements of a representative polyclonal yet monospecific tumor-reactive T cell population. The results obtained from quantitative assays of intracellular Ca(2+) mobilization, TCR down-regulation, cytokine production and induction of effector cell differentiation indicate that the naturally produced Melan-A peptides are weak agonists and are clearly suboptimal for T cell activation. In contrast, optimal T cell activation was obtained by stimulation with recently defined peptide analogues. These findings provide a molecular basis for the low immunogenicity of tumor cells and suggest that patient immunization with full agonist peptide analogues may be essential for stimulation and maintenance of anti-tumor T cell responses in vivo.

Reactive power dispatch and planning using a non-linear branch-and-bound algorithm

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Incorporating voltage/reactive representation to short-term generation scheduling models

This paper proposes a methodology to incorporate voltage/reactive representation to Short Term Generation Scheduling (STGS) models, which is based on active/reactive decoupling characteristics of power systems. In such approach STGS is decoupled in both Active (AGS) and Reactive (RGS) Generation Scheduling models. AGS model establishes an initial active generation scheduling through a traditional dispatch model. The scheduling proposed by AGS model is evaluated from the voltage/reactive points of view, through the proposed RGS model. RGS is formulated as a sequence of T nonlinear OPF problems, solved separately but taking into account load tracking between consecutive time intervals. This approach considerably reduces computational effort to perform the reactive analysis of the RGS problem as a whole. When necessary, RGS model is capable to propose active generation redispatches, such that critical reactive problems (in which all reactive variables have been insufficient to control the reactive problems) can be overcome. The formulation and solution methodology proposed are evaluated in the IEEE30 system in two case studies. These studies show that the methodology is robust enough to incorporate reactive aspects to STGS problem.

An active/reactive predispatch model incorporating ramp rate constraints solved by dual decomposition/lagrangian relaxation

The Predispatch model (PD) calculates a short-term generation policy for power systems. In this work a PD model is proposed that improves two modeling aspects generally neglected in the literature: voltage/reactive power constraints and ramp rate constraints for generating units. Reactive power constraints turn the PD into a non-linear problem and the ramp rate constraints couple the problem dynamically in time domain. The solution of the PD is turned into a harder task when such constraints are introduced. The dual decomposition/ lagrangian relaxation technique is used in the solution approach for handing dynamic constraints. As a result the PD is decomposed into a series of independent Optimal Power Flow (FPO) sub problems, in which the reactive power is represented in detail. The solution of the independent FPO is coordinated by means of Lagrange multipliers, so that dynamic constraints are iteratively satisfied. Comparisons between dispatch policies calculated with and without the representation of ramp rate constraints are performed, using the IEEE 30 bus test system. The results point-out the importance of representing such constraints in the generation dispatch policy. © 2004 IEEE.