Mathematical optimisation techniques applied to power system operation and planning

Nowadays, power system operation becomes more complex because of the critical operating conditions resulting from the requirements of a market-driven operation. In this context, efficient methods for optimisation of power system operation and planning become critical to satisfy the operational (technical), financial and economic demands. Therefore, the detailed analysis of modern optimisation techniques as well as their application to the power system problems represent a relevant issue from the scientific and technological points of view. This paper presents a brief overview of the developments on modern mathematical optimisation methods applied to power system operation and planning. Copyright © 2007 Inderscience Enterprises Ltd.

A combinatorial approach for transmission expansion & reactive power planning

A metaheuristic technique for solving the short-term transmission network expansion and reactive power planning problems, at the same time, in regulated power systems using the AC model is presented. The problem is solved using a real genetic algorithm (RGA). For each topology proposed by RGA an indicator is employed to identify the weak buses for new reactive power sources allocation. The fitness function is calculated using the cost of each configuration as well as constraints deviation of an AC optimal power flow (OPF) in which the minimum reactive generation of new reactive sources and the active power losses are objectives. With allocation of reactive power sources at load buses, the circuit capacity increases and the cost of installation could be decreased. The method is tested in a well known test system, presenting good results when compared with other approaches. © 2011 IEEE.

Strategies to reduce the number of variables and the combinatorial search space of the multistage transmission expansion planning problem

This paper proposes strategies to reduce the number of variables and the combinatorial search space of the multistage transmission expansion planning problem (TEP). The concept of the binary numeral system (BNS) is used to reduce the number of binary and continuous variables related to the candidate transmission lines and network constraints that are connected with them. The construction phase of greedy randomized adaptive search procedure (GRASP-CP) and additional constraints, obtained from power flow equilibrium in an electric power system are employed for more reduction in search space. The multistage TEP problem is modeled like a mixed binary linear programming problem and solved using a commercial solver with a low computational time. The results of one test system and two real systems are presented in order to show the efficiency of the proposed solution technique. © 1969-2012 IEEE.

Optimisation of High Hydrostatic Pressure Processing of Pêra Rio Orange Juice

The influence of high hydrostatic pressure (HHP) on Pêra Rio orange juice was investigated using response surface methodology. A central composite design was used to evaluate the effects of three processing conditions (independent variables), namely pressure (100-600 MPa), temperature (30-60 °C) and time (30-360 s), on the native microflora and pectin methylesterase (PME) activity of orange juice. Analysis of variance showed that second-order polynomial models fitted well with the experimental data for PME residual activity (R2 = 0.9586, p < 0.001) and aerobic microorganism count (R2 = 0.9879, p < 0.001). The optimum HHP processing conditions to produce orange juice with PME residual activity of less than 20 % and low microorganism count (<2 log cycles CFU/mL) were 550 to 600 MPa, 55 to 60 °C and 330 to 360 s. © 2013 Springer Science+Business Media New York.

Optimisation of a sample preparation method for the determination of fumonisin B-1 in rice

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Reactive power planning under conditional-value-at-risk assessment using chance-constrained optimisation

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