Secundino Soares

A network flow model for short-term hydro-dominated hydrothermal scheduling problems

This paper is concerned with the short-term hydrothermal scheduling (STHS) of hydro-dominated power systems. The problems formulation includes the representation of operational constraints such as the hydraulic coupling between hydro plants in cascade and the transmission limits in the electric network. In order to allow the problems decomposition into hydraulic and electric subproblems, a linear-quadratic penalty approach is applied to enforce the coupling between hydro and electric variables. As a result, the problems natural network flow structure is fully exploited through special-purposed network flow algorithms. The technique has been implemented in FORTRAN in a SUN SPARCstation IPX and tested in a 440 kV subsystem of the main interconnected Brazilian power system. >

An Efficient Hydrothermal Scheduling Algorithm

In this paper a Hydrothermal Scheduling Algorithm (HSA) for deterministic inflows is presented. A nonlinear network flow model representing the hydrothermal scheduling problem is used. By exploiting the special network structure, a temporally expanded arborescence [1], the HSA code attains important computational savings in processing time and memory requirements allowing the use of microcomputers even for large scale problems. The main feature of the HSA code is a dynamic base choice that enables the decision maker to improve the convergence of the optimization procedure by the use of his practical experience. The performance of the algorithm is tested on the Southeast Brazilian Power System.

Minimum loss predispatch model for hydroelectric power systems

A predispatch model that minimizes generation and transmission losses on hydroelectric power systems is presented and applied to the hydroelectric power system of COPEL, a Brazilian utility located in Southern Brazil. Power loss in the generation and transmission systems is minimized on an hourly basis throughout a day. Load demand as well as generation, transmission and interchange operational constraints are satisfied. Hydroelectric generation characteristics are described in detail. Power loss in hydroelectric generation is associated with a reduction in turbine-generator efficiency and effective water head. The latter is due to an increase in tailrace elevation and penstock head loss. Power loss upon transmission is calculated as a quadratic function of active power flow, as represented in a DC load flow model. The predispatch model was evaluated under typical operational conditions, achieving significant savings with respect to actual operation.

A second order network flow algorithm for hydrothermal scheduling

This paper presents a second-order network flow algorithm specially designed for hydrothermal scheduling problems. The algorithm is based on the truncated Newton method and takes advantage of the particular layout of the hydro scheduling network. The three-diagonal structure of the Hessian matrix is also exploited. Heuristic strategies for variable partition into basic-superbasic-nonbasic sets are suggested to improve the algorithms efficiency. Tests with systems of dimensions up to 27 hydro plants in cascade have been performed in order to evaluate the algorithms performance and compare some variable partition strategies. Results have demonstrated the high efficiency of the code. >

Learning algorithms for a class of neurofuzzy network and application

A class of neurofuzzy networks and a constructive, competition-based learning procedure is introduced. Given a set of training data, the learning procedure automatically adjusts the input space portion to cover the whole space and finds membership functions parameters for each input variable. The network processes data following fuzzy reasoning principles and, due to its structure, it is dual to a rule-based fuzzy inference system. The neurofuzzy model is used to forecast seasonal streamflow, a key step to plan and operate hydroelectric power plants and to price energy. A database of average monthly inflows of three Brazilian hydroelectric plants located at different river basins was used as source of training and test data. The performance of the neurofuzzy network is compared with period regression, a standard approach used by the electric power industry to forecast streamflows. Comparisons with multilayer perceptron, radial basis network and adaptive neural-fuzzy inference system are also included. The results show that the neurofuzzy network provides better one-step-ahead streamflow forecasting, with forecasting errors significantly lower than the other approaches.

Optimal active power dispatch combining network flow and interior point approaches

In this paper, the optimal active power dispatch is formulated as a network flow optimization model and solved by interior point methods. The primal-dual and predictor-corrector versions of such interior point methods are developed and the resulting matrix structure is explored. This structure leads to very fast iterations since it is possible to reduce the linear system either to the number of buses or to the number of independent loops. Either matrix is invariant and can be factored offline. As a consequence of such matrix manipulations, a linear system which changes at each iteration has to be solved; its size, however, reduces to the number of generating units. These methods were applied to IEEE and Brazilian power systems and the numerical results were obtained using a C implementation. Both interior point methods proved to be robust and achieved fast convergence in all instances tested.

Optimal active power dispatch by network flow approach

An optimal active power dispatch problem is formulated as a nonlinear capacitated network flow problem with additional linear constraints. Transmission flow limits and both Kirchhoff laws are taken into account. The problem is solved by a generalized upper bounding technique that takes advantage of the network flow structure of the problem. The proposed approach has potential applications to power system problems such as economic dispatch, load supplying capability, minimum load shedding and generation-transmission reliability. The authors also review the use of transportation models for power system analysis. A detailed illustrative example is presented. >

A general parametric optimal power flow

This paper is a generalization and extension of earlier research in parametric optimal power flow (OPF). Its principal features are: (i) the use of a full nonlinear OPF model, (ii) a more general parameterization of equalities, inequalities and objective function, and (iii) an algorithm that exactly tracks the OPF behaviour in terms of the continuation parameter. The parameterization allows one to distinguish between two phases. One serves to find the OPF solution to a static problem for a fixed load and network starting from an arbitrary initial condition. The second, finds the trajectories corresponding to varying loads. The optimal trajectories, in both phases, offer an excellent visualization of the complex nature of the OPF solutions, that is, the highly nonlinear behaviour and the sensitivity of the solutions to parameter variations. >

Long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models

This paper proposes an operational policy for long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models using an open-loop feedback control framework. The optimization model precisely represents hydropower generation by taking into consideration water head as a nonlinear function of storage, discharge and spillage. The inflow is made available by a forecasting model based on a fuzzy inference system that captures the nonlinear correlation of consecutive inflows on an annual basis, then disaggregating it on a monthly basis. In order to focus on the ability of the approach to handle the stochastic nature of the problem, a case study with a single-reservoir system is considered. The performance of the proposed approach is evaluated by simulation over the historical inflow records and compared to that of the stochastic dynamic programming approach. The results show that the proposed approach leads to a better operational performance of the plant, providing lower spillages and higher average hydropower efficiency and generation.

Dynamic dispatch of hydro generating units

This work is concerned with the dynamic dispatch of hydro generating units. A performance criterion that takes into account variations in tailrace elevation, penstock head losses and turbine-generator efficiencies is considered. A heuristic procedure based on Lagrangian relaxation is applied to solve the dynamic dispatch problem of scheduling generation on an hourly basis during a day. The approach has been tested on a hydro system composed of nine hydro plants of the Brazilian power system. The generation scheduling verified on a typical day was considered for comparison with the solution provided by the approach and the results show a significant improvement in terms of hydro generation efficiency.