Reinaldo Castro Souza

Stochastic Dynamic Programming Applied to Hydrothermal Power Systems Operation Planning Based on the Convex Hull Algorithm

This paper presents a new approach for the expected cost-to-go functions modeling used in the stochastic dynamic programming (SDP) algorithm. The SDP technique is applied to the long-term operation planning of electrical power systems. Using state space discretization, the Convex Hull algorithm is used for constructing a series of hyperplanes that composes a convex set. These planes represent a piecewise linear approximation for the expected cost-to-go functions. The mean operational costs for using the proposed methodology were compared with those from the deterministic dual dynamic problem in a case study, considering a single inflow scenario. This sensitivity analysis shows the convergence of both methods and is used to determine the minimum discretization level. Additionally, the applicability of the proposed methodology for two hydroplants in a cascade is demonstrated. With proper adaptations, this work can be extended to a complete hydrothermal system.

HYDROELECTRIC ENERGY FORECAST

The aim of this paper is to propose a new methodology for hydroelectric energy forecasting. A new approach for selection of the number of eigenvalues in SSA is also proposed. In this paper it is proposed the hierarchical clustering associated to PCA and integrated to ARIMA models. The proposed approach is applied to forecast the affluent flow in a hydroelectric plant located at Parana River Basin, Brazil. As a matter of fact, modeling such series is quite important for the optimal dispatch of the energy generation in Brazil due to the heavy participation of hydro plants in the country (over 85% of the generated energy comes from hydro plants).

RESIDENTIAL ELECTRICITY CONSUMPTION FORECASTING USING A GEOMETRIC COMBINATION APPROACH

The forecasting of electricity consumption and demand plays a pivotal role in electric power systems planning. This paper proposes the combination of forecasts from two approaches with the aim of improving the forecasting accuracy, in order to make the best use of the installed transmission and generating capacity. In the first approach, the consumption time series is decomposed by wavelet analysis and a Box-Jenkins model is fitted to each wavelet component, following which the individual components forecasts are added to compute the total consumption forecast. The alternative approach, uses the Singular Spectrum Analysis technique to model the consumption time series in order to shrink the noise level. Thereafter, the Box-Jenkins model is used to forecast the filtered time series, producing a second forecast for the consumption series. Eventually, the two forecasts are combined geometrically in order to minimize the mean square error. The proposed methodology is illustrated by a computational experiment with the time series of residential consumption of electricity in Brazil.

Optimal operation of hydrothermal systems with Hydrological Scenario Generation through Bootstrap and Periodic Autoregressive Models

In electrical power systems with strong hydro generation, the use of adequate techniques to generate synthetic hydrological scenarios is extremely important for the evaluation of the ways the system behaves in order to meet the forecast energy demand. This paper proposes a new model to generate natural inflow energy scenarios in the long-term operation planning of large-sized hydrothermal systems. This model is based on the Periodic Autoregressive Model, PAR (p), where the identification of the p orders is based on the significance of the Partial Autocorrelation Function (PACF) estimated via Bootstrap, an intensive computational technique. The scenarios generated through this new technique were applied to the operation planning of the Brazilian Electrical System (BES), using the previously developed methodology of Stochastic Dynamic Programming based on Convex Hull algorithm (SDP-CHull). The results show that identification via Bootstrap is considerably more parsimonious, leading to the identification of lower orders models in most cases which retains the statistical characteristics of the original series. Additionally it presents a closer total mean operation cost when compared to the cost obtained via historic series.

The Cost Efficiency of the Brazilian Electricity Distribution Utilities: A Comparison of Bayesian SFA and DEA Models

The purpose of this study is to evaluate the efficiency indices for 60 Brazilian electricity distribution utilities. These scores are obtained by DEA (Data Envelopment Analysis) and Bayesian Stochastic Frontier Analysis models, two techniques that can reduce the information asymmetry and improve the regulators skill to compare the performance of the utilities, a fundamental aspect in incentive regulation schemes. In addition, this paper also addresses the problem of identifying outliers and influential observations in deterministic nonparametric DEA models.

A new approach to identify the structural order of par (p) models

The periodic autoregressive model, a particular structure of the Box & Jenkins family, denoted by PAR (p), is employed to model the series of hydrological streamflow used for estimating the operational costs of the Brazilian hydro-thermal optimal dispatch. Recently, some aspects of this approachbegan to be studied and several researches on this topic are being developed. This paper focuses on the identification stage of the orders p of these models. Nowadays, the identification is based on evaluating the significance of the coefficients of the partial autocorrelation function (PACF), based on the asymptoticresults of Quenouille. The purpose of this study is on the application of the computer-intensive Bootstraptechnique to estimate the significance of such coefficients. The results show that identification via Bootstrap is considerably more parsimonious, leading to the identification of lower orders in most cases andcorroborating some points raised in previous studies on the traditional approach.

Effective probabilistic stopping rules for randomized metaheuristics: GRASP implementations

The main drawback of most metaheuristics is the absence of effective stopping criteria. Most implementations stop after performing a given maximum number of iterations or a given maximum number of consecutive iterations without improvement in the best known solution value, or after the stabilization of the set of elite solutions found along the search. We propose probabilistic stopping rules for randomized metaheuristics such as GRASP and VNS. We first show experimentally that the solution values obtained by GRASP fit a Normal distribution. Next, we use this approximation to obtain an online estimation of the number of solutions that might be at least as good as the best known at the time of the current iteration. This estimation is used to implement effective stopping rules based on the trade off between solution quality and the time needed to find a solution that might improve the best found to date. This strategy is illustrated and validated by a computational study reporting results obtained with some GRASP heuristics.

Modelling and Forecasting the Residential Electricity Consumption in Brazil with Pegels Exponential Smoothing Techniques

Abstract The importance of the residential class in the consumption of electricity in the Brazilian Electric System (BES) can be recognized by its quantitative size, as it, in 2013, concentrates 27% of the total consumption and 85% among all consumers. Also, in this class are the main public policies such as subsidies for consumer units inhabited by low-income families, labelling and increased energy efficiency of appliances used in the home and others. This paper aims to model and forecast the Brazilian residential energy consumption, up to 2050, with Pegels exponential smoothing techniques. In addition to the forecasts with the best model in sample, an optimization procedure of the models hyper parameters is carried out in order to adjust the projections provided by the Energy Research Company (ERC). The results obtained show that it is possible to predict satisfactorily the electricity consumption for the proposed horizon with minimum error in sample. And the exercise of optimization proved to be important for providing level and trend equations for the official expectations regarding the residential electricity consumption in Brazil.

ARTIFICIAL NEURAL NETWORK AND WAVELET DECOMPOSITION IN THE FORECAST OF GLOBAL HORIZONTAL SOLAR RADIATION

This paper proposes a method (denoted by WD-ANN) that combines the Artificial Neural Networks (ANN) and the Wavelet Decomposition (WD) to generate short-term global horizontal solar ra- diation forecasting, which is an essential information for evaluating the electrical power generated from the conversion of solar energy into electrical energy. The WD-ANN method consists of two basic steps: firstly, it is performed the decomposition of level p of the time series of interest, generating p + 1 wavelet orthonormal components; secondly, the p + 1 wavelet orthonormal components (generated in the step 1) are inserted simultaneously into an ANN in order to generate short-term forecasting. The results showed that the proposed method (WD-ANN) improved substantially the performance over the (traditional) ANN method.

Combining singular spectrum analysis and PAR(p) structures to model wind speed time series

Singular spectrum analysis (SSA) is a technique that decomposes a time series into a set of components, such as, trend, harmonics, and residuals. Leaving out the residual components and adding up the others, the time series can be smoothed. This procedure has been used to model Brazilian electricity consumption and flow series. The PAR(p), periodic autoregressive models, has been broadly used in modelling energy series in Brazil. This paper presents an approach of this decomposition method, by fitting the PAR(p), considering its multivariate version known as multivariate SSA (MSSA). The method was applied to a vector of two wind speed series recorded at two locations in the Brazilian Northeast region. The obtained results, when compared to the univariate decomposition of each series, were far superior, showing that the spatial correlation between the two series were considered by MSSA decomposition stage.