Jesús Manuel Riquelme Santos

Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques

This paper presents a simple technique to forecast next-day electricity market prices based on the weighted nearest neighbors methodology. First, it is explained how the relevant parameters defining the adopted model are obtained. Such parameters have to do with the window length of the time series and with the number of neighbors chosen for the prediction. Then, results corresponding to the Spanish electricity market during 2002 are presented and discussed. Finally, the performance of the proposed method is compared with that of recently published techniques.

Path-based distribution network modeling: application to reconfiguration for loss reduction

This paper is devoted to efficiently modeling the connectivity of distribution networks, which are structurally meshed but radially operated. A new approach, based on the path-to-node concept, is presented, allowing both topological and electrical constraints to be algebraically formulated before the actual radial configuration is determined. In order to illustrate the possibilities of the proposed framework, the problem of network reconfiguration for power loss reduction is considered. Two different optimization algorithms-one resorting to a genetic algorithm and the other solving a conventional mixed-integer linear problem-are fully developed. The validity and effectiveness of the path-based distribution network modeling are demonstrated on different test systems.

An evolutive algorithm for wind farm optimal design

An evolutive algorithm for the optimal design of wind farms is presented. The algorithm objective is to optimize the profits given an investment on a wind farm. Net present value will be used as a figure of the revenue. To work out this figure, several economic factors such as the initial capital investment, the discount rate, the value of the generated energy and the number of the years spanned by the investment are considered. All this factors depends on the preliminary design of the wind park (number, type, tower height and layout situation of wind generators), which are the variables to set.

Time-Series Prediction: Application to the Short-Term Electric Energy Demand

This paper describes a time-series prediction method based on the kNN technique. The proposed methodology is applied to the 24-hour load forecasting problem. Also, based on recorded data, an alternative model is developed by means of a conventional dynamic regression technique, where the parameters are estimated by solving a least squares problem. Finally, results obtained from the application of both techniques to the Spanish transmission system are compared in terms of maximum, average and minimum forecasting errors.

A New and Efficient Method for Optimal Design of Large Offshore Wind Power Plants

This work addresses the problem of the optimal micro-siting of the wind turbines in large offshore wind power plants with the aim of maximizing the economic profitability of the project. To achieve this goal it is first necessary to estimate the required investment and, secondly, the yearly operation and maintenance costs as well as the yearly income resulting from the operation of the wind power plant over its life span. With this purpose, a complete and realistic model of economic behavior for offshore wind farms has been developed. The optimal turbines layout of a wind farm is a challenge both from a mathematical and technological point of view. The size of the solution space (computational complexity) of the problem addressed in this work dramatically increases with an increase in size of the wind farm. In order to address this difficulty, a new and computationally efficient algorithm is proposed. The method is based in the division of available marine plot in smaller areas of suitable size, sequentially optimized by an improved genetic algorithm.

Short-term hydro-thermal coordination based on interior point nonlinear programming and genetic algorithms

This paper presents a combined primal-dual logarithmic-barrier interior point and genetic algorithm for short-term hydro-thermal coordination. The genetic algorithm is used to compute the optimal on/off status of thermal units, while the interior point module deals with the optimal solution of the hydraulically-coupled short-term economic dispatch of thermal and hydro units. Inter-temporal constraints both due to cascaded reservoirs and maximum up and down ramps of thermal units are included in the latter module. Results from realistic cases based on the Spanish power system are reported.

Electricity Market Price Forecasting: Neural Networks versus Weighted-Distance k Nearest Neighbours

In todays deregulated markets, forecasting energy prices is becoming more and more important. In the short term, expected price profiles help market participants to determine their bidding strategies. Consequently, accuracy in forecasting hourly prices is crucial for generation companies (GENCOs) to reduce the risk of over/underestimating the revenue obtained by selling energy. This paper presents and compares two techniques to deal with energy price forecasting time series: an Artificial Neural Network (ANN) and a combined k Nearest Neighbours (kNN) and Genetic algorithm (GA). First, a customized recurrent Multi-layer Perceptron is developed and applied to the 24-hour energy price forecasting problem, and the expected errors are quantified. Second, a k nearest neighbours algorithm is proposed using a Weighted-Euclidean distance. The weights are estimated by using a genetic algorithm. The performance of both methods on electricity market energy price forecasting is compared.

A Comparison of Two Techniques for Next-Day Electricity Price Forecasting

In the framework of competitive markets, the markets participants need energy price forecasts in order to determine their optimal bidding strategies and maximize their benefits. Therefore, if generation companies have a good accuracy in forecasting hourly prices they can reduce the risk of over/underestimating the income obtained by selling energy. This paper presents and compares two energy price forecasting tools for day-ahead electricity market: a k Weighted Nearest Neighbours (kWNN) the weights being estimated by a genetic algorithm and a Dynamic Regression (DR). Results from realistic cases based on Spanish electricity market energy price forecasting are reported.

Finding improved local minima of power system optimization problems by interior-point methods

This paper presents a simple heuristic technique to deal with multiple local minima in nonconvex, nonlinear power system optimization problems by solving a sequence of interior-point subproblems. Both the real-valued and the mixed-integer cases are separately discussed. The method is then applied to the unit commitment problem and its performance on realistic cases is compared with that of a genetic algorithm (GA).

Influence of kNN-Based Load Forecasting Errors on Optimal Energy Production

This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast is used to compute the optimal on/off status and generation scheduling of the units. Finally, the influence of forecasting errors on both the status and generation level of the units over the scheduling period is studied.