The Merit-Order Effect of Load-Shifting: An Estimate for the Spanish Market

Renewable producers can offer selling bids with very low marginal cost since they are not obliged to include on any cost related to the use of energy from the wind or sun. Accordingly, when the Market Operator integrates a renewable bid in the merit-order generation curve, all the generators based on conventional technologies, with higher marginal cost due to the cost of fuels, are displaced to the right. The right-shifting of the merit-order generation curve leads to a lower clearing price, a small increment of the traded energy (almost inelastic demand curve), and a reduction of the total cost of the energy traded in the wholesale market. This is the key mechanism of the well-known merit-order effect of renewables. Load-shifting (demand-side management) plans are expected to yield a reduction of the cost of the traded energy for the customers, since the cost-saving due to the energy eschewed at peak hours would be greater than the extra cost due to the increased demand at off-peak hours. This work will show that the main effects of load-shifting on the market are qualitatively similar to that of renewables, which exemplify the existence a “merit-order effect of load-shifting”. To analyse the characteristics of the merit-order effect of load-shifting, a simplified model has been developed, based on the displacement of the generation and demand curves. A set of scenarios has been generated in order to quantify the main effects on the Spanish/Iberian market for 2015.Ministerio de Economía y Competitividad, España (Ministry of Economy and Competitiveness, Spain) grant ENE2016-77650-

Evolutionary techniques applied to the optimal short-term scheduling of the electrical energy production

This paper presents an evolutionary technique applied to the optimal short-term scheduling (24 h) of the electric energy production. The equations that define the problem lead to a non-convex non-linear programming problem with a high number of continuous and discrete variables. Consequently, the resolution of the problem based on combinatorial methods is rather hard. The required heuristics, introduced to assure the feasibility of the constraints, are analyzed, along with a brief description of the proposed genetic algorithm (GA). The GA is used to compute the optimal on/off status of thermal units and the fitness function is obtained by solving a quadratic programming problem by means of a standard non-linear Interior Point (IP) method. The results from real-world cases based on the Spanish power system are reported, which show the good performance of the proposed algorithm, taking into account the complexity and dimensionality of the problem. Finally, an IP algorithm is adapted to deal with discrete variables that appear in this problem and the obtained results are compared with that of the proposed GA.Ministerio de Ciencia y Tecnología TIN2004-00159Junta de Andalucia ACPAI-2003/032Junta de Andalucia P05-TIC-0053

New Trends in the Control of Grid-Connected Photovoltaic Systems for the Provision of Ancillary Services

Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).The gradual displacement of conventional generation from the energy mix to give way to renewable energy sources represents a paradigm shift in the operation of future power systems: on the one hand, renewable technologies are, in general, volatile and difficult to predict; and on the other hand, they are usually connected to the grid through electronic power converters. This decoupling due to power converters means that renewable generators lack the natural response that conventional generation has to the imbalances between demand and generation that occur during the regular operation of power systems. Renewable generators must, therefore, provide a series of complementary services for the correct operation of power systems in addition to producing the necessary amount of energy. This paper presents an overview of existing methods in the literature that allow photovoltaic generators to participate in the provision of ancillary services, focusing on solutions based on power curtailment by modifying the traditional maximum power point tracking algorithm

Partitioning-Clustering Techniques Applied to the Electricity Price Time Series

Clustering is used to generate groupings of data from a large dataset, with the intention of representing the behavior of a system as accurately as possible. In this sense, clustering is applied in this work to extract useful information from the electricity price time series. To be precise, two clustering techniques, K-means and Expectation Maximization, have been utilized for the analysis of the prices curve, demonstrating that the application of these techniques is effective so to split the whole year into different groups of days, according to their prices conduct. Later, this information will be used to predict the price in the short time period. The prices exhibited a remarkable resemblance among days embedded in a same season and can be split into two major kind of clusters: working days and festivities

Assessing the decarbonisation effect of household photovoltaic self-consumption

Article number 128501The combination of falling renewable technology costs, with high and rising electricity prices and non-obstructive national regulations are making distributed generation increasingly attractive. In the case of photovoltaic (PV) systems, even domestic consumers find it profitable to self-produce part of their electricity demand, instead of purchasing all their energy from the grid, which is changing the current way of obtaining and consuming electricity. The purpose of this work is to estimate the decarbonisation effect in the Iberian/Spanish market, produced by domestic PV self-consumption, once the new regulation, passed in 2019, has removed the previous regulatory barriers in Spain. To achieve this goal, the nationwide, distributed, domestic PV self-production was turned into a reduction of the aggregate demand in the market, and the new clearing point and the corresponding dispatched generators list was established, by emulating the performance of the market operator. Based on 2016–2019 market data, the results suggest that self-consumption could decarbonise the Iberian electricity market, with an average rate of just over 300 tCO2-eq/year for each GWh/year of household PV self-consumed energy.Consejeria de Economia y Conocimiento 718RT0564Consejeria de Economia y Conocimiento US-1265887Centro de Desarrollo Industrial y Tecnológico de España CER-20191019Feder (UE) ENE2016-77650-

A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system

The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.Ministerio de Economía y Competitividad Proyectos ENE2016-77650-R, PCIN-2015-04 y TIN2017-88209-C2-R

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

Application of Evolutionary Computation Techniques to the Optimal Short-Term Scheduling of the Electrical Energy Production

In this paper, an evolutionary technique applied to the optimal short-term scheduling (24 hours) of the electric energy production is presented. The equations that define the problem lead to a nonlinear mixed-integer programming problem with a high number of real and integer variables. Consequently, the resolution of the problem based on combinatorial methods is rather complex. The required heuristics, introduced to assure the feasibility of the constraints, are analyzed, along with a brief description of the proposed genetic algorithm. Finally, results from realistic cases based on the Spanish power system are reported, revealing the good performance of the proposed algorithm, taking into account the complexity and dimension of the problem

A Low-Cost Non-Intrusive Method for In-Field Motor Speed Measurement Based on a Smartphone

Induction motors are broadly used as drivers of a large variety of industrial equipment. A proper measurement of the motor rotation speed is essential to monitor the performance of most industrial drives. As an example, the measurement of rotor speed is a simple and broadly used industrial method to estimate the motor’s efficiency or mechanical load. In this work, a new low-cost non-intrusive method for in-field motor speed measurement, based on the spectral analysis of the motor audible noise, is proposed. The motor noise is acquired using a smartphone and processed by a MATLAB-based routine, which determines the rotation speed by identifying the rotor shaft mechanical frequency from the harmonic spectrum of the noise signal. This work intends to test the hypothesis that the emitted motor noise, like mechanical vibrations, contains a frequency component due to the rotation speed which, to the authors’ knowledge, has thus far been disregarded for the purpose of speed measurement. The experimental results of a variety of tests, from no load to full load, including the use of a frequency converter, found that relative errors on the speed estimation were always lower than 0.151%. These findings proved the versatility, robustness, and accuracy of the proposed method.Spanish MEC-Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness), co-funded by the European Commission (ERDF-European Regional Development Fund) ENE2016-77650-RMinisterio de Ciencia e Innovación (España) CERVERA research program of CDTI (Industrial and Technological Development Centre of Spain) research Project HySGrid+ CER-2019101

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

In the framework of competitive markets, the market’s 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