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This paper addressesthe problem of real-time state estimation in European-type four-wire low-voltage distribution networks with advanced metering infrastructure based on power line communication (PLC). The main drawback of this type of infrastructure is its high latency and a sequential data sampling process. Both factors make it difficult to obtain instantaneous values from the smart meters in a timely and adequate manner to estimate the real-time state of the system. This problem may be solved in the future with the deployment of metering systems based on other technologies such as 5G. However, PLC technology prevails in many countries, and is not likely to be replaced in the medium term. In the meantime, a solution is needed to provide acceptable real-time state estimates in low voltage networks, not to hinder the active management of these assets in the context of smart grids. In this work, a solution is proposed that allows estimating the state of the low voltage network with a reasonable accuracy considering the low quality of the measurements provided by the deployed infrastructure. The system is validated using a portion of a real pilot network located in the north of Spain fed by a transformer station with seven feeders. [ABSTRACT FROM AUTHOR]

The large penetration of renewable energy resources is demanding additional flexibility for the operation of power systems. In this sense, real world applications are proving that wind power units are able to provide part of the required flexibility. Therefore, these new sources of flexibility need to be considered by power system planners in order to make appropriate generation capacity expansion decisions. This paper proposes a generation and storage capacity expansion formulation considering that wind power units are able to participate in the reserve provision service. The proposed model is a stochastic linear program that is solved using a commercial solver. A case study based on the isolated power system of Lanzarote and Fuerteventura in Spain is solved to quantify the impact of considering the reserve provision of wind power in capacity investment decisions. [ABSTRACT FROM AUTHOR]

Non-technical losses (NTL) in electricity utilities are responsible for major revenue losses. In this paper, we propose a novel end-to-end solution to self-learn the features for detecting anomalies and frauds in smart meters using a hybrid deep neural network. The network is fed with simple raw data, removing the need of handcrafted feature engineering. The proposed architecture consists of a long short-term memory network and a multi-layer perceptrons network. The first network analyses the raw daily energy consumption history whilst the second one integrates non-sequential data such as its contracted power or geographical information. The results show that the hybrid neural network significantly outperforms state-of-the-art classifiers as well as previous deep learning models used in NTL detection. The model has been trained and tested with real smart meter data of Endesa, the largest electricity utility in Spain. [ABSTRACT FROM AUTHOR]

This paper describes and analyzes the Spanish AGC system. The differences with respect to a standard hierarchical structure are explained. A simple model useful for simulation is proposed. Using this model, the performance of the system and the influence of several parameters in system response is evaluated. The paper also suggests some changes in the configuration and parameter settings of the system to improve its performance. [ABSTRACT FROM AUTHOR]

This two-part paper surveys the frequency and voltage control ancillary services in power systems from various parts of the world. In this first part, the nomenclature used to describe active power reserves across 11 systems is first reviewed in order to facilitate the comparison of frequency control ancillary services. The essential technical features of frequency and voltage control ancillary services are then described. Finally, the technical requirements adopted in eight jurisdictions (North America, continental Europe, Germany, France, Spain, the Netherlands, Belgium, and Great Britain) are compared. The companion paper surveys the economic features of these ancillary services. [ABSTRACT FROM AUTHOR]

This paper presents an approach to determine the optimal daily generation scheduling based on the Spanish electricity market rules. The Spanish power system considers the pool-based daily market and the technical constraints resolution as two different and consecutive processes. After the daily market has been cleared, a centralized constraints resolution process is applied to this initial scheduling through the redispatch of previously matched energy. In this paper, an algorithm based on Benders decomposition, arranged in three levels, is proposed to solve the technical constraints solution process in order to define a preventive secure dispatch. The constraints resolution process includes a full ac network and security model. This method determines the active power committed to each generating unit so as to minimize the energy redispatch cost subject to dispatch, network, and security constraints. The solution also provides the reactive power output of the generating units, the value of the transformers taps, and the committed voltage control devices. The model has been tested in an actual example of the Spanish power system. Some relevant results are reported. [ABSTRACT FROM AUTHOR]

The solution of power system constraints is an important issue that has to be addressed to achieve a fair operation of a competitive electricity market. In the Spanish electricity market, the System Operator (SO) is in charge of determining the technical feasibility of the generation dispatch provided by the Market Operator (MO). An optimal solution method of power system constraints in the Spanish market must take into account the connection of off-line units to solve both branch overloads and voltage constraints, the adjustment of voltage control resources, the solution of the postulated n-I and n-2 contingencies with a preventive criteria and the coupling of the solution in the 24 hourly scenarios due to the start-up cost of nonconnected units. This paper details the management of power system constraints in the Spanish market, and contains a comprehensive review of solution methods of power system constraints. In addition, the paper proposes a novel approach to solve Spanish power system constraints that overcomes the limitations of existing methods, by decoupling the solution of branch overloads and voltage constraints. [ABSTRACT FROM AUTHOR]

This paper describes the application of sliced inverse regression to model the electrochemical process of aluminum smelter plants. Real data measurements obtained during several years in a Spanish industrial environment are used to illustrate the main dependencies between parameters. Nonlinear relations between the output variables and relevant linear combinations of input variables are deduced. An exploratory statistical analysis is also presented, checking for correlations and possible linear dependencies. [ABSTRACT FROM AUTHOR]

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn. [ABSTRACT FROM AUTHOR]

The level of secondary reserve needed in a power system is traditionally settled by system operators according to engineering criteria. This paper proposes a novel methodology to determine the optimum level of secondary reserve based on both engineering and economic criteria. Within the proposed approach a price elastic-quantity demand curve for the secondary reserve is built. This approach assumes that the provision of secondary reserve is made under a competitive market. In this market, the supply curve that collects the bids from generators providing secondary reserve is matched with the calculated elastic demand cost curve. The cross of the supply and demand curves determines the optimal level of secondary reserve and the price for provision of the service. The developed approach is illustrated with a case study based on the current day-ahead secondary reserve market in Spain. [ABSTRACT FROM AUTHOR]

In a competitive electricity market, an accurate forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions. An adaptive wavelet neural network (AWNN) is proposed in this paper for short-term price forecasting (STPF) in the electricity markets. A commonly used Mexican hat wavelet has been chosen as the activation function for hidden-layer neurons of feed-forward neural network (FFNN). To demonstrate the effectiveness of the proposed approach, day-ahead prediction of market clearing price (MCP) of Spain market, which is a duopoly market with a dominant player, and locational marginal price (LMP) forecasting in PJM electricity market, are considered. The forecasted results clearly show that AWNN has good prediction properties compared to other forecasting techniques, such as wavelet-ARIMA, multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as recently proposed fuzzy neural network (FNN). [ABSTRACT FROM AUTHOR]

The aim of the Spanish voltage control ancillary ser- vice is the minimization of the transmission losses while keeping the system away from voltage instability. The latter target can be achieved maintaining an adequate voltage profile in normal operating condition and assuring that generators exhibit enough reactive margins that guarantee that the system voltages will remain under acceptable values in case of contingencies. The service has been designed in Spain to be partially mandatory and partially subject to payment based on market-driven rules and performance evaluation. In the annual time scope, the optional reactive offers submitted by the service suppliers are selected analyzing a number of forecasted scenarios. This paper explains the implementation of the Spanish voltage control ancillary service (VCAS) that is provided by generators in the annual time scope. For this purpose, a mixed-integer linear programming optimal power flow (OPF) is used. Reactive bids of generators are modeled within each scenario by modifying the reactive limits of the generators. The use of the proposed OPF in the assignment of the voltage control ancillary service is explained and illustrated using actual scenarios of the Spanish power system. [ABSTRACT FROM AUTHOR]

In this paper, an efficient method based on a new fuzzy neural network is proposed for short-term price fore- casting of electricity markets. This fuzzy neural network has inter-layer and feed-forward architecture with a new hypercubic training mechanism. The proposed method predicts hourly market-clearing prices for the day-ahead electricity markets. By combination of fuzzy logic and an efficient learning algorithm, an appropriate model for the nonstationary behavior and outliers of the price series is presented. The proposed method is examined on the Spanish electricity market. It is shown that the method can provide more accurate results than the other price forecasting techniques, such as ARIMA time series, wavelet-ARIMA, MLP, and RBF neural networks. [ABSTRACT FROM AUTHOR]

In this paper, a probabilistic methodology for estimating the energy costs in the market for wind generators associated with wind prediction errors is proposed. Generators must buy or sell energy production deviations due to prediction errors when they bid in day-ahead or hour-ahead energy markets. The prediction error is modeled through a probability density function that represents the accuracy of the prediction model. Production hourly energy deviations and their associated trading costs are then calculated. Three study cases based on real wind power installations in Spain are analyzed. The three study cases show that the error prediction costs can reach as much as 10% of the total generator energy incomes. [ABSTRACT FROM AUTHOR]