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• Application of the ampacity control system is necessary for wind energy integration. • DLR application reduced the time in which wind farms were out of service by 4100 h in 44 months. • DLR application increased the energy supplied by wind farms by 70.9 GWh in 44 months. • DLR application saved more than 7800 t of CO 2 in 44 months. The total renewable wind energy capacity of Spain currently accounts for more than 20% of the total installed energy capacity, which makes integration into the grid challenging for wind farm owners as well as Distribution System Operator (DSO). Electrical companies require new techniques to integrate renewable energies safety and with low investment costs. Dynamic line rating (DLR) is one of these techniques, and is used by DSOs to maximise the capacity of existing infrastructure. This paper presents a successful case of DLR application by a DSO over several years to reduce the time that wind farms were out of service due to an excess of electrical energy generated. Over the period from January 2015 to September 2018, the application of the DLR technique prevented 4,100 hours of out-of-service time, increasing the energy supplied by the wind farms by 70.9 GWh and by extension saving 7,800 tons of CO2. [ABSTRACT FROM AUTHOR]

Hydropower stands out for its fast response ability and flexible operation, playing a predominant role in the provision of regulation reserves. As hydro power is an energy-constrained generation technology, it needs to be protected against any possible deployment of the scheduled reserves. However, the few models that formulate a detailed hourly co-optimization of energy and regulation reserves, neglect infeasibilities that could be derived from the requested reserves in real-time. As the regulation reserve market is becoming increasingly important, hydro producers can no longer neglect such effect. This paper presents a stochastic optimization model to find the optimal hourly schedule of a set of hydraulically coupled hydropower plants to obtain the weekly operation that simultaneously maximizes the expected profit in both energy and regulation reserves markets. The model is formulated for a price-taker agent, and it considers a very detailed representation of the system including minimum–maximum water flows, net head dependency, and fractional water travel time. The main contribution is that the obtained solution protects a multi-reservoir system against risk of water and storage unavailability due to the uncertainty in real-time use of regulation-up and down reserves, respectively, and that the reserves deem the net head dependency. The paper presents a realistic case study where the proposed formulation has been tested successfully with real data from the Spanish electricity market. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of a new online real-time hybrid load-forecasting model based on an autoregressive model and neural networks. This new system is currently running at the Spanish Transport System Operator (REE) and provides an hourly forecast for the current day and the next nine days timely every hour for the national system as well as 18 regions of Spain. These requirements impose a heavy computational burden that needs to be considered during the design phase. The system is developed to improve forecasting accuracy specifically on difficult days like hot, cold and special days. In order to achieve this goal, a deep analysis of the temperature series from 59 stations is made for each region and the relevant series are included individually in the model. Special days are also analyzed and a thorough classification of days is proposed for the Spanish national and regional system. The model is designed and tested with data from 2005 to 2015. The results provided for the period from December 2014 to October 2015 show how the addition of the proposed model to the TSO’s ensemble causes a 5% RMSE overall error reduction and a 15% reduction on the 59 difficult days considered in the testing period. [ABSTRACT FROM AUTHOR]

• Evaluation of the most recent Spanish grid code requirements. • A new tool to speed up the commissioning of new solar PV power plants is presented. • The accuracy of the aggregated PV power plant dynamic model proposed is demonstrated. • The aggregated model is shown to be ten times faster than the detailed equivalent. • The aggregated solar PV power plant simulation model approved by the Spanish TSO. Renewable energy and, in particular, solar PV power, will play a crucial role in achieving net-zero emissions scenarios over the coming decades. Increasing the share of renewables in countries' energy mixes is therefore a major challenge that must be urgently addressed. Within this context, the present work proposes an innovative tool that may accelerate the commissioning process of new solar PV power plants, in the form of an aggregated simulation model to assess a power plant's responses under the technical requirements established in the Spanish grid code. Our analyses, based on the comparison of the proposed model with a detailed model -both representing the same installation, show, from a quantitative and qualitative viewpoint, the accuracy and usefulness of the dynamic simulation model proposed. The aggregated model is shown to be ten times faster than the detailed equivalent, while the errors encountered in the simulation responses of both models are below 0.1% in all cases. It must also be noted that the present paper has allowed for the introduction, in the most recent version of the Spanish grid code and following approval of the Spanish Transmission System Operator (TSO), Red Eléctrica de España (REE), of this new and advantageous PV power plant model. Finally, it is worth mentioning that this study could form the basis for encouraging other countries to also adopt this model in their grid codes as a new tool to help integrate PV power into power grids. [Display omitted] [ABSTRACT FROM AUTHOR]

• Holidays affect electricity load greatly, causing inaccurate forecasts. • This effect varies widely among holidays, special days and adjacent days. • Wide and detailed classification of each special day can reduce forecasting error. • The proper methodology eliminates the analisys effort needed for classification. • An automatic classification can outperform an expert-level classification. Electricity demand presents a repetitive pattern following daily, weekly and seasonal patterns. However, factors like temperature or social events tend to disrupt these patterns introducing outlying data that is difficult to forecast. This paper introduces a new methodology to classify special days without any prior knowledge of the database. Simple classification of special days into two or three categories is insufficient as the consumers' behavior has many shades on these days. However, classifying special days in a wide range of categories required a deep understanding of the consumers' behavior on different days and periods of the year. The methodology proposed describes an algorithm to automate this classification starting from a simple day-of-the-week classification and branching into as many categories as needed to fit a real database. Categories with similar profiles are merged to avoid overfitting and actual outliers are detected to ensure that no false categories are created. The methodology is developed using data from 2010 to 2017 and tested in three different systems. The benchmark used is the classification used by the Transmission System Operator in Spain and the test show that the proposed methodology provides more accurate results without the need of an expert to develop the classification. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a long term generation expansion model that uses system dynamics to capture the interrelations between different variables and parameters. Using this model, it is possible to estimate the long term evolution of the demand and of the electricity price that are then used by generation agents to prepare individual expansion plans. These plans are submitted to a coordination analysis to check some global indicators, as the reserve margin and the LOLE. The developed approach is illustrated using a realistic generation system based on the Portuguese/Spanish system with an installed capacity of nearly 120GW and an yearly demand of 312TWh in 2010. Large investments were directed in the last 20 years to the Iberian generation system both regarding traditional technologies and dispersed generation (namely wind parks and solar systems). Today, the excess of installed capacity together with the demand reduction poses a number of questions that should be addressed carefully namely to investigate the impact of several options. The planning exercise aims at identifying the most adequate expansion plans in view of the increased renewable generation (namely wind parks). For illustration purposes, we also conducted a sensitivity analysis to evaluate the impact of increasing the installed capacity in wind parks, of internalizing CO2 emission costs and of incorporating a capacity payment. These analyses are relevant in order to get more insight on the possible long term evolution of the system and to allow generation companies to take more sounded decisions. [Copyright &y& Elsevier]

Abstract: We present a novel transmission expansion planning model that explicitly considers a multi-year planning horizon. The proposed formulation is a mixed-integer linear problem that can be solved using commercially available software. The algorithm determines the best overall transmission expansion plan, reflecting both investment and operational costs. To analyze the results obtained from the model, we use a set of efficiency metrics to appraise the effect of the expansion among generators and demands, as well as congestion metrics to measure changes in nodal prices and line congestions. This paper also provides a case study that considers the expansion plan for a transmission system based on the one of mainland Spain. [Copyright &y& Elsevier]

Abstract: This paper proposes an approach to calibrate incentives for continuity of supply in electricity distribution in Spain. This approach consists on the estimation of the impact of continuity of supply improvements on distribution network costs. For this purpose, distribution costs resulting from different continuity of supply requirements are computed by a reference network model (RNM). The results obtained from the RNM are used to estimate a cost-function that considers continuity of supply a cost driver for distribution costs. This methodology is applied to three Spanish areas of service (an urban one, a semi-urban one and a rural one) in such a way that differences in distribution costs caused by the characteristics of the area type can be taken into account. The analysis indicated that distribution costs are more sensitive to continuity of supply improvements in rural areas of service. It also demonstrated that the incentive for reducing number of service interruptions should be higher than the incentive for reducing duration of service interruptions. Finally, the current Spanish incentive scheme for continuity of supply was analyzed under the perspective of the proposed approach. [Copyright &y& Elsevier]

Abstract: This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California. [Copyright &y& Elsevier]

• Formulates a model for market equilibrium among producers and prosumers. • Converted into an equivalent optimization model through its KKT conditions. • Analyzes a Spain-like case study for the period 2019–2037. • Results show possible prosumer response to tariff structure. • Having different objective functions shows an example of incentive misalignment. Power systems will face important structural changes in the near future due to the empowerment of consumers, who may resort on self-consumption, and reduce their purchases of electricity from the grid. The avoided costs of purchasing energy, as compared to the investment costs of installing their own self-generation capacity, could be one of the drivers of the consumers' decision making. The system expansion will therefore result from the interaction of the traditional market agents, maximizing their profits by investing in and operating centralized generation assets, and the new active consumers, minimizing their expenses while meeting their energy needs. This paper presents a Nash equilibrium model that considers centralized and behind-the-meter distributed generation expansion, by representing the operation and investments decisions of both types of agents with their own conceptually different strategies. To simplify the resolution, the equilibrium model is transformed into an equivalent minimization problem from its Karush-Kuhn-Tucker conditions. The model application to Spain-like system case example allows to assess the impact of the network access-tariff (whether being mainly volumetric-based or power-based) and the impact of the big industrial market power on the generation expansion, for the time horizon 2019-2037. [ABSTRACT FROM AUTHOR]