Abstract
This paper deals with integrating energy storage systems (ESS) into existing electricity markets. We explain why ESS increase flexibility of power systems and energy markets and why more flexible systems and markets are desirable, particularly in a context of high integration of variable renewable energy sources (RES). The Dutch electricity markets are introduced as the case studies. As opposing to the existing literature, we focus on implementation of a dual technology ESS, which we believe is more beneficial than a single ESS. To show this, we introduce an optimal control model, in which the goal is to maximize the revenues of the dual technology energy storage system applied into two different energy markets, assuming the selling and buying electricity prices are exogenous. Subsequently, we introduce our model, using a simple strategy and present its results, showing the impact of the devices nominal rating on the potential revenues.

Abstract
In this paper a Distribution State Estimator (DSE) tool suitable for real-time monitoring in poorly characterized low voltage networks is presented. An Autoencoder (AE) properly trained with Extreme Learning Machine (ELM) technique is the "brain" of the DSE. The estimation of system state variables, i.e., voltage magnitudes and phase angles is performed with an Evolutionary Particle Swarm Optimization (EPSO) algorithm that makes use of the already trained AE. By taking advantage of historical data and a very limited number of quasi real-time measurements, the presented approach turns possible monitoring networks where information of topology and parameters is not available. Results show improvements in terms of estimation accuracy and time performance when compared to other similar DSE tools that make use of the traditional back-propagation based algorithms for training execution.

Abstract
The three-phase state estimation algorithms developed for distribution systems (DS) are based on traditional approaches, requiring components modeling and the complete knowledge of grid parameters. These algorithms are capable of dealing with the particular characteristics of DS but cannot be used in cases where grid topology and parameters are unknown, which is the most common situation in existing low voltage grids. This paper presents a novel three-phase state estimator for DS that enables the explicit estimation of voltage magnitudes and phase angles in all phases, neutral, and ground wires even when grid topology and parameters are unknown. The proposed approach is based on the use of auto-associative neural networks, the autoencoders (AE), which only require an historical database and few quasi-real-time measurements to perform an effective state estimation. Two test cases were used to evaluate the algorithm's performance: a low and a medium voltage grid. Results show that the algorithm provides accurate results even without information about grid topology and parameters. Several tests were performed to evaluate the best AE configuration. It was found that training an AE for each network feeder leads generally to better results than having a single AE for the entire system. The same happened when different AE were trained for each network phase in comparison with a single AE for the three phases.

Abstract
This paper presents the analysis of the importance of a set of explanatory (input) variables for the day-ahead price forecast in the Iberian Electricity Market (MIBEL). The available input variables include extensive hourly time series records of weather forecasts, previous prices, and regional aggregation of power generations and power demands. The paper presents the comparisons of the forecasting results achieved with a model which includes all these available input variables (EMPF model) with respect to those obtained by other forecasting models containing a reduced set of input variables. These comparisons identify the most important variables for forecasting purposes. In addition, a novel Reference Explanatory Model for Price Estimations (REMPE) that achieves hourly price estimations by using actual power generations and power demands of such day is described in the paper, which offers the lowest limit for the forecasting error of the EMPF model. All the models have been implemented using the same technique (artificial neural networks) and have been satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). The relative importance of each explanatory variable is identified for the day-ahead price forecasts in the MIBEL. The comparisons also allow outlining guidelines of the value of the different types of input information.

Abstract
This paper addresses the provision of frequency control services with multi-terminal HVDC grids interconnecting several asynchronous AC control areas and integrating offshore wind farms. Regarding the operational performance of the multi-terminal HVDC grid, it is discussed and proposed a communication-free regulation scheme that allows these type of infrastructures to actively participate in primary frequency regulation services and provision of inertial emulation capabilities among the non-synchronous areas. Additionally, the proposed control scheme is extended such that offshore wind generators can also actively provide inertia and primary frequency control to the mainland AC grid. The main rational of the proposed control scheme relies of a cascading control mechanism based on the modulation of active power as a function of the frequency in the HVDC converter stations connected to mainland AC grids and on the control of the frequency in the HVDC converters associated to offshore wind farms. The DC grid voltage variations resulting from this principle is used as a natural communication channel to develop the control loops to be used in all the converter stations. The effectiveness of the proposed strategy is illustrated in the case of two non-synchronous areas linked by a multi-terminal HVDC system connecting two offshore wind farms.

Abstract
This study presents a methodology for siting and sizing static synchronous compensator (STATCOM) devices in the Portuguese transmission system in order to improve system security following severe grid faults. Security issues arise since the Portuguese transmission system incorporates significant levels of wind generation without fault ride through and reactive current injection capabilities during grid faults. As the transmission system operator (TSO) is responsible for assuring system security, the goal of the study is to take advantage of the proved STATCOM ability for injecting reactive current in order to mitigate the disconnection of large amounts of wind farms in case of severe grid faults. The proposed methodology was developed and tested in coordination with the Portuguese TSO and it is based on the formulation of an optimisation problem in order to minimise the installed STATCOM power while ensuring its compliance with the current grid code requirements, namely in what concerns to the system stability and security. Given the discrete and complex nature of the problem, a hybrid approach, combining both a heuristic method and an evolutionary particle swarm optimisation (EPSO) algorithm was developed. Results show the effectiveness of the proposed methodology as well as its robustness regarding the validity of the obtained solutions while facing the most severe operational scenarios.