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The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm&rsquo, s advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology.

The fundamental changes in power systems have led to an increased complexity in their operation and control. Wide area monitoring systems (WAMS) are considered as a key solution in order to deal with these challenges. In this paper, a general description of WAMS is presented along with a literature review on the topic. Emphasis is given on documenting real up to date applications of WAMS in the European power network. Moreover, wide area damping control systems for power system oscillations are also presented. A description is provided of two innovative WAMS combined with damping control using FACTS devices and Battery Energy Energy Storage Systems (BESS) that are currently implemented in the European network.

In this paper we examine and compare the efficiency of four European electricity markets (NordPool, Italian, Spanish and Greek) of different microstructure and level of maturity, by testing the weak form of the Efficient Market Hypothesis (EMH). To quantify the level of efficiency deviation of each market from the &lsquo, ideal&rsquo, or &lsquo, benchmark market of random walk&rsquo, we have constructed a Composite Electricity Market Efficiency Index (EMEI), inspired by similar works on other energy commodities. The proposed index consists of linear and nonlinear components each one measuring a different feature or dimension of the market efficiency such as its complexity, fractality, entropy, long-term memory or correlation, all connected to the associated benchmark values of the Random Walk Process (RWP). The key findings are that overall, all examined electricity markets are inefficient in respect to the weak form of EMH and the less inefficient market, as measured by the EMEI is the NordPool, closely followed by the Spanish market, with the Italian being the third. The most inefficient market is the Greek one. These results are in accordance with the predominant view about the maturity of these markets. This study contributes significantly on improving the research framework in developing consistent and robust tools for efficiency measurement, while the proposed index can be a valuable tool in designing improved guidelines towards enhancing the efficiency of electricity markets.

Renewable energy source (RES) installations can inject large amounts of power into the main grid either via ac or dc connections. For certain applications such as long-distance located wind turbine (WT) farms, high-voltage dc (HVDC) transmission is preferred due to engineering and economical reasons. In this paper, the dynamic behavior of a fully controlled HVDC-link transmission system is studied and reviewed under two distinct cases, namely, the dc-link connection of a WT system to a stiff ac power system and the connection to a weak one. Critical aspects for the efficient and stable performance of such a HVDC-link transmission are related to the capability of the control scheme applied to achieve maximum power extraction and simultaneously to effectively regulate the reactive power injected to a strong ac grid or the ac voltage magnitude of a weak ac grid. As shown by this study, cascaded control schemes designed in a manner that guarantees stability provide an excellent system performance with fast and smooth response.

The large-scale integration of variable renewable energy (VRE) in power systems, such as wind and solar, increases the flexibility needed to maintain the load-generation balance. In order for the power operators to plan for secure and reliable operation, they must examine whether there exists sufficient power system flexibility to meet ramps caused by the increased VRE integration and the system demand. In this context, the paper aims to propose an improved flexibility metric to accurately evaluate the flexibility level of a power system in the planning stage. The proposed metric is based on kernel density estimators and expresses the probability of the flexibility residual (the difference between the available flexibility and the expected net load ramps) being less than zero. The Greek power system is used as case study in order to evaluate the proposed index. In particular, the unit commitment optimization problem with flexibility constraints for ten different scenarios based on the ENTSO-E methodology for the time period 2020–2024 is solved and then the proposed metric is calculated. Finally, this index is compared to the well known insufficient ramping resource expectation (IRRE) metric to further evaluate it.

The increasing penetration of variable renewable energy (VRE) sources into the electrical grid poses significant challenges for balancing the power system supply and demand. In order to cope with these challenges, system flexibility is needed. Therefore, system operators need to evaluate and plan ahead flexibility adequacy for their power systems in order to ensure reliable operation under high VRE penetration. In this context, we present a suitable high-level flexibility assessment study applied on a real system, namely the Greek power system, that is based on actual data and can provide useful preliminary results regarding the flexibility level and the VRE integration potential. More specifically, in the first step, the flexibility needs and flexibility resources are determined. In the second step, the flexibility index (FIX) and the present VRE penetration potential (PVP) metric are calculated in order to quantify how flexible the Greek power system is and how much VRE capacity can be reliably balanced. Finally, some initial results are provided that quantity how different modern flexibility solutions impact the VRE penetration potential of the Greek system.