The high proliferation of converter-dominated Distributed Renewable Energy Sources (DRESs) at the distribution grid level has gradually replaced the conventional synchronous generators (SGs) of the transmission system, resulting in emerging stability and security challenges. The inherent characteristics of the SGs are currently used for providing ancillary services (ASs), following the instructions of the Transmission System Operator, while the DRESs are obliged to offer specific system support functions, without being remunerated for these functions, but only for the energy they inject. This changing environment has prompted the integration of energy storage systems as a solution for transfusing new characteristics and elaborating their business in the electricity markets, while the smart grid infrastructure and the upcoming microgrid architectures contribute to the transformation of the distribution grid. This review investigates the existing ASs in transmission system with the respective markets (emphasizing the DRESs’ participation in these markets) and proposes new ASs at distribution grid level, with emphasis to inertial response, active power ramp rate control, frequency response, voltage regulation, fault contribution and harmonic mitigation. The market tools and mechanisms for the procurement of these ASs are presented evolving the existing role of the Operators. Finally, potential barriers in the technical, regulatory, and financial framework have been identified and analyzed.

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’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.

In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC) technique and the traditional multiple regression (PC-regression), for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004–2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.

Italy, Greece, and, to a lesser degree, Bulgaria have experienced fast growth in their renewable generation capacity (RESc) over the last several years. The consequences of this fact include a decrease in spot wholesale prices in electricity markets and a significant effect on cross border trading (CBT) among neighboring interconnected countries. In this work, we empirically analyzed historical data on fundamental market variables (i.e., spot prices, load, RES generation) as well as CBT data (imports, exports, commercial schedules, net transfer capacities, etc.) on the Greek, Italian, and Bulgarian electricity markets by applying the Granger causality connectivity analysis (GCCA) approach. The aim of this analysis was to detect all possible interactions among the abovementioned variables, focusing in particular on the effects of growing shares of RES generation on the commercial electricity trading among the abovementioned countries for the period 2015–2018. The key findings of this paper are summarized as the following: The RES generation in Italy, for the period examined, drives the spot prices in Greece via commercial schedules. In addition, on average, spot price fluctuations do not affect the commercial schedules of energy trading between Greece and Bulgaria.

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 ‘ideal’ or ‘benchmark market of random walk’, 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.

The European Commission’s Target Model’s main objective is to integrate European electricity markets, leading to a single internal energy market and guaranteeing the instantaneous balance between electricity generation and demand. According to the target model for electricity trading, proposed by the European Network Transmission System Operators for Electricity (ENTSO-E), within each zone, electricity can be traded freely without taking into consideration network limitations. In contrast, for cross-border trading, the exchanges with other market areas are taken into account. Cross-border trade poses a further burden on the interconnection lines, resulting in increasing network congestion, which in turn restricts electricity trading. Thus, calculating the available capacity for trade has a significant ramification on the market. Today, the Available Transfer Capacity (ATC) mechanism dominates cross-border trading, but this methodology may be replaced by the Flow-Based (FB) approach across Europe. This paper investigates both approaches regarding the cross-border congestion management under the market coupling procedure. In our case study, the Southeast Europe (SEE) region is taken into consideration; it consists of both the FB and ATC approach in a five country (Greece, North Macedonia, Bulgaria, Serbia, and Romania) scenario. The purpose of our tests is to perform, compare, and evaluate the effectiveness of each method for the SEE region, while the main findings are the maximization of social welfare, better cross-border trading opportunities, and price convergence via the FB method.