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Cybersecurity is an emerging challenge for power systems, as it strongly affects their reliability and the whole energy system cost. The paper uses several Unit Commitments (UC) models, applying different methods to tackle renewables’ uncertainty. The selected power system is IEEE RTS 96. The UC models are used to assess the impact of different cybersecurity threats. The focus is to assess their impact on the total operating cost and the power grid adequacy to handle them. The comparison between the UC models shows that more robust UC models lead to higher total operating costs. The cost, unit dispatching, and energy mix evolution have a non-linear trend, depending on the power system characteristics and the cyberattacks types. However, the paper provides evidence of considerable price signals in the case of the examined cyberattacks. Each Transmission System Operator (TSO) should examine combinations of cyberattacks and operating conditions to identify crucial cases for system stability and power system cost operation. The applied methodology would also require substantial developments or supplementary approaches to assess cyberthreats at the distribution level.

This paper aims at tackling how the bilateral contracts affect wholesale electricity markets. It examines different levels of bilateral contracts among producers and demand aggregators, aiming to quantify their effect. In addition, it focuses on markets where bilateral contracts could be used as a tool by market participants with a dominant position. Further, the paper examined a case with asymmetrical portfolios, namely where a market participant has a dominant position as in case of Greece, aiming to investigate if bilateral contracts can be used as a tool to manipulate the market. The simulations have been done by an optimization model that provides the economic dispatch and clearing of the day-ahead electricity market. The model incorporated bilateral contracts with committed generating capacity from producers, as well as dynamic bidding strategy per market participant. Results provide useful insights on the design of electricity markets, especially in case of designing voluntary energy exchanges where a market participant has a dominant position.

The paper examines both the time-varying price and volatility transmission between US natural gas and crude oil wholesale markets, over the period 1990–2017. Short iterations suggest that neither commodity determines other’s returns, but sub-periods with very short-lived causal relationships exist. It can be asserted that the markets are decoupled, where unconventional production further enhances the already established commodities’ independence. Using Momentum Threshold Autoregressive (MTAR) cointegration methodology, we find evidence of positive asymmetry from crude oil to natural gas prices, i.e., oil price increases cause faster adjustments to natural gas prices than decreases. We also find that an 1% change of oil price has positive and significantly larger long-term impact (between 0.01% to 0.02%) to the gas price, compared to the negligible impact of gas to oil. Volatility transmission is examined using the Dynamic Conditional Covariance (DCC)-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) methodology, presenting their time-varying correlation. Results show that both commodities influence each other’s volatility at the aggregate level. Finally, we conclude that both regional commodity markets are liquid and integrated, where the market fundamentals drive their price formulation. However, although markets are decoupled and not appropriate for perfect hedging of each other, the existence of bidirectional volatility transmission and their substitutability might be useful for diversified portfolio allocation.

A vital component for the development of a functioning internal electricity market is the adoption by each European member state of the Pan-European Hybrid Electricity Market Integration (EUPHEMIA) for the day-ahead market solution. The consideration of the national power market’s characteristics enables more realistic market design towards the implementation of the so-called “Target Model”. This work considers a series of factors, including the EUPHEMIA order types, their use by market participants, the relative competitiveness of power generators, the impact of interconnected markets, the existence of market players with dominant positions, and the existence of specific regulations such as the minimum average variable cost restriction on offers by producers, as well as the strategy adopted by market participants. The main goal of this paper is to provide a comprehensive analysis on the adoption of EUPHEMIA’s algorithm in case of the Greek wholesale market, based on a relevant research project funded by the Joint Research Centre of the European Commission to support the Hellenic Regulatory Authority of Energy on its decision-making. The paper contributes to the relevant literature on the quantification of the impacts of the EUPHEMIA algorithm in the case of the Greek wholesale market, providing insights on the crucial aspects affecting realistic, market-based decision-making.

Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers’ decision-making framework in competitive electricity markets.