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This study aims to investigate the effects of thermal–hydro interconnection on the revenues, market value and curtailment of variable renewable energy (VRE). The increasing market shares of VRE sources in the Northern European power system cause declining revenues for VRE producers, because of the merit-order effect. A sparsely studied flexibility measure for mitigating the drop in the VRE market value is increased interconnection between thermal- and hydropower-dominated regions.

A comprehensive partial equilibrium model with a high spatial and temporal resolution is applied for the analysis.

Model simulation results for 2030 show that thermal–hydro interconnection will cause exchange patterns that to a larger extent follow VRE production patterns, causing significantly reduced VRE curtailment. Wind value factors are found to decrease in the hydropower-dominated regions and increase in thermal power-dominated regions. Because of increased average electricity prices in most regions, the revenues are, however, found to increase for all VRE technologies. By only assuming the planned increases in transmission capacity, total VRE revenues are found to increase by 3.3 per cent and VRE electricity generation increases by 3.7 TWh.

The current study is, to the authors' knowledge, the first to analyze the effect of interconnection between thermal- and hydropower-dominated regions on the VRE market value, and the authors conclude that this is a promising flexibility measure for mitigating the value-drop of VRE caused by the merit-order effect. The study results demonstrate the importance of taking the whole power system into consideration when planning future transmission capacity expansions.

Since the liberalization of electricity markets in the European Union, prices are subject to market dynamics. Hence, understanding the short-term drivers of electricity prices is of major interest to electricity companies and policymakers. Accordingly, this paper aims to study movements of prices in the combined German and Austrian electricity market.

This paper estimates an autoregressive model with exogenous variables (ARX) in a two-step procedure. In the first step, both time series, which inherently feature seasonality, are de-seasonalized, and in the second step, the influence of all model variables on the two dependent variables, i.e. the day-ahead and intraday European Power Energy Exchange prices, is measured.

The results reveal that the short-term market is largely driven by seasonality, consumer demand and short-term feed-ins from renewable energy sources. As a contribution to the existing body of literature, this paper specifically compares the price movements in day-ahead and intraday markets. In intraday markets, the influences of renewable energies are much stronger than in day-ahead markets, i.e. by 24.12 per cent for wind and 116.82 per cent for solar infeeds.

Knowledge on the price setting mechanism in the intraday market is particularly scarce. This paper contributes to existing research on this topic by deriving drivers in the intraday market and then contrasting them to the day-ahead market. A more thorough understanding is especially crucial for all stakeholders, who can use this knowledge to optimize their bidding strategies. Furthermore, the findings suggest policy implications for a more stable and efficient electricity market.

Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts..

This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis.

This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior.

The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting.

When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors.

The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.

This study is a targeted review of some of the major changes in European regulation that guided energy policy decisions in the Iberian Peninsula and how they may have aggravated the problem of lack of flexibility. This study aims to assess some of the proposed short-term solutions to address this issue considering the underlying root causes and suggests a different course of action, that in turn, could help alleviate future market strains.

The evolution of the most important (macro) energy and price-related variables in both Portugal and Spain is assessed using market and grid operator data. In addition, the authors present critical viewpoints on some of the most recent EU and national regulation changes (official document analysis).

The Iberian energy policy and regulatory agenda has successfully promoted a rapid adoption of renewables (main goal), although with insufficient diversification of generation technologies. The compulsory closings of thermal plants and an increased tax (mainly carbon) added pressure toward more environmentally friendly thermal power plants. However, inevitably, this curbed the bidding price competitiveness of these producers in an already challenging market framework. Moving forward, decisions must be based on “a bigger picture” that does not neglect system flexibility and security of supply and understands the specificities of the Iberian market and its generation portfolio.

This work provides an original account of unprecedented spikes in energy prices in 2021, specifically in the Iberian electricity market. This acute situation worries consumers, industry and governments. Underlining the instability of the market prices, for the first time, this study discusses how some of the most important regulatory changes, and their perception and absorption by involved parties, contributed to the current environment. In addition, this study stresses that if flexibility is overlooked, the overall purpose of having an affordable and reliable system is at risk.

The electric power industry has been moving from a regulated monopoly structure to a deregulated market structure in many countries. The purpose of this study is to comprehensively review the existing markets to study advantages, issues involved and lessons learnt to benefit emerging electricity markets.

The paper employs a comprehensive review of existing competitive electricity market models in USA (California), UK, Australia, Nordic Countries (Norway), and developing country (Chile) to analyze the similarities, differences, weaknesses, and strengths among these markets based on publically available data, literature review and information.

Ongoing or forthcoming electricity sector restructuring activities in some countries can be better designed based on lessons learnt from existing markets and incorporating their own political, technical and economical contexts. A template for design of successful electricity market has also been presented.

This study is limited to a comparative analysis of five markets and can be extended in the future for other existing and emerging electricity markets.

The discussed weaknesses and strengths of existing electricity markets in this study can be practically utilized to improve the electricity industry market structures leading to several social benefits including lower electricity cost.

The comprehensive review and analysis of five existing markets, physically located in different continents, may be used as an assistance or reference guide to benefit the emerging electricity markets in other countries.

The study aims to find out an appropriate volume of power to be procured through long-term power purchase agreements (PPAs), the volume to be sourced from the power exchange through day-ahead and term-ahead options and also a suitable volume to be sold at different points of time within a day, which would finally lead to the optimum cost of power procurement.

The study has considered a Delhi-based power distribution utility and has collected all relevant data from its archival sources. A stochastic optimization model has been developed to capture the problem of power procurement faced by the distribution utility, which is modelled as a mixed integer linear programming problem. Sensitivity analyses were carried out on the important parameters including hourly demand of power, unit variable cost of power available through PPAs, maximum back-down percentage allowed under PPAs, etc., to investigate their impact on daily cost of power under PPAs, daily cost of power under day-ahead and term-ahead options, daily sales revenue and also the net total daily cost of power procurement.

The findings include the appropriate volume of power procured from different suppliers through PPAs and from the power exchange under day-ahead and term-ahead options and also the surplus volume of power sold under the day-ahead arrangement. It has also computed the total cost of power purchased under PPAs, the cost of power purchased from the power exchange under day-ahead and term-ahead options and also the revenue generated out of the sale of surplus power under the day-ahead arrangement. In addition, it has also presented the results of sensitivity analyses, which provide rich managerial insights.

The paper makes two significant contributions to the existing body of power procurement literature. First, the stochastic mixed-integer linear programming model helps decision makers in determining the right volume of power to be purchased from different sources. Second, based on the findings of the procurement model, a power procurement framework is developed considering the dimensions of uncertainty in power supply and the cost of power procurement. This power procurement framework would aid managers in making procurement decisions under different scenarios.

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a comprehensive survey and comparison of these techniques.

The present article provides an overview of the statistical short‐term price forecasting (STPF) models. The basic theory of these models, their further classification and their suitability to STPF has been discussed. Quantitative evaluation of the performance of these models in the framework of accuracy achieved and computation time taken has been performed. Some important observations of the literature survey and key issues regarding STPF methodologies are analyzed.

It has been observed that price forecasting accuracy of the reported models in day‐ahead markets is better as compared to that in real time markets. From a comparative analysis perspective, there is no hard evidence of out‐performance of one model over all other models on a consistent basis for a very long period. In some of the studies, linear models like dynamic regression and transfer function have shown superior performance as compared to non‐linear models like artificial neural networks (ANNs). On the other hand, recent variations in ANNs by employing wavelet transformation, fuzzy logic and genetic algorithm have shown considerable improvement in forecasting accuracy. However more complex models need further comparative analysis.

This paper is intended to supplement the recent survey papers, in which the researchers have restricted the scope to a bibliographical survey. Whereas, in this work, after providing detailed classification and chronological evolution of the STPF techniques, a comparative summary of various price‐forecasting techniques, across different electricity markets, is presented.

The purpose of this paper is to introduce a neural network‐based market agent, which develops optimal bidding strategies for a power generating company (Genco) in a day‐ahead electricity market.

The problem of finding optimal bidding strategy for a Genco is formulated as a two‐level optimization problem. At the top level, the Genco aims at maximizing its total daily profit, and at the bottom level, the independent system operator obtains the power dispatch quantity for each market participant with the objective of maximizing the social welfare. The neural network is trained using a particle swarm optimization (PSO) algorithm with the objective of maximizing daily profit for the Genco.

The effectiveness of the proposed approach is established through several case studies on the benchmark IEEE 30‐bus test system for the day‐ahead market, with an hourly clearing mechanism and dynamically changing demand profile. Both block bidding and linear supply function bidding are considered for the Gencos and the variation of optimal bidding strategy with the change in demand is investigated. The performance is also evaluated in the context of the Brazilian electricity market with real market data and compared with the other methods reported in the literature.

Strategic bidding is a peculiar phenomenon observed in an oligopolistic electricity market and has several implications on policy making and mechanism design. In this work, the transmission line constraints and demand side bidding are taken into account for a more realistic simulation.

To the best of the authors' knowledge, this paper has introduced, for the first time, a neural network‐based market agent to develop optimal bidding strategies of a Genco in an electricity market. Simulation results obtained from the IEEE 30‐bus test system and the Brazilian electricity market demonstrate the superiority of the proposed approach, as compared to the conventional PSO‐based method and the genetic fuzzy rule‐based system approach, respectively.

This article aims to verify if there are detectable barriers in price levels that are understood to be psychologically important (psychological barriers) in a set of hourly electricity prices. These barriers manifest themselves when the market struggles with a difficulty in crossing the barrier to a different level. Psychological barriers focus on directional price movements around regions of the barrier, thus the importance of understanding investor behavior. The authors intend to contribute empirically to the scarce literature on psychological influences in individuals trading in the energy market, hereby enhancing the knowledge concerning the behavior of investors in this market.

The present work aims to test psychological barriers in the Nord Pool electricity market. Through a sample of hourly data on the Elspot day-ahead market, from 2013 to 2017, three groups of tests were made, following the M-values methodology: (1) uniformity tests, which clearly rejected the uniformity in hourly prices; (2) barrier tests, which included the barrier proximity and barrier hump tests, evidencing psychological barriers and (3) conditional effects tests, which allowed us to conclude in favor of effects of positive returns after approaching a barrier on an upward movement, i.e. the barrier breaches due to the fact that increasing prices tend to lead to further price increases, on average.

Uniformity tests, rejected the uniformity in hourly prices; barrier tests, included the barrier proximity and barrier hump tests, evidencing psychological barriers and conditional effects tests, allowed us to conclude in favor of effects of positive returns after approaching a barrier on an upward movement, i.e. the barrier breaches due to the fact that increasing prices tend to lead to further price increases, on average. Another relevant conclusion is that the period from midnight to 9 a.m. is very sensitive, since there is evidence of return and variance effects simultaneously. The implications of these results are potentially relevant, since changes on the variance are usually perceived as a proxy for risk, with changes on the return. It was also concluded that with the increase of the time span from 5 to 10 days on the conditional effects difference tests, there were significant changes on the results, the variance effect is stronger, while the return effect weakens.

However, this research presents some limitations that result in representing opportunities for future research. The fact that there are reduced data available for other markets end up limiting the study of the global electricity market. Although Nord Pool is Europe's leading energy market and is seen as one of the most successful energy markets in the world, it would be interesting to do a study with more than one electricity market to make comparative considerations. Although the spot market is the main arena for energy trade, while the intraday market works as a compliment, it would be equally interesting to do a similar study for the intraday market and then compare conclusions. Moreover, in the present study, it was used standard methods in the literature on psychological barriers, but other methods could have been used–for example, those that assume that prices follow the Benford's distribution (Lu and Giles, 2010), which also present a path for future research and opportunity for confirming the robustness of the present results.

When the presence of psychological barriers is detected it means that the risk-return relationship becomes weaker around the psychological barrier (round numbers, meaning that electricity traders anchor). Identification of psychological barriers supports the claim that technical analysis strategies based on price support and resistance can be profitable. Therefore, more profitable strategies can be built by traders, but no reconciliation with the efficient market hypothesis (EMH) (provided that in inefficient markets prices should not exhibit any particular pattern). The finding of significant psychological barriers in specific hourly time intervals implies the need to address its practical implications in electricity markets, being so specific, namely, the possibility to earn extraordinarily profits exploiting this anomaly and who wins.

The electricity sector is a determinant sector in economic growth and a factor of development. Herein lies the importance of studying this market, which until now has not occurred in this subject, as far as it was possible to gauge. Are there barriers in the electricity market and should such a presence be taken into account? Investigating the existence of psychological barriers in the electric market becomes relevant, because knowing that investors are psychologically affected by a psychological barrier, can become a useful tool in negotiation, as it can function as another variable in the “equation” which is to trade in a complex market like this. Proving the potential presence of a psychological barrier may lead investors to believe in the idea of levels of resistance or levels of support, affecting their decision-making and price dynamics.

The purpose of this paper is to examine how active consumers, i.e. consumers that can inter-temporally shift their load, can influence electricity prices. As demonstrated in this paper, inter-temporal load shifting can induce negative electricity prices, a recurring phenomenon on power exchanges.

The paper presents a novel electricity-market model assuming a nodal-pricing, energy-only spot market with active consumers. This study formulates an economic equilibrium problem as a linear program and uses an established six-node case study to compare equilibrium prices of a model with inflexible demand to a model with flexible demand of active consumers.

This study illustrates that temporal coupling of hourly market clearing through load shifting of active consumers can cause negative electricity prices that are not observed in a model with ceteris paribus inflexible demand. In such situations, where compared to the case of inflexible demand more flexibility is available in the system, negative electricity prices signal lower total system costs. These negative prices result from the use of demand flexibility, which, however, cannot be fully exploited due to limited transmission capacities, respectively, loop-flow restrictions.

Literature indicates that negative electricity prices result from lacking flexibility. The results illustrate that active consumers and their additional flexibility can lead to negative electricity prices in temporally coupled markets, which in general contributes to increased system efficiency as well as increased use of renewable energy sources. These findings extend existing research in both the area of energy flexibility and causes for negative electricity prices. Therefore, policymakers should be aware of such (temporal coupling) effects and, e.g. continue to allow negative electricity prices in the future that can serve as investment signals for active consumers.