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In this article, we provide a structured review of crude oil price dynamics. Specifically, we summarize evidence on important factors determining oil prices, cover the impact of oil market shocks o...

This work presents a generic optimization framework (ANNEX model), including three alternative algorithms for the electricity market-clearing process in order to optimally determine the annual ener...

To implement the process of European power markets’ integration, a market-clearing algorithm has been developed among European power exchanges under the title EUPHEMIA (Pan-European Hybrid Electricity Market Integration Algorithm), being a strictly economic dispatch algorithm and providing several options and products to market participants. This paper proposes an optimization-based methodological framework for the optimal economic dispatch problem in power exchanges, further enhancing the EUPHEMIA algorithm’s block order module. More specifically, through the development of a mixed-integer linear programming (MILP) model and utilizing an iterative process, it quantifies the impacts of the proposed new options to the optimal energy mix, the wholesale prices, and on the market players’ economic performance. The model considers all the current market products of the EUPHEMIA algorithm, as well as introduces new market products such as the flexibility provision of the main aspects of block orders (minimum acceptance ratio, price offer, and block time limits), the modification of existing block orders (exclusive groups), the development of new linkage structure (linked block orders with parallel relationship), as well as the activation of mixed schemes combining existing types of block orders into new integrated forms. The developed optimization framework has been assessed in the Greek interconnected power system, including its interconnections with neighboring power systems. The proposed methodological approach suggests a robust and systematic methodological formation to provide useful insights on policy issues and ongoing debate regarding the shaping of more efficient market designs and structures to deal with the new operational challenges of low-carbon flexible power systems.

The shale gas developments over the last two decades have challenged the gas price linkage with crude oil. The decoupling of the US wholesale gas from oil markets is mainly attributed to the rapid development of unconventional production, which formed a regional natural gas market based on regional market fundamentals. Moreover, investments in exporting facilities in the US made more quantities available to the rest of the world making global integration more plausible. This paper provides empirical evidence on the price and volatility transmission among the main European (NBP and TTF) and the Japan-Korean Marker (JKM) gas markets with that of Brent crude oil market, a crude oil benchmark used in Europe and Asia. The paper provides evidence that there are no price spillovers among oil and gas in European gas hubs. The European markets, contrary to the JKM market, seem to be mature enough as in the case of the US gas market. Finally, the paper provides policy recommendations on key elements for establishing functional gas hubs.Keywords: natural gas and oil markets; price and volatility spillovers; Europe, JapanJEL Classifications: Q40, Q41, C5DOI: https://doi.org/10.32479/ijeep.9774

This study aims to re-investigate the long-run relationship among energy prices and economic growth within the periphery of the European Union. We rely on the Engle–Granger methodology to estimate a Vector-Error Correction Model. We also employ Variance Decomposition Analysis to estimate the causal effect of energy prices on economic growth. We provide evidence on the conservation hypothesis for the case of real GDP and residential electricity prices, as well as on the growth hypothesis for the case of real output and industrial electricity prices. The residential electricity sector exhibits the highest level of influence, as industrial electricity price and crude oil price “Granger cause” residential electricity prices. We also find signs of the feedback hypothesis concerning final energy consumption and residential electricity price. Lastly, the level of economic growth proxied by real GDP is strongly endogenous in the short-run, whereas shocks from other covariates seem to have a transitory effect.

Developing economies are in process of liberalizing their electricity markets, following similar process in developed economics. This process aims at establishing liquid energy exchanges that provide clear price signals, providing indications on the profitability of different operations: production, retail, trading in interconnections. This paper aims at developing a unit commitment model for examining zonal market pricing in Kazakhstan. The latter has an extensive landscape but sparsely populated, while is also characterized by the high availability of domestic fossil fuels, but located in different sub-regions of the country. The provision of zonal price signals in such a power system in invaluable, as it enables the provision of clear price signals on the needed infrastructure and the estimation of the zonal hourly energy and technology mix. Moreover, in enables the formation of dynamic bidding strategies by market participants in cases with favourable conditions, such as the implementation of scarcity pricing. This paper presents a unit commitment model that used to assess different bidding strategies and to provide zonal price signals. The strategies are formed, depending on the technology type and fuel prices comparison. Results provide clear signals on needed infrastructure among zones in Kazakhstan. It also shows that dynamic biding can lead to market coupling. Finally, it indicates the importance of institutional capability to monitor bidding strategies, eliminating speculation.

Developing economies are in process of liberalizing their electricity markets, following similar process in developed economics. This process aims at establishing liquid energy exchanges that provide clear price signals, providing indications on the profitability of different operations: production, retail, trading in interconnections. This paper aims at developing a unit commitment model for examining zonal market pricing in Kazakhstan. The latter has an extensive landscape but sparsely populated, while is also characterized by the high availability of domestic fossil fuels, but located in different sub-regions of the country. The provision of zonal price signals in such a power system in invaluable, as it enables the provision of clear price signals on the needed infrastructure and the estimation of the zonal hourly energy and technology mix. Moreover, in enables the formation of dynamic bidding strategies by market participants in cases with favourable conditions, such as the implementation of scarcity pricing. This paper presents a unit commitment model that used to assess different bidding strategies and to provide zonal price signals. The strategies are formed, depending on the technology type and fuel prices comparison. Results provide clear signals on needed infrastructure among zones in Kazakhstan. It also shows that dynamic biding can lead to market coupling. Finally, it indicates the importance of institutional capability to monitor bidding strategies, eliminating speculation.Keywords: Electricity markets, unit commitment model, KazakhstanJEL Classifications: Q4, Q47, L94DOI: https://doi.org/10.32479/ijeep.9022

Energy union and climate stands as one of the priorities of the European commission, aiming at the provision of secure, environmentally friendly and affordable energy. European energy policy over the last two decades have reshaped energy markets challenging the profitability and viability of energy companies. The latter must prove flexible in their management, including diversification of their portfolio, proceeding on structural unbundling and extending their operations in new markets and regions. Scope of the paper is to assess the financial and liquidity performances of key European energy companies over the period 2008-2017. The focus of the analysis concerns liquidity, profitability, operational performance and capital structure. The analysis is carried out in key energy companies, selected to have an extended geographical representation. Results indicate that gas and oil companies have less risk compared to power companies, attributed mainly to debt exposure. The renewable sector, although underrepresented in the examined sample, implies potential for high profitability. The profitability of power companies is affected by the ownership of assets with low operating costs and by diversification of operations, including regulated network operations. Eastern European power companies are favored by the derogation of EU regulation, though provision of free emission allowances.

The Generation Expansion Planning (GEP) stands as one as one of the most discussed topics within the academia and decision makers in the energy sector, especially related to meeting deep emission reduction targets. Every country, aiming at decarbonizing its economy, focuses on the application of policies that could enhance the penetration of Renewable Energy Sources (RES) in its power capacity mix. GEP is a complex task, combining techno-economic, financial, spatial and environmental characteristics. Several models are developed to model GEP, applying different methodological approaches. The underlying theory is very important as it might inherit bias in the resulted outcomes. The debate on the appropriateness of each methodology is increased, especially as projected outlooks deviate from reality. The paper aims to provide a review of the models employed to integrate RES in the GEP. The paper classifies models in three generic categories: optimisation models, general/partial equilibrium models and alternative models, not adopting the optimum integration of RES in the GEP. It provides insights on the characteristics, advantages and disadvantages of the theoretical approaches implemented, as well on their suitability for different aspects of the problem, contributing in the better understanding on the expected outcomes of each methodology.

The integration of electric vehicles (EVs) in the power system is a challenging issue for the power system and network operators. The paper uses several Unit Commitments (UC) models which incorporate high levels of wind power production, applying different methods to tackle the renewables’ uncertainty. The selected power system is IEEE RTS 96. The UC models are further extended to integrate the EVs. Our focus is to assess the EVs impact on the total operating cost and the power grid adequacy to handle the extra load, by examining different charging profiles and penetration levels of EVs with the different UC models. Simulation results show that an optimized charging strategy is considerably more efficient than the random charging strategy, both in the total operating cost and the ability to integrate more EVs. The comparison between the UC models show that the most robust UC model leads to higher total operating cost, due to its more conservative methodology to tackle the stochastic nature of wind. There exists a non-linear trade-off between power system robustness and the total operating cost, depending on each power system characteristics, affecting also the penetration level of EVs.Keywords: Electric Vehicles, Unit Commitment, Renewables, Uncertainty, Power System, IEEE RTS 96JEL Classifications: Q47, L94DOI: https://doi.org/10.32479/ijeep.7125

The abundance of renewable energy in Ghana can play an important role in the rural electrification program rolled out by the Government of Ghana to promote energy that everyone can assess for improved living standards. It has the potential to meet the objectives of the energy sector which include; 10% renewable energy in the total generation mix, minimize the adverse effects of energy production on the environment, reduce poverty, and improve the socio-economic development of the country, mainly, in rural communities, creating community-based employment, etc. In this study, Nkrankrom was selected as a case study to evaluate the possibility of meeting their energy needs with a solar mini-grid. To determine the energy demand of the community, a structured questionnaire was used to collect data. The HOMER software was used to perform the financial viability of utilizing the solar resource available. The result shows that it is possible to meet the energy demand of the community from the solar resource available. The proposed system configuration included PV/Battery/Converter with a Levelized Cost of Energy of $0.107/kWh compared to $0.124/kWh (using $1 = GH₵4.98 rate), if the area is to be connected to the national grid. The breakeven distance or Electric Distance Limit (EDL) between standalone mini-grid and grid extension in this analysis was found to be 1.11 km. The study also revealed that solar mini-grid could have an immense benefit to the community both economically and socially such as improve the standard of living as well as meeting the rural development objectives of Ghana.

The gas developments in Eastern Mediterranean facilitate discussions on potential regional cooperation, but as well as on strengthening political tensions. Although energy can be seen as a facilitator on resolving regional political disputes, recent illegal drilling activities by Turkey on the Exclusive Economic Zone of Cyprus offset any relevant progress. On the other hand, regional activities, such as the establishment of the Eastern Mediterranean Gas Forum by the energy ministers of seven countries, pave the way on the formation of regional institutional capability for cooperation on the energy issues. This chapter aims at presenting the European perspective on the energy developments in Eastern Mediterranean and South East Europe. It presents key elements of the European energy policy, highlighting the importance of Projects of Common Interest. However, the chapter comments on the suitability of Cost Benefits Analysis, providing a review of alternative approaches and proposing an optimization model for an integrated natural gas and power systems planning. Such model, requiring comprehensive information on the European energy system, would be able to provide a robust assessment of critical energy projects. It would be able to identify if there are bottlenecks among the different regions in the EU and providing useful insights to the decision makers, involved companies and market participants on the bankability and viability of critical infrastructure.

Market concentration is a sensitive and contradicting issue in power and natural gas markets. This issue concerns the whole European competition policy, as it concerns liberalization process, considers assets with state aid support schemes and covers antitrust and mergers cases. Market concentration is usually tackled under Competition Authorities, through ex-post and ad-hoc evaluation of each case. This creates an uncertainty on whether a market participant is considered to have dominant position in a market, as well as on what and when is allowed for a participant to do concerning its bidding and tariffs formation strategies. The Directorate General for Competition in Europe considers that “if a company has a market share of less than 40%, it is unlikely to be dominant”, however there is not specific threshold which identifies a dominant position. On the other hand, National Regulatory Authorities for Energy are responsible for the regulation and market monitoring of power and natural gas markets, however they do not tackle market concentration issues with a coherent and permanent methodology. This chapter describes an ex-ante Market Monitoring and Regulation Mechanism for Market Concentration in Power and Natural Gas Markets. The mechanism concerns the available capacity in both supply and demand sides. In the supply side, it estimates the available capacity of all market participants, considering the capacity per resource type (i.e., lignite, natural gas, large hydro, renewables), market participation type (i.e., FiT, FiP, commissioning), interconnection (entry) point type and existence of bilateral contracts. It imposes a common threshold, i.e., 40% or 50%, for both aggregate and disaggregate markets, namely capacity of each resource/entry point type. In the demand side, it considers load, storage and pumping as well as export interconnections. However, regulation can exclude resource/entry point types with low capacity under another threshold, i.e., disaggregate market up to 2%/2.5% or 4%/5% of the aggregate market, depending on the values of the second threshold. The mechanism also considers the relative size of market participants in the supply and demand side, implementing a fourth threshold, i.e., at 1% or 2/2.5%, depending on the value of the third threshold. The mechanism is a coherent and permanent mechanism. The mechanism provides indicative results concerning the Hellenic power market. It can assist Energy Regulators to design clear rules on tackling market concentration ex-ante, to be implemented by the Transmission System Operators.

This work presents a generic mixed integer linear programming model to determine the optimal energy and reserves scheduling of a renewable-based multi-zonal power system. In particular, through a detailed unit commitment model implementing a co-optimization of energy and reserves market with a cost minimization objective function, the developed approach determines the optimal annual energy and reserves mix of the Romanian power system in a future year (2040). The model outputs highlight the impacts in the power system in terms of operational, economic, and environmental performance. The developed optimization framework enables the provision of useful insights on the determination of the optimal transition roadmaps, by highlighting the influences and the challenges of each phase.

An accurate fuel consumption prediction system for transportation units is the pillar that a more efficient fuel management can rely on. This in turn may eventually lead to cost and emission savings for the unit&rsquo, s owner. Numerous studies have been conducted for predicting the fuel usage in various means of transportation (i.e., airplanes, trucks, and vehicles). However, there is a limited number of researches that focus on passenger ships. These researches involve traditional machine learning models. There is a lack of literature on deep-learning-based forecasting models. The present paper serves as an initial study for exploring the potential of deep learning in day-ahead fuel consumption on a passenger ship. Firstly, a discussion is provided for the parameters that influence the fuel consumption. Secondly, the day-ahead fuel forecasting problem is formulated. To fully examine the influence of exogenous parameters on the consumption, various scenarios are formulated that differ in the types and number of inputs. The proposed forecasting model combines shallow and deep learning. Several machine learning and time series models were compared, and the results indicate the robustness of the proposed approach.

This paper provides an econometric analysis of Saudi Arabia's crude oil strategy, as related to market fundamentals and macro-economic factors. Specifically, we develop three alternative econometric models over the period 1980–2017. The empirical findings provide sufficient evidence on the drivers of Saudi Arabia's crude oil production strategy. The results show that the kingdom of Saudi Arabia has a long-term supply sharing strategy, resilient to short-run price fluctuations. Saudi Arabia's strategy, aims at meeting about half of the world demand increase, both in the long and the short run, leaving limited space to the rest of the world producers. Saudi Arabia's production strategy is slightly affected by the OECD stocks, only in the short-run, and it is rather competitive. We argue that its production is inelastic to world crude oil demand fluctuations. Saudi Arabia's production lends support to the trade-off theory, since the kingdom produces more crude oil to defend its total petroleum exports. Lastly, we argue that Saudi Arabia adjusts its production strategy away from the optimal production level, in order to meet wider policy goals, such as the preservation of its petroleum exports and its market share in the global crude oil market.

The European electricity markets’ integration aims at the market coupling among interconnected power systems and the enhancement of market competitive forces. This process is facilitated by the adoption of a common clearing algorithm among European power exchanges, entitled EUPHEMIA (Pan-European Hybrid Electricity Market Integration Algorithm), which however lacks to capture critical technical aspects of power systems, as done by the unit commitment problem including start-up and shut-down decisions, time constraints (minimum on- and off-times), as well as the consideration of ancillary services. This paper presents an optimization-based framework for the optimal joint energy and reserves market clearing algorithm, further utilizing the hourly offers module of the EUPHEMIA algorithm. In particular, through the formulation of a mixed integer linear programming (MILP) model and employing an iterative approach, it determines the optimal energy and reserves mix, the resulting market clearing prices, and it calculates the welfares of the market participants. The model incorporates intra-hourly power reserve constraints, as well as introduces new market products such as the option of forming linked groups of power units, aiming at supplying additional flexibility in the decision-making of the market participants. The model applicability has been assessed in the Greek power system and its interconnections with neighboring power systems in Southeast Europe. The proposed optimization framework can provide useful insights on the determination of the optimal generation and interconnection portfolios that address the new market-based operational challenges of contemporary power systems subject to technical and economic constraints.

The integration of European electricity markets aims at market coupling among interconnected power systems and the evolution of environmentally friendly technologies. This process is anticipated to utilize more efficiently the flexible generation and interconnections transmission capacity and provide environmental and economic benefits to final consumers. This paper presents a mixed integer linear programming model for the optimal scheduling of a power system (unit commitment problem) simulating the day-ahead electricity market. The model determines the optimal daily power generation mix, the electricity trade with neighboring countries, the evolution of the system's marginal price and the resulting environmental impact. The model incorporates CO2 emissions intensity constraints and introduces flexible ramping products, in addition to reserve requirements, aiming to identify their impacts on both operational and economic decisions. The model is applied on the Greek power system and its interconnections with neighboring power systems in Southeast Europe. The proposed approach can provide useful insights on the optimal generation and interconnections portfolio that meets the real electricity market operating needs of contemporary power systems with environmental and ramping capacity constraints.

The long-term Generation Expansion Planning (GEP) problem determines the optimal type of energy technologies, size, location, and time construction of new power generation plants, while minimizing total cost over a long planning horizon and being subject to a series of constraints. Due to its complex nature, its effective implementation requires the consideration of a wide range of aspects including economic, environmental, regulatory, technical, operational, social, as well as potential interdependencies with other complementary sectors. As a consequence, the traditional cost-based approaches have been extensively modified and updated, leading to more advanced ones including, at least partially, some of the above described aspects. This work provides a comprehensive review of the most recently developed approaches dealing with the Generation Expansion Planning problem from a variety of perspectives, organizing them into seven key categories including the interaction of generation expansion planning with: the transmission expansion planning, natural gas system, short-term operation of power markets, electric vehicles, demand-side management and storage, risk-based decision-making, as well as with applied energy policy including security of supply. The main goal of this work is to stress the multi-dimensionality of the generation expansion planning execution, creating the need for an in-depth investigation and consideration of synergies with other complementary sectors. Reviewing results have the objective of providing useful insights into the current state and future challenges of the GEP decision-making.

This article explores the phenomenon of the enduring state control over the Russian natural gas sector. We suggest that explanations of the underlying motives can be classified under two broad theo...

This work aims to detect the impact of fundamentals and regulatory reforms on the Greek Wholesale Electricity Market, applying SARMAX/GARCH models. The System Marginal Price (SMP) is considered a stochastic, nonlinear process, reflecting not only the effects of endogenous/fundamental market factors but also the effects of exogenous variables including regulatory reforms, which also affect the market dynamics. To capture the dynamics of the conditional mean and volatility of SMP, a number of SARMAX/GARCH models have been estimated using as regressors an extensive set of fundamental factors in the Greek Electricity Market (GEM), as well as dummy variables that mimic the history of Regulator's interventions. The best-found model captures adequately the dependency of the spot price to the regulatory reforms. The findings reassure the typical sign and the magnitude of the effect of fundamentals, and detects successfully the impacts of the reforms. The most interesting finding is that the GEM does not exhibit asymmetries or leverage effect, in the volatility of its wholesale price, as the most European markets do. The outcome of this paper can be useful to a wide variety of GEM's participants and specifically to the decision makers in GEM.

The paper aims to quantify the impact of the penetration of renewables on the flexibility needs and their price signal. It uses a generic Mixed Integer Linear Programming (MILP) model that integrates long-term power system planning with a Unit Commitment (UC) model, which performs the simulation of the Day-Ahead Electricity Market (DAEM). The integrated model evaluates the need of flexibility services, under different conditions of renewable penetration. A case study of the Greek interconnected electric system is examined. Results show that the main flexibility needs concern photovoltaics causing the sunset effect, while the needs from stochastic wind are alleviated from the fact that wind output is de-linked from the demand evolution and that wind installations’ positions are diversified. The identification of flexibility needs from the Transmission System Operators (TSOs) require detailed data to depict the spatial and technical characteristics of each power system, which can reveal that ramping rates, and not just the magnitude of ramping capacity, can be an important flexibility requirement, due to large single-hour ramp contribution in some months. Such an analysis can also reveal the options for increasing flexibility, which are power system specific.

There is lot of discussion on the level of the oil dependence of the Russian economy, as well as on whether the Russian Federation presents signs of the Dutch disease or even if already suffers by it. In this paper, we develop econometric models for examining the oil dependence of the Russian economy. We construct two VARs and then we proceed with VECMs models. The models consider macroeconomic factors, such as industrial production index, unemployment, GDP and government expenditure, as well as oil factors, such crude oil price and Russian oil production. We employ impulse response functions to catch the interactions among variables. We find strong evidence on the oil dependence of the Russian economy; however, we do not find firm established proof of the Dutch disease.Keywords: Russia, oil, cointegrationJEL Classifications: Q43, C01, P48

This paper introduces a model that integrates a Unit Commitment (UC) model, which performs the simulation of the day-ahead electricity market, combined with an econometric model that estimates the income and price elasticities of electricity demand. The integrated model is further extended to estimate the retailers’ profitability with demand responsive consumers. The applicability of the proposed model is illustrated in the Greek day-ahead electricity market. The model is designed to identify the effects of demand responsiveness to the fluctuations of spot prices, based on their short-term price elasticities. It provides price signals on the profitability of retailers/demand aggregators, when forming their tariffs. We argue that the non-linearity between demand response and evolution of wholesale price, inherits risk for retailers. This finding could lead even to losses for some time periods, affecting strongly their viability. The model provides useful insights into the risk of retailers from their price responsive customers and therefore acts as a pivotal study to policy makers and government officials (i.e. regulators, transmission and distribution system operators) active in the electricity market.

This paper presents an integrated model for risk management of electricity traders. It integrates the Unit Commitment (UC) problem, which provides the power generation units' dispatch and the electricity price forecasting of a power system, with artificial neural network (ANN) models, which provide electricity price forecasting of a neighbouring power system by incorporating a clustering algorithm. The integrated model is further extended to estimate the traders' profitability and risk, incorporating risk provisions. The integrated model is applied in bi-directional trading between the Italian and Greek day-ahead electricity markets. The UC and neural network models provide forecasts of the wholesale electricity price in Greece and Italy respectively. The model attributes a confidence level of the price forecasts, depending on the data clustering and the forecasting performance of each model. The integrated model identifies periods with high price margins for trading for each power flow, aligned with a forecasting confidence and a risk level. The integrated model can provide price signals on the profitability of traders and useful insights into the risk of traders.

Load profiling refers to a procedure which leads to the formulation of daily load curve and consumer categories regarding the similarity of their curves shapes. This procedure incorporates a set of pattern recognition algorithms. While many crisp clustering algorithms have been purposed for grouping load curves into classes, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM). Since the benefits of the soft clustering is demonstrated in a variety of applications, we examine the potential of introducing soft clustering algorithms in the electricity demand patterns segmentation. This paper introduces in the load profiling studies, two soft clustering algorithms which have been already used in other clustering problems, namely the Possibilistic C-Means (PCM) and the Gustafson-Kessel Fuzzy C-Means (GKFCM). A detailed comparison takes places between the algorithms and their performance is checked by a set of adequacy measures that have been proposed in the load profiling related literature and by a set of traditional fuzzy clustering validity measures.

Accurate forecasts of natural gas demand can be essential for utilities, energy traders, regulatory authorities, decision makers and others. The aim of this paper is to test the robustness of a novel hybrid computational intelligence model in day-ahead natural gas demand predictions. The proposed model combines the Wavelet Transform (WT), Genetic Algorithm (GA), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Feed-Forward Neural Network (FFNN). The WT is used to decompose the original signal in a set of subseries and then a GA optimized ANFIS is employed to provide the forecast for each subseries. ANFIS output is fed into a FFNN to refine the initial forecast and upgrade the overall forecasting accuracy. The model is applied to all distribution points that compose the natural gas grid of a country, in contradiction to the majority of the literature that focuses on a limited number of distribution points. This approach enables the comparison of the model performance on different consumption patterns, providing also insights on the characteristics of large urban centers, small towns, industrial areas, power generation units, public transport filling stations and others.

This paper presents a generic Mixed Integer Linear Programming (MILP) model that integrates a Mid-term Energy Planning (MEP) model, which implements generation and transmission system planning at a yearly level, with a Unit Commitment (UC) model, which performs the simulation of the Day-Ahead Electricity Market. The applicability of the proposed model is illustrated in a case study of the Greek interconnected power system. The aim is to evaluate a critical project in the Ten Year Network Development Plan (TYNDP) of the Independent Power Transmission System Operator S.A. (ADMIE), namely the electric interconnection of the Crete Island with the mainland electric system. The proposed modeling framework identifies the implementation (or not) of the interconnection of the Crete Island with the mainland electric system, as well as the optimum interconnection capacity. It also quantifies the effects on the Day-Ahead electricity market and on the energy mix. The paper demonstrates that the model can provide useful insights into the strategic and challenging decisions to be determined by investors and/or policy makers at a national and/or regional level, by providing the optimal energy roadmap and management, as well as clear price signals on critical energy projects under real operating and design constraints.

Traditionally, short-term electricity price forecasting has been essential for utilities and generation companies. However, the deregulation of electricity markets created a competitive environment and the introduction of new market participants, such as the retailers and aggregators, whose economic viability and profitability highly depends on the spot market price patterns. The aim of this study is to examine artificial neural network (ANN) based models for Day-ahead price forecasting. Specifically, the models refer to the sole application of ANNs or to hybrid models, where the ANN is combined with clustering algorithm. The training data are clustered in homogenous groups and for each cluster, a dedicated forecaster is employed. The proposed models are characterized by comprehensive operation and by high level of flexibility; different inputs can be taken under consideration and different ANN topologies can be examined. The models are tested on a data set that consists of atypical price patterns and many outliers. This approach makes the price forecasting problem a more challenging task, providing evidence that the proposed models can be considered as useful and robust forecasting tools to the actual needs of market participants, including the traditional generation companies and self-producers, but also the retailers/suppliers and aggregators.

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 increasing interactions and interdependences of the wholesale and retail markets in the power sector have created the need for development of integrated approaches for the optimal portfolio management of vertically integrated utilities, aiming at drastically limiting their risk exposure. This work presents a generic mixed integer linear programming (MILP) model for the optimal clearing of a wholesale power market including market products widely used in power exchanges such as block and hourly orders. Based on the market clearing results, it then calculates the economic balance of vertically integrated utilities participating in retail markets, covering the whole value chain in the power sector. By considering a wide range of technology options in the wholesale market, including fossil fuel-based thermal units with or without carbon capture and sequestration capability (CCS), nuclear power, renewable energy, electricity trading and storage options, as well as different types of consumers in the retail market (low, medium and high voltage), the model determines the optimal electricity generation mix, the system’s marginal price and its environmental performance, as well as the net economic position of each power utility in both wholesale and retail markets. A series of sensitivity analyses on the CO2 emission pricing, the renewables’ penetration, and the applied environmental policy has been conducted to investigate the influence of several economic, technical and policy parameters on the operational scheduling and financial planning of each utility. The model applicability has been assessed in an illustrative case study of a medium-sized power system considering also its interconnections with neighboring power systems. The model results quantify the risk facing electric utilities participating in both wholesale and retail markets under several market conditions and with different schemes on the generation side, as well as with different representation shares on the demand side. The proposed optimization framework can provide useful insights on the determination of the optimal integrated generation and retail portfolios that address the new market operating challenges of contemporary power systems, subject to several technical and economic constraints, enabling also the design of medium-term operational strategies for vertically integrated power utilities.

We conduct an econometric analysis to investigate, the carbon pass-through in the Greek electricity sector. For this reason, we utilize a rich micro-level panel dataset, including hourly data for 24 thermal power plants spanning the period from January 2014 to December 2017. In order to study the pass-through of emissions costs to wholesale electricity prices, we employ an instrumental variable approach. Our findings survived several robustness checks, accounting for logged linear and non-linear econometric specifications. The empirical results indicate the existence of an almost complete pass-through, revealing that retailers fully internalize the cost of CO2 permits. This study incurs important policy implications, since the complete pass-through signify that wholesale electricity prices will increase at least in the short run. However, the overall effect on retail prices will be mostly affected by the amount of CO2 permits and the fuel energy mix.

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.

The number and types of inputs of the prediction system is crucial to a load forecasting task. In many cases, the number of inputs is increased, a fact that may lead to poor forecasting accuracy. The scope of the present paper is to examine the effect of dimensionality reduction methods in the forecasting performance of Feed Forward Neural Networks (FFNN). The test case refers to the day-ahead forecasting of the loads connected to a high-to-medium voltage transformer of the transmission system of Greece.

The European Union formed a strategy with the Energy Union towards the delivery of secure, competitive, and sustainable energy. The Energy Union targets at a fully integrated internal energy market, which requires infrastructure, leaving market fundamentals to drive energy pricing. The European Union swifts to natural gas as its primary energy source. So far the natural gas supply has been conducted via inter-state pipelines and priced through oil-indexed contracts. In our research, we study whether there are price spillovers from oil to natural gas, and vice versa. This paper studies the oldest gas virtual trading point in Europe, National Balancing Point. It is considered as one of the most liquid and transparent gas markets. In order to fully cover every aspect, we use Wald tests, cointegration tests, asymmetric price transmission methodology, and Diebold and Mariano tests. We reach conclusions on commodities’ independence and we present their time varying relationship.

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.

Day-ahead peak load forecasting is an essential tool for generation units scheduling, unit commitment and generally, in power systems operation in short-term horizon. The scope of the present study is to develop a robust peak load forecasting model for the power system of Greece. The peak load series is decomposed via the Discrete Wavelet Transform (DWT) into low-frequency and high-frequency subseries in the wavelet domain. For each subseries the Support Vector Regression (SVR) model is trained and applied. The final peak load series is obtained by the inverse DWT. The proposed approach corresponds to low execution time and robust performance and thus, can be a valuable tool for utilities, systems operators and others.

Demand Side Management (DSM) refers to a set of measures targeting at load modifications in periods of high electricity generation costs, supply shortage and grid" s techno-economic constrains. This paper proposes a novel 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 an ideal tool for Retailers" decision making framework in day-ahead competitive electricity markets.

The paper aims at assessing the impact of the economic crisis on energy poverty in Greece. It monitors the electricity consumption per capita, its relationship with the economic growth and its comparison with other European countries. Moreover, the paper provides new indicators and information, monitoring data related to the capability of people to pay their electricity bills, the power cuts made due to the economic crisis and the social policy of the government for sensitive social groups. Results show that the standard of living in Greece has been increased considerably compared to other countries and that people require time to respond to the new economic conditions and to change their habits. It provides evidence that the economic crisis has considerable effect on the electricity consumption and on the capability of people to pay their bills. However, the power cuts depict mainly the unwillingness of customers to continue paying bills for properties that they do not use or do not provide any revenue for them. The incapability of customers to pay the electricity bills on time, create serious liquidity problem for the Public Power Corporation, which enables the danger of transforming an energy poverty issue to an energy security issue.

There is an urgent need to limit greenhouse gas emissions from cities if ambitious mitigation targets are to be met. On the other hand the economic crisis and the ambiguous relationship of inequality with economic growth have raised the issue of energy poverty. The need to connect economic activity with employment, energy poverty, climate change is becoming increasingly recognised. This paper describes the socioeconomic–energy–environmental component of an urban integrated assessment facility developed by the Tyndall Centre for Climate Change Research, which simulates socio-economic change, energy demand, climate impacts and greenhouse gas emissions over the course of the twenty first century at the city scale. The research is focussed upon London, UK, a city that has taken a lead role in the UK and globally with respect to energy poverty and climate protection. The paper demonstrates, through the implementation of several scenarios, quantifiable synergies and conflicts between economic development, employment and energy poverty in order to improve decision making in achieving sustainable and equality outcomes for cities.

This paper presents a mixed-integer linear programming (MILP) model for the optimal long-term energy planning of a (national) power generation system. In order to capture more accurately the spatial and technical characteristics of the problem, the underlying geographical area (country) is divided into a number of individual networks that interact with each other. The proposed model determines the optimal planning of the power generation system, the selection of the power generation technologies, the type of fuels and the plant locations so as to meet the expected electricity demand, while satisfying environmental constraints in terms of CO2 emissions. Furthermore, the suggested model determines the electricity imports from neighbouring countries, the electricity transmission as well as the transportation of primary energy resources between domestic networks. A real case study concerning the Greek energy planning problem demonstrates the applicability of the proposed approach, which can provide policy makers with a systematic computer-aided tool to analyse various scenarios and technology options. Finally, a sensitivity analysis was conducted in order to capture the influence of some key parameters such as electricity demand, natural gas and CO2 emission price as well as wind power investment cost.

This paper attempts to cast light into the relationship between electricity consumption and economic growth in Greece in a multivariate framework. For this purpose we used cointegration techniques and the vector error correction model in order to capture short-run and long-run dynamics over the sample period 1970–2011. The empirical results reveal that in the long-run electricity demand appears to be price inelastic and income elastic, while in the short-run the relevant elasticities are below unity. We also argue that the causal relationship between electricity consumption and economic growth in Greece is bi-directional. Our results strengthen the notion that Greece is an energy dependent country and well directed energy conservation policies could even boost economic growth. Furthermore, the implementation of renewable energy sources should provide significant benefits ensuring sufficient security of supply in the Greek energy system. This evidence can provide a new basis for discussion on the appropriate design and implementation of environmental and energy policies for Greece and other medium sized economies with similar characteristics.

The international negotiations concerning climate change, taken place during the UNFCCC conference in Durban at the end of year 2011, have failed to establish a new global agreement to reduce global emissions. Therefore, the G8 commitments on 80% reduction by 2050 seems to be the most realistic climate change mitigation framework for the time being, enhanced by the political will of the EU and USA administrations. For the needs of this paper, the G8 80% target is further extended to cover the whole EU-27 region, where the reduction commitments of the EU-27 member states are allocated based on the relevant allocation weights considered for the Kyoto Protocol obligations. This paper examines the implementation of the EU-27 and USA 80% emissions reduction target using a macro-economic hybrid model E4M-GAIA of the global economy, standing for Energy-Economy-Environment-Engineering Model of the Earth. The E4M-GAIA model, which adopts similar theoretical background with the “New Economics” school depicted mainly in the well-established Cambridge University’ E3 models, is used to implement this target and to compare it with a reference scenario, where no reduction target is pursued. Both scenarios consider that impact of the financial crisis, with updated information to the end of 2010. This paper aims to provide evidence that the proper direction of a portfolio of policies including: regulation, behavioral shift, revenue recycling, energy investments, energy and carbon pricing, can lead to double dividend, namely meeting a deep reduction target and providing gains for the economy.

Cities are faced with a number of sustainability challenges in the context of climate change. There is an urgent need to limit greenhouse gas emissions from cities if ambitious mitigation targets are to be met. Meanwhile, cities are vulnerable to the impacts of climate change unless adaptation plans can be put in place. The need to connect climate change adaptation and mitigation with broader assessment of sustainability is becoming increasingly recognised. This paper describes an urban integrated assessment facility developed by the Tyndall Centre for Climate Change Research, which simulates socio-economic change, climate impacts and greenhouse gas emissions over the course of the twenty first century at the city scale. The urban integrated assessment facility adopts a broad-scale systems approach to urban development and sustainability assessment. It incorporates a multi-sectoral model of the regional economy, hierarchical city-scale spatial interaction model and modules for assessment of climate impacts, adaptation options and greenhouse gas emissions. The paper demonstrates how the urban integrated assessment facility quantifies synergies and conflicts between adaptation to climate change and mitigation of greenhouse gas emissions in order to improve decision making and facilitate the development of portfolios of planning policies that together have a realistic prospect of achieving sustainable outcomes for cities.

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.

This paper examines the macroeconomic rebound effect for the global economy arising from energy-efficiency policies. Such policies are expected to be a leading component of climate policy portfolios being proposed and adopted in order to achieve climate stabilisation targets for 2020, 2030 and 2050, such as the G8 50% reduction target by 2050. We apply the global “New Economics” or Post Keynesian model E3MG, developing the version reported in IPCC AR4 WG3. The rebound effect refers to the idea that some or all of the expected reductions in energy consumption as a result of energy-efficiency improvements are offset by an increasing demand for energy services, arising from reductions in the effective price of energy services resulting from those improvements. As policies to stimulate energy-efficiency improvements are a key part of climate-change policies, the likely magnitude of any rebound effect is of great importance to assessing the effectiveness of those policies. The literature distinguishes three types of rebound effect from energy-efficiency improvements: direct, indirect and economy-wide. The macroeconomic rebound effect, which is the focus of this paper, is the combination of the indirect and economy-wide effects. Estimates of the effects of no-regrets efficiency policies are reported by the International Energy Agency in World Energy Outlook, 2006, and synthesised in the IPCC AR4 WG3 report. We analyse policies for the transport, residential and services buildings and industrial sectors of the economy for the post-2012 period, 2013–2030. The estimated direct rebound effect, implicit in the IEA WEO/IPCC AR4 estimates, is treated as exogenous, based on estimates from the literature, globally about 10%. The total rebound effect, however, is 31% by 2020 rising to 52% by 2030. The total effect includes the direct effect and the effects of (1) the lower cost of energy on energy demand in the three broad sectors as well as of (2) the extra consumers’ expenditure from higher (implicit) real income and (3) the extra energy-efficiency investments. The rebound effects build up over time as the economic system adapts to the higher real incomes from the energy savings and the investments.