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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...

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.

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.

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.

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.

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.

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 liberalization of the Greek electric market (Law 2773/99, updated with Laws 3175/2003 and 3426/2005 for incorporating Directive 2003/54 into the Greek legislation) is in its final structural transformation, which includes the fact that from 1.7.2007 each customer can select its electricity provider. This new status together with the procedure towards the formation of a post-Kyoto plan, raise the need of examining different energy saving strategies in the consumption side for evaluating their economic and environmental consequences. Such strategies may be useful for the decision makers or the electricity retail companies. This paper examines the influence of several post-Kyoto electricity consumption strategies in the Greek interconnected electric system for the period 2005–2025. The aim of the paper is to be used as a decision makers’ tool for investigating the potential of electricity consumption policies. The results show that policies related either to seasonal peak demand control, or targeting at the total electric consumption lead to significant gains and emission reduction. Moreover the influence of factors, such as the weather conditions, the discount rate of the energy investments, the fuel prices evolution and the consumers’ behavior linkage with oil prices are examined.

The obligations resulting from the Kyoto Protocol (KP) are implemented by many participants, e.g. Greece, by a combination of two methods: (a) application of regional measures which restrict CO2 emissions and (b) procurement of green certificates e.g. in a Climate Exchange Market (CEX). Therefore, the cost for compliance with the KP depends on the extent each method is used and also on the traded values in the CEX. The energy policy and planning to be considered are long-term items and now extension of the KP to a post-KP is discussed which reaches year 2020. In Greece, the electricity sector is with a weighted CO2 contribution of 73%, the dominantly emission sector. The paper analyzes the cost and other merits of different scenarios for the expansion of electrical power system in Greece. For different scenarios, the total cost of the electrical power system expansion is calculated as a function of the price of emission certificates. It has been shown that there is a price of 27.5€/tCO2, above which specific mitigation measures may lead to cost optimum solutions. A sensitivity analysis is also presented concerning the variation of key parameters like the participation in the system expansion of new supercritical coal units, the price evolution of natural gas, the RES usage rate and the discount rates of the expansion investments. Results may be of interest for a decision on the cost optimum electrical power system expansion.

With the exception of Albania, the countries of South-Eastern Europe (SEE) have high shares of electricity generation from an ageing fleet of coal-fired power units with quite low efficiencies. Energy planning-related decision-making regarding whether to modernize or replace a significant share of this old generating capacity has to be fixed within a global clean energy transition context. At the same time, the SEE region has a huge potential for renewable energy deployment and energy efficiency implementation. This work presents a generic mixed integer linear programming model to determine the optimal energy scheduling of a multi-zonal power system following the roadmaps of its energy transition process. 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 mixes of the Romanian power system through the day by day iterative solution of all the dates of three specific time milestones (2020, 2030, and 2040), where the outputs of a specific date comprise the inputs of the next one. The model outputs highlight the impacts on the power system in terms of technical, economic, and environmental performance. The energy transition towards a renewables dominant capacity and production mix guarantees the system’s security of supply, and improves noticeably its environmental performance, reporting a significant decrease in the amount of resulting CO2 emissions. With regard to the economic impacts, the wholesale price follows an increasing trend due to the parallel increase of the CO2 emission pricing, on the grounds that natural gas-fired units and electricity interconnections set the system’s price during most of the hours of all dates and years. The developed optimization framework enables the provision of useful insights on the determination of the optimal energy roadmaps, by highlighting the influences and the challenges of each phase.