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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 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 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 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 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 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.