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Under the current European market environment, transmission companies have to decide network expansion by maximizing social welfare. However, generation companies (GENCOs) decide their capacity expansion with the aim of maximizing their own profit. This process, in addition to the increasing penetration of renewable energy, storage and distributed generation, might represent a rupture between short and long-term signals. Therefore, this paper proposes a bi-level formulation for the generation and transmission coordination problem (GEPTEP). We consider a proactive framework in which a centralized TSO represents the upper level while the decentralized GENCOs, that trade in the market, represent the lower level. As a novel feature, an enhanced representative period framework is employed, which allows us to consider operation in both short and long-term storage technologies. A case study is presented to compare the results between perfect and imperfect competition in the market. • A two-level proactive transmission expansion model is developed. • A conjectured-price framework is applied to compare perfect and oligopolistic behavior. • Short and long-term storage constraints are jointly considered (Hydro and Batteries). [ABSTRACT FROM AUTHOR]

Combined cycle gas turbine power plants (CCGTs) play a key role in modern electric power systems due to their operational flexibility and the firmness they provide to the networks where they operate. Due in part to this flexibility and firmness, the function of CCGTs has experienced a significant evolution with the advent of intermittent renewable energy sources such as wind: CCGTs are increasingly required to rapidly vary load levels in order to counterbalance the fluctuations of renewable energy sources and satisfy overall system demand. The purpose of this paper is to develop a methodology that can be used for assessing the optimal operational strategy of a CCGT when subjected to cycling requirements. This is achieved by introducing a unit commitment formulation that takes into account useful life expenditure costs due to fatigue damage accumulation associated to cycling. The case studies presented in this paper show that neglecting fatigue cycling costs when making operational decisions generates suboptimal cost and profit outcomes for CCGT operators. Furthermore, it is shown that profitability of CCGTs is compromised in systems with very significant penetration of intermittent renewable energy sources, due to the increase in cycling costs derived from continuous load-following operation. [ABSTRACT FROM AUTHOR]

We propose a multi-objective bi-level optimization model for analyzing the different investment options (in natural gas pipelines and regasification terminals) within the EU framework under a market perspective, considering the different interest of market participants and the multiple criteria that need to be achieved simultaneously (i.e. market integration, security of supply and competition). The model consists of the objectives of the network planner at the upper level optimizing a multi-objective function and a lower level that represents the downstream European gas market. The model is used for the assessment of the optimal infrastructure investment in the North-South Gas Interconnections in Western Europe. • Analysis of the different investment options in pipelines and regasification capacity. • Natural sequence of investment and operation decisions in a gas market model. • Multi-objective model for evaluating expansion plans under different criteria. • Assessment of the optimal infrastructure investment in Western Europe. [ABSTRACT FROM AUTHOR]

Short-term energy storage systems, e.g., batteries, are becoming one promising option to deal with flexibility requirements in power systems due to the accommodation of renewable energy sources. Previous works using medium- and long-term planning tools have modeled the interaction between short-term energy storage systems and seasonal storage (e.g., hydro reservoirs) but despite these developments, opportunity costs considering the impact of short-term energy storage systems in stochastic hydrothermal dispatch models have not been analyzed. This paper proposes a novel formulation to include short-term energy storage systems operational decisions in a stochastic hydrothermal dispatch model, which is based on a Linked Representative Periods approach. The Linked Representative Periods approach disposes of both intra- and inter-period storage constraints, which in turn allow to adequately represent both short- and long-term storage at the same time. Apart from the novelty of the model formulation itself, one of the main contributions of this research stems from the underlying economic information that can be extracted from the dual variables of the intra- and inter-period constraints, which allows to derive an hourly opportunity cost of storage. Such a detailed hourly economic value of storage has not been proposed before in the literature and is not possible in a classic Load Duration Curve model that does not adequately capture short-term operation. This advantage is reflected in the case study results. For instance, the model proposed in this paper and based on Linked Representative Periods obtains operating decisions of short-term energy storage systems with errors between 5% and 10%, while the classic Load Duration Curve approach fails by an error greater than 100%. Moreover, the Load Duration Curve model cannot determine opportunity costs on an hourly basis and underestimates these opportunity costs of hydro (also known as water value) by 6%–24% for seasonal hydro reservoirs. The proposed Linked Representative Periods model produces an error on the opportunity cost of hydro units lower than 3%. Hourly opportunity costs for short-term battery energy storage systems using dual variables from both intra- and inter-period storage balance equations in the proposed model are also presented and analyzed. The case study shows that the proposed approach successfully internalizes both short- and long-term opportunity costs of energy storage systems. These results are useful for planning and policy analysis, as well as for bidding strategies of ESS owners in day-ahead markets and not taking them into account may lead to infeasible operation or to suboptimal planning. • Representation of short-term operational decisions in hydrothermal dispatch models. • Integration of short-term storage (e.g., batteries) drives this representation. • This paper compares two models: classic Load Duration Curve vs Linked-Representative Periods. • The Linked-Representative Periods model can determine hourly opportunity costs for short-term energy storage. • These types of results cannot be obtained in the classic Load Duration Curve model. [ABSTRACT FROM AUTHOR]

This paper assesses the difference of optimal operation of energy communities (ECs) with respect to economic and technical goals. ECs have emerged as a promising solution for accelerating the transition to more sustainable energy systems and therefore climate change mitigation. While cost optimization (economic goal) is most commonly used in ECs, optimizing their resilience (technical goal) can be an important part of operating a distribution grid with high photovoltaic (PV) and electrical vehicle (EV) penetration in the future. This paper presents a comparative analysis of the impact of those two objective functions on overall EC costs as well as individual member costs. The findings highlight the trade-off between the flexibility measures required for a resilient EC and the cost associated with them. This study helps quantify the subsidies that would be required to incentives EC to operate in a resilient matter as a form of grid service. • An energy community optimization model with multiple objective function options. • Energy production, flexibility and grid constraints in single energy community model. • Considering grid tariffs and energy allocation to members. • Cost optimization results show high demand/production peaks. • Resiliency optimization results show high financial burden on members. [ABSTRACT FROM AUTHOR]

Due to computational constraints, power system planning models are typically unable to incorporate full annual temporal resolution. In order to represent the increased variability induced by large amounts of variable renewable energy sources, two methods are investigated to reduce the time dimension: the integral approach (using typical hours based on demand and renewable output) and the representative days method (using typical days to capture annual variability). These two approaches are tested with a benchmark implementation that incorporates full time representation in order identify their suitability for assessing power systems with high renewable penetration. The integral method predicts renewable capacities within a 10% error margin, this paper's main performance metric, using just 32 time steps, while the representative days approach needs 160–200 time steps before providing similarly accurate renewable capacity estimates. Since the integral method generally cannot handle variation management, such as trade and storage, without enhancing the state-space representation, it may be more applicable to one-node models, while the representative days method is suitable for multi-regional models. In order to assess power systems with increasing renewable policy targets, models should be designed to handle at least the 160 time steps needed to provide results that do not systematically overestimate the renewable capacity share. [ABSTRACT FROM AUTHOR]

We analyse the (techno- and macro-)economic and distributive effects of a transformation to a renewable electricity system in Austria by 2030, as stipulated by the Austrian government. For the analysis, the macroeconomic model DYNK and ATLANTIS , a partial model of the electricity market, were expanded and linked. Four transformation scenarios conforming with the 100% renewable electricity target in Austria on a national balance are examined, integrated into a consistent scenario for the development of the European electricity system. Additionally, sensitivity analyses with respect to the gas price are performed. Although all scenarios achieve 100% renewable electricity on a national balance, the analysis shows that electricity from gas-fired power plants will still be needed in 2030 to balance variable renewable generation, to avoid grid congestion, and for heat generation from combined heat and power plants in winter months. Another main conclusion from the simulations is that the transition towards a renewable electricity sector is almost neutral from a socio-economic perspective. It does neither reveal harmful impacts nor lead to high multiplier effects from additional investment. With high natural gas prices in the sensitivity scenarios a decrease in GDP and household income, which might motivate redistributive policies, can be observed. • We link a macroeconomic and an electricity system model to study Austria's RES-E transition. • Four transition scenarios are simulated to illustrate the range of potential effects. • Sensitivity analysis with respect to the gas price is performed. • Electricity from gas-fired power plants will still be needed in Austria in 2030. • The transition towards RES-E is almost neutral from a socio-economic perspective. [ABSTRACT FROM AUTHOR]