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The electricity consumption of non-residential productive sectors represents a significant share of the overall energy consumption in the Member States of the European Union. Therefore, determining the main factors affecting the intensity of such consumption (defined as the ratio of the electricity consumed by the productive system of a given country to gross value added) is a research effort worth undertaking. The aim of this paper is to identify the main factors which affect such intensity with the help of a static panel data model, which is estimated with three different methods. Our results show that a higher degree of technological progress in the production systems and higher retail prices of electricity for non-residential consumers reduce such intensity. Other variables, which are statistically significant, lead to an increase of electricity intensity, including the accumulated stock of physical capital and the lagged gross fixed capital formation. The accumulated capital stock per unit of GDP is the variable with the highest influence on electricity intensity. Whereas the influence of price changes is limited, these results indicate that there is some rigidity to change energy intensity through demand-side policy measures, suggesting that they will need to be complemented with supply-side measures aimed at increasing low-carbon electricity generation capacity. [ABSTRACT FROM AUTHOR]

This paper presents a multistage adaptive robust generation expansion planning model, which accounts for short-term unit commitment and ramping constraints, considers multi-period and multi-regional planning, and maintains the integer representation of generation units. The uncertainty of electricity demand and renewable power generation is taken into account through bounded intervals, with parameters that permit control over the level of conservatism of the solution. The multistage robust optimization model allows the sequential representation of uncertainty realization as they are revealed over time. It also guarantees the non-anticipativity of future uncertainty realizations at the time of decision-making, which is the case in practical real-world applications, as opposed to two-stage robust and stochastic models. To render the resulting multistage robust problem tractable, decision rules are employed to cast the uncertainty-based model into an equivalent mixed integer linear (MILP) problem. The re-formulated MILP problem, while tractable, is computationally prohibitive even for moderately sized systems. We, thus, propose a solution method relying on the reduction of the information basis of the decision rules employed in the model, and validate its adequacy to efficiently solve the problem. The importance of considering multistage robust frameworks for accounting for net-load uncertainties in generation expansion planning is illustrated, particularly under a high share of renewable energy penetration. A number of renewable penetration scenarios and uncertainty levels are considered for a case study covering future generation expansion planning in Europe. The results confirm the effectiveness of the proposed approach in coping with multifold operational uncertainties and for deriving adequate generation investment decisions. Moreover, the quality of the solutions obtained and the computational performance of the proposed solution method is shown to be suitable for practical policy-making generation expansion planning problems, seeking to evaluate the impact of uncertainty on future system-wide performance. • A multistage adaptive robust optimization model for generation expansion planning. • Key operational uncertainties due to large shares of renewable energy are considered. • Novel solution method based on the reduction of information basis for decision rules. • Significant reduction in calculation time for solving the multistage robust model. • Practical relevance of the proposed approach for system planning under uncertainties. [ABSTRACT FROM AUTHOR]

This paper provides a detailed account of the impact of different offshore wind siting strategies on the design of the European power system. To this end, a two-stage method is proposed. In the first stage, a highly-granular siting problem identifies a suitable set of sites where offshore wind plants could be deployed according to a pre-specified criterion. Two siting schemes are analysed and compared within a realistic case study. These schemes essentially select a pre-specified number of sites so as to maximize their aggregate power output and their spatiotemporal complementarity, respectively. In addition, two variants of these siting schemes are provided, wherein the number of sites to be selected is specified on a country-by-country basis rather than Europe-wide. In the second stage, the subset of previously-identified sites is passed to a capacity expansion planning framework that sizes the power generation, transmission and storage assets that should be deployed and operated in order to satisfy pre-specified electricity demand levels at minimum cost. Results show that the complementarity-based siting criterion leads to system designs which are up to 5% cheaper than the ones relying on the power output-based scheme when offshore wind plants are deployed with no consideration for country-based deployment targets. On the contrary, the power output-based scheme leads to system designs which are consistently 2% cheaper than the ones leveraging the complementarity-based siting strategy when such constraints are enforced. The robustness of the reported results is supported by a sensitivity analysis on offshore wind capital expenditure and inter-annual weather variability, respectively. • Impact of offshore wind power plant siting strategies is analysed. • Method combining siting and capacity expansion planning models is proposed. • Siting schemes maximizing production or spatiotemporal complementarity are studied. • First scheme consistently cheaper across different problem instances. • Results robust to offshore cost assumptions and inter-annual weather variability. [ABSTRACT FROM AUTHOR]

• Climate change affects long-lived infrastructures like electricity systems. • Decarbonization will largely be based on variable renewable energies. • Impacts of climate change work differently in size and direction. • Two investment planning strategies are compared: Anticipate climate change yes or no. • Total effects but also isolated effects on potential welfare loss are estimated. Ongoing climate change affects complex and long-lived infrastructures like electricity systems. Particularly for decarbonized electricity systems based on variable renewable energies, there is a variety of impact mechanisms working differently in size and direction. Main impacts for Europe include changes in wind and solar resources, hydro power, cooling water availability for thermoelectric generation and electricity demand. Hence, it is not only important to understand the total effects, i.e., how much welfare may be gained when accounting for climate change impacts in all dimensions, but also to disentangle various effects in terms of their marginal contribution to the potential welfare loss. This paper applies a two-stage modeling framework to assess very strong climate change impacts on a cost-optimally decarbonized, highly renewables-based European electricity system. Thereby, the performance of two electricity system design strategies – one based on no anticipation of climate change and one anticipating impacts of climate change – is studied under a variety of climate change impacts. Impacts on wind and solar resources are found to cause the largest system effects in 2100. Combined climate change impacts increase system costs of a system designed without climate change anticipation due to increased fuel and carbon permit costs. Applying a system design strategy with climate change anticipation increases the cost-optimal share of variable renewable energy based on additional wind offshore capacity in 2100, at a reduction in nuclear, wind onshore and solar photovoltaics capacity. Compared to a no anticipation strategy, total system costs are reduced. [ABSTRACT FROM AUTHOR]