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In the current situation with the unprecedented deployment of clean technologies for electricity generation, it is natural to expect that storage will play an important role in electricity networks. This paper provides a qualitative methodology to select the appropriate technology or mix of technologies for different applications. The multiple comparisons according to different characteristics distinguish this paper from others about energy storage systems. Firstly, the different technologies available for energy storage, as discussed in the literature, are described and compared. The characteristics of the technologies are explained, including their current availability. In order to gain a better perspective, availability is cross-compared with maturity level. Moreover, information such as ratings, energy density, durability and costs is provided in table and graphic format for a straightforward comparison. Additionally, the different electric grid applications of energy storage technologies are described and categorised. For each of the categories, we describe the available technologies, both mature and potential. Finally, methods for connecting storage technologies are discussed.

In the current situation with the unprecedented deployment of clean technologies for electricity generation, it is natural to expect that storage will play an important role in electricity networks. This paper provides a qualitative methodology to select the appropriate technology or mix of technologies for different applications. The multiple comparisons according to different characteristics distinguish this paper from others about energy storage systems. Firstly, the different technologies available for energy storage, as discussed in the literature, are described and compared. The characteristics of the technologies are explained, including their current availability. In order to gain a better perspective, availability is cross-compared with maturity level. Moreover, information such as ratings, energy density, durability and costs is provided in table and graphic format for a straightforward comparison. Additionally, the different electric grid applications of energy storage technologies are described and categorised. For each of the categories, we describe the available technologies, both mature and potential. Finally, methods for connecting storage technologies are discussed.

This paper develops a data-driven model for assessing the availability of flexibility from individual household devices, at house level. The model predicts the potential shift, increase or decrease of the energy consumption of a particular type of device at a given time, in response to a price signal, and for each house separately. Therefore, the location of the flexibility source is known with accuracy. The model has an Auto-Encoder architecture based on Convolution Neural Network. The augmented model demonstrates good performance in terms of predicting the time-shift in load.

This paper develops a data-driven model for assessing the availability of flexibility from individual household devices, at house level. The model predicts the potential shift, increase or decrease of the energy consumption of a particular type of device at a given time, in response to a price signal, and for each house separately. Therefore, the location of the flexibility source is known with accuracy. The model has an Auto-Encoder architecture based on Convolution Neural Network. The augmented model demonstrates good performance in terms of predicting the time-shift in load.

Owing to the complexity of the sector, industrial activities are often represented with limited technological resolution in integrated energy system models. In this study, we enriched the technological description of industrial activities in the integrated energy system analysis optimisation (IESA-Opt) model, a peer-reviewed energy system optimisation model that can simultaneously provide optimal capacity planning for the hourly operation of all integrated sectors. We used this enriched model to analyse the industrial decarbonisation of the Netherlands for four key activities: high-value chemicals, hydrocarbons, ammonia, and steel production. The analyses performed comprised 1) exploring optimality in a reference scenario; 2) exploring the feasibility and implications of four extreme industrial cases with different technological archetypes, namely a bio-based industry, a hydrogen-based industry, a fully electrified industry, and retrofitting of current assets into carbon capture utilisation and storage; and 3) performing sensitivity analyses on key topics such as imported biomass, hydrogen, and natural gas prices, carbon storage potentials, technological learning, and the demand for olefins. The results of this study show that it is feasible for the energy system to have a fully bio-based, hydrogen-based, fully electrified, and retrofitted industry to achieve full decarbonisation while allowing for an optimal technological mix to yield at least a 10% cheaper transition. We also show that owing to the high predominance of the fuel component in the levelled cost of industrial products, substantial reductions in overnight investment costs of green technologies have a limited effect on their adoption. Finally, we reveal that based on the current (2022) energy prices, the energy transition is cost-effective, and fossil fuels can be fully displaced from industry and the national mix by 2050.

Owing to the complexity of the sector, industrial activities are often represented with limited technological resolution in integrated energy system models. In this study, we enriched the technological description of industrial activities in the integrated energy system analysis optimisation (IESA-Opt) model, a peer-reviewed energy system optimisation model that can simultaneously provide optimal capacity planning for the hourly operation of all integrated sectors. We used this enriched model to analyse the industrial decarbonisation of the Netherlands for four key activities: high-value chemicals, hydrocarbons, ammonia, and steel production. The analyses performed comprised 1) exploring optimality in a reference scenario; 2) exploring the feasibility and implications of four extreme industrial cases with different technological archetypes, namely a bio-based industry, a hydrogen-based industry, a fully electrified industry, and retrofitting of current assets into carbon capture utilisation and storage; and 3) performing sensitivity analyses on key topics such as imported biomass, hydrogen, and natural gas prices, carbon storage potentials, technological learning, and the demand for olefins. The results of this study show that it is feasible for the energy system to have a fully bio-based, hydrogen-based, fully electrified, and retrofitted industry to achieve full decarbonisation while allowing for an optimal technological mix to yield at least a 10% cheaper transition. We also show that owing to the high predominance of the fuel component in the levelled cost of industrial products, substantial reductions in overnight investment costs of green technologies have a limited effect on their adoption. Finally, we reveal that based on the current (2022) energy prices, the energy transition is cost-effective, and fossil fuels can be fully displaced from industry and the national mix by 2050.

SummaryEnvironmentally extended multiregional input‐output (EE MRIO) tables have emerged as a key framework to provide a comprehensive description of the global economy and analyze its effects on the environment. Of the available EE MRIO databases, EXIOBASE stands out as a database compatible with the System of Environmental‐Economic Accounting (SEEA) with a high sectorial detail matched with multiple social and environmental satellite accounts. In this paper, we present the latest developments realized with EXIOBASE 3—a time series of EE MRIO tables ranging from 1995 to 2011 for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply‐use tables (SUTs) in a 163 industry by 200 products classification as the main building blocks. In order to capture structural changes, economic developments, as reported by national statistical agencies, were imposed on the available, disaggregated SUTs from EXIOBASE 2. These initial estimates were further refined by incorporating detailed data on energy, agricultural production, resource extraction, and bilateral trade. EXIOBASE 3 inherits the high level of environmental stressor detail from its precursor, with further improvement in the level of detail for resource extraction. To account for the expansion of the European Union (EU), EXIOBASE 3 was developed with the full EU28 country set (including the new member state Croatia). EXIOBASE 3 provides a unique tool for analyzing the dynamics of environmental pressures of economic activities over time.