• Energy Research

A multi-objective optimization method was developed for the design of trigeneration plants. The optimization is carried out on technical, economical, energetic and environmental performance indicators in a multi-objective optimization framework. Both construction (equipment sizes) and discrete operational (pricing tariff schemes and operational strategy) variables were optimized based on realistic conditions. The problem is solved using a multi-objective evolutionary algorithm. An example of a trigeneration system in a 300 bed hospital was studied in detail in order to demonstrate the design procedure, the economic and energetic performance of the plant, as well as the effectiveness of the proposed approach even under fluctuating energy prices.

The coupling of the heating and the electricity sectors is of utmost importance when it comes to the achievement of the decarbonisation and the energy efficiency targets set for the 2020 and 2030 in the EU. Centralised cogeneration plants connected to district heat networks are fundamental element of this coupling. Despite the efficiency benefits, the effects of introducing combined generation to the power system are sometimes adverse. Reduced flexibility caused by contractual obligations to deliver heat may not always facilitate the penetration of renewable energy in the energy system. Thermal storage is acknowledged as a solution to the above. This work investigates the optimal operation of cogeneration plants combined with thermal storage. To do so, a combined heat and power (CHP) plant model is formulated and incorporated into Dispa-SET, a JRC in-house unit commitment and dispatch model. The cogeneration model sets technical feasible operational regions for different heat uses defined by temperature requirements. Different energy system scenarios are used to assess the implications of the heating–electricity coupling to the flexibility of the power system and to the achievement of the decarbonisation goals in an existing non interconnected power system where CHP plants provide heating and electricity to nearby energy dense areas. The analysis indicates that the utilisation of CHP plants contributes to improve the overall system efficiency and reduce total cost of the system. In addition, the incorporation of thermal storage increases the penetration of renewable energy in the system.

Abstract Modern greenhouses are good candidates for combined cooling, heating and power generation (CCHP) systems. An analytical framework to assess the affordability of such systems for two different problems, is proposed; (a) the grassroots problem, i.e. the CCHP system is designed along with the greenhouse, and, (b) the retrofit problem, i.e. the addition of a CHP unit to an existing greenhouse. As a case study, the analysis is applied in three locations of Greece (North, Central, South) with different meteorological conditions, for two traditional products (tomato, cucumber). The results indicate that cogeneration is a cost-effective solution improving the economic and energetic efficiency of the facility. Furthermore, with the use of active cooling, the operating period of the greenhouse is increased, which significantly improves the cashflow. Product cultivation parameters and climatic conditions affect to a significant degree the overall performance. It is proved that the combined use of a CCHP and a conventional gas boiler for heating, is more profitable for tomato cultivation, achieving R O I 23%, 28%, and 27%, in North, Central and South Greece, respectively. The sensitivity of the investment’s feasibility is quantified in regards to energy prices, and pricing policies, which is one of the most important factors.

# ERA-NUTS (1980-2021) This dataset contains a set of time-series of meteorological variables based on Copernicus Climate Change Service (C3S) ERA5 reanalysis. The data files can be downloaded from here while notebooks and other files can be found on the associated Github repository. This data has been generated with the aim of providing hourly time-series of the meteorological variables commonly used for power system modelling and, more in general, studies on energy systems. An example of the analysis that can be performed with ERA-NUTS is shown in this video. Important: this dataset is still a work-in-progress, we will add more analysis and variables in the near-future. If you spot an error or something strange in the data please tell us sending an email or opening an Issue in the associated Github repository. ## Data The time-series have hourly/daily/monthly frequency and are aggregated following the NUTS 2016 classification. NUTS (Nomenclature of Territorial Units for Statistics) is a European Union standard for referencing the subdivisions of countries (member states, candidate countries and EFTA countries). This dataset contains NUTS0/1/2 time-series for the following variables obtained from the ERA5 reanalysis data (in brackets the name of the variable on the Copernicus Data Store and its unit measure): - t2m: 2-meter temperature (`2m_temperature`, Celsius degrees) - ssrd: Surface solar radiation (`surface_solar_radiation_downwards`, Watt per square meter) - ssrdc: Surface solar radiation clear-sky (`surface_solar_radiation_downward_clear_sky`, Watt per square meter) - ro: Runoff (`runoff`, millimeters) There are also a set of derived variables: - ws10: Wind speed at 10 meters (derived by `10m_u_component_of_wind` and `10m_v_component_of_wind`, meters per second) - ws100: Wind speed at 100 meters (derived by `100m_u_component_of_wind` and `100m_v_component_of_wind`, meters per second) - CS: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky) - HDD/CDD: Heating/Cooling Degree days (derived by 2-meter temperature the EUROSTAT definition. For each variable we have 367 440 hourly samples (from 01-01-1980 00:00:00 to 31-12-2021 23:00:00) for 34/115/309 regions (NUTS 0/1/2). The data is provided in two formats: - NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as `int16` type using a `scale_factor` to minimise the size of the files. - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly) All the CSV files are stored in a zipped file for each variable. ## Methodology The time-series have been generated using the following workflow: 1. The NetCDF files are downloaded from the Copernicus Data Store from the ERA5 hourly data on single levels from 1979 to present dataset 2. The data is read in R with the climate4r packages and aggregated using the function `/get_ts_from_shp` from panas. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders. 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R 4. The NetCDF are created using `xarray` in Python 3.8. ## Example notebooks In the folder `notebooks` on the associated Github repository there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in `xarray` and how to visualise them in several ways by using matplotlib or the enlopy package. There are currently two notebooks: - exploring-ERA-NUTS: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them. - ERA-NUTS-explore-with-widget: explorer interactively the datasets with [jupyter]() and ipywidgets. The notebook `exploring-ERA-NUTS` is also available rendered as HTML. ## Additional files In the folder `additional files`on the associated Github repository there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region. ## License This dataset is released under CC-BY-4.0 license.

Abstract The IAEA DEEP software has been used worldwide for the economic evaluation of desalination plants (thermal or electrical) coupled with various energy sources (nuclear, fossil fueled or renewable). Throughout the years, the software was updated constantly. Such updates included the user interface and model structure but not the economic models. Previous continuous development was culminated in the development of the DEEP 3.2 version which has been recently released in 2009. This paper presents a step forwards in the continuous effort to maintain high standards and reliability of DEEP. It also scrutinizes methods used, assumptions made, and constants or default values originally used. The validity of calculations as well as the identification of the most important parameters is presented. Sensitivity analysis is used to identify the most important parameters in the DEEP model. Overall, the review proves that both the DEEP economic model and software implementation are solid for economic evaluation of dual purpose plants. Based on results presented and recommendations made, a new version of DEEP is expected to be released in 2010 which will address minor issues and improvements.

This work examines the role of centralised cogeneration plants as one of the potential pathways of a future decarbonised energy system. Even in this context, thermal power plants will still exist and the utilisation of their excess heat via district heating networks can assist the decarbonisation of the built environment. In particular, the potential of existing thermal power plants to operate as combined heat and power (CHP) plants is assessed and their impact on the power system quantified. To do so, the European heat demand for the built environment is described, focused on the heat demand supplied with fossil fuels, and the European power sector is discussed. Then, a power system model (Dispa-SET) is used to evaluate this coupling pathway in terms of operating costs, efficiencies and associated CO2 emissions. The analysis is developed for the current and future European power system. Results show that the conversion of thermal into CHP plants increases the efficiency and reduces both the operating costs and the environmental impact of the energy system. Not only that, it also offers alternative flexibility options when coupled with thermal storage. Still, large investments regarding the deployment of thermal networks are required to leverage the full CHP potential.

# ERA-NUTS (1980-2020) This dataset contains a set of time-series of meteorological variables based on Copernicus Climate Change Service (C3S) ERA5 reanalysis. The data files can be downloaded from here while notebooks and other files can be found on the associated Github repository. This data has been generated with the aim of providing hourly time-series of the meteorological variables commonly used for power system modelling and, more in general, studies on energy systems. An example of the analysis that can be performed with ERA-NUTS is shown in this video. Important: this dataset is still a work-in-progress, we will add more analysis and variables in the near-future. If you spot an error or something strange in the data please tell us sending an email or opening an Issue in the associated Github repository. ## Data The time-series have hourly/daily/monthly frequency and are aggregated following the NUTS 2016 classification. NUTS (Nomenclature of Territorial Units for Statistics) is a European Union standard for referencing the subdivisions of countries (member states, candidate countries and EFTA countries). This dataset contains NUTS0/1/2 time-series for the following variables obtained from the ERA5 reanalysis data (in brackets the name of the variable on the Copernicus Data Store and its unit measure): - t2m: 2-meter temperature (`2m_temperature`, Celsius degrees) - ssrd: Surface solar radiation (`surface_solar_radiation_downwards`, Watt per square meter) - ssrdc: Surface solar radiation clear-sky (`surface_solar_radiation_downward_clear_sky`, Watt per square meter) - ro: Runoff (`runoff`, millimeters) There are also a set of derived variables: - ws10: Wind speed at 10 meters (derived by `10m_u_component_of_wind` and `10m_v_component_of_wind`, meters per second) - ws100: Wind speed at 100 meters (derived by `100m_u_component_of_wind` and `100m_v_component_of_wind`, meters per second) - CS: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky) - HDD/CDD: Heating/Cooling Degree days (derived by 2-meter temperature the EUROSTAT definition. For each variable we have 359 424 hourly samples (from 01-01-1980 00:00:00 to 31-12-2020 23:00:00) for 34/115/309 regions (NUTS 0/1/2). The data is provided in two formats: - NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as `int16` type using a `scale_factor` to minimise the size of the files. - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly) All the CSV files are stored in a zipped file for each variable. ## Methodology The time-series have been generated using the following workflow: 1. The NetCDF files are downloaded from the Copernicus Data Store from the ERA5 hourly data on single levels from 1979 to present dataset 2. The data is read in R with the climate4r packages and aggregated using the function `/get_ts_from_shp` from panas. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders. 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R 4. The NetCDF are created using `xarray` in Python 3.8. NOTE: air temperature, solar radiation, runoff and wind speed hourly data have been rounded with two decimal digits. ## Example notebooks In the folder `notebooks` on the associated Github repository there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in `xarray` and how to visualise them in several ways by using matplotlib or the enlopy package. There are currently two notebooks: - exploring-ERA-NUTS: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them. - ERA-NUTS-explore-with-widget: explorer interactively the datasets with [jupyter]() and ipywidgets. The notebook `exploring-ERA-NUTS` is also available rendered as HTML. ## Additional files In the folder `additional files`on the associated Github repository there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region. ## License This dataset is released under CC-BY-4.0 license.

The EU aims to become the world’s first climate-neutral continent by 2050. In order to meet this target, the integration of high shares of Renewable Energy Sources (RESs) in the energy system is of primary importance. Nevertheless, the large deployment of variable renewable sources such as wind and photovoltaic power will pose important challenges in terms of power management. For this reason, increasing the system flexibility will be crucial to ensure the security of supply in future power systems. This work investigates the flexibility potential obtainable from the diffusion of Demand Response (DR) programmes applied to residential heating for different renewables penetration and power system configuration scenarios. To that end, a bottom-up model for residential heat demand and flexible electric heating systems (heat pumps and electric water heaters) is developed and directly integrated into Dispa-SET, an existing unit commitment optimal dispatch model of the power system. The integrated model is calibrated for the case of Belgium and different simulations are performed varying the penetration and type of residential heating technologies, installed renewables capacity and capacity mix. Results show that, at country level, operational cost could be reduced up to €35 million and curtailment up to 1 TWh per year with 1 million flexible electric heating systems installed. These benefits are significantly reduced when nuclear power plants (non-flexible) are replaced by gas-fired units (flexible) and grow when more renewable capacity is added. Moreover, when the number of flexible heating systems increases, a saturation effect of the flexibility is observed.