Operation Mode of Integrated Energy System with Liquid Air Energy Storage

This paper takes the energy supply in the park as the research background, the integrated energy system as the research object and establishes the integrated energy system model including liquid air energy storage, distributed photovoltaic, gas turbines and other equipment. When the integrated energy system operates the mode of “ordering heat by power”, the heat wasted as high as 14.647MWh and the cold wasted as high as 24.13MWh. When the system is not equipped with LAES, the output power of the CCHP unit increases by 21MWh, the electricity purchase in power grid increases by 8.123MWh, the heat waste increases by 21.696MWh and the cold waste increases by 12.421MWh. When the integrated energy system operates the mode of [[CHECK_DOUBLEQUOT_ENT]] ordering power by heat ", heat energy of the system has been reasonably utilized. When the system is not equipped with LAES, the power output and heat of the CCHP unit in the system are the same, the thermal energy output results of the system are the same and the electricity purchased by the power grid increases by 32.14MWh.

How to Reach the New Green Deal Targets: Analysing the Necessary Burden Sharing within the EU Using a Multi-Model Approach

The Green Deal of the European Union defines extremely ambitious climate targets for 2030 (−55% emissions compared to 1990) and 2050 (−100%), which go far beyond the current goals that the EU member states have agreed on thus far. The question of which sectors contribute how much has already been discussed, but is far from decided, while the question of which countries shoulder how much of the tightened reduction targets has hardly been discussed. We want to contribute significantly to answering these policy questions by analysing the necessary burden sharing within the EU from both an energy system and an overall macroeconomic perspective. For this purpose, we use the energy system model TIMES PanEU and the computational general equilibrium model NEWAGE. Our results show that excessively strong targets for the Emission Trading System (ETS) in 2030 are not system-optimal for achieving the 55% overall target, reductions should be made in such a way that an emissions budget ratio of 39 (ETS sector) to 61 (Non-ETS sector) results. Economically weaker regions would have to reduce their CO2 emissions until 2030 by up to 33% on top of the currently decided targets in the Effort Sharing Regulation, which leads to higher energy system costs as well as losses in gross domestic product (GDP). Depending on the policy scenario applied, GDP losses in the range of −0.79% and −1.95% relative to baseline can be found for single EU regions. In the long-term, an equally strict mitigation regime for all countries in 2050 is not optimal from a system perspective; total system costs would be higher by 1.5%. Instead, some countries should generate negative net emissions to compensate for non-mitigable residual emissions from other countries.

Towards Sustainable Heat Supply with Decentralized Multi-Energy Systems by Integration of Subsurface Seasonal Heat Storage

In the energy transition, multi-energy systems are crucial to reduce the temporal, spatial and functional mismatch between sustainable energy supply and demand. Technologies as power-to-heat (PtH) allow flexible and effective utilisation of available surplus green electricity when integrated with seasonal heat storage options. However, insights and methods for integration of PtH and seasonal heat storage in multi-energy systems are lacking. Therefore, in this study, we developed methods for improved integration and control of a high temperature aquifer thermal energy storage (HT-ATES) system within a decentralized multi-energy system. To this end, we expanded and integrated a multi-energy system model with a numerical hydro-thermal model to dynamically simulate the functioning of several HT-ATES system designs for a case study of a neighbourhood of 2000 houses. Results show that the integration of HT-ATES with PtH allows 100% provision of the yearly heat demand, with a maximum 25% smaller heat pump than without HT-ATES. Success of the system is partly caused by the developed mode of operation whereby the heat pump lowers the threshold temperature of the HT-ATES, as this increases HT-ATES performance and decreases the overall costs of heat production. Overall, this study shows that the integration of HT-ATES in a multi-energy system is suitable to match annual heat demand and supply, and to increase local sustainable energy use.

An Optimization Framework for Low-Carbon Oriented Integrated Energy System Management in Commercial Building under Electric Vehicle Demand Response

As the carbon emissions of commercial buildings are attracting considerable attention, the integrated energy system (IES) has become a promising low-carbon method in response. In this paper, an optimization framework for low-carbon oriented integrated energy system management under electric vehicles (EV) demand response is proposed. After analyzing the charging behavior, EV charging demand is simulated. Then, the low-carbon integrated energy system model is proposed with the optimization framework considering carbon reduction. Subsequently the objective function containing carbon emission is obtained for the whole operation optimization. The results of the studied case show that the optimization framework proposed can reduce the carbon emission greatly as well as moderate economic cost, which declined because of the revenue from charging demand response. In general, the optimization of low-carbon oriented IES in commercial buildings is feasible.

Selected ‘Starter Kit’ energy system modelling data for Paraguay (#CCG)

Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Paraguay, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

Selected ‘Starter Kit’ energy system modelling data for Uruguay (#CCG)

Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Uruguay, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

Selected ‘Starter Kit’ energy system modelling data for Peru (#CCG)

Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Peru, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

Selected ‘Starter Kit’ energy system modelling data for Venezuela (#CCG)

Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Venezuela, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.