• Energy Research

Abstract Laboratory protocols based on water heating procedures represent the most widespread tool for the evaluation of Improved Cooking Stoves (ICSs) thermal performance. Nevertheless, the performance of the cooking system can vary substantially when the boundary conditions – ambient conditions, burn sequence – differ from those experienced in a specific lab. Consequently, we developed and experimentally validated a Cooking Stoves Thermal Performance Simulator (Cook-STePS), based on a 1-D heat and mass transfer model and implemented in an Excel® and VBA environment, in order to provide additional information about how the performance of a cooking system would change, in selected conditions, as compared to the baseline laboratory performance. The study shows how the tool can be effectively applied to a generic cooking system through a two-steps procedure, based on (i) a preliminary set of experimental tests in controlled conditions and (ii) the simulation of performance in the desired conditions.

AbstractEnergy models are used to study emissions mitigation pathways, such as those compatible with the Paris Agreement goals. These models vary in structure, objectives, parameterization and level of detail, yielding differences in the computed energy and climate policy scenarios. To study model differences, diagnostic indicators are common practice in many academic fields, for example, in the physical climate sciences. However, they have not yet been applied systematically in mitigation literature, beyond addressing individual model dimensions. Here we address this gap by quantifying energy model typology along five dimensions: responsiveness, mitigation strategies, energy supply, energy demand and mitigation costs and effort, each expressed through several diagnostic indicators. The framework is applied to a diagnostic experiment with eight energy models in which we explore ten scenarios focusing on Europe. Comparing indicators to the ensemble yields comprehensive ‘energy model fingerprints’, which describe systematic model behaviour and contextualize model differences for future multi-model comparison studies.

The decarbonisation of residential heat through integration with the power system and deployment of refurbishment policies is at the core of European energy policies. Yet, heat-electricity integration may be challenged, in practice, by the large variability of heat demand across weather years. Current approaches for residential heat demand simulation fail to provide insights about the extent of such variability across many weather years and about the benefits potentially brought about by nearly zero-energy buildings. To fill this gap, this work develops an open-source space-heating demand simulation workflow that is applicable to any country's building stock. The workflow, based on a well-established lumped-parameter thermodynamic model, allows capturing sub-national weather-year variability and the mitigation effects of refurbishment. For Italy, different weather years lead to variations in heat demand up to 2 TWh/day, lasting for several days. Moreover, some weather regimes produce spatial asymmetries that may further complicate heat-electricity integration. The refurbishment of about 55% of buildings constructed before 1975 could substantially mitigate such oscillations, leading to a 31–37% reduction of yearly heat demand, primarily in colder regions. Intra-day heat demand variations, driven by user behaviour, are not substantially impacted by refurbishment, calling for the simultaneous deployment of flexible heat generating technologies.

Abstract The relevance of sector coupling is increasing when shifting from the current highly centralised and mainly fossil fuel-based energy system to a more decentralized and renewable energy system. Cross-sectoral linkages are already recognized as a cost-effective decarbonisation strategy that provides significant flexibility to the system. Modelling such cross-sectoral interconnections is thus highly relevant. In this work, these interactions are considered in a long-term perspective by uni-directional soft-linking of two models: JRC-EU-TIMES, a long term planning multisectoral model, and Dispa-SET, a unit commitment and optimal dispatch model covering multiple energy sectors such as power, heating & cooling, transportation etc. The impact of sector coupling in future Europe-wide energy systems with high shares of renewables is evaluated through five scenarios. Results show that the contributions of individual sectors are quite diverse. The transport sector provides the highest flexibility potential in terms of power curtailment, load shedding, congestion in the interconnection lines and resulting greenhouse gas emissions reduction. Nevertheless, allowing combinations of multiple flexibility options such as hydro for the long-term, electric vehicles and flexible thermal units for the short-term provides the best solution in terms of system adequacy, greenhouse gas emissions and operational costs.

Distributed Generation is driving a paradigm shift in traditional power systems, allowing the production of renewable electricity and thermal energy close to the main energy consumption nodes (e.g., buildings). In this sense, thermal networks and electrical grids for Smart districts /cities have a key role, allowing the interconnections of distributed energy resources (renewables, combined heat and power generators, etc storage systems, and loads (electrical and thermal), locally dispatching supply and demand. In the present work, a multi-energy system for a new nearly zero energy and low carbon district near Milan (Italy) is proposed and analysed. After the evaluation of thermal and electrical energy needs of the district, an innovative thermal and electrical multi-energy system is designed with the specific aim to integrate multiple renewable energy sources (i.e., photovoltaics and groundwater energy) and energy storage technologies (i.e., sensible thermal storages), obtaining the best matching with thermal and electrical loads. Final results demonstrate the achievable benefits of the proposed solution, which can be successfully applied in several contexts.

Robust sizing of rural micro-grids is hindered by uncertainty associated with the expected load demand and its potential evolution over time. This study couples a stochastic load generation model with a two-stage stochastic micro-grid sizing model to take into account multiple probabilistic load scenarios within a single optimisation problem. As a result, the stochastic-optimal sizing of the system ensures an increased robustness to shocks in the expected load compared to a best-case (lowest-demand) sizing, though with a lower cost and better dispatch flexibility compared to a worst-case (highest-demand) sizing. What is more, allowing just a 1% unmet demand enables to significantly improve the cost-competitiveness and the renewables penetration as all the not supplied energy is located in a negligible fraction of the unlikeliest highest demand scenarios.

Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options Based on the pre-built Sector-coupled Euro-Calliope model developed by Bryn Pickering This model is pre-packaged and ready to be loaded into Calliope, based on 2015 input data. To run the model as done in the associated publication you will need to do the following: Install a specific conda environment to be working with the correct version of Calliope ( conda env create -f requirements.yml ) Run the model including only those scenarios that relate to the power sector and SPORES Main and parallel batches of SPORES To facilitate this second point and the reproduction of results, you'll find some pre-packaged python script with all and only those model scenarios that allow you to run either the "main batch" of SPORES (spores_model_run.py) or any of the "parallel batches" of SPORES (e.g., excl_bio and max_bio, which generate SPORES while minimising and, respectively, maximising bioenergy deployment). Strength of the anchoring to extremes of the decision space To tweak the strength of the anchoring to a specific technology feature, as we do in the paper, you need to modify the euro_calliope/spores.yaml override file. More precisely, you need to change the excl_score parameter in the objective function at the end of the file: max_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': -1} excl_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': 1} A value of 1 (for maximisation) or -1 (for minimisation) is the default by which we generate the primary results in the paper. By changing it to 0.1, you can reproduce as well the secondary results that we use as a sensitivity for a "weaker anchoring" to extreme technology features of the decision space. Weight-assignment method Finally, to change the weight-assignment method, you need to modify the euro_calliope/eurospores/model.yaml file. More precisely, the scoring_method parameter, which can be one of the following: integer, relative_deployment, random or evolving_average. run.spores_options.scoring_method: integer Hard-coded changes to be aware of The files in this model theoretically allow accounting for all energy sectors (power, heat, transport, industry). Yet, we subset the analysis in the associated publication to only the power sector. To this end, we have modified the original electricity demand file (euro_calliope/eurospores/electricity-demand.csv). In fact, the original file did not account for the fraction of electricity associated with heat, transport or industry consumption, which was instead allocated to sector-specific demand files. In such a way, the model was free to decide whether to electrify these sectoral demands or not. In the present study, instead, we wanted to run our analysis based on the current electricity demand, inclusive of the currently electrified sector-specific demand. Therefore, we have replaced the original file with a new one that includes the present-day electricity demand, with no subtractions. If you want to run the analysis for all sectors, unlike we do in the study, you'll first need to recover the original file. You'll quickly find it in the same folder, named as __electricity-demand.csv. Summary of results from the paper The folder paper_summary_results features some CSV files that summarise the results we obtained for our study across all the different tested search strategies.

Abstract Efforts towards ensuring clean and affordable electricity for all have been progressing slowly in rural, off grid areas of developing countries. In this context, hybrid microgrids may offer reliable and potentially clean electricity for isolated locations. Nevertheless, the process of planning and operation of these systems faces several challenges, often due to the uncertainties related to the renewable resources and to the stochastic nature of electricity consumption in rural contexts. This paper tackles this problem and contributes to the literature in bridging the gap between field practices and two-stage stochastic modeling approaches by identifying an open-source modeling framework which is then applied to real local data. As reference case-study, we consider a microgrid built in 2015 in Bolivia. Overall, the optimal system results from a compromise between the Net Present Cost, the peak capacity installed and the flexibility (to balance variable generation). Different approaches to size isolated microgrids are tested, with the conclusion that methods accounting for the uncertainty in both demand and renewable generation may lead to a more robust configuration with little impacts on the final cost for the community.