• Energy Research
• 2021-2025close
• 7. Clean energy close
• 12. Responsible consumption close

These datasets support the findings of the study titled “A spatial cost-benefit-externality modelling framework for siting of variable renewable energy farms: A case in Bohol, Philippines.” This study introduces a four-stage energy modelling framework for siting variable renewable energy farms using Geographic Information System and Cost-Benefit Analysis. There are only two files in this repository. The data with the filename “Cost-Benefit-Externality Model” holds the implementation of the model in assessing the viability of VRE in the province of Bohol. Input parameters of the model can be varied in this file. The data with the filename “Sensitivity Analysis” contains the sensitivity analysis results we conducted for the economical parameters of the model.

The increased global demand for energy will require additional tools to help guide policy and management actions to conserve wildlife. Grouse (Tetraoninae) are adversely affected by infrastructure associated with energy development, but the magnitude of effects are difficult to quantify in a singular management prescription. Advancement in monitoring and analysis techniques have allowed researchers to evaluate complex questions surrounding the effects of infrastructure on grouse populations, rapidly increasing our knowledge. To better inform management decisions, especially with the emergence of renewable energy, a quantitative synthesis of previous research evaluating the effects of infrastructure on grouse populations is needed. We reviewed studies evaluating the effect of energy infrastructure on grouse, with the main objective to determine the magnitude of effect on grouse lek attendance, resource selection, and survival to help inform future conservation actions. We modeled slope coefficients for distance to energy infrastructure, standardized by scale, on various behaviors to determine overall effect sizes in a meta-analysis. We used 93 study-result combinations from 21 studies that directly evaluated resource selection, survival, or lek attendance relative to energy infrastructure. Trends in overall effect sizes suggest an adverse effect of distance to energy infrastructure on grouse behavior; however, the combination of non-significant pooled regression slopes and high among-study heterogeneity suggest the effect of distance to energy infrastructure is context dependent. While distance to infrastructure is a common metric used in many grouse management plans, our results suggest distance to infrastructure may not be a reliable predictor of grouse behavior and the effect is context dependent making management prescriptions based solely on distance to infrastructure in a one size fits all approach difficult. Our analysis points to numerous aspects that scientists can improve upon by evaluating density in conjunction with distance to energy infrastructure as well as reporting the necessary statistics for future meta-analyses. Data was obtained from peer-reviewed literature. We extracted regression coefficients, standard erros or confidence intervals, and sample sizes from statistical models that evaluated resource selection, survival, and lek attendance relative to distance to energy infrastructure. We contacted authors that assessed the effects of distance to energy infrastructure on grouse if they did not report relevant statistics. 

This dataset contains data and codes required to replicate the results in the article "Joint assessment of generation adequacy with intermittent renewables and hydro storage: A case study in Finland" to be published in Electric Power Systems Research. See the enclosed Readme for further instructions.

These are data sets from thesis with the above title.

QTDIAN - Quantification of Technological DIffusion and sociAl constraiNts - is a toolbox of qualitative and quantitative descriptions of socio-technical and political aspects of the energy transition that influence the overall potential, the rate of energy-related technology and service diffusion and the design of the future energy system. The output of QTIDIAN is empirically founded datasets of social and political drivers and barriers of the transition, both in the form of raw data describing past and current developments and manipulated to constitute consistent quantifications of the storylines. Here you can download the data for six QTDIAN themes: Socially feasible scaling of energy technologies Policy preferences & dynamics Barriers to infrastructural development (wind energy, grid development) Citizen energy Private energy demand Further information on the QTDIAN modelling toolbox and the data can be found in the SENTINEL Deliverable 2.3 and Deliverable 2.4: S��sser, D., al Rakouki, H., & Lilliestam, J.(2021). The QTDIAN modelling toolbox���Quantification of social drivers and constraints of the diffusion of energy technologies. Deliverable 2.3. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS). S��sser, D., Pickering, B., Chatterjee, S., Oreggioni, G., Stavrakas, V., & Lilliestam, J.(2021). Integration of socio-technological transition constraints into energy demand and systems models. Deliverable 2.5. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS).

Regional integrated energy system coupling with multi-energy devices, energy storage devices and renewable energy devices has been regarded as one of the most promising solutions for future energy systems. Existing natural gas network and electricity networks expansion planning, regional integrated energy systems siting planning, interior equipment’s type and capacity planning are urgent problems in practical industrial establishments. This paper proposed a joint planning model to address above problems firstly; however, joint planning model misleads the ownerships of three agents which investment decisions are generally made by different investors. Furthermore, the joint planning model is decomposed into three planning sub-problems related to corresponding stakeholders, and the alternating direction method of multipliers is adopted to solve the tripartite distributed planning problem. The effectiveness of the planning model is verified on an updated version of IEEE 24-bus electric system, Belgian 20-node natural gas system, and three assumed integrated energy systems. Simulation results illustrated that distributed planning model is more sensitive to individual load differences, which is precisely the defect of the joint planning model. Moreover, the algorithm performance considering convergence errors, rates of convergence and the impacts of penalty parameters are further analyzed.

Dataset supporting publication: “A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol” (publication available for download: GEOFIT Zenodo) Datasets resultant from simulation of the integrated system into buildings. Conference paper presented in IBPSA-England Building Simulation and Optimisation Conference 2020 This paper presents a novel Reduced Order grey box Model (ROM) methodology, based on a Resistor-Capacitor (RC) network, which supports the creation of the baseline energy consumption and the estimation of energy savings due to Energy Conservation Measures (ECMs) under the Measurement and Verification protocol. Within this scope, a description of the RC network, including a calculation of the parameters’ needed to execute the ROM, are presented. This ROM methodology is demonstrated on an educational building located in Sant Cugat, Spain as part of the H2020 GEOFIT project. The results presented in this paper demonstrate that the ROM is sufficiently accurate for the creation of the baseline energy consumption and for estimating the energy savings of different ECMs.

Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).