This page has only limited features, please log in for full access.
Thanks to their modularity and their capacity to adapt to different contexts, hybrid microgrids are a promising solution to decrease greenhouse gas emissions worldwide. To properly assess their impact in different settings at country or cross-country level, microgrids must be designed for each particular situation, which leads to computationally intractable problems. To tackle this issue, a methodology is proposed to create surrogate models using machine learning techniques and a database of microgrids. The selected regression model is based on Gaussian Processes and allows to drastically decrease the computation time relative to the optimal deployment of the technology. The results indicate that the proposed methodology can accurately predict key optimization variables for the design of the microgrid system. The regression models are especially well suited to estimate the net present cost and the levelized cost of electricity (R2 = 0.99 and 0.98). Their accuracy is lower when predicting internal system variables such as installed capacities of PV and batteries (R2 = 0.92 and 0.86). A least-cost path towards 100% electrification coverage for the Bolivian lowlands mid-size communities is finally computed, demonstrating the usability and computational efficiency of the proposed framework.
Sergio Balderrama; Francesco Lombardi; Nicolo Stevanato; Gabriela Peña; Emanuela Colombo; Sylvain Quoilin. Surrogate models for rural energy planning: Application to Bolivian lowlands isolated communities. Energy 2021, 232, 121108 .
AMA StyleSergio Balderrama, Francesco Lombardi, Nicolo Stevanato, Gabriela Peña, Emanuela Colombo, Sylvain Quoilin. Surrogate models for rural energy planning: Application to Bolivian lowlands isolated communities. Energy. 2021; 232 ():121108.
Chicago/Turabian StyleSergio Balderrama; Francesco Lombardi; Nicolo Stevanato; Gabriela Peña; Emanuela Colombo; Sylvain Quoilin. 2021. "Surrogate models for rural energy planning: Application to Bolivian lowlands isolated communities." Energy 232, no. : 121108.
Energy system models for off-grid systems usually tend to focus solely on the provision of electricity for powering simple appliances, thus neglecting more energy-intensive and critical needs, such as water heating. The adoption of a Multi-Energy System (MES) perspective would allow us not only to provide comprehensive solutions addressing all types of energy demand, but also to exploit synergies between the electric and thermal sectors. To this end, we expand an existing open-source micro-grid optimization model with a complementary thermal model. Results show how the latter achieves optimal solutions that are otherwise restricted, allowing for a reduction in the Levelized Cost of Energy (LCOE) of 59% compared to a conventional microgrid, and an increase of reliance on renewable sources of 70%.
Nicolo' Stevanato; Lorenzo Rinaldi; Stefano Pistolese; Sergio Luis Balderrama Subieta; Sylvain Quoilin; Emanuela Colombo. Modeling of a Village-Scale Multi-Energy System for the Integrated Supply of Electric and Thermal Energy. Applied Sciences 2020, 10, 7445 .
AMA StyleNicolo' Stevanato, Lorenzo Rinaldi, Stefano Pistolese, Sergio Luis Balderrama Subieta, Sylvain Quoilin, Emanuela Colombo. Modeling of a Village-Scale Multi-Energy System for the Integrated Supply of Electric and Thermal Energy. Applied Sciences. 2020; 10 (21):7445.
Chicago/Turabian StyleNicolo' Stevanato; Lorenzo Rinaldi; Stefano Pistolese; Sergio Luis Balderrama Subieta; Sylvain Quoilin; Emanuela Colombo. 2020. "Modeling of a Village-Scale Multi-Energy System for the Integrated Supply of Electric and Thermal Energy." Applied Sciences 10, no. 21: 7445.
Hybrid microgrids represent a cost-effective and viable option to ensure access to energy in rural areas located far from the main grid. Nonetheless, the sizing of rural microgrids is complicated by the lack of models capable of accounting for the evolution of the energy demand over time, which is likely to occur in such contexts as a result of the modification of users' lifestyles. To tackle this issue, the present study aims at developing a novel, long-term optimisation model formulation, capable of accounting for load evolution and performing suitable investment decisions for capacity expansion along the time horizon. Multiple scenarios of load evolution are considered to evaluate the beneficial effects of this novel approach, through the coupling of the model with a tool for stochastic load profiles generation. The results show how this implementation brings lower Net Present Cost to the project and improved correspondence between actual electricity demand and microgrid sizing. Finally, a sensitivity analysis evaluates the robustness of the approach with respect to input data variability and the Loss of Load parameter.
Nicolò Stevanato; Francesco Lombardi; Giulia Guidicini; Lorenzo Rinaldi; Sergio L. Balderrama; Matija Pavičević; Sylvain Quoilin; Emanuela Colombo. Long-term sizing of rural microgrids: Accounting for load evolution through multi-step investment plan and stochastic optimization. Energy for Sustainable Development 2020, 58, 16 -29.
AMA StyleNicolò Stevanato, Francesco Lombardi, Giulia Guidicini, Lorenzo Rinaldi, Sergio L. Balderrama, Matija Pavičević, Sylvain Quoilin, Emanuela Colombo. Long-term sizing of rural microgrids: Accounting for load evolution through multi-step investment plan and stochastic optimization. Energy for Sustainable Development. 2020; 58 ():16-29.
Chicago/Turabian StyleNicolò Stevanato; Francesco Lombardi; Giulia Guidicini; Lorenzo Rinaldi; Sergio L. Balderrama; Matija Pavičević; Sylvain Quoilin; Emanuela Colombo. 2020. "Long-term sizing of rural microgrids: Accounting for load evolution through multi-step investment plan and stochastic optimization." Energy for Sustainable Development 58, no. : 16-29.
For decades, electrification planning in the developing world has often focused on extending the national grid to increase electricity access. This article draws attention to the potential complementary role of decentralized alternatives – primarily micro-grids – to address universal electricity access targets. To this aim, we propose a methodology consisting of three steps to estimate the LCOE and to size micro-grids for large-scale geo-spatial electrification modelling. In the first step, stochastic load demand profiles are generated for a wide range of settlement archetypes using the open-source RAMP model. In the second step, stochastic optimization is carried by the open-source MicroGridsPy model for combinations of settlement size, load demand profiles and other important techno-economic parameters influencing the LCOE. In the third step, surrogate models are generated to automatically evaluate the LCOE using a multivariate regression of micro-grid optimization results as a function of influencing parameters defining each scenario instance. Our developments coupled to the OnSSET electrification tool reveal an important increase in the cost-competitiveness of micro-grids compared to previous analyses.
J.G. Peña Balderrama; S. Balderrama Subieta; F. Lombardi; N. Stevanato; A. Sahlberg; M. Howells; E. Colombo; Sylvain Quoilin. Incorporating high-resolution demand and techno-economic optimization to evaluate micro-grids into the Open Source Spatial Electrification Tool (OnSSET). Energy for Sustainable Development 2020, 56, 98 -118.
AMA StyleJ.G. Peña Balderrama, S. Balderrama Subieta, F. Lombardi, N. Stevanato, A. Sahlberg, M. Howells, E. Colombo, Sylvain Quoilin. Incorporating high-resolution demand and techno-economic optimization to evaluate micro-grids into the Open Source Spatial Electrification Tool (OnSSET). Energy for Sustainable Development. 2020; 56 ():98-118.
Chicago/Turabian StyleJ.G. Peña Balderrama; S. Balderrama Subieta; F. Lombardi; N. Stevanato; A. Sahlberg; M. Howells; E. Colombo; Sylvain Quoilin. 2020. "Incorporating high-resolution demand and techno-economic optimization to evaluate micro-grids into the Open Source Spatial Electrification Tool (OnSSET)." Energy for Sustainable Development 56, no. : 98-118.
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.
C. Del Pero; F. Leonforte; Francesco Lombardi; Nicolo' Stevanato; J. Barbieri; N. Aste; H. Huerto; E. Colombo. Modelling Of An Integrated Multi-Energy System For A Nearly Zero Energy Smart District. 2019 International Conference on Clean Electrical Power (ICCEP) 2019, 246 -252.
AMA StyleC. Del Pero, F. Leonforte, Francesco Lombardi, Nicolo' Stevanato, J. Barbieri, N. Aste, H. Huerto, E. Colombo. Modelling Of An Integrated Multi-Energy System For A Nearly Zero Energy Smart District. 2019 International Conference on Clean Electrical Power (ICCEP). 2019; ():246-252.
Chicago/Turabian StyleC. Del Pero; F. Leonforte; Francesco Lombardi; Nicolo' Stevanato; J. Barbieri; N. Aste; H. Huerto; E. Colombo. 2019. "Modelling Of An Integrated Multi-Energy System For A Nearly Zero Energy Smart District." 2019 International Conference on Clean Electrical Power (ICCEP) , no. : 246-252.
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.
Nicolo' Stevanato; Francesco Lombardi; Emanuela Colmbo; Sergio Balderrama; Sylvain Quoilin. Two-Stage Stochastic Sizing of a Rural Micro-Grid Based on Stochastic Load Generation. 2019 IEEE Milan PowerTech 2019, 1 -6.
AMA StyleNicolo' Stevanato, Francesco Lombardi, Emanuela Colmbo, Sergio Balderrama, Sylvain Quoilin. Two-Stage Stochastic Sizing of a Rural Micro-Grid Based on Stochastic Load Generation. 2019 IEEE Milan PowerTech. 2019; ():1-6.
Chicago/Turabian StyleNicolo' Stevanato; Francesco Lombardi; Emanuela Colmbo; Sergio Balderrama; Sylvain Quoilin. 2019. "Two-Stage Stochastic Sizing of a Rural Micro-Grid Based on Stochastic Load Generation." 2019 IEEE Milan PowerTech , no. : 1-6.