Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects
The long-term planning and optimisation of renewable and sustainable energy systems is indispensable for the efficient allocation of finite resources, especially in the context of the growing deployment of localised and distributed energy generation systems. These integrated resource planning endeav...
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Elsevier
2025-01-01
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| Series: | Energy Strategy Reviews |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X25000033 |
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| author | Soheil Mohseni Alan C. Brent |
| author_facet | Soheil Mohseni Alan C. Brent |
| author_sort | Soheil Mohseni |
| collection | DOAJ |
| description | The long-term planning and optimisation of renewable and sustainable energy systems is indispensable for the efficient allocation of finite resources, especially in the context of the growing deployment of localised and distributed energy generation systems. These integrated resource planning endeavours primarily aim to minimise total discounted system costs while adhering to a network of interconnected technical constraints, encompassing considerations of reliability, resilience, and the integration of renewable energy sources. In this intricate landscape, the interdependence of optimisation variables that underlie sustainable energy system planning and optimisation processes underscores the critical role of advanced computational models, notably optimisation algorithms and forecasting techniques, as well as innovative business models tailored to renewable energy in the context of diverse distributed energy options. Nevertheless, despite the well-documented drivers that promote the adoption of renewable energy, an array of social, technical, economic, and regulatory barriers poses significant impediments to the realisation of optimally sized and operated renewable and sustainable energy systems. Consequently, a notable disjunction exists between the pace of advancements in enhancing the efficiency and utilisation of such systems and their practical implementation in decentralised energy initiatives. In this context, the paper undertakes a systematic and comprehensive review, synthesising existing empirical evidence concerning the long-term procurement of renewable energy resources with differing optimisation objectives and within the framework of diverse constraints. To shed light on potential strategies for mitigating the so-called “simulation-to-reality gaps” in the development of sustainable energy systems, the paper endeavours to correlate key trends in the design of renewable energy networks with critical socio-techno-economic and regulatory barriers. The review culminates in the formulation of specific recommendations aimed at surmounting these associated barriers, leveraging the pivotal drivers that bolster clean energy production and utilisation. |
| format | Article |
| id | doaj-art-5aaeec40495f43da8f33f4a82595bbd2 |
| institution | Kabale University |
| issn | 2211-467X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Energy Strategy Reviews |
| spelling | doaj-art-5aaeec40495f43da8f33f4a82595bbd22025-01-22T05:41:48ZengElsevierEnergy Strategy Reviews2211-467X2025-01-0157101640Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospectsSoheil Mohseni0Alan C. Brent1Sustainable Energy Systems, School of Engineering and Computer Science, Wellington Faculty of Engineering, Victoria University of Wellington, PO Box 600, Wellington, 6140, New Zealand; University of Technology Sydney, Institute for Sustainable Futures, Sydney, NSW, 2007, Australia; Corresponding author. Sustainable Energy Systems, School of Engineering and Computer Science, Wellington Faculty of Engineering, Victoria University of Wellington, PO Box 600, Wellington, 6140, New Zealand.Sustainable Energy Systems, School of Engineering and Computer Science, Wellington Faculty of Engineering, Victoria University of Wellington, PO Box 600, Wellington, 6140, New Zealand; Department of Industrial Engineering and the Centre for Renewable and Sustainable Energy Studies, Stellenbosch University, Stellenbosch, 7600, South AfricaThe long-term planning and optimisation of renewable and sustainable energy systems is indispensable for the efficient allocation of finite resources, especially in the context of the growing deployment of localised and distributed energy generation systems. These integrated resource planning endeavours primarily aim to minimise total discounted system costs while adhering to a network of interconnected technical constraints, encompassing considerations of reliability, resilience, and the integration of renewable energy sources. In this intricate landscape, the interdependence of optimisation variables that underlie sustainable energy system planning and optimisation processes underscores the critical role of advanced computational models, notably optimisation algorithms and forecasting techniques, as well as innovative business models tailored to renewable energy in the context of diverse distributed energy options. Nevertheless, despite the well-documented drivers that promote the adoption of renewable energy, an array of social, technical, economic, and regulatory barriers poses significant impediments to the realisation of optimally sized and operated renewable and sustainable energy systems. Consequently, a notable disjunction exists between the pace of advancements in enhancing the efficiency and utilisation of such systems and their practical implementation in decentralised energy initiatives. In this context, the paper undertakes a systematic and comprehensive review, synthesising existing empirical evidence concerning the long-term procurement of renewable energy resources with differing optimisation objectives and within the framework of diverse constraints. To shed light on potential strategies for mitigating the so-called “simulation-to-reality gaps” in the development of sustainable energy systems, the paper endeavours to correlate key trends in the design of renewable energy networks with critical socio-techno-economic and regulatory barriers. The review culminates in the formulation of specific recommendations aimed at surmounting these associated barriers, leveraging the pivotal drivers that bolster clean energy production and utilisation.http://www.sciencedirect.com/science/article/pii/S2211467X25000033Strategic asset allocationMicro-gridsEnergy planningSustainabilityOptimisationSystematic literature review |
| spellingShingle | Soheil Mohseni Alan C. Brent Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects Energy Strategy Reviews Strategic asset allocation Micro-grids Energy planning Sustainability Optimisation Systematic literature review |
| title | Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects |
| title_full | Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects |
| title_fullStr | Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects |
| title_full_unstemmed | Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects |
| title_short | Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects |
| title_sort | long term planning optimisation of sustainable energy systems a systematic review and meta analysis of trends drivers barriers and prospects |
| topic | Strategic asset allocation Micro-grids Energy planning Sustainability Optimisation Systematic literature review |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X25000033 |
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