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Cited by 98Integrated Energy Systems OptimizationRenewable Energy and SustainabilityPower Systems and Renewable Energy

Integration of Renewable Energy into Present and Future Energy Systems

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Sims, Ralph, Mercado, Pedro, Krewitt, Wolfram, Bhuyan, Gouri, Flynn, Damian, Holttinen, Hannele, Jannuzzi, Gilberto, Khennas, Smail +25 more·2011·Renewable Energy Sources and Climate Change Mitigation
DOI 10.1017/cbo9781139151153.012
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Abstract

To achieve higher renewable energy (RE) shares than the low levels typically found in present energy supply systems will require additional integration efforts starting now and continuing over the longer term.These include improved understanding of the RE resource characteristics and availability, investments in enabling infrastructure and research, development and demonstrations (RD&D), modifi cations to institutional and governance frameworks, innovative thinking, attention to social aspects, markets and planning, and capacity building in anticipation of RE growth.In many countries, suffi cient RE resources are available for system integration to meet a major share of energy demands, either by direct input to end-use sectors or indirectly through present and future energy supply systems and energy carriers, whether for large or small communities in Organisation for Economic Co-operation and Development (OECD) or non-OECD countries.At the same time, the characteristics of many RE resources that distinguish them from fossil fuels and nuclear systems include their natural unpredictability and variability over time scales ranging from seconds to years.These can constrain the ease of integration and result in additional system costs, particularly when reaching higher RE shares of electricity, heat or gaseous and liquid fuels.Existing energy infrastructure, markets and other institutional arrangements may need adapting, but there are few, if any, technical limits to the planned system integration of RE technologies across the very broad range of present energy supply systems worldwide, though other barriers (e.g., economic barriers) may exist.Improved overall system effi ciency and higher RE shares can be achieved by the increased integration of a portfolio of RE resources and technologies.This can be enhanced by the fl exible cogeneration of electricity, fuels, heating and cooling, as well as the utilization of storage and demand response options across different supply systems.Real-world case studies outlined throughout the chapter exemplify how different approaches to integration within a specifi c context have successfully achieved RE deployment by means of a combination of technologies, markets, and social and institutional mechanisms.Examples exist of islands, towns and communities achieving high shares of RE, with some approaching 100% RE electricity penetration and over a 50% share of liquid fuels for their light duty vehicle fl eets.Several mature RE technologies, including wind turbines, small and large hydropower generators, geothermal systems, bioenergy cogeneration plants, biomethane production, fi rst generation liquid biofuels, and solar water heaters, have already been successfully integrated into the energy systems of some leading countries.Further integration could be encouraged by both national and local government initiatives.Over the longer term, integration of other less mature, pre-commercial technologies, including advanced biofuels, solar fuels, solar coolers, fuel cells, ocean energy technologies, distributed power generation, and electric vehicles, requires continuing investments in RD&D, infrastructure, capacity building and other supporting measures.To reach the RE levels being projected in many scenarios over future decades will require integration of RE technologies at a higher rate of deployment than at present in each of the electricity generation, heating/ cooling, gas and liquid fuel distribution, and autonomous energy supply systems.RE can be integrated into all types of electricity supply systems, from large, interconnected, continental-scale grids to on-site generation and utilization in small, autonomous buildings.Technically and economically feasible levels of RE penetration depend on the unique characteristics of a system.These include the status of infrastructure development and interconnections, mix of generation technologies, control and communication capability, demand pattern and geographic location in relation to the RE resources available, market designs, and institutional rules.The distribution, location, variability and predictability of the RE resources will also determine the scale of the integration challenge.Short time-variable wind, wave and solar resources can be more diffi cult to integrate than dispatchable reservoir hydro, bioenergy and geothermal resources, which tend to vary only over longer periods (years and decades).As variable RE penetration levels increase, maintaining system reliability becomes more challenging and costly.Depending on the specifi cs of a given electricity system, a portfolio of solutions to minimize the risks to the system and the costs of RE integration can include the development of complementary, fl exible generation; strengthening and extending the network infrastructure;

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  • AI-driven energy management systems use machine learning algorithms to forecast renewable energy availability and reallocate energy distribution to ensure constant supply and reduce non-renewable energy use.

    “Such systems use machine learning algorithms to forecast when the sun or wind will be available, and energy distribution is then appropriately re-allocated to ensure a constant supply and diminish the use of non-renewable energy sources43–45.”

    From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [43]

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Sims, R., Mercado, P., Krewitt, W., Bhuyan, G., Flynn, D., Holttinen, H., Jannuzzi, G., Khennas, S., Liu, Y., Nilsson, L. J., Ogden, J., Ogimoto, K., O'Malley, M., Outhred, H., Ulleberg, Ø., van Hulle, F., Bazilian, M., Beerepoot, M., Demayo, T., Denny, E., Infield, D., Keane, A., Lee, A., Milligan, M., Mills, A., Power, M., Smith, P., Söder, L., Tuohy, A., Ueckerdt, F., Zhang, J., Skea, J., & Strunz, K.
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Renewable Energy Sources and Climate Change Mitigation
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Cambridge University Press
Published
2011-11-21
Pages
609-706
DOI
10.1017/cbo9781139151153.012
OpenAlex
W1608021527
Cited by
98
References
288
Citations by year
2026: 2 · 2025: 5 · 2024: 5 · 2023: 5 · 2022: 2 · 2021: 4 · 2020: 4 · 2019: 3 · 2018: 6 · 2017: 6 · 2016: 13 · 2015: 6 · 2014: 11 · 2013: 16 · 2012: 7
FWCI
45.58
Type
book chapter
Language
en
Open access
Licence
www.cambridge.org/core/terms (unspecified, 14 d)
Locations
  • Cambridge University Press eBooks (publishedVersion)
  • Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion)
  • Lund University Publications (Lund University) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Renewable Energy and Sustainability
  • Physical Sciences › Energy › Energy Engineering and Power Technology › Power Systems and Renewable Energy
Keywords
renewable energy integration, renewable energy share, energy flexibility, combined heat and power, energy storage, demand response, renewable energy market, renewable energy deployment, 100% renewable electricity, biofuels, solar fuels, ocean energy, distributed generation, electric vehicles, capacity building, OECD countries, non-OECD countries

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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