Optimizing the production and distribution system of bioenergy villages

Uhlemair, Harald; Karschin, Ingo; Geldermann, Jutta

doi:10.1016/j.ijpe.2012.10.003

Cited by 21 publications

(10 citation statements)

References 9 publications

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“…The case study aims to illustrate the MAMCA method's functionalities and to highlight this method's distinctive contributions to decisions in the context of energy scenario planning. We do so by examining a bioenergy village in Lower Saxony, Germany [7,60]. The village's goal is to transition to a self-sufficient power supply by expanding the capacity of its renewable energy technologies.…”

Section: Application Of the Multiactor Multicriteria Analysis For Thementioning

confidence: 99%

“…Allowing citizens and companies to invest in renewable energy and thereby become independent power producers has not only advanced the population's acceptance of renewable energy but has also accelerated the move towards a more decentralised and sustainable power supply [6,7]. Consequently, energy policies need to take divergent groups of key actors and stakeholders' viewpoints into account.…”

Section: Introductionmentioning

confidence: 99%

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Adopting Multiactor Multicriteria Analysis for the Evaluation of Energy Scenarios

Schär

Geldermann

2021

Sustainability

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The assessment of future options and pathways for sustainable energy systems requires considering multiple techno-economic, ecological and social issues. Multicriteria analysis methods, which are useful tools that aid decision processes involving various and even conflicting qualitative and quantitative criteria, could support such comprehensive analyses. With regard to energy policies, the key actors and stakeholders’ acceptance of emerging and innovative technologies for generating, converting and storing electricity, heat and fuels is crucial for their future implementation. The multiactor multicriteria (MAMCA) methodology was developed to involve stakeholders with vastly different views and objectives when addressing complex societal problems. We extend the MAMCA methodology to include the outranking approach PROMETHEE, which allows us to explicitly consider the stakeholders’ objectives in the evaluation process. The MAMCA method with PROMETHEE is applied to a case study of four different transition pathways of providing electricity to a bioenergy village in Germany. The explicit mapping at hand of an illustrative case study could help researchers and decision makers greatly in the assessment of pathways for sustainable energy systems; it is also applicable in other contexts requiring extensive stakeholder involvement and where qualitative and quantitative criteria are to be considered simultaneously. The detailed sensitivity analysis provided by the extension of the MAMCA method with PROMETHEE not only reveals the stakeholders’ crucial trade-offs when allowing each stakeholder group to develop its own set of criteria and weights but also indicates compromise options.

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Section: Application Of the Multiactor Multicriteria Analysis For Thementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adopting Multiactor Multicriteria Analysis for the Evaluation of Energy Scenarios

Schär

Geldermann

2021

Sustainability

Self Cite

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“…Sustainable development advocates that consumption should be close to the site of generation to minimize the length of the distribution line, which is critical to pressure drop, heat loss and investment cost. In terms of solution approaches, it is feasible to evaluate the optimal configuration of the piping network using mixed-integer programming models, genetic algorithm, and probabilistic search heuristic [39], [81], [82], [83] and [84]. Alternative distribution schemes are assessed thoroughly before construction to understand the distribution system configuration and to further minimize the installation expenditure and operation cost.…”

Section: Fig 6 Three Types Of Distribution Networkmentioning

confidence: 99%

District heating and cooling optimization and enhancement – Towards integration of renewables, storage and smart grid

Liu

Rezgui

Zhu

2017

Renewable and Sustainable Energy Reviews

133

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Optimization and Enhancement of DHC networks to reduce (a) fossil fuel consumption, CO 2 emission, and heat losses across the network, while (b) increasing return on investment, forming key challenges faced by decision makers in the fast developing energy landscape. While the academic literature is abundant of research based on field experiments, simulations, optimization strategies and algorithms etc., there is a lack of a comprehensive review that addresses the multi-faceted dimensions of the optimization and enhancement of DHC systems with a view to promote integration of smart grids, energy storage and increased share of renewable energy. The paper focuses on four areas: energy generation, energy distribution, heat substations, and terminal users, identifying state-of-the-art methods and solutions, while paving the way for future research.

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“…Kong et al (2005); Makkonen and Lahdelma (2006); and Mixed Integer Linear Programming, (MILP) e.g. Dimitriadis et al (1997); Gustafsson (1998); Yokoyama et al (2002); Hiremath et al (2007); Sugihara et al (2008); Chinese (2008); Lozano et al (2009) ;Casisi et al (2009) ;Li et al (2010b); Keirstead et al (2012b); Mehleri et al (2012a); Fazlollahi and Marchal (2013); Pruitt et al (2013); Omu et al (2013a); Uhlemair et al (2014); Pantaleo et al (2014); Bischi et al (2014); Marquant et al (2015b); Moreno et al (2015); Silvente et al (2015); Morvaj et al (2016b). Recently numerous holistic methods have been developed to tackle the complex problem of the design and operation of multiple energy systems at different scales.…”

Section: Research In Energy Optimisationmentioning

confidence: 99%

A holarchic approach for multi-scale distributed energy system optimisation

et al. 2017

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The benefits of decentralized energy systems can be realised through the optimal siting of distributed energy systems and the design of highly interlinked district heating networks within existing electrical and gas networks. The problem is often formulated as a Mixed Integer Linear Programming (MILP) problem. MILP formulations are efficient and reliable, however the computational burden increases drastically with the number of integer variables, making detailed optimisation infeasible at large urban scales. To tackle complex problems at large scale the development of an efficient and robust simplification method is required. This paper presents an aggregation schema to facilitate the optimisation of urban energy systems at city scale.Currently, spatial and/or temporal aggregation are commonly employed when modelling energy systems at spatiotemporal resolutions from plant scheduling up to national scenarios. This paper argues for solving different scales separately using a bottom-up approach, while keeping track of the error made by reducing the resolution when moving from building to urban scale. Novel modelling formulations and optimisation techniques are presented. They enable drastic reduction of the computational time (by up to a factor of 100) required to find an optimal solution in reasonable time without sacrificing the quality of the results (no more than 1% loss in accuracy).A density-based clustering algorithm enables intelligent division of a large city-scale problem into sub-optimisation problems by creating clusters of different density. In each cluster, the trade-off between centralized and decentralized energy systems and the associated district heating network design is evaluated. A solution is selected based on a local optimisation of the network costs. Demand profiles of each building are assigned appropriately, then at an upper level the energy optimisation problem is solved considering the network losses at lower levels. This method enables large-scale modelling of urban energy systems while taking into account building-scale levels of detail. The clustering method enables assessment of the potential of district heating networks on city scale based on building characteristics and available urban energy systems.

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Optimizing the production and distribution system of bioenergy villages

Cited by 21 publications

References 9 publications

Adopting Multiactor Multicriteria Analysis for the Evaluation of Energy Scenarios

Adopting Multiactor Multicriteria Analysis for the Evaluation of Energy Scenarios

District heating and cooling optimization and enhancement – Towards integration of renewables, storage and smart grid

A holarchic approach for multi-scale distributed energy system optimisation

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