A holarchic approach for multi-scale distributed energy system optimisation

Marquant, Julien F.; Evins, Ralph; Bollinger, L. Andrew; Carmeliet, Jan

doi:10.1016/j.apenergy.2017.09.057

Cited by 62 publications

(23 citation statements)

References 53 publications

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“…Clusters and typologies have often been applied at the district scale, in identifying the most cost-effective low carbon energy solution for different types of districts (Hargreaves et al 2017;McKenna et al 2016McKenna et al , 2017aSu et al 2017), as well as at the building scale, for example in the context of residential heat demand studies (McKenna et al 2016(McKenna et al , 2017a. In addition, Marquant et al (2017) present a holistic approach for optimisation of multi-scale distributed energy systems, by employing clusters of similar buildings at the district level.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing a municipality typology for modelling decentralised energy systems

2019

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The recent rapid expansion of renewable energy capacities in Germany has been dominated by decentralised wind, photovoltaic (PV) and bioenergy plants. The spatially disperse and partly unpredictable nature of these resources necessitates an increasing exploitation of integration measures such as curtailment, supply and demand side flexibilities, network strengthening and storage capacities. Indeed, one solution to the large-scale integration of renewable energies could be decentralised autonomous municipal energy systems. The achievement of grid parity for some renewable energy technologies has strengthened the desire of some communities to become independent from central markets. Whilst many communities in Germany already strive for socalled energy autonomy, the vast majority do so only on an annual basis. Several studies have already analysed the technical and economic implications of the mainly decentralised future energy system, but most are restricted in their insights by limited temporal and spatial resolution. The large number (11,131) of German municipalities means that a national analysis at this resolution is not feasible. Hence, this study employs a cluster analysis to develop a municipality typology in order to analyse the techno-economic suitability of these municipalities for autonomous energy systems. A total of 34 socio-technical indicators are employed at the municipal level, with a particular focus on the sectors of Private Households and Transport, and the potentials for decentralised renewable energies. The first step is to scale the indicator values and reduce their number by using a factor analysis. Several alternative methods are weighed against each other, and the most suitable methods for the factor analysis are chosen. Secondly, selected quantitative cluster validation methods are employed alongside qualitative criteria to determine the optimal number of clusters. This results in a total of ten clusters, which show a large variation as well as some overlap with respect to specific indicators. For example, one cluster contains all major German cities and has a low potential for renewable energies. Another cluster, on the other hand, contains the municipalities with a higher potential for renewable energies due to their high hydrothermal potential for geothermal power. An analysis of the municipalities from three German renewable energy projects "Energy Municipalities", "Bioenergy Villages" and "100% Renewable Energy Regions" shows that in eight of the ten clusters municipalities are aiming for energy autonomy (in varying degrees). It is challenging to differentiate between the clusters regarding readiness for energy autonomy projects, however, especially if the degree of social acceptance and engagement for such projects is to be considered. To answer the more techno-economical part of this question, future work will employ the developed clusters in the context of an energy system optimisation. Insights gained at the

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Section: Literature Reviewmentioning

confidence: 99%

Developing a municipality typology for modelling decentralised energy systems

2019

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“…An excellent resource, containing software tools for energy hub modelling can be found in [6]. The energy hub modelling approach has been applied to a variety operational challenges associated with MES such as expansion planning [7] and [8], optimal design [9] and [10], resource optimisation [11] and [12] and reliability assessment [13]. The energy hub is capable of representing MES flexibility provided by energy storage and the increased degrees of freedom in the energy resource dispatch.…”

Section: A Related Literaturementioning

confidence: 99%

Generalised control-oriented modelling framework for multi-energy systems

2019

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A requirement for new sources of flexibility in meeting energy demand and the growing interest in the interaction between energy sectors have drawn attention to multienergy systems, which can offer numerous benefits, e.g. providing flexibility to counteract the intermittency of renewable generation and increase energy efficiency. In order to maximise these benefits, advanced control methods are required. The model predictive control methodology is a promising candidate for such applications as it is capable of incorporating economic and operational objectives whilst respecting various technical, regulatory and environmental constraints. In order to implement such a control strategy effectively, it is necessary to develop appropriate system models. This paper presents a novel generalised modelling framework for multi-energy systems that is particularly well suited, though not limited to, predictive control applications. The proposed approach is capable of representing energy converter arrangements of arbitrary complexity containing multiple energy vectors, as well as multi-directional energy flow, multi-generation and multi-mode devices, a wide range of controllable producers/consumers, energy storage and flexible loads. The effectiveness of the approach is demonstrated using a representative case study based on three buildings at the University of Manchester where the developed model is incorporated into a model predictive control scheme. The simulation results show how the controller minimises the cost of purchasing energy whilst satisfying operational constraints, and managing various types of flexible demand.

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“…The computational effort can be reduced if the whole problem is faced by defining two (or more) levels, which can be solved dealing with a reduced number of independent variables [15], or if it is decomposed into many sub-problems, which have to be solved with an iterative procedure [16][17][18]. In both cases the solution could be more easily obtained if the problem were described with sufficient precision by means of a linear model (MILP), even if additional variables are introduced for considering strong non-linear relations among flows of both energy and mass [10,11].…”

Section: Introductionmentioning

confidence: 99%

Two-Level Evolutionary Multi-Objective Optimization of a District Heating System with Distributed Cogeneration

Casisi¹,

Costanzo²,

Pinamonti³

et al. 2018

Preprint

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The paper deals with the modelization and optimization of an integrated multi-component energy system. On-off operation and presence-absence of components must be described by means of binary decision variables, besides equality and inequality constraints; furthermore, the synthesis and the operation of the energy system should be optimized at the same time. In this paper a hierarchical optimization strategy is used, adopting a genetic algorithm in the higher optimization level, to choose the main binary decision variables, whilst a MILP algorithm is used in the lower level, to choose the optimal operation of the system and to supply the merit function to the genetic algorithm. The method is then applied to a distributed generation system, which has to be designed for a set of users located in the center of a small town in the North-East of Italy. The results show the advantage of distributed cogeneration, when the optimal synthesis and operation of the whole system are adopted, and significant reduction in the computing time by using the proposed two-level optimization procedure.

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A holarchic approach for multi-scale distributed energy system optimisation

Cited by 62 publications

References 53 publications

Developing a municipality typology for modelling decentralised energy systems

Developing a municipality typology for modelling decentralised energy systems

Generalised control-oriented modelling framework for multi-energy systems

Two-Level Evolutionary Multi-Objective Optimization of a District Heating System with Distributed Cogeneration

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