A distributed decision framework for building clusters with different heterogeneity settings

Jafari-Marandi, Ruholla; Hu, Mengqi; Omitaomu, Olufemi A.

doi:10.1016/j.apenergy.2015.12.088

Cited by 46 publications

(11 citation statements)

References 41 publications

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“…Figure 15 shows both of these parameters per cluster on the two heat-map diagonals and the evolution of those parameters when two clusters are grouped, which can be seen as interconnecting two clusters. Figure 15a shows the Homogeneity Index (HI) as defined by Jafari-Marandi et al (2016), which is calculated for the heating demand profiles. This index represents the average value of the correlations in the heating load time series between houses within a cluster (on the diagonal), or within houses of two different clusters (on the upper or lower triangle).…”

Section: Discussionmentioning

confidence: 99%

“…A decrease of the homogeneity index indicates an increase in the heterogeneity of the cluster, meaning the possibility of having shifted loads between energy consumers. It is calculated per cluster according to the definition of Jafari-Marandi et al (2016), where i is the index of different clusters and M cl i j is the j-th building member of cluster i. N cl i is the number of buildings within a cluster: Figure 15a can be seen as a matrix of the possibility of flattening the load curve which ensues from aggregating building loads within a cluster (in the diagonal) or with another cluster. The lower and greener the HI, the more heterogeneously are the loads of the different buildings distributed in the corresponding clusters.…”

Section: Discussionmentioning

confidence: 99%

“…Outliers are detected and removed manually based on high energy demand profiles. Lately, Jafari-Marandi et al (2016) investigated possibilities of clustering based on load profiles considering the heterogeneity index of a set of buildings.…”

Section: Research In Energy Optimisationmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A holarchic approach for multi-scale distributed energy system optimisation

et al. 2017

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“…A self-organizing map based clustering algorithm is proposed in Ref. [26] to group different micro-grids into different clusters based on their energy profiles, and a distributed decision model is proposed to study homogeneous and heterogeneous micro-grid clusters.…”

Section: Introductionmentioning

confidence: 99%

Balancing collective and individual interests in transactive energy management of interconnected micro-grid clusters

Chen

2016

Energy

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“…Hu et al [31,32] developed a decentralized operation determination model for an integrated building cluster with distributed energy systems. They also published a following study to investigate the building cluster self-organizing for heterogeneity settings [33]. Similar to these building cluster studies, another recent publication proposed a multi-party energy management for smart building cluster with PV energy generation for demand response [34].…”

Section: Introductionmentioning

confidence: 99%

An operation optimization and decision framework for a building cluster with distributed energy systems

Wen

Malkawi

2016

Applied Energy

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h i g h l i g h t sDeveloped and validated energy forecasting models for a building cluster with distributed energy systems. Proposed an operation optimization and decision framework for the building cluster. Optimized the operation strategies through particle swarm algorithm based multi-objective optimizations. Derived the Pareto frontiers to balance the energy cost saving and thermal comfort. a b s t r a c tDriven by the development of smart buildings and smart grids, numerous of research has focused on developing optimal operation strategies for smart buildings with the aims of reducing energy consumption and cost, as well as improving the grid reliability. Unfortunately, most of the studies from smart building perspective only target on a single building with elaborated energy forecasting models. Few of them addresses the effects of multiple buildings on power grid operation. On the other hand, a few studies from smart grid area focus on multiple buildings and their influence on power grid, they usually, however, use simplified linear energy forecasting models, which are hard to guarantee the findings reflecting the cases in real fields. As a result, this research proposes to bridge this research gap, through developing and validating high fidelity energy forecasting models for a building cluster with multiple buildings and distributed energy systems, as well as creating a collaborative operation framework to determining the optimal operation strategies of this building cluster. The operation framework utilizes multi-objective optimizations to determine the operation strategies: building temperature setpoints, energy storage charging and discharging schedules, etc., using particle swarm optimization. Pareto curves for energy cost saving and thermal comfort maintaining are also derived with different thermal comfort requirements. The results from this study show that the developed building cluster collaborative operation framework is able to reduce the energy cost by 12.1-58.3% under different electricity pricing plans and thermal comfort requirements.

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A distributed decision framework for building clusters with different heterogeneity settings

Cited by 46 publications

References 41 publications

A holarchic approach for multi-scale distributed energy system optimisation

A holarchic approach for multi-scale distributed energy system optimisation

Balancing collective and individual interests in transactive energy management of interconnected micro-grid clusters

An operation optimization and decision framework for a building cluster with distributed energy systems

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