Multi-objectives, multi-period optimization of district energy systems: III. Distribution networks

Fazlollahi, Samira; Becker, Gwénaëlle; Maréchal, François

doi:10.1016/j.compchemeng.2014.02.018

Cited by 48 publications

(21 citation statements)

References 29 publications

(41 reference statements)

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“…Equation (4) indicates that the greater the similarity between the synthesis result and the reference image, the larger the peak signal to noise ratio is, and the stronger the noise immunity performance of the algorithm is.…”

Section: As Follows)mentioning

confidence: 99%

“…In the multiple synthesis methods, the threshold synthesis method has been widely applied in the image processing field due to its simplicity and effectiveness [3], in which, the maximum between-class variance method is one of the most common and important synthesis methods. The maximum between-class variance method only takes the gray value information of the pixel into consideration while ignoring its spatial correlation; hence, the algorithm has relatively poor noise immunity [4,5]. To solve this issue, the two-dimensional Otsu method is put forward by introducing the mean value of the pixel neighborhood, which has enhanced the noise immunity of the algorithm to some extent.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive wireless network multi-objective optimization algorithm based on image synthesis

Zhang

Xueya

2018

J Image Video Proc.

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Multi-objective optimization problems can be divided into continuous multi-objective optimization problems and discrete multi-objective optimization problems, and discrete multi-objective optimization is not universal. In practical applications, there are many discrete multi-objective optimization problems. The solution of different problems needs to design different evolutionary multi-objective algorithms according to their specific conditions. The threshold in the traditional multi-objective optimization of wireless network is a preset constant. It has relatively poor performance in the synthesis of images with different noise levels. An adaptive wireless network multi-objective optimization algorithm based on image synthesis is proposed. Based on the maximum inter class variance and maximum peak signal to noise ratio (SNR), an adaptive wireless network multi-objective optimization algorithm is established. The accuracy and noise immunity of image synthesis are also considered. In order to avoid the effect of threshold increase on algorithm efficiency, multi-objective optimization algorithm is introduced into the algorithm. Experiments show that the method proposed in this paper is accurate and robust and has good universality for the synthesis of different noise images.

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Section: As Follows)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive wireless network multi-objective optimization algorithm based on image synthesis

Zhang

Xueya

2018

J Image Video Proc.

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“…Bujalski [7] has proposed a solution for optimal scheduling of a CHP plant operation, with the use of a storage tank. Fazlollahi et al [8] have proposed a procedure for optimization in DH on the stage of design and operation. Ziębik and Gładysz [9] have proposed an empirical formula for determination of the optimal value of the share of cogeneration in cogeneration systems with thermal storages.…”

Section: Current State Of Researchmentioning

confidence: 99%

Modeling of District Heating Networks for the Purpose of Operational Optimization with Thermal Energy Storage

Leśko

Bujalski

2017

Archives of Thermodynamics

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The aim of this document is to present the topic of modeling district heating systems in order to enable optimization of their operation, with special focus on thermal energy storage in the pipelines. Two mathematical models for simulation of transient behavior of district heating networks have been described, and their results have been compared in a case study. The operational optimization in a DH system, especially if this system is supplied from a combined heat and power plant, is a difficult and complicated task. Finding a global financial optimum requires considering long periods of time and including thermal energy storage possibilities into consideration. One of the most interesting options for thermal energy storage is utilization of thermal inertia of the network itself. This approach requires no additional investment, while providing significant possibilities for heat load shifting. It is not feasible to use full topological models of the networks, comprising thousands of substations and network sections, for the purpose of operational optimization with thermal energy storage, because such models require long calculation times. In order to optimize planned thermal energy storage actions, it is necessary to model the transient behavior of the network in a very simple way -allowing for fast and reliable calculations. Two approaches to building such models have been presented. Both have been tested by comparing the results of simulation of the behavior of the same network. The characteristic features, advantages and disadvantages of both kinds of models have been identified. The results can prove useful for district heating system operators in the near future.

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“…Domínguez-Muñoz et al (2011) select a user-chosen number of typical days optimally selected using a k-medoids clustering algorithm. Based on a k-means clustering algorithm assisted by an constraint optimisation technique, Fazlollahi et al (2014a) use typical periods selected from multiple demand profiles, minimising the number of typical periods while accounting for extreme demand peaks. To compute the optimal design of a distributed energy systems for a neighbourhood of 10 buildings, Mehleri et al (2012b) divide a year into 18 different time periods (6 periods per day, for summer, mid-season and winter).…”

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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Multi-objectives, multi-period optimization of district energy systems: III. Distribution networks

Cited by 48 publications

References 29 publications

Adaptive wireless network multi-objective optimization algorithm based on image synthesis

Adaptive wireless network multi-objective optimization algorithm based on image synthesis

Modeling of District Heating Networks for the Purpose of Operational Optimization with Thermal Energy Storage

A holarchic approach for multi-scale distributed energy system optimisation

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