A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China

Xy, Zheng; Wu, Guoce; Qiu, Yuwei; Zhan, Xiangyan; Shah, Nilay; Li, Ning; Zhao, Yingru

doi:10.1016/j.apenergy.2017.06.038

Cited by 98 publications

(44 citation statements)

References 40 publications

(44 reference statements)

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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%

“…A particular PM is denoted, p j,ni , where j is an index corresponding to the PM when more than one is connected to a terminal ECM node n i . The net energy demand of a particular prosumer at sampling instant k, denoted as P pj,n i (k) is given by (8).…”

Section: Storage Modelmentioning

confidence: 99%

“…positive P pj,n i (k) represents production and negative P pj,n i (k) represents consumption. As such, end users that are either sole consumers or producers can be represented by (8) with G m (k) = 0 or L n (k) = 0 respectively. As discussed in the following sub-sections prosumer components can be one of two types: fixed or flexible.…”

Section: Storage Modelmentioning

confidence: 99%

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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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Section: A Related Literaturementioning

confidence: 99%

Section: Storage Modelmentioning

confidence: 99%

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Generalised control-oriented modelling framework for multi-energy systems

2019

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“…Unlike previous studies, which ignored the real time variations of energy demand, they considered the effect of hourly demand fluctuations [24].…”

Section: Introductionmentioning

confidence: 99%

“…We can divide the optimization models for optimal design and operation of DER systems into two main groups: while the majority of the initial studies ( [2,[4][5][6][7][8][9]) deal with the determination of optimal configuration, in other words, optimal system combination and operational strategies for a single DER system, more recent works ( [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]) also take into account the pipeline network, in more detail. Nevertheless, there is no doubt that the complexity is further increased when the DER systems are designed for covering the demands of multiple end users at the neighborhood scale, which often involves also the design of a heating pipeline network, concurrently.…”

Section: Introductionmentioning

confidence: 99%

Allocation of Distributed Energy Systems at District-Scale over Wide Areas for Sustainable Urban Planning with a MILP Model

Atak

2018

Mathematical Problems in Engineering

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An optimal allocation of centralized district-scale distributed energy resource (DER) systems with district heating and cooling network (DHCN) is studied. A generic mixed integer linear programming (MILP) model is constructed to increase the system efficiency and decrease costs by reducing energy distribution losses and transportation costs in energy distribution network. Initial investment costs based on size and type (co/trigeneration) of the facility and demand-weighted transportation costs are minimized by the capacitated fixed charge facility location (FCFL) model. However, unlike the standard FCFL model, by adding the maximum coverage distance in the set covering problem, a new method has been proposed. Thus, its aim is to avoid assigning a demand point to a candidate facility from a point farther than the predetermined distance. That means that the weakness of disregarding the distance between supply and demand points in the FCFL problem is eliminated. Additionally, this model, in which the annual inputs are used, has a generic framework suitable to form infrastructure needs in consideration of distributed energy systems in the general planning level for sustainable urban planning. For this purpose, the applications of the model, both case study and tests, have been made over wide areas, with annual demand and capacity values. Consequently, a case study with different coverage distances has been conducted to see the effect of coverage distance on the model, and also test problems with different sizes have been carried out to demonstrate the capability of the proposed model.

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An inexact optimization model for distributed multi‐energy systems management in sustainable airports

Jin

2021

Int J Energy Res

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This paper proposes a fuzzy chance-constrained fractional programming (FCFP) method for planning distributed multi-energy systems (DMES). FCFP can deal with uncertainties expressed as fuzzy information, probability distributions, and multiple objectives. The FCFP-DMES model was applied to a real airport in a case study, and a series of scenarios were selected to examine the effects of the uncertainty on the energy supply and technology selection. Additionally, a comparison related to conventional energy system (CES) and DMES are discussed from energy consumption, economic, and environmental aspects. The results revealed the following: the combined cooling, heat, and power would serve as a primary distributed energy resource providing heating, cooling, and electricity in different seasons, accounting for approximately 40% of the total; among different alternative technologies, heating supplied by gas-fired boiler and thermal storage would serve as auxiliary heaters to cover 6.6% and 15.2% of the heating load, respectively, under high-level demand; although the DMES cannot bring cost-cutting, it has better environmental performance and a peak shaving function. Compared with the DMES, the CES would almost double the electricity purchasing cost (reaching $9.56 million), and an additional 136.24 MW of electricity would be needed, which would result in 127.5 tons/year of pollutant emissions. The findings of this study indicate that the FCFP-DMES model can provide a comprehensive and systematic strategy considering the multi-energy, multi-technology, and multi-uncertainty within the DMES.

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A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China

Cited by 98 publications

References 40 publications

Generalised control-oriented modelling framework for multi-energy systems

Generalised control-oriented modelling framework for multi-energy systems

Allocation of Distributed Energy Systems at District-Scale over Wide Areas for Sustainable Urban Planning with a MILP Model

An inexact optimization model for distributed multi‐energy systems management in sustainable airports

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