Bi-stage stochastic model for optimal capacity and electric cooling ratio of CCHPs—a case study for a hotel

Zhang, Tao; Wang, Minli; Wang, Peihong; Gu, Jiangqi; Zheng, Weidong; Dong, Yali

doi:10.1016/j.enbuild.2019.04.004

Cited by 38 publications

(14 citation statements)

References 36 publications

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“…For the sake of simplicity, some literature directly gives a number of typical scenarios to describe randomness. Reference [22] gives the typical curves of cooling, heating, and electrical load and solar radiation in different seasons to illustrate these uncertainties, which does not make cluster analysis or scenario reduction. Furthermore, there are many frequently used scenario-clustering methods applied in the energy field, such as K-mean [23,24], Fuzzy c-mean [25,26], K-harmonic means [27,28], and K-shape [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

Liu

Nie

2019

Sustainability

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With the rapid transformation of energy structures, the Integrated Energy System (IES) has developed rapidly. It can meet the complementary needs of various energy sources such as cold, thermal, and electricity in industrial parks; can realize multi-energy complements and centralized energy supplies; and can further improve the use efficiency of energy. However, with the extensive access of renewable energy, the uncertainty and intermittentness of renewable energy power generation will greatly reduce the use efficiency of renewable energy and the supply flexibility of IES so as to increase the operational risk of the system operator. With the goal of minimum sum of the system-operating cost and the carbon-emission penalty cost, this paper analyzes the combined supply of cooling, heating, and power (CCHP) influence on system efficiency, compared with the traditional IES. The flexible modified IES realizes the decoupling of cooling, thermal, and electricity; enhances the flexibility of the IES in a variety of energy supply; at the same time, improves the use efficiency of multi-energy; and reasonably avoids the occurrence of energy loss and resource waste. With the aim of reducing the risk that the access of renewable energy may bring to the IES, this paper introduces the fuzzy c-mean-clustering comprehensive quality (FCM-CCQ) algorithm, which is a novel method superior to the general clustering method and performs cluster analysis on the output scenarios of wind power and photovoltaic. Meanwhile, conditional value at risk (CVaR) theory is added to control the system operation risk, which is rarely applied in the field of IES optimization. The model is simulated in a numerical example, and the results demonstrate that the availability and applicability of the presented model are verified. In addition, the carbon dioxide emission of the traditional operation mode; thermoelectric decoupling operation mode; and cooling, thermal, and electricity decoupling operation mode of the IES decrease successively. The system flexibility is greatly enhanced, and the energy-use rate of the system is improved as a whole. Finally, IES, after its flexible transformation, significantly achieve energy conservation, emission reduction, and environmental protection.

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Section: Literature Reviewmentioning

confidence: 99%

Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

Liu

Nie

2019

Sustainability

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“…In the CCHP system, input technical parameters have been summarized in Table 2 [36,37]. The commercial electric price and natural gas price of Shanghai are shown in Table 3 [38].…”

Section: Pre-set Parametersmentioning

confidence: 99%

Sustainability Assessment of Combined Cooling, Heating, and Power Systems under Carbon Emission Regulations

2019

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The combined cooling, heating, and power (CCHP) system, which is a sustainable distributed energy system, has attracted increasing attention due to the associated economic, environmental, and energy benefits. Currently, the enforcement of carbon emission regulations has become an increasingly concerning issue globally. In this paper, a multi-objective optimization model is established to evaluate the CCHP system under two different carbon emission regulation policies in terms of economic benefit, environmental sustainability, and energy advantage. A nonlinear programming optimization model is formulated and solved by using the particle swarm optimization (PSO) algorithm. The results from the case studies demonstrate that when considering carbon tax regulation, the cost savings of the optimal CCHP system strategy were on average 10.0%, 9.1%, 17.0%, 22.1%, and 20.9% for the office, supermarket, hotel, school, and hospital in China, respectively, compared with the conventional energy supply system. On the other hand, when considering carbon trading regulation, the optimal CCHP system strategy can lead to a 10.0%, 8.9%, 16.8%, 21.6%, and 20.5% cost-saving for the five different building categories, respectively. Furthermore, the optimal CCHP system strategy for the five buildings, i.e., an average of 39.6% carbon dioxide emission (CDE) reduction and 26.5% primary energy consumption (PEC) saving, can be achieved under carbon emission regulations.

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“…Several approaches can be used to address those impacts, like stochastic optimization [13]- [14], chance-constrained stochastic optimization [15]- [16], fuzzy optimization [17], and robust optimization [18]. Zhang et al [19] proposed a bi-stage stochastic model to optimize both facility capacity allocation and the electric cooling ratio as well to improve the integrated performance under several uncertainties in energy supply and demand sides. Onishi et al [20] proposed a stochastic model to optimize the design and operation of a trigeneration system considering the uncertainties of multi-energy demand and the long-term energy prices.…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Demand-Controlled Operation Strategy for a Multi-Energy System

et al. 2021

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The multi-energy system is a promising energy-efficient technology to supply electric and thermal energy to end-users simultaneously, which can realize the energy cascade utilization. However, it is challenging to optimize the operation of multi-energy systems due to their inherent structural complexity, as well as the highly coupled nature of multiple energy flows and the uncertainty of renewable energy generation. This paper proposed a collaborative demand-controlled operation strategy for a multi-energy system, which consists of an upper-level model and a lower-level model. In the upper-level model, a robust linear optimization method is adopted to optimize the system operation in a day-ahead stage. In the lowerlevel model, a stochastic rolling optimization method is applied to achieve a dynamic adjustment to cope with the fluctuation in both renewable electricity generation and electric load. The multiple energy demandcontrolled strategy is also applied in the optimal operation strategy to achieve load shifting and to create flexibility in energy demand despite the "source-load" imbalance power fluctuation. A case study is carried out and simulation results verify the effectiveness and correctness of the proposed model of the coordinated operation framework.INDEX TERMS Multi-energy system; robust linear optimization; indoor temperature control; demand response; optimal operation This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Bi-stage stochastic model for optimal capacity and electric cooling ratio of CCHPs—a case study for a hotel

Cited by 38 publications

References 36 publications

Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

Sustainability Assessment of Combined Cooling, Heating, and Power Systems under Carbon Emission Regulations

A Collaborative Demand-Controlled Operation Strategy for a Multi-Energy System

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