Energy and emission management of CCHPs with electric and thermal energy storage and electric vehicle

Mohammadkhani, Nahid; Sedighizadeh, Mostafa; Esmaili, Masoud

doi:10.1016/j.tsep.2018.10.005

Cited by 66 publications

(26 citation statements)

References 34 publications

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“…AUGMECON has been employed in various applications and systems, including supply chain management (Torabi et al 2013;Bootaki et al 2014Bootaki et al , 2016Canales-Bustos et al 2017;Musavi and Bozorgi-Amiri 2017;Ravat et al 2017;Vieira et al 2017;Sazvar et al 2018;Ehrenstein et al 2019;Oiu et al 2019;Shekarjan et al 2019;Xin et al 2019), energy planning (Hombach and Walther 2015;Tartibu et al 2015;Arancibia et al 2016;Mohammadkhani et al 2018;Rabbani et al 2018;Sedighizadeh et al 2018;Razm et al 2019), waste management Mavrotas et al 2015a, b;Inghels et al 2016), portfolio analysis (Xidonas et al 2010(Xidonas et al , 2011 Mavrotas and Florios (2013) further extended this algorithm in AUGMECON 2, by introducing a bypass coefficient as well as a type of lexicographic optimisation to all objective functions, the order of which was insignificant in AUGMECON:…”

Section: A Brief Overview Of the Augmented ε-Constraint Methodsmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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Systems can be unstructured, uncertain and complex, and their optimisation often requires operational research techniques. In this study, we introduce AUGMECON-R, a robust variant of the augmented ε-constraint algorithm, for solving multiobjective linear programming problems, by drawing from the weaknesses of AUG-MECON 2, one of the most widely used improvements of the ε-constraint method. These weaknesses can be summarised in the ineffective handling of the true nadir points of the objective functions and, most notably, in the significant amount of time required to apply it as more objective functions are added to a problem. We subsequently apply AUGMECON-R in comparison with its predecessor, in both a set of reference problems from the literature and a series of significantly more complex problems of four to six objective functions. Our findings suggest that the proposed method greatly outperforms its predecessor, by solving significantly less models in emphatically less time and allowing easy and timely solution of hard or practically impossible, in terms of time and processing requirements, problems of numerous objective functions. AUGMECON-R, furthermore, solves the limitation of unknown nadir points, by using very low or zero-value lower bounds without surging the time and resources required.

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Section: A Brief Overview Of the Augmented ε-Constraint Methodsmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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“…However, the thermal energy storage, allowing the recovered heat to be stored for later use, is considered to be an effective way to dealing with the mismatch between electric and thermal demands. Mohammadkhani et al [16] proposed an energy management of a CCHP system with thermal and electrical energy storages. A following the hybrid electric-thermal load (FHL) operation strategy was proposed to cut the cost and emissions of the CCHP system.…”

Section: Introductionmentioning

confidence: 99%

An Effective Operation Strategy for CCHP System Integrated with Photovoltaic/Thermal Panels and Thermal Energy Storage

Mao

Jiekang

Zhang³

2020

Energies

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The combined cooling, heating and power (CCHP) system is a promising energy-efficient technology to realize energy cascade utilization. With the integration of photovoltaic/thermal panels and thermal energy storage, the comprehensive performance of the system can be further improved. However, the performance is also affected by the operation strategy. This paper proposes an effective operation strategy to deal with the energy flow of the system well to achieve a better performance. The mathematical model of a CCHP system hybridized with photovoltaic/thermal panels and thermal energy storage is established in this paper. The optimal size of key components of the CCHP system is determined by the particle swarm optimization (PSO) algorithm. Case studies of four scenarios of a residential zone in Beijing are conducted to verify the effectiveness of the system structure and efficiency of the proposed method. The results show that by adding photovoltaic/thermal (PV/T) panels and thermal energy storage, the economic and energetic benefits can be effectively improved and the proposed operation strategy is effective.

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“…Liu et al proposed a CCHP system with TES as an energy station [7]. Mohammadkhani et al proposed a residential microgrid equipped by CCHP with considering electrical energy storage and thermal energy storage [8].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Optimization of Multi-Energy Complementary Combined Cooling, Heating, and Power Systems Considering Schedulable Loads

et al. 2020

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Combined cooling, heating, and power (CCHP) systems are a promising energy-efficient and environment-friendly technology. However, their performance in terms of energy, economy, and environment factors depends on the operation strategy. This paper proposes a multi-energy complementary CCHP system integrating renewable energy sources and schedulable heating, cooling, and electrical loads. The system uses schedulable loads instead of energy storage, at the same time, a collaborative optimization scheduling strategy, which integrates energy supply and load demand into a unified optimization framework to achieve the optimal system performance, is presented. Schedulable cooling and heating load models are formulated using the relationship between indoor and outdoor house temperatures. A genetic algorithm is employed to optimize the overall performance of energy, economy, and environment factors and obtain optimal day-ahead scheduling scheme. Case studies are conducted to verify the efficiency of the proposed method. Compared with a system involving thermal energy storage and demand response (DR), the proposed method exhibits a higher primary energy saving rate, greenhouse gas emission reduction rate, and operation costs saving rate of 7.44%, 6.59%, and 4.73%, respectively, for a typical summer day, thereby demonstrating the feasibility and superiority of the proposed approach.

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Energy and emission management of CCHPs with electric and thermal energy storage and electric vehicle

Cited by 66 publications

References 34 publications

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

An Effective Operation Strategy for CCHP System Integrated with Photovoltaic/Thermal Panels and Thermal Energy Storage

Collaborative Optimization of Multi-Energy Complementary Combined Cooling, Heating, and Power Systems Considering Schedulable Loads

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