Energy matching and optimization analysis of waste to energy CCHP (combined cooling, heating and power) system with exergy and energy level

Gao, Penghui; Dai, Yanjun; Tong, Yen Wah; Dong, Pinliang

doi:10.1016/j.energy.2014.11.050

Cited by 62 publications

(19 citation statements)

References 18 publications

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“…It was recommended doing minor changes to the operation mode of the airport to reduce energy consumption and pollution's emission. Gao et al [8] are implemented energy level and exergy analysis on energy conversion processes to reveal the variation of energy amount and quality in the operation of CCHP system. Moreover, based on the energy level analysis, the methodology of energy matching and optimization for the CCHP system is proposed by them.…”

Section: Introductionmentioning

confidence: 99%

Exergoeconomic comparison of three novel trigeneration systems using SOFC, biomass and solar energies

Baghernejad

Yaghoubi

Jafarpur

2016

Applied Thermal Engineering

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Section: Introductionmentioning

confidence: 99%

Exergoeconomic comparison of three novel trigeneration systems using SOFC, biomass and solar energies

Baghernejad

Yaghoubi

Jafarpur

2016

Applied Thermal Engineering

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“…Various energy management strategies have been used in the literature for scheduling microgrids/multi-microgrids [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Among those, the cooperative multi-microgrid community has gained popularity due to its merits like minimum operation cost of the entire network, network level resource-aware optimization, better utilization of efficient units of individual microgrids, etc.…”

Section: System Configurationmentioning

confidence: 99%

“…Same is the case with startup indicator of large CHP and ADC as given by Equations (21) and (22), respectively. Equation (23) shows that commitment status indicators (u k,m (t), u L (t), and u a (t)) of small CHPs, large CHP, and ADC are binary variables and can take either 1 or 0 value at each time interval t.…”

Section: Generation Constraintsmentioning

confidence: 99%

Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations

et al. 2017

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Abstract:In this paper, an optimal energy management strategy for a cooperative multi-microgrid system with combined cooling, heat and power (CCHP) is proposed and has been verified for a test case of building microgrids (BMGs). Three different demand types of buildings are considered and the BMGs are assumed to be equipped with their own combined heat and power (CHP) generators. In addition, the BMGs are also connected to an external energy network (EEN), which contains a large CHP, an adsorption chiller (ADC), a thermal storage tank, and an electric heat pump (EHP). By trading the excess electricity and heat energy with the utility grid and EEN, each BMG can fulfill its energy demands. Seasonal energy demand variations have been evaluated by selecting a representative day for the two extreme seasons (summer and winter) of the year, among the real profiles of year-round data on electricity, heating, and cooling usage of all the three selected buildings. Especially, the thermal energy management aspect is emphasized where, bi-lateral heat trading between the energy supplier and the consumers, so-called energy prosumer concept, has been realized. An optimization model based on mixed integer linear programming has been developed for minimizing the daily operation cost of the EEN while fulfilling the energy demands of the BMGs. Simulation results have demonstrated the effectiveness of the proposed strategy.

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“…The Optimal Condition of the Operation Strategy A. the optimal strategy in state 1 and state 2 Using TEWM strategy in state 1, the PGU, absorption chiller and electric chiller work between their maximum and minimum limit, and the auxiliary boil's output and the purchasing electricity are aero that is their minimum limit. So the KKT multipliers are 0； 0； 0.…”

Section: The Optimal Operation Model Of Cchpmentioning

confidence: 99%

“…The most significant feature of the distributed energy system is the recovery of waste heat from power generation to supply heat energy for users around. There is a tight relationship between generating and heating in combined heating power system which is the key equipment in the distributed energy system, and the generating and heating efficiency change with the power generating [2] . The operation strategy of combined heating power system to meet the load profile is an important issue.…”

Section: Introductionmentioning

confidence: 99%

A Simulation Algorithm Based on Optimal Operation Strategy for CCHP System

Zhao¹,

Zou²

2018

dtcse

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Abstract. The operation problem is modeled as nonlinear programming in which the relationship between generating and heating and the generating efficiency change with the power generating are considered as the constraints. The KKT condition of the optimal operation model are investigated for the typical operation strategy, and the condition that the typical operation strategy is optimal operation strategy are obtained. A CCHP simulation algorithm based on optimal operation strategy is presented in this paper. The numerical results demonstrated that the operation cost based the presented operation strategy reduces significantly comparison with the traditional operation strategy.

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Energy matching and optimization analysis of waste to energy CCHP (combined cooling, heating and power) system with exergy and energy level

Cited by 62 publications

References 18 publications

Exergoeconomic comparison of three novel trigeneration systems using SOFC, biomass and solar energies

Exergoeconomic comparison of three novel trigeneration systems using SOFC, biomass and solar energies

Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations

A Simulation Algorithm Based on Optimal Operation Strategy for CCHP System

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