The impact of thermal storage on the operational behaviour of residential CHP facilities and the overall CO2 emissions

Haeseldonckx, Dries; Peeters, Leen; Helsen, Lieve; D’haeseleer, William

doi:10.1016/j.rser.2005.09.004

Cited by 148 publications

(56 citation statements)

References 4 publications

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“…The maximum rectangle (MR) method is normally used to size the energy generation equipment in a power system [4,14]. Compared with other optimisation algorithms, the MR method focuses on finding the best capacity of an energy generator which can cover majority of energy consumption instead of covering the maximal demand.…”

Section: The Maximum Rectangle Methodsmentioning

confidence: 99%

Sizing Combined Heat and Power Units and Domestic Building Energy Cost Optimisation

Meng

Yan

et al. 2017

Energies

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Many combined heat and power (CHP) units have been installed in domestic buildings to increase energy efficiency and reduce energy costs. However, inappropriate sizing of a CHP may actually increase energy costs and reduce energy efficiency. Moreover, the high manufacturing cost of batteries makes batteries less affordable. Therefore, this paper will attempt to size the capacity of CHP and optimise daily energy costs for a domestic building with only CHP installed. In this paper, electricity and heat loads are firstly used as sizing criteria in finding the best capacities of different types of CHP with the help of the maximum rectangle (MR) method. Subsequently, the genetic algorithm (GA) will be used to optimise the daily energy costs of the different cases. Then, heat and electricity loads are jointly considered for sizing different types of CHP and for optimising the daily energy costs through the GA method. The optimisation results show that the GA sizing method gives a higher average daily energy cost saving, which is 13% reduction compared to a building without installing CHP. However, to achieve this, there will be about 3% energy efficiency reduction and 7% input power to rated power ratio reduction compared to using the MR method and heat demand in sizing CHP.Keywords: sizing combined heat and power (CHP); domestic buildings; the maximum rectangle (MR) method; the genetic algorithm (GA)

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Section: The Maximum Rectangle Methodsmentioning

confidence: 99%

Sizing Combined Heat and Power Units and Domestic Building Energy Cost Optimisation

Meng

Yan

et al. 2017

Energies

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“…The electric power and rated thermal power of 0 th generation systems comprised of simple cogeneration systems is sized to maximize its thermal output within the scope of the application in order to reduce primary energy and GHG emissions with CHPgenerated electricity without wasting any of the heat generated [43]. For applications with a lower utilization of the CHP system such as spaceheating purposes, Voorspools and D'haeseleer have already demonstrated that static formulas may lead to large errors in calculated savings when compared to the actual primaryenergy savings and emission sector [4446].…”

Section: Methodology: Sizing the Pv+chp Systemmentioning

confidence: 99%

Expanding photovoltaic penetration with residential distributed generation from hybrid solar photovoltaic and combined heat and power systems

Pearce

2009

Energy

113

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The recent development of small scale combined heat and power (CHP) systems has provided the opportunity for inhouse power backup of residentialscale photovoltaic (PV) arrays. This paper investigates the potential of deploying a distributed network of PV+CHP hybrid systems in order increase the PV penetration level in the U.S. The temporal distribution of solar flux, electrical and heating requirements for representative U.S. single family residences were analyzed and the results clearly show that hybridizing CHP with PV can enable additional PV deployment above what is possible with a conventional centralized electric generation system. The technical evolution of such PV+CHP hybrid systems was developed from the present (near market) technology through four generations, which enable high utilization rates of both PVgenerated electricity and CHPgenerated heat. A method to determine the maximum percent of PVgenerated electricity on the grid without energy storage was derived and applied to an example area. The results show that a PV+CHP hybrid system not only has the potential to radically reduce energy waste in the status quo electrical and heating systems, but it also enables the share of solar PV to be expanded by about a factor of five.

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“…This characteristic, together with the fact that micro-CHP units are mostly coupled to heat storage systems [17], creates flexibility in power generation. This paper analyzes the potential value of flexibility in micro-CHP operation.…”

Section: Objective Of the Papermentioning

confidence: 99%

Demand Response With Micro-CHP Systems

Houwing¹,

2011

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Abstract-With the increasing application of distributed energy resources and novel information technologies in the electricity infrastructure, innovative possibilities to incorporate the demand side more actively in power system operation are enabled. A promising, controllable, residential distributed generation technology is a micro combined heat and power system (micro-CHP). Micro-CHP is an energy efficient technology that simultaneously provides heat and electricity to households. In this paper we investigate to what extent domestic energy costs could be reduced with intelligent, price-based control concepts (demand response). Hereby, first the performance of a standard, so-called heat-led micro-CHP system is analyzed. Then, a model predictive control strategy aimed at demand response is proposed for more intelligent control of micro-CHP systems. Simulation studies illustrate the added value of the proposed intelligent control approach over the standard approach in terms of reduced variable energy costs. Demand response with micro-CHP lowers variable costs for households by about 1-14 %. The cost reductions are highest with the most strongly fluctuating real-time pricing scheme.Index Terms-micro combined heat and power systems, demand response, model predictive control.

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The impact of thermal storage on the operational behaviour of residential CHP facilities and the overall CO2 emissions

Cited by 148 publications

References 4 publications

Sizing Combined Heat and Power Units and Domestic Building Energy Cost Optimisation

Sizing Combined Heat and Power Units and Domestic Building Energy Cost Optimisation

Expanding photovoltaic penetration with residential distributed generation from hybrid solar photovoltaic and combined heat and power systems

Demand Response With Micro-CHP Systems

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