Load sharing with a local thermal network fed by a microcogenerator: Thermo-economic optimization by means of dynamic simulations

doi:10.1016/j.applthermaleng.2013.09.055

Applied Thermal Engineering

Volume 71, Issue 2, 22 October 2014, Pages 628-635

https://doi.org/10.1016/j.applthermaleng.2013.09.055 Get rights and content

Highlights

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Load sharing approach between house and office is proposed.
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A system consisting of MCHP, heat storage, boiler and thermal network is simulated.
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Two different geographical locations in Italy are considered.
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An optimal thermo-economic control of MCHP system is implemented.
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The energy, environmental and economic implication of the system are investigated.

Abstract

The cogeneration is the combined production of electric and/or mechanical and thermal energy starting by a single energy source; in particular in this paper the analysis will be focused on a cogeneration system with electric power lower than 15 kW (micro-cogeneration). The paper analyzes a system consisting of a natural gas-fired micro-cogeneration unit (MCHP), a heat storage and a peak boiler. The system provides thermal and electric energy to two end-users, the former is a tertiary building (office), where the generation system is located, and the latter is a residential building connected to the former through a district heating micro-grid. In order to analyze the influence of climatic conditions, two different geographical locations in Italy (Benevento and Milano) are considered, that are also characterized by different natural gas and electricity tariffs. Particular attention is paid to the choice of the users, in order to obtain more stable and continuous electric and thermal loads (load sharing approach) and to increase the operating hours per year of the MCHP unit.

The operation of the MCHP is governed by a control system, aimed to optimize a thermo-economic objective function. The models representing the components, the thermo-economic objective function and the buildings have been implemented in a widely used commercial software for building simulations. The models are calibrated and validated through data obtained from experimental tests carried out in the laboratory of the University of Sannio (Benevento). The results of the simulations highlight the potential benefits of the thermal load sharing approach. In particular, this study shows that an MCHP unit connected by means of a thermal micro-grid to different users in “load sharing mode” can obtain a high number of operating hours as well as significant energy (Primary Energy Saving) and environmental (avoided CO₂ equivalent emissions) benefits with respect to an appropriate reference system, even in Mediterranean areas, where the climatic conditions are not always suitable for cogeneration.

Introduction

European Union [1], defines the cogeneration as the simultaneous generation of thermal and electrical and/or mechanical energy. A micro-cogeneration unit (MCHP – Micro Combined Heat and Power) is defined as a cogeneration unit with a maximum electric power lower than a limit value. In Ref. [1] this value is set at 50 kWel. In literature, several authors refer to an upper value of 15 kWel. Cogeneration represents one of the most important measures to achieve energy savings and reduce greenhouse gas emissions. In Mediterranean area the diffusion of residential MCHP systems is hindered by the relatively mild climate, that causes low operating hours per year and consequently high payback periods. To overcome this problem many researches focus on trigeneration (CCHP – Combined Cool Heat and Power) systems based on thermal activated heat pumps [2], [3], or on desiccant HVAC system driven by MCHP [4]. Another interesting solution could be the load sharing approach [5]: selecting suitable users sharing electrical and thermal loads could be an effective way to increase the operating hours of the MCHP. In this case, the MCHP (or CHP) system is very often managed according to a thermo-economic optimization control, as it would be typically operated by an Energy Service Company (ESCo), interested in revenues maximization or cost minimization.

In Ref. [6], an optimal online operation strategy for MCHP systems, which is more efficient than the conventional pre-determined (either heat-led or electricity-led) operation strategies, is presented. A generic optimal online linear programming optimiser was developed for operating an MCHP system, capable of minimising the daily operation costs of such a system.

In Ref. [7], an energy supply chain network based on residential-scale microgeneration systems is proposed, modelled and optimized. A mathematical programming framework is developed for the operational planning of such energy supply chain networks. The minimization of total costs constitutes the objective function. Additionally, an alternative micro-grid structure that allows the heat interchange within subgroups of the overall micro-grid is proposed.

In Ref. [8], an energy dispatch algorithm that minimizes the cost of energy (electricity from the grid and natural gas for the engine and the boiler), based on energy efficiency constrains for each component, is presented. A deterministic network flow model of a typical CHP system is developed as part of the algorithm. This algorithm was used in simulations of a case study on the operation of an existing MCHP system. The results from the simulation demonstrate the economic advantages resulting from optimal operation.

In Ref. [9], a general environomic methodology is applied to a district heating (DH) system with centralized pumps, cogeneration units and an auxiliary furnace, supplemented by decentralized heat pumps; it includes models of the thermodynamic, economic, and environmental characteristics of the considered system. Optimization results, in terms of synthesis, design and operation of the network, are presented in Ref. [10].

In Ref. [11], a numerical optimisation method has been applied to a small-scale cogenerator, based on a micro-gas turbine and driven by the heat demand of a medium-size building located in the north of Portugal. The mathematical model yields a non-linear objective function, subject to physical constraints of system operation, and defined as the maximisation of the annual worth of the CHP system. A purchase cost equation was used for each major plant component, taking into account size and performance variables.

An economic optimization is also used for the design of trigeneration systems. In Ref. [12], an optimization model is developed, using mixed integer linear programming, to determine the preliminary design of a CCHP system with thermal storage for urban districts. The objective function to be minimized is the total annual cost. The effect of legal constraints in the design and operation of CCHP systems is also highlighted.

In Ref. [13], an optimal planning method is proposed for sizing a cogeneration plant to be installed within a public/commercial micro-grid. The optimization tool is based on a mixed integer linear programming model, aimed at determining the annual operational strategy able to minimize the total operational cost of the micro-grid and finding the optimal size of the CHP to be installed. A numerical example is proposed with the aim of investigating the economic advantages coming from the adoption of an optimal cogeneration operation strategy.

In Ref. [14], the simulation of a district heating system with a biomass-fired microcogeneration system and a heat storage was performed by means of a tool based on data collected from a real DH system (city of Turin). The analysis performed show the variation of the system performance and the possibility to guarantee higher PES values with a careful design and layout of the system. In particular, the role of heat storage has been shown to be essential to optimize the global DH system, with improvement in the efficiency, reduction of the peak of heat demand and increase of CHP production resulting in primary energy savings and environmental benefits.

In this paper, the components of a local thermal network (MCHP, heat storage, peak boiler, heating system and final users) are modelled, mainly by means of experimental data, and implemented in a dynamic commercial simulation software. The MCHP is managed by means of a thermo-economic control function, aimed at minimizing the operation cost of the system. Simulations have been performed considering different scenarios (in terms of climatic conditions and prices of energy vectors); the results show that the thermal load sharing approach is an effective way to increase the operating hours of the MCHP system in moderate climatic conditions, like Italian ones, typically characterized by low thermal load and few hours of heating system operation, achieving satisfying economic, energy and environmental performance.

Section snippets

The experimental MCHP system

In Fig. 1 the analysed micro-cogeneration system is shown. It consists of an MCHP, a thermal storage tank and a peak boiler, installed in a tertiary building for office use (User #1). The thermal energy “produced” can also be transferred to a second residential user (User #2), connected to the MCHP system through a district heating micro-grid. The MCHP used is based on reciprocating internal combustion (RIC) engine fuelled by natural gas, Table 1. The MCHP can operate in two modes: “electric”

Models

The described system has been simulated in two different geographical locations (Benevento and Milano). The buildings and the related thermal loads have been properly sized to take into account the different climatic conditions of the two cities, as derived from the corresponding “Meteonorm” climate file, [15].

Table 2 shows the main dimensional characteristics and energy requirements of residential and office users for the two considered locations.

In Table 3, the main characteristics of the

Optimization

Fig. 4 shows the control scheme implemented in the simulation software. It is possible to define the input information to the Energy Management System (EMS) that has to operate the cogeneration system and the integration device through the control variables of the cogenerator (x_CHP) and the boiler (x_B), in the range: 0 ÷ 1. The state “0” characterizes the condition of an inactive device, while the state “1” characterizes the operation at full load, intermediate values are associated with

Simulation results

Table 8 shows the main results obtained from the simulations in Benevento and Milano. Two different scenarios were analyzed. In the first (Table 8) it was assumed that the unit costs of electricity and natural gas are equal for the traditional system and for the proposed one. The simulations were performed for the period of year during which the heating system is allowed to be used.

The load sharing approach guarantees a high number of operation hours for the proposed system in Benevento

Conclusion

In this paper a model of an MCHP system has been described and implemented in TRNSYS software [22]. The system provides thermal and electric energy to two end-users, connected by means of district heating micro-grid in a load sharing approach. Simulations were performed considering two different Italian cities.

The results show that the analyzed system allows to obtain a high number of operating hours of the micro-cogenerator, also in Mediterranean areas where the climate conditions are not

Acknowledgements

This work was developed in the framework of an agreement between Italian Ministry of Economic Development and ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development), within the 2009–2011 three-year Research Plan for the National Electricity System (Ministerial Decree 19/03/2009; area: Electric energy rationalization and saving; project: Studies and assessments on the rational use of energy: tools and technologies for energy efficiency in the tertiary

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Load sharing with a local thermal network fed by a microcogenerator: Thermo-economic optimization by means of dynamic simulations

Highlights

Abstract

Introduction

Section snippets

The experimental MCHP system

Models

Optimization

Simulation results

Conclusion

Acknowledgements

Prog. Energy Combust. Sci.

Prog. Energy Combust. Sci.

Energy Build.

Renewable Energy

Energy Build.

Appl. Energy

Energy Build.

Int. J. Therm. Sci.

Int. J. Therm. Sci.

Energy

Energy