Proceedings of Clima 2007 WellBeing Indoors
Modelling and optimization of multi-energy source building systems in the design concept phase Vincenzo Corrado, Enrico Fabrizio and Marco Filippi Dipartimento di Energetica (DENER), Politecnico di Torino, Torino, Italy Corresponding email:
[email protected]
SUMMARY A growing interest has been addressed to multi-energy systems in buildings that involve the integration of different energy sources to cover the thermal and electrical loads of the building. A modelling approach to multi-energy systems in buildings based on the concept of hybrid energy hub is presented. The model has been customised to be used in the concept phase of the building design, either as a system simulation tool or as a system design optimization tool. Given the prices of technologies and of energy-wares, under a certain set of constraints it is possible to determine the configuration that minimises the capital cost, the primary energy consumption or the life-cycle cost. This approach allows avoiding a burdensome simulation and ranking of a set of different systems. In the end, an application on a case study is provided.
INTRODUCTION The energy system of a building is the complex of building plants that transform primary (chemical, thermal, solar, wind) and secondary (electricity, hydrogen) energy-wares into energy used to cover the building energy demand. In recent years a growing interest has been addressed to zero energy homes or buildings that produce more energy than that they consume. The efforts to attain this goal have produced a progressive integration of renewable and conventional energy sources. This means that new technologies, such as solar plants, biomass plants, geothermal heat pumps, co/tri-generators, fuel cells, are spreading rapidly. The application of these technologies leads to a sort of building that can be referred to as a multi-energy source building, since the thermal, cooling and electrical loads are covered by a mix of energy sources, at least one of them renewable. The aim is to design and manage such systems with the best efficiency [1]. It is necessary to investigate the coupling between a building, characterized by energy demand profiles for heating, cooling and electricity, and a system, characterized by production profiles (solar, wind, electricity, etc…). The study of the integration of different energy sources in buildings has to be the foremost in order to fully exploit the energy savings potential of renewable sources. Being conscious that the degree of the design effort is greater during the program pre-design and schematic design phases [2], it is of a great importance to concentrate the research activities on the elaboration of a methodology to model and optimize the coupling between energy demand and energy supply in a building at the design concept phase.
Proceedings of Clima 2007 WellBeing Indoors
MULTI-ENERGY SOURCE BUILDING SYSTEMS Several examples of multi-energy source building systems can be found in literature, combining cogeneration with solar energy (both thermal and PV) and with wind energy [3], exploiting geothermal and solar energy through solar assisted heat pumps [4], combining CHP with absorption chillers and desiccant cooling [5], exploiting solar energy to produce both heating and cooling [6], exploiting wind energy through a fuel cell stack [7]. One of the main problems of a multi-energy system [8] is the mismatch between energy supply and energy demand. This is especially true for renewable sources. This problem, as for the plants that exploit solar energy to produce cold [6], is usually dealt with the integration of a storage. Typical storage mediums are water (refrigerated or ice), ground, PCM or hydrogen. As an example, in the wind fuel cell hybrid energy system described in [7] electricity surplus provided by a wind turbine is utilized to produce hydrogen that is used, later when necessary, in a fuel cells stack, which also represents a cogeneration system. The objectives of multi-energy systems are the followings [8]: • to reduce the primary energy consumption; • to produce energy on site; It is out of doubt that in the near future this kind of systems will be used both for new buildings and for renovations. MULTI-ENERGY SOURCE BUILDING SYSTEM MODELLING The presented modelling approach to multi-energy systems in buildings is based on the concept of hybrid energy hub, which was developed by Andersson, Fröhlich, Geidl et al. [9,10] to model and optimize the multi-carrier energy network of the future [11]. The building is modelled as an energy system that is supplied with different energy carriers and must meet heating, cooling and electrical loads. The coupling between the sources (inputs) and the loads (outputs) is established by a coupling matrix that depends on the technology and on the conversion efficiencies of systems and plants installed. Within the same modelling approach, different levels of complexity are possible depending on the input data and on the coupling matrix entries. With reference to the optimization of a multi-energy source building system in the design concept phase, the data selected to model flow and conversion of energies within the hub are the design power P and the annual (or seasonal) energy E. Each energy carrier is identified by a superscript (e.g. e for electricity, t for thermal). The subscript identifies the demand (out) or the supply (in). The coupling between energy demand and energy supply can be written as Ein = D Eout where Ein is the vector of energy inputs [Eein , Etin ,…]T, Eout is the vector of energy outputs [Eeout , Etout ,…]Tand D is the coupling matrix which depends on systems and plants installed and on their conversion efficiencies. The same relation can be written for design power, assuming design efficiencies instead of mean seasonal efficiencies. Each matrix entry dab accounts for both connection and conversion efficiency between energy carriers. A value of dab equal to 0 means that no connection between the energy carrier a and energy carrier b is provided by the hub; a value of dab equal to 1 means that all power of energy carrier a flows into energy carrier b without conversion losses.
Proceedings of Clima 2007 WellBeing Indoors
Multi-energy source building system modelling application procedure The application procedure to model a multi-energy source building system is the following: 1) identify the set of available energy sources and the set of building loads; 2) identify the components that can be used to cover the building loads, given the set of energy sources available: this can lead to two different approaches: 2.1) a generic hub which takes into account all conversions (and the relative components) that energy sources can undergo before covering the loads; 2.2) a tailored hub which takes into account only the conversions (and the relative components) that are of practical application and of interest to the building owner; 3) identify all the parameters (such as efficiencies) that can model the performance of the components both at the design condition and at operational conditions, and assign to them a numeric value; 4) define the costs of the energy-wares and of the technologies adopted; 5) perform an optimization to select the best multi-energy system. MULTI-ENERGY SOURCE BUILDING SYSTEM OPTIMIZATION Several criteria can be adopted to optimize a multi-energy source building system. Some of them are the followings: - reduce the running costs; - reduce the capital costs; - reduce the amount of energy consumed in a period of time; - maximize the efficiency of the system (both in terms of energy and of exergy); - reduce the environmental impact of the building and services; - maximize the use of natural resources that are available at no cost. The most appropriate optimization criterion for the design concept of a multi-energy source building system in the design concept phase seems to be the economic approach. It has been adopted in the case study presented. Two different procedures can be outlined. The first one is based on the net present value calculation. The procedure steps are the followings: 1) set a reference configuration (converters, allowable conversions, power flows, design powers of the converters) of the system; 2) set the period (number of years) for the investment analysis; 3) determine the capital cost difference and the running cost difference between alternative scenarios and the reference case; 4) calculate the net present value for each scenario based on capital and operational cost differences over the fixed period. As regards the design alternatives, usually after a greater capital cost in the first year, savings on running costs are expected. The optimal scenario is the one that has the greater net present value (this comparison can be done if all net present values are calculated assuming the same period of time. The determination of the internal rate of return (IIR) is also useful. The second procedure tends to minimise a yearly cost based on the sum of one year running costs and of the investment costs divided by the years of expected life of each component. This procedure can be implemented without defining a reference configuration of the system. In any case, the lifetime of components installed must be defined in order to compare capital and running costs.
Proceedings of Clima 2007 WellBeing Indoors
APPLICATION TO A CASE STUDY The methodology presented has been applied to the design of the energy system serving the Valcasotto castle in Piedmont. A tailored energy hub procedure has been carried out as follows: 1) The available energy sources are: wood (w), LPG and electricity (e) from the network; the building thermal and the electric load are to be covered. Four demand scenario (the first one considering thermal energy only) have been identified in terms of design power Pout and annual demand Eout and are reported in Table 1. 2) The converters that can be used to cover the building loads are a wood boiler (WB), a boiler (B), an internal combustion engine (ICE) and a steam turbine (ST). The internal combustion engine and the steam turbine provide both electricity and heat. The heatpower ratios are respectively 1 and 2. The internal combustion engine is fed either by wood through a wood gasifier (WG) or by LPG. The steam turbine is considered to be an ensemble of a wood boiler and a steam turbine. The schematic representation of the energy hub considered is designed in Figure 1. The ε coefficients represent the load fractions covered by a certain converter (the superscript identifies the energy carrier, the subscript the converter): for example ε tWB represents the fraction of the thermal load covered by the output of the wood boiler. 3) The efficiencies η of the components (divided into thermal and electrical for cogeneration converters) are reported in table 2 including both design efficiencies and yearly mean efficiencies. Table 1. Components of Pout and Eout vectors (respectively in kW and MWh/year) Energy Thermal Cooling Electricity
Scenario 1 Pout Eout 1857 6169 0 0 0 0
Scenario 2 Pout Eout 1284 4267 0 0 573 1902
Scenario 3 Pout Eout 929 3085 0 0 929 3085
Figure 1. Schematic representation of the Valcasotto tailored energy hub
Scenario 4 Pout Eout 573 1902 0 0 1284 4267
Proceedings of Clima 2007 WellBeing Indoors
Table 2. Design efficiencies and mean annual efficiencies of the converters Design efficiency ηWB ηB ηWG ηtICE ηeICE ηtST ηeST
Mean efficiency
0.75 0.95 0.75 0.40 0.40 0.60 0.30
ηWB ηB ηWG ηtICE ηeICE ηtST ηeST
0.65 0.85 0.65 0.30 0.30 0.45 0.23
After having defined the efficiencies of each converter η (for example ηtICE is the thermal efficiency of the internal combustion engine) the input powers at the entrance port of the hub can be determined summing up the contributions of each converter as follows: Pinw = PinLPG
1 t t 1 t t 1 1 1 εWB Pout + t ε ST Pout + e ε eST Poute / 2 + k t ε tICE Poutt + k e ε eICE Poute / 2 ηGCL ηST ηICE ηWG ηST ηICE ηWG 1 t t 1 1 = ε B Pout + (1 − k ) t εtICE Poutt + (1 − k ) e ε eICE Poute / 2 ηB ηICE ηICE
(1)
Pine = ε ee Poute
Assuming a linear correlation between inputs and outputs, this can be rewritten in a matrix form as t εWB εt εt + STt + k t ICE 2ηICE ηWG Pinw ηWB 2ηST εtB εtICE LPG ( ) P = + 1 − k in ηB 2ηtICE Pine 0
0 0 0
ε eST ε eICE + k 2ηeST 2ηeICE ηWG P t out e f (1 − k ) ε ICE P out , 2ηeICE e Pout ε ee
(2)
e t where the products ε eICE ,ST Pout and ε tICE ,ST Pout are related, in the case of a cogenerator, by the relation
t e ε tICE ,ST Pout = ε eICE ,ST Pout
ηtICE ,ST ηeICE ,ST
,
(3)
that must be included as a further equation to (3) in order to model the hub performance. The optimisation of the hub can be performed by minimizing the objective equation
(
C y = f Ein , Pk
)
which takes into account the energy costs in a period of time and the capital cost of the converter devices installed. It is therefore necessary to determine the design power of each converter Pk and then (5) can be rewritten as:
(4)
Proceedings of Clima 2007 WellBeing Indoors
c P c P c Pe c P c Pe min C y = min c w Einw + c LPG EinLPG + c e Eine + WB WB + B B + ICE ICE + WG WG + ST ST (5) yWB yB yICE yWG yST where cost for energy consumed c, costs of converters (in terms of design power) ck are resumed in tables 3 and 4. The expected number of years is fixed to 20 years for each converter. The minimisation of this yearly cost function has been performed by means of a commercially available reduced gradient method algorithm. The problem is then to find the values of ε that minimize the cost function ε αi : min f (Ein , Pk ) under the constraint of the hub: Ein= D Eout ; Pin= D’ Pout Ein > 0 ; Pin > 0 and the constraints relative to the ε coefficients: 0 ≤ ε iα ≤ 1 ∀ α , ∀ i ∈ {GCL, GC , ME ,...}
∑i ε iα
=1 ∀α
ε
t 2
t ME ,TV
P =ε
e ME ,TV
ηtME ,TV P e ηME ,TV e 2
Table 3. Cost structure c of the energy wares Energy-ware Wood LPG Electricity
[€/kWh] 0.014 0.060 0.150
Table 4. Cost structure ck of the energy converters Energy converter Wood boiler Boiler Wood gasifier ICE Gas turbine Electricity
[€/kW] 200 100 50 900 1100 100
The results of the optimization of each scenario are shown in figure 2. The model can be used to simulate, for a given demand scenario, other hub structures. In the case of scenario number 2, it is possible to determine the primary energy that must be supplied using the internal combustion engine feed by gas via the wood gasifier or the internal combustion engine fed by LPG instead of the wood gasifier and the steam turbine. This can be done by fixing the values of coefficient ε instead of letting them to freely change as in the optimisation. Those two alternative scenarios simulated are presented in figure 3 and it can be verified that the resulting values of Cy are higher than that of the optimized configuration.
Proceedings of Clima 2007 WellBeing Indoors
Figure 2. Schematic representation of the Valcasotto tailored energy hub optimized for four different demand scenarios (1 to 4 from right to left)
Figure 3. Schematic representation of two further simulation of the Valcasotto tailored energy hub for the second demand scenario
DISCUSSION A modelling approach to multi-energy systems in buildings has been presented. It is based on the concept of the hybrid energy hub has been customised to be used in the design concept phase. The model is quite simple and allows analysis to be performed in presence of design power and annual or seasonal energy demand data only. Such a model meets the requirements of simplicity that characterize the design concept phase, but a factor of uncertainty is represented by the choice of the values of the efficiencies. Values of mean seasonal efficiencies greatly affect the results and appropriate values of these properties are difficult to determine a priori and must be based on the consultant experience. Further research activity is currently carried out to overcome this drawback; values of efficiencies dependent on design power installed and on the part load ratio may be integrated into the model.
Proceedings of Clima 2007 WellBeing Indoors
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