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the thermal supply using costly compressor technology is likely to be low. Only operational aspects are considered. No attempt is made to establish the feasibility of operating the system over any longer planning period. Investment costs, including the costs of replac- ing existing equipment, are not considered. Fixed operational costs are assumed to be similar for TB and the existing conventional option and are thus excluded from the analysis. Furthermore, no mechanical considerations are offered with respect to the physical integration of the TB. However, the existing conventional tech- nology chosen for analysis is expected to make the replacement relatively straightforward while maintaining central elements of the existing heating and cooling distribution systems, thus, keeping investment costs relatively low. 3.1. COMPOSE:Softwareforcomparingenergyoptionsina system perspective The analysis is performed using the COMPOSE software [12,13] that combines detailed operational simulation under the deter- ministic techno-economic constraints of the TB and the existing appliances with a least-cost marginal-dispatch model for the energy system in which the TB is analyzed. The energy system model allows for an identification of the marginal system-wide consequences with respect to the intermittency-friendliness of operation and CO2 emissions. These particular system analysis methodologies are described in further detail below. In COMPOSE, the user defines an energy option in terms of end- use requirements, storages, and conversion processes (e.g. heat pump). Options may be designed from scratch or based on build-in libraries. Furthermore, the user defines an energy system in terms of spot markets, candidate marginal power producers, electricity demands, and intermittent production. For both option and sys- tem, parameters are specified on an hourly basis for each year of analysis. System specific parameters may be imported from utility databases, or adapted from COMPOSE’s build-in libraries. COMPOSE then identifies the option’s optimal operational strat- egy by mixed-integer linear programming under the objective function of minimizing the economic cost of meeting heating and cooling demands for the period of simulation under given techno-economic constraints and boundaries, including hourly values for end-use requirements, capacities and efficiencies, mar- ket prices, variable O&M costs. The resulting detailed energy balance includes e.g. fuel and electricity consumption, storage states, energy losses, energy costs. For TB, based on the identi- fied least-cost operational strategy, COMPOSE uses the resulting net electricity profile – the TB’s hourly electricity consumption profile – as a basis for calculating the resulting energy system impacts, including intermittency-friendliness Rc and marginal CO2 emissions. Several other COMPOSE results are available from such analysis, but are not considered here. 3.2. The intermittency-friendliness coefficient Rc An intermittency-friendliness coefficient Rc has been intro- duced as a means of measuring how well an electricity demand (or supply) support the integration of intermittent renewables, thus enabling Smart Grid. Rc is defined as the statistical correla- tion between the net electricity exchange end-user and grid, and the energy system’s net electricity requirements, as stated in Eq. (1) [14]. (e − em)(d − dm) Rc= (1) (e − em)2 (d − dm)2 where e is the net electricity exchange between end-user and sys- tem, d is the net electricity requirements vis-à-vis the system’s electricity load minus intermittent electricity production, and m- subscript refers to mean values. Eq. (1) is based on the Pearson correlation, which is a dimen- sionless index obtained by dividing the covariance of two variables by the product of their standard deviations. By definition, the index ranges from −1 to 1, where −1 expresses that two variables have an extreme negative association, while 1 expresses that the two vari- ables have an extreme positive association. For the variables in the intermittency-friendliness coefficient Rc, net electricity exchange (using negative values for consumption) and net grid require- ments (electricity demand minus intermittent supply), a coefficient of 1 identifies an electricity consumption pattern that perfectly support the system’s balancing challenges by using more electric- ity when net requirements are low (high wind/PV periods), and M.B. Blarke et al. / Energy and Buildings 50 (2012) 128–138 131 Fig. 2. PG&E load, intermittent renewables production, and net requirements on July 19, 2011 (MWh/h).PDF Image | Thermal battery with CO2 compression heat pump
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