MODELING THE TECHNICAL AND ECONOMIC-POTENTIAL OF THERMAL-ENERGY STORAGE-SYSTEMS USING PSEUDO-DATA ANALYSIS

At the request of public service commissions nationwide electric utili ties are implementing demand-side management (DSM) programs to increas e their customers' efficiency of electricity use.1 Many of those progr ams are designed to encourage customer investments in innovative techn ologies whose use achieves such efficiency without significantly alter ing the quality of energy services. But whether specific programs shou ld even be designed and implemented depends critically on their viabil ity (i.e., on whether individual customers will find the technology fe asible). Feasibility can be defined in terms of both engineering crite ria (technical feasibility) and economic criteria (economic feasibilit y). This paper extends a technique, known as pseudo-data analysis (e.g ., Griffin, J.M., 1977, Long run production modeling with pseudo-data: Electric power generation, Bell Journal of Economics 8 (1), 112-127) to approximate technical and economic potential (i.e., market potentia l) - the number of utility customers who will find a new technology te chnically feasible and within the range of their typical criteria for investment projects. Our application determines the potential of a rel atively new technology, thermal energy storage (TES) systems, among Fl orida Power and Light Company's (FPL's) commercial customers under a w ide variety of electricity-rate and incentive scenarios.2 We created p seudo data by completing simulations with COOLAID, an engineering cost model developed by the Electric Power Research Institute (EPRI) to ev aluate alternative commercial cooling systems. Those data were used to estimate statistical functions that related a key investment criterio n, payback, to alternative DSM policy features under consideration. Th en the simulation results were combined with customers' known technica l facility constraints and stated investment cut-off criteria to deter mine the number for whom TES systems would be technically and economic ally feasible. Our method demonstrates a generic technique that can be used to estimate the market potential for new technologies in general , especially for commercial and industrial business establishments, wh en little or no historical data are available on responses to variatio ns in prices and incentives. The results of this kind of analysis can be especially useful to any utility to assess how DSM program options - pricing and incentive levels will alter customers' investment choice s for new technologies and, consequently, the cost-effectiveness of th e DSM program.