From hospital to municipal cogeneration systems: an Italian case study

Abstract

A mixed integer linear programming model combined with a more traditional design by scenarios is proposed to optimize facilities size and operation mode of a municipal energy system involving significant civil centres and a hospital. Moving from the need of a new heat and power station for the local hospital due to the construction of new pavilions, the opportunity of involving other centres in the neighbourhood in a distributed cogeneration system is analysed, increasing system complexity step by step. Smaller cogeneration units tailored to hospital needs are rewarding ventures with relatively low risks but, in a country whose traditional power generation systems heavily rely on fossil fuels and where energy policy and market conditions can make it profitable to sell surplus power, district heating systems foster the installation of larger cogenerators and lead thereby to higher profits and to better performance as for primary energy savings and greenhouse gases emission reduction

Constructal design of distributed cooling on the landscape

“…where L/D 0 is furnished by Equations (12) and (13). To make analytical progress in closed form, we note that the monotonic increase of Z II with size (suggested by Figure 1) is approximated locally for by a power-law expression…”

“…Here, L r is the total length of the radial network, correlated in Equation (13), and D r is the diameter of ducts in the radial network. For turbulent flow, the pumping power is…”

“…Considerable research has been devoted to the improvement of the COP of refrigeration plants [4][5][6][7][8][9][10][11][12] and to the optimal design of networks for distributing hot and chilled water [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. However, the allocation of refrigeration on the landscape of a distributed cooling system has not yet been considered.…”

An inexact optimization model for regional energy systems planning in the mixed stochastic and fuzzy environment

“…5 cost for natural-gas import in period t (million $ PJ À1 ) C 9t 5 cost for electricity import in period t (million $ PJ À1 ) Conv R 1t 5 conversion ratio of coal to electricity in period t (million $ PJ À1 ) Conv R 2t 5 conversion ratio of natural gas to electricity in period t (million $ PJ À1 ) Conv R 3t 5 conversion ratio of hydropower to electricity in period t (million $ PJ À1 ) Conv R 4t 5 conversion ratio of solar energy to electricity in period t (million $ PJ À1 ) Conv R 5t 5 conversion ratio of wind energy to electricity in period t (million $ PJ À1 ) Conv R 6t 5 conversion ratio of wind energy to electricity in period t (million $ PJ À1 ) DIC t 5 industrial demand for coal in period t (PJ) DID t 5 industrial demand for diesel in period t (PJ) DIE t 5 industrial demand for electricity in period t (PJ) DIG t 5 industrial demand for gasoline in period t (PJ) DIN t 5 industrial demand for natural gas in period t (PJ) DMC t 5 municipality/commercial demand for coal in period t (PJ) DMD t 5 municipality/commercial demand for diesel in period t (PJ) DME t 5 municipality/commercial demand for electricity in period t (PJ) DMG t 5 municipality/commercial demand for gasoline in period t (PJ) DMN t 5 municipality/commercial demand for natural gas in period t (PJ) DTD t 5 transportational demand for diesel in period t (PJ) DTG t 5 transportational demand for gasoline in period t (PJ) EC 1mt 5 capacity expansion for coal-fired power plant with option m at the beginning of period t (PJ) EC 2mt 5 capacity expansion for naturalgas-fired power plant with option m at the beginning of period t (PJ) EC 3mt 5 capacity expansion for hydropower station with option m at the beginning of period t (PJ) EC 4mt 5 capacity expansion for solar power facility with option m at the beginning of period t (PJ) EC 5mt 5 capacity expansion for wind power facility with option m at the beginning of period t (PJ) EC 6mt 5 capacity expansion for nuclear power plant with option m at the beginning of period t (PJ) i 5 index for energy resources (i 5 1, 2,y,9) IC 1mt 5 capital cost for coal-fired power plant with option m at the beginning of period t (million $ PJ À1 ) IC 2mt 5 capital cost for natural-gas-fired power plant with option m at the beginning of period t (million $ PJ À1 ) IC 3mt 5 capital cost for hydropower station with option m at the beginning of period t (million $ PJ À1 ) IC 4mt 5 capital cost for solar power facility with option m at the beginning of period t (million $ PJ À1 ) IC 5mt 5 capital cost for wind power facility with option m at the beginning of period t (million $ PJ À1 ) IC 6mt 5 capital cost for nuclear power plant with option m at the beginning of period t (million $ PJ À1 ) j 5 index for electricity-generating technologies (j 5 1, 2,y, 6) k 5 index for probability levels (k 5 1, 2, 3) m 5 index for electricity capacity-expansion types (m 5 1, 2, 3) RC 1 5 residual capacity for converting coal to electricity (PJ) RC 2 5 residual capacity for converting natural gas to power (PJ) 5 residual capacity for converting hydropower to electricity (PJ) RC 4 5 residual capacity for converting solar energy to electricity (PJ) RC …”

Performance evaluation of an electricity base load engine cogeneration system

“…Load duration curves (LDC) were used by Orlando [1]. Mixed integer linear programming (MILP) was employed by Chinese et al [2]. Renedo et al [3] did a case study using monthly electricity consumptions and thermal consumptions in three periods (winter, spring-autumn and summer) to evaluate the performance of a cogeneration system.…”