Journal
IET RENEWABLE POWER GENERATION
Volume 12, Issue 11, Pages 1263-1268Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-rpg.2018.0090
Keywords
renewable energy sources; linear programming; cooling; optimisation; cogeneration; power generation economics; integer programming; photovoltaic solar energy; economic optimisation; energy supply; conversion system; energy demands; sanitary hot water; steam; conventional equipment; cogeneration modules; renewable energy resources; solar photovoltaic energy; mixed integer linear programming; optimal economic solution; operation strategy; operational year; total annual costs; variable costs; maintenance; energy costs; reference system; solar utilities; biomass utilities; annual cost; reference solution; economic optimal solution; solar resources; biomass resources; annual minimum cost
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In the energy supply and conversion system proposed herein, the following energy demands were considered for a hospital: electricity, sanitary hot water, steam, and cooling. A superstructure representing all options of equipment and energy resources was built, with conventional equipment as well as more complex technologies, such as absorption chillers and cogeneration modules. Two renewable energy resources were available: solar photovoltaic energy and biomass (sugarcane bagasse). The solution of a mathematical model based on mixed integer linear programming provided the optimal economic solution, constituted by the configuration of the system (equipment installed) and its operation strategy (how to operate each equipment, throughout one operational year). The objective function considered the minimisation of total annual costs, which encompassed fixed costs (equipment) and variable costs (maintenance and energy costs). A reference system was designed, where only conventional equipment was utilised (no cogeneration, no solar or biomass utilities available). The optimal economic solution included the utilisation of biomass to produce hot water and steam, with an annual cost that was 11% lower than the reference solution. Although the economic optimal solution did not install cogeneration modules, it took advantage of solar and biomass resources to achieve annual minimum cost.
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