Abstract
Distributed generation (DG) using renewable energy sources is of widespread interest. For example, modern centralized conventional fossil fuel power generation commonly adds DG using renewable energy resources to the grid. Therefore, in these changes, it is necessary to optimize renewable energy systems to increase energy efficiency and reduce emissions. In previous studies, meta-heuristic algorithms were used to optimize DG location and capacity, but different types of DG systems and integrated energy hub conditions were not considered. Determining the most effective DG type for an integrated energy hub is critical. Accordingly, this study presented a methodology for selecting the most cost-efficient DG for metropolitan residential customers of energy hubs. In this paper, we model energy hubs for residential customers and the most cost-efficient DG type using MATLAB and HOMER software, considering microturbine (MT), photovoltaic (PV), wind turbine, and fuel cell (FC) power sources. For this purpose, the energy hub was modeled as a combined cooling heat and power (CCHP) system and selected a specific metropolitan area as a testbed (Atlanta, USA). For practical simulation, the total active power of the Atlanta community was measured by multiplying the average load profile data of residential houses collected by open energy information (OpenEI). The first case study showed that optimal-blast MTs without absorption chillers (AbCs) were the most cost-efficient compared to other optimal-blast DG systems without AbCs. Additional second case studies for optimal and full-blast MTs with AbCs were performed to verify the results for energy consumption, costs, and emissions savings. As a result, full-blast MTs with AbCs comprise the most cost-efficient DG type in the CCHP system for metropolitan residential customers, reducing energy consumption, cost, and emissions.