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City-integrated renewable energy design for low-carbon and climate-resilient communities
- Bagheri, Mehdi, Delbari, Seyed Hamid, Pakzadmanesh, Mina, Kennedy, Christopher A.
- Applied energy 2019 v.239 pp. 1212-1225
- batteries, biomass, capital, consumers (people), decision making, discount rate, electricity, energy costs, greenhouse gas emissions, greenhouse gases, issues and policy, models, natural gas, planning, renewable electricity, solar collectors, solar energy, uncertainty, wind, wind power
- Urban electrification with renewables is a crucial strategy for achieving low-carbon and climate-resilient communities. Given the different types of power customers (e.g., residential, commercial and industrial), this work develops a systematic and straightforward framework for the optimal planning of urban solar/wind/biomass (/natural gas) systems at neighbourhood scale using the actual real-time hourly electric loads. In achieving this objective, we defined three power scenarios (i) 100% natural gas; (ii) natural gas and renewables; (iii) 100% renewables (e.g., solar/wind/biomass) and identified the hybrid systems with the least NPC for each of the three power scenarios. Our results indicate that providing per kilowatt-hour renewable electricity to the industrial sector (0.385 USD/kWh) costs 4% less than the commercial (0.399 USD/kWh) and about 5% less than the residential sector (0.418 USD/kWh) at neighbourhood scales. The more significant cost of electricity (COE) of the residential system is primarily due to the greater batteries to solar PV fractions. Also, COE of solar/wind/biomass plant showed to be three times less than the equivalent solar/wind power system. Likewise, by integrating a low-emission natural gas (NG) generator to the hybrid solar/wind/biomass plant, the system's COE reduced by 30% while resulting in close to three order-of-magnitude higher annual greenhouse gas (GHG) emissions. To address the model accuracy concerning the uncertainty and variations associated with input variables, we further conducted the sensitivity analysis of the systems' COE address the model accuracy concerning the uncertainty and variations associated with input variables by changes in the discount rate and capital cost of the PV panels and batteries. As a result, systems’ COE was detected to be more sensitive to the capital cost of batteries than solar panels. This study can help decision-makers in developing more effective policies and mechanisms to support the urban hybrid renewable energy systems.