A framework for analyzing city-wide impact of building-integrated renewable energy

As the building-integrated renewables increase in the urban energy mix, it is important to assess the collective effects of building-integrated renewables at city scale. Estimation of the total energy supply and capacity of renewables, variations in the energy from the grid, and CO2 emission would aid renewable energy policy evaluation. However, analyzing the collective effects considering the optimal energy system for each building in a city is difficult due to a large number of buildings (on the order of 10(5)). Therefore, this study proposes a new framework for analyzing the city-wide impact of increased building-integrated renewables. To reduce the number of optimization, clusters of buildings with similar characteristics and one virtual representative building for each cluster are generated. For apartment buildings, the characteristics are the floor area and roof area per household. For non-residential buildings, the characteristics are the shapes of the monthly electricity and gas usages, the ratio between the annual gas and electricity usages, and normalized roof area. To generate clusters of similar non-residential buildings, k-Means Clustering Algorithm and Genetic Algorithm have been applied. The proposed framework has been validated by comparing the collective results from i) optimization of 4,425 actual apartment buildings and 2,779 actual non-residential buildings in an urban district; and ii) optimization of corresponding 957 representative apartment buildings and 176 representative non-residential buildings. Total energy supply and capacity of each renewable energy source, total monthly electricity and gas from the grid, and total hourly electricity from the grid show good agreement. As a demonstration, the proposed framework has been applied to the city of Seoul, Korea for a future scenario of building energy obligation - i) to estimate the total capacities and energy supply of the building-integrated renewables and the change in the energy from the grid; and ii) to evaluate the cost-effectiveness of the obligation based on the unit cost of CO2 reduction for varying renewable energy requirements for the buildings.