Local Energy Optimization (LEO) model and input data for cost-efficient decarbonization of local energy systems by whole-system based design optimization

There are two files, one is an Excel file including the data inputs to the optimization model, and the other is the text file of the optimization model written as GAMS code.

In the Excel file, the 1^st Sheet defines the five aggregators marked as B1~B5 and the associated aggregator names. The 2^nd Sheet provides the shiftable demand at certain hours for dishwasher, laundry, and electric vehicle when modelling demand response. The unit for the values is all ‘kWh’. The 3^rd Sheet provides the hourly original electricity demand (before implementing demand response) for each aggregator on each typical day. The unit for the values is all ‘kWh’. The 4^th Sheet provides the hourly original heating demand (before implementing building energy retrofit) for residential buildings on each typical day. The unit for the values is all ‘kWh’. The 5^th Sheet provides the hourly heating demand savings on each typical day when implementing different building energy retrofit options (i.e., k1~k3). The unit for the values is all ‘kWh’. The 6^th Sheet provides the hourly energy prices for each aggregator, including the electricity purchasing price (i.e., grid_buy), electricity feed-in price (i.e., grid_sell), and natural gas for boiler price (i.e., NG_b). The unit for the values is all ‘£/kWh’. The 7^th Sheet provides the hourly solar radiation index (SRI) for each typical day. The unit for the values is all ‘W/m²’. The “sprw” … “winx” in the sheets represent different typical days, where “spr”, “hot”, “sum”, “aut” and “win” represent “spring”, “hot summer”, “summer”, “autumn” and “winter”, and “w”, “n” and “t” represent “weekday”, “weekend” and “extreme day” respectively.

The text file provides the local energy optimization (LEO) model written as GAMS code. In the model, all sets, parameters, variables, equations, model solving settings, results output settings are provided in detail.

Research results based upon these data are published at http://doi.org/10.1016/j.apenergy.2022.119921