This is the repository for a community battery storage (CBS) sizing study that considers receding horizon operation (RHO). A key part of this study is to develop an accurate CBS operation model that continuously adjusts to changes in power system forecasts. The uncertainties include wholesale spot prices and residential users' consumption. While the forecasts of spot prices can be obtained from the pre-dispatch prices published by AEMO, the forecasts of residential users' consumption are not available. Therefore, the study first considers a price-responsive behaviour model of residential electricity users to obtain the dynamic consumption forecasts. The study then uses the price and consumption forecasts to determine the optimal CBS operation plan in a receding horizon manner. There exists different battery sizing studies in the literature, however, most of them do not consider the RHO. The study aims to compare the CBS sizing results with and without RHO.
The end users model is implemented in the optimisation_models/prosumer_rho_model.py file and can be run using the prosumer_rolling_operation.ipynb file. The output from the model is the varying consumption behaviour over time.
The CBS operation model is implemented in the optimisation_models/battery_rho_model.py file and can be run using the battery_rolling_operation.ipynb file. The model considers the dynamic consumption forecasts of residential users and the dynamic wholesale spot prices forecasts. The output from the model is the optimised CBS operation plan over time.
The global optimal battery capacity can be found by exhaustively searching for the capacity that minimises the ground truth cost. Examples are provided in the battery_cost_calculation.ipynb file.
A common battery sizing approach is to assume a perfect prediction of uncertain parameters and solve a planning problem over the entire sizing horizon. Additionally, to explore the impact of forecast prices, we replace the actual prices with 30-minute look-ahead pre-dispatch prices. This sizing model is implemented in optimisation_models/battery_without_rh_model.py and can be run using the battery_without_rh_sizing.ipynb.
The CBS sizing model with coupled receding horizons simultaneously considers all receding horizons in one optimisation problem. The model is implemented in optimisation_models/battery_coupled_rh_model.py and can be run using the battery_coupled_rh_sizing.ipynb.