The paper proposes a bi-level multi-objective optimization model to optimally design and operate renewable energy sources and storage systems in an existing electrical grid with increasing demand. while respecting network constraints based. The bi-level optimization model combines particle swarm optimization (PSO) and dynamic linear AC-optimal power flow (DLOPF). The aim of the bi-objective model is to minimize costs while limiting carbon emissions. PSO sizes and identifies the placement of battery energy storage (BES) systems, and DLOPF locates and sizes RES in a spatial–temporal framework using the Levelized Cost of Energy and Storage. Different scenarios were applied to the IEEE-30 bus system to reveal the behavior of the dynamic network for the different cases, and the associated effect of integrating RES and BES. The model resulted in optimized scenarios in terms of placement and sizing of RES and BES at the lowest cost while considering cost minimization and carbon constraints. This shows how the complexity of the CO2 constraints requires more RES and BES installations and thus more funds. The maximum CO2 limit achieved is 30%, which has reached the storage limits and increased the overall cost by $15 m.