Industry plays a significant role in the energy transition due to its share of energy consumption. More complex energy systems are proposed to accelerate the energy transition, including coupling renewable energy sources and energy storage to supply part of the industrial loads locally. In this work, we used a multi-objective genetic algorithm to optimally size an industrial hybrid power system comprising a PV system, a battery energy storage system, and a diesel generator to minimise energy costs and overall equivalent CO2 emissions. The results suggest that the system does not require high power and capacity components to minimise the energy cost and equivalent CO2 emissions, highlighting the importance of the EMS strategy. In our case scenario, the optimal HPS reduced the emission cost by 46.7 % and the energy cost by 8.7 %. For the EMS, we proposed a rolling horizon average approach, which defines a setpoint for the power exchanged with the grid to minimise its change rate in time. The EMS dispatched the power to minimise the sudden changes in the demand from the network, with a power allocation priority order of PV, BESS, and generator. We also evaluated the effect of adding the optimally sized hybrid power system into a CIGRE medium-voltage distribution network, using a real industrial load profile for each node. The hybrid power system improved the voltage sag on the hybrid power energy system node and its neighbouring nodes.