# Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm
## 5 Conclusions  
A valuable tool has been implemented to test, simulate and analyze the behavior of the modelled HESS with battery models. The tool integrates a techno-economical model of a microgrid, including loads and RES. The model includes two battery types with their respective round trip efficiencies and costs of depreciation related to battery degradation during cycling. This simulation tool facilitates the sizing of the HESS installation, as well as the development and testing of control algorithms for scheduling the HESS operation. The graphical interface allows easy provisioning of input data while also allowing a visualization and analysis of the output data. The authors have implemented HESS control methods, including a simple energy balancing algorithm and using an energy cost optimization. The model and methods have been tested with real energy profiles recorded at a research centre. The results explain the difference between the tested methods. The simple balancing algorithm stores surplus RES energy in the HESS and increases self-consumption rate to reduce the cost of energy; it does not do arbitrage as such, as it does not care about prices or tariffs. The advantage of this algorithm is its simplicity and moderate financial outcomes-using energy storages with such an algorithm brings profits in comparison to the setup without storage. The disadvantage is that this approach relies on the surplus of RES energy, otherwise the batteries are not used at all. Such an algorithm can be profitable when the averaged production from RES exceeds the usage of the facility. The economical optimization method on the other hand minimizes the costs of the operation during a single day. It uses the fact that there is a sufficient difference in cost of energy in different zones of the tariffs-using batteries for arbitrage can become profitable. The total cost of using the VRFB battery (taking into account depreciation cost and losses related to the round trip efficiency) is not compensated by the difference between the prices in morning peak and off peak tariff zones. Adding an LFP battery, which has different properties and therefor cost balance, allows for reducing usage in all price peaks. The combination of both batteries allow to improve the cost balance of the operation and prolong the lifetime of the batteries. In the presence of overproduction of PV installation, the simple balancing algorithm at times outperformed the economical optimization method. The reason for this is that the methods employ a 24 h time window, during which the simple balancing algorithm tried to charge the battery as much as possible (it did not count the cost), whereas the economical optimization limited the charging to what was necessary for this day. This meant that in the economical optimization, a subsequent day might start with a lower SOC level, even though there was the potential to charge them the day before. To compensate for this, a modification of the economic optimization was performed, where the surplus of energy in a day was used to charge the HESS as much as possible. This modification improved the economical optimization method compared to the simple balancing algorithm. The economical optimization method uses the HESS for arbitrage and, as a result, causes a more intense cycling of both batteries within the HESS. The simulation with realistic technical and economic data show that the arbitrage introduced by the economic optimization method has a small effect on the overall financial result. Although the energy consumption in the peak hours, and thus the energy cost, is reduced, there are additional costs of battery depreciation and energy losses in the batteries. The potential for energy price reduction comes at a cost--the final economic result is only slightly better than the simple energy balancing when the battery depreciation cost is included.