With increasing shares of variable renewable energy sources in the power mix, the need for energy storage solutions is projected to increase as well. Storage can in such combined systems help mitigate the issues with relying on intermittent sources by time-shifting the supply and smoothing out frequency fluctuations, to name some examples. This thesis has focused on Azelio ABs flagship product, the TES.POD, which is a long-duration thermal energy storage technology. When integrated with, for example, solar PV power, the TES.POD can store excess energy and dispatch it during times of low supply or when during the evening/night. The aim of the thesis has been the development of a day-ahead dispatch optimization tool for systems that include multiple TES.PODs, combined into a Cluster, and solar PV. The model was to be built using the Python programming language and based on Mixed-Integer-Linear-Programming (MILP) methods. The PV+storage system was then allowed to be connected to supplementary power sources such as a larger electric grid, or diesel generators in off-grid locations. The purpose of the optimization model is to find the most economic way to operate the individual TES.PODs while also keeping track of other system components, using a cost-based objective function (minimize costs). A focus has been on using high time resolution (small time step) in order to investigate the impact that the TES.PODs dynamic constraints has on operation. Another strength compared to pre-existing models was the ability to operate individual units indifferent to each other, as opposed to having them all operated in unison. Final results from benchmarking tests and two case studies indicated that using the optimization tool with smaller time steps had an effect on key indicators, and could lead to improved economy in the system. It was observed in both cases that the cost of electricity was reduced by running the optimization tool with time steps of either two or three minutes when compared with using an hourly resolution. Furthermore, several usage parameters for the TES.PODs, notably the total amount of operated hours and energy output per cycle, saw improvements which could lead to reduced cost of operation and maintenance. While not the main intent, testing different Cluster sizes and amount of installed PV capacity with the model, it could also be used in strategic decisions for system sizing. However, due to rapidly growing computational times in systems with large TES.POD clusters and using smaller time steps, the possibility of adding more complexity to the model in future work must be done with caution. To combat this issue, either improvements to the model formulation could be attempted, or by using more powerful hardware or optimizer (imported software algorithm that handles solving the model).