The cost of peak power for end-users subject to a demand charge may be substantial, expecting to increase further with the vast growth of power-demanding devices. In cases where load-shifting is not a viable option for cost reduction, battery storage systems used for peak shaving purposes are emerging as a promising solution. In this paper, the economic benefits of implementing battery storage into an existing grid-connected photovoltaic system for a medium-scale swimming facility is studied. The objective is to minimize the total cost of electricity for the facility, including the cost of energy and peak power demand, while ensuring the longevity of the battery. An optimization model based on multi-integer linear programming is built, and simulated using a one-year time horizon in GAMS and Matlab. The main results reveal that installing a battery storage system is economically attractive today, with net savings on the total system cost of 0.64% yearly. The cost of peak power is reduced by 13.9%, and the savings from peak shaving operation alone is enough to compensate for the yearly cost of the battery. Moreover, the battery ensures additional revenue by performing price arbitrage operations. When simulating the system for an assumed 2030 scenario, the battery is found to be more profitable with a yearly net savings of 4.15%.

In this paper, we introduce an Interior Point-based (IP) Multi-Period AC Optimal Power Flow (MPOPF) solver for the integration of Stationary Energy Storage Systems (SESS) and Electric Vehicles (EV). The primary methodology is based on: 1) analytic and exact calculation of partial differential equations of the Lagrangian sub-problem, and 2) exploiting the sparse structure and pattern of the coefficient matrix of Newton-Raphson approach in the IP algorithm. Extensive results of the application of proposed methods on several benchmark test systems are presented and elaborated, where the advantages and disadvantages of different existing algorithms for the solution of MPOPF, from the standpoint of computational efficiency, are brought forward. We compare the computational performance of the proposed Schur-Complement algorithm with a direct sparse LU solver. The comparison is performed for two different applicational purposes: SESS and EV. The results suggest the substantial computational performance of Schur-Complement algorithm in comparison with that of a direct LU solver when the number of storage devices and optimisation horizon increase for both cases of SESS and EV. Also, some situations where computational performance is inferior are discussed

With the introduction of massive renewable energy sources and storage devices, the traditional process of grid operation must be improved in order to be safe, reliable, fast responsive and cost efficient, and in this regard power flow solvers are indispensable. In this paper, we introduce an Interior Point-based (IP) Multi-Period AC Optimal Power Flow (MPOPF) solver for the integration of Stationary Energy Storage Systems (SESS) and Electric Vehicles (EV). The primary methodology is based on: 1) analytic and exact calculation of partial differential equations of the Lagrangian sub-problem, and 2) exploiting the sparse structure and pattern of the coefficient matrix of Newton-Raphson approach in the IP algorithm. Extensive results of the application of proposed methods on several benchmark test systems are presented and elaborated, where the advantages and disadvantages of different existing algorithms for the solution of MPOPF, from the standpoint of computational efficiency, are brought forward. We compare the computational performance of the proposed Schur-Complement algorithm with a direct sparse LU solver. The comparison is performed for two different applicational purposes: SESS and EV. The results suggest the substantial computational performance of Schur-Complement algorithm in comparison with that of a direct LU solver when the number of storage devices and optimisation horizon increase for both cases of SESS and EV. Also, some situations where computational performance is inferior are discussed.