Optimizing energy cost via battery sizing in residential PV/battery systems

Abstract

Application of renewable sources of energy is vital for the mankind due to global warming. Residential buildings consume major portion of electricity. Therefore, a grid-connected photovoltaic system of domestic level with battery storage backup (PV/storage system) is addressed in this article. This system has a significant effect on decreasing energy costs and contributes to meet the requirements of a nearly net-zero energy building. In this article, Mixed Integer Linear Programming (MILP) is applied to optimize the energy cost of a single-family house with battery energy storage for four different scenarios of installing solar panels. Optimization is performed on monthly scale for a house in Helsinki, Finland. The numerical results indicate that it is reasonable to integrate a storage in grid-connected PV systems in residential applications and represent the impact of battery size on household's monthly energy cost.

Robustness Evaluation of PV-Battery Sizing Principle Under Mission Profile Variations

“…Two of the main disadvantages of such sizing approach are high complexity and computational burden. Moreover, developed optimization methods are often tested for single case study [11]- [13] and their accuracy under other operating conditions, e.g., mission profiles, have not been investigated yet. Additionally, it is worth mentioning that PV power generation and load demand time series profile including longer time periods (e.g., years) are not always accessible.…”

Assessing the required life-cycle characteristics of electricity storage for ensuring sustainability in buildings

“…Optimal design is investigated for maximizing different cost indicators, such as the 'home economy' (Wu et al 2017), the net present value (Zhang et al 2017). In Doroudchi et al (2015), the optimal capacity is considered to be the smallest necessary, thereby enabling the minimization of operational costs with a scoring function. By assessing the probability of exceeding the maximum storage availability, Guarino and colleagues (2015) investigated the best trade-off between storage size and system efficiency.…”

“…Zhang et al 2017;Vuarnoz et al 2018). The minimization of cost is achieved by different computational approaches, for example, convex programming (Wu et al 2017), mixed-integer linear programming (Doroudchi et al 2015), and adaptative dynamic programming (Huang and Liu 2011). Zhang and colleagues (2017) found that the dynamic price load shifting strategy has a similar performance than the conventional operational strategy when electricity price variation is not large enough.…”

Robust optimisation for deciding on real‐time flexibility of storage‐integrated photovoltaic units controlled by intelligent software agents

“…To integrate demand response and control distributed generators, the current tariff-based electricity price paradigm is expected to change towards dynamic pricing [12][13][14]. Considerable work has been done on the use of residential batteries for minimising the electrical energy cost of its owner in the presence of real-time tariffs [15] or dynamic electricity prices [16]. The latter introduce uncertainty to an end-user who wants to schedule his EES to minimise his electricity cost or profit from the price fluctuations [17].…”