Energy storage systems (ESSs) are being considered to overcome issues in modern grids, caused by increasing penetration of renewable generation. Nevertheless, integration of ESS should also be supplemented with an optimal energy management framework to ensure maximum benefits from ESS. Conventional energy management of battery, used with PV system, maximises self-consumption but does not mitigate grid congestion or address battery degradation. Model predictive control (MPC) can alleviate congestion, degradation while maximizing self-consumption. As such, studies will be carried out, in this work, to highlight the improvement with MPC based energy management over conventional method using simulations of one-year system behaviour. As MPC uses forecast information in decision making, the impact of forecast uncertainties will be assessed and addressing the same through constraint tightening will be presented. It is concluded that MPC provides improvement in system behaviour over multiple performance criteria.

Deployment of a battery energy storage system for the photovoltaic (PV) application has been increasing at a fast rate. Depending on the number of power conversion units and their type of connection, the PV-battery system can be classified into DC- and AC-coupled configurations. The number of the components and their electrical loading directly affects the reliability of each of the configurations. Hence, in order to assure high efficiency and lifetime of the PV-battery system, reliability assessment of power conversion units (representing the most reliability-critical system components) is necessary. With respect to that, in this paper, a reliability assessment of the PV-battery system is performed and a comparison of the DC- and AC-coupled configuration reliability is conducted. In the analysis, all parts of the power conversion system, i.e., DC/DC and DC/AC converter units, are taken into consideration and component-, converter- and system-level reliability is assessed. A case study of 6 kW PV system with integrated 3 kW/7.5 kWh battery system has shown that higher reliability is achieved for DC-coupled configuration. The obtained results indicate that the probability of failure for the 15% of the population for DC-coupled configuration occurs 7 years later than that is a case for AC-coupled configuration. Finally, the presented analysis can serve as a benchmark for lifetime and reliability assessment of power conversion units in PV-battery systems for both configuration types. It provides information about differences in electrical and thermal loading of the power conversion units and resulting reliability of the two configurations.

Increasing integration of photovoltaic (PV) system in electric grids cause congestion during peak power feed-in. Battery storage in PV systems increases self-consumption, for consumer's benefit. However with conventional maximising self consumption (MSC) control for battery scheduling, the issue of grid congestion is not addressed. The batteries tend to be fully charged early in the day and peak power is still fed-in to grid. This also increases battery degradation due to increased dwell time at high state of charge (SOC) levels. To address this issue, this work uses a model predictive control (MPC) for scheduling in PV system with battery storage to achieve multiple objectives of minimising battery degradation, grid congestion, while maximising self consumption. In order to demonstrate the improvement, this work compares the performances of MPC and MSC schemes when used in battery scheduling. The improvement is quantified through performance indices like self consumption ratio, peak power reduction and battery capacity fade for one-year operation. An analysis on computation burden and maximum deterioration in MPC performance under prediction error is also carried out. It is concluded that, compared to MSC, MPC achieves similar self consumption in PV systems while also reducing grid congestion and battery degradation.

This paper focuses on the sizing of a battery energy storage system providing frequency containment reserves in a power system with a large wind power penetration level. A three-stage sizing methodology including the different aspect of battery energy storage system performance is proposed. The first stage includes time-domain simulations, investigating battery energy storage system dynamic response and its capability of providing frequency reserves. The second stage involves lifetime investigation. An economic assessment of the battery unit is carried out by performing the last stage. The main outcome of the proposed methodology is to choose the suitable battery energy storage system size for providing frequency containment reserve from augmented wind power plants while fulfilling relevant evaluation criteria imposed for each stage.

In recent years, the transition process towards clean energy has caused many challenges to power systems, especially in the field of operation and power system control. Conventional generators, which have been providing ancillary services to maintain stability and reliability to the system, are being replaced by intermittent renewable generators. Therefore, maintaining system quality and stability in terms of power system frequency control is one of the major challenges that require new resources and system integration. Battery energy storage systems (BESSs), as fast‐acting energy storage systems, with the capability to act as a controllable source and sink of electricity are one of the prominent solutions for system services. This study investigates the primary frequency control provision from BESSs to the renewable energy sources dominated power system. The simulation results for various cases have shown that integration of BESSs has significantly improved power system frequency stability.