The future uptake of electric vehicles (EV) in low-voltage distribution networks can cause increased voltage violations and thermal overloading of network assets, especially in networks with limited headroom at times of high or peak demand. To address this problem, this paper proposes a distributed battery energy storage solution, controlled using an additive increase multiplicative decrease (AIMD) algorithm. The improved algorithm (AIMD+) uses local bus voltage measurements and a reference voltage threshold to determine the additive increase parameter and to control the charging, as well as discharging rate of the battery. The used voltage threshold is dependent on the network topology and is calculated using power flow analysis tools, with peak demand equally allocated amongst all loads. Simulations were performed on the IEEE LV European Test feeder and a number of real U.K. suburban power distribution network models, together with European demand data and a realistic electric vehicle charging model. The performance of the standard AIMD algorithm with a fixed voltage threshold and the proposed AIMD+ algorithm with the reference voltage profile are compared. Results show that, compared to the standard AIMD case, the proposed AIMD+ algorithm further improves the network’s voltage profiles, reduces thermal overload occurrences and ensures a more equal battery utilisation.

In the attempt to tackle the issue of climate change, governments across the world have agreed to set global carbon reduction targets.

Increasing domestic demand for electric energy is expected to put significant strain on the existing power distribution networks. In order to delay or prevent costly network reinforcement, some UK Distribution Network Operators (DNOs) are investigating the use of Battery Energy Storage Solutions (BESS), or other demand response systems, in the Low-Voltage (LV) power distribution networks to reduce peak demand. In most cases the control strategies, and metrics of success, are evaluated on a half-hourly basis and so sub-half-hourly (i.e. minute by minute) variations in demand are not effectively addressed. In this work, a closed-loop optimisation methodology is proposed that adjusts the pre-scheduled charging profile of a BESS in a sub-half-hourly manner in order to improve network operation whilst maintain the same average net energy flow over the half-hour period. This new approach guarantees that the BESS follows its predetermined half-hourly schedule, yet voltage and power imbalance, network losses, and feeder overloading are additionally mitigated through sub-half-hourly control actions. For validation, this paper presents a case study based on the real BESS installed in Bracknell as part of Thames Valley Vision project with Scottish and Southern Energy Power Distribution (SSE-PD) evaluated on the IEEE LV test case feeder model.

Change in consumer behaviour through uptake of low carbon technologies is likely to put existing distribution networks under strain and worsen the operational requirements of the network. Deployment of energy storage and power electronics is a feasible alternative to traditional network reinforcement. This study presents two control algorithms used with an energy storage device deployed as part of New Thames Valley Vision Project. The two algorithms are aimed at (i) equalising phase loading with correction of power factor and (ii) providing voltage support with Additive Increase Multiplicative Decrease algorithm for active and reactive power control.