Timur Yunusov

A Peak Reduction Scheduling Algorithm for Storage Devices on the Low Voltage Network

Reinforcing the Low Voltage (LV) distribution network will become essential to ensure it remains within its operating constraints as demand on the network increases. The deployment of energy storage in the distribution network provides an alternative to conventional reinforcement. This paper presents a control methodology for energy storage to reduce peak demand in a distribution network based on day-ahead demand forecasts and historical demand data. The control methodology pre-processes the forecast data prior to a planning phase to build in resilience to the inevitable errors between the forecasted and actual demand. The algorithm uses no real time adjustment so has an economical advantage over traditional storage control algorithms. Results show that peak demand on a single phase of a feeder can be reduced even when there are differences between the forecasted and the actual demand. In particular, results are presented that demonstrate when the algorithm is applied to a large number of single phase demand aggregations that it is possible to identify which of these aggregations are the most suitable candidates for the control methodology.

Sub-agent elements for control methods in multi-agent energy management system

Increased penetration of generation and decentralised control are considered to be feasible and effective solution for reducing cost and emissions and hence efficiency associated with power generation and distribution. Distributed generation in combination with the multi-agent technology are perfect candidates for this solution. Pro-active and autonomous nature of multi-agent systems can provide an effective platform for decentralised control whilst improving reliability and flexibility of the grid.

Smart Storage Scheduling and Forecasting for Peak Reduction on Low-Voltage Feeders

The transition to a low carbon economy will likely bring new challenges to the distribution networks, which could face increased demands due to low-carbon technologies and new behavioural trends. A traditional solution to increased demand is network reinforcement through asset replacement, but this could be costly and disruptive. Smart algorithms combined with modern technologies can lead to inexpensive alternatives. In particular, battery storage devices with smart control algorithms can assist in load peak reduction. The control algorithms aim to schedule the battery to charge at times of low demand and discharge, feeding the network, at times of high load. This study analyses two scheduling algorithms, model predictive control (MPC) and fixed day-ahead scheduler (FDS), comparing against a set-point control (SPC) benchmark. The forecasts presented here cover a wide range of techniques, from traditional linear regression forecasts to machine learning methods. The results demonstrate that the forecasting and control methods need to be selected for each feeder taking into account the demand characteristics, whilst MPC tends to outperform the FDS on feeders with higher daily demand. This chapter contributes in two main directions: (i) several forecasting methods are considered and compared and (ii) new energy storage control algorithm, MPC with half-hourly updated (rolling) forecasts designed for low voltage network application, is introduced, analysed and compared.

On-line adjustment of battery schedules for supporting LV distribution network operation

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.

Cost function for sub-agent elements in multi-agent energy management system

Distributed generation plays a key role in reducing CO2 emissions and losses in transmission of power. However, due to the nature of renewable resources, distributed generation requires suitable control strategies to assure reliability and optimality for the grid. Multi-agent systems are perfect candidates for providing distributed control of distributed generation stations as well as providing reliability and flexibility for the grid integration.