The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

Matthew Rowe,Timur Yunusov,Stephen Haben,William Holderbaum,Ben Potter

doi:10.3390/en7063537

Matthew Rowe, Timur Yunusov + Show 3 more

Open Access

https://doi.org/10.3390/en7063537

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Journal: Energies	Publication Date: May 30, 2014
Citations: 93	License type: CC BY 3.0

Affiliation: University of Reading, University of Oxford

Abstract

Energy storage is a potential alternative to conventional network reinforcement of the low voltage (LV) distribution network to ensure the grid’s infrastructure remains within its operating constraints. This paper presents a study on the control of such storage devices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, where the objective is to achieve the greatest peak reduction in demand, for a given storage device specification, taking into account the high level of uncertainty in the prediction of LV demand. The algorithms presented in this paper are compared to a standard set-point controller and bench marked against a control algorithm with a perfect forecast. A specific case study, using storage on the LV network, is presented, and the results of each algorithm are compared. A comprehensive analysis is then carried out simulating a large number of LV networks of varying numbers of households. The results show that the performance of each algorithm is dependent on the number of aggregated households. However, on a typical aggregation, the novel SRHC algorithm presented in this paper is shown to outperform each of the comparable storage control techniques.

Highlights

As demand increases on the low voltage (LV) network due to changes in consumer behaviour and the electrification of transport and heating [1], the distribution network operators (DNO) will be forced to change the way in which they operate and reinforce this part of the network
The stochastic receding horizon control (SRHC) controller treats the demand as a stochastic process and formulates a scenario tree based on a priori demand data
If the demand is below the set-point, the storage device will charge until the storage device capacity is full; else, if the demand is above the set-point, the storage device will discharge at the set rate, until the storage device capacity is empty or the demand goes below the set-point again, reducing the demand up the network from the storage device

Summary

Introduction

As demand increases on the low voltage (LV) network due to changes in consumer behaviour and the electrification of transport and heating [1], the distribution network operators (DNO) will be forced to change the way in which they operate and reinforce this part of the network. We present algorithms using real-time data available from the network, to make a control decision for the storage device on the LV network, firstly incorporating demand forecasts into the control of DNO owned storage devices and treating the demand as a stochastic element in the controller. It has been shown that storage control incorporating uncertainty in the electricity market is possible using a receding horizon and no forecasts [23]; the work successfully reduces the impact of prediction errors using a heuristic-based approach. The paper has multiple contributions: the work studies and presents novel controllers for the control of the LV network connected DNO owned storage device’s, where the objective is to achieve the greatest possible peak reduction, given a pre-defined storage device.

The Energy Storage System and LV Network Demand Model

Selecting Nodes

Sampling from the Historic Data

Results and Discussion

A Specific Example

Conclusions