Stephen Haben

Stephen Haben

Doctor of Philosophy

About

37
Publications
12,171
Reads
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983
Citations
Citations since 2016
23 Research Items
871 Citations
2016201720182019202020212022050100150
2016201720182019202020212022050100150
2016201720182019202020212022050100150
2016201720182019202020212022050100150
Additional affiliations
August 2018 - present
University of Oxford
Position
  • Researcher
August 2018 - present
Energy Systems Catapult
Position
  • Analyst
October 2013 - July 2018
University of Oxford
Position
  • PostDoc Position
Education
September 2007 - May 2011
University of Reading
Field of study
  • Data Assimilation
September 2006 - September 2007
University of Bath
Field of study
  • Applied Mathematics
October 2002 - June 2006
The University of Warwick
Field of study
  • Mathematics

Publications

Publications (37)
Article
The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a nece...
Article
Full-text available
The energy sector is moving towards a low-carbon, decentralised, and smarter network. The increased uptake of distributed renewable energy and cheaper storage devices provide opportunities for new local energy markets. These local energy markets will require probabilistic price forecasting models to better describe the future price uncertainty. Thi...
Preprint
Full-text available
The increased digitalisation and monitoring of the energy system opens up numerous opportunities % and solutions which can help to decarbonise the energy system. Applications on low voltage (LV), localised networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and managem...
Article
With low voltage (LV) distribution networks increasingly being re-purposed beyond their original design specifications to accommodate low carbon technologies, the ability to accurately calculate their actual spare capacity is critical. Traditionally, within the Great Britain (GB) power system, there has been limited monitoring of LV distribution ne...
Article
Full-text available
This paper aims to present the significance of predicting stochastic loads to improve the performance of a low voltage (LV) network with an energy storage system (ESS) by employing several optimal energy controllers. Considering the highly stochastic behaviour that rubber tyre gantry (RTG) cranes demand, this study develops and compares optimal ene...
Article
In low voltage networks, Energy Storage Systems (ESSs) play a significant role in increasing energy cost savings, peak reduction and energy efficiency whilst reinforcing the electrical network infrastructure. This paper presents a stochastic optimal management system based on a Genetic Algorithm (GA) for the control of an ESS equipped with a networ...
Article
Short term load forecasts will play a key role in the implementation of smart electricity grids. They are required for optimising a wide range of potential network solutions on the low voltage (LV) grid, including the integration of low carbon technologies (such as photovoltaics) and the utilisation of battery storage devices. Despite the need for...
Article
An Energy Storage System (ESS) is a potential solution to increase the energy efficiency of low voltage distribution networks whilst reinforcing the power system. In this article, energy management systems have been developed for the control of an ESS connected to a network of electrified Rubber Tyre Gantry (RTG) cranes. Considering the highly vola...
Article
The increasing use and spread of low carbon technologies are expected to cause new patterns in electric demand and set novel challenges to a distribution network operator (DNO). In this study, we build upon a recently introduced method, called “buddying”, which simulates low voltage (LV) networks of both residential and non-domestic (e.g. shops, of...
Article
Full-text available
Short term load forecasts will play a key role in the implementation of smart electricity grids. They are required to optimise a wide range of potential network solutions on the low voltage (LV) grid, including integrating low carbon technologies (such as photovoltaics) and utilising battery storage devices. Despite the need for accurate LV level l...
Chapter
This chapter presents some advanced tools for low voltage (LV) network demand simulation. Such methods will be required to help distribution network operators (DNOs) cope with the increased uptake of low carbon technologies and localised sources of generation. This will enable DNOs to manage the current network, simulate the effect of various scena...
Article
Full-text available
Given the increase in international trading and the significant energy and environmental challenges in ports around the world, there is a need for a greater understanding of the energy demand behaviour at ports. The move towards electrified rubber-tyred gantry (RTG)cranes is expected to reduce gas emissions and increase energy savings compared to d...
Article
Full-text available
In variational data assimilation a least-squares objective function is minimised to obtain the most likely state of a dynamical system. This objective function combines observation and prior (or background) data weighted by their respective error statistics. In numerical weather prediction, data assimilation is used to estimate the current atmosphe...
Chapter
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 c...
Article
Full-text available
This article presents a study of optimal control strategies for an energy storage system connected to a network of electrified Rubber Tyre Gantry (RTG) cranes. The study aims to design optimal control strategies for the power flows associated with the energy storage device, considering the highly volatile nature of RTG crane demand and difficulties...
Article
Distribution network operators (DNOs) are increasingly concerned about the impact of low carbon technologies on the low voltage (LV) networks. More advanced metering infrastructures provide numerous opportunities for more accurate power flow analysis of the LV networks. However, such data may not be readily available for DNOs and in any case is lik...
Conference Paper
Full-text available
Estimating the demand on the low voltage network is essential for the distribution network operator (DNO), who is interested in managing and planning the network. Such concerns are particularly relevant as the UK moves towards a low carbon economy, and the electrification of heating and transport. Furthermore, small to medium enterprises (SMEs) con...
Article
Full-text available
We present a model for generating probabilistic forecasts that combines the kernel density estimation (KDE) and quantile regression techniques, as part of the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014. Initially, the KDE method is implemented with a time-decay parameter, but we later improve this method...
Research
Full-text available
Optimal state estimation is a method that requires minimising a weighted, nonlinear, least squares objective function in order to obtain the best estimate of the current state of a dynamical system. Often the minimisation is non-trivial due to the large scale of the problem, the relative sparsity of the observations and the nonlinearity of the obje...
Article
Full-text available
Clustering methods are increasingly being applied to residential smart meter data, which provides a number of important opportunities for distribution network operators (DNOs) to manage and plan low-voltage networks. Clustering has a number of potential advantages for DNOs, including the identification of suitable candidates for demand response and...
Article
More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we...
Article
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 redu...
Article
Full-text available
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 contr...
Article
Full-text available
As low carbon technologies become more pervasive, distribution network operators are looking to support the expected changes in the demands on the low voltage networks through the smarter control of storage devices. Accurate forecasts of demand at the individual household-level, or of small aggregations of households, can improve the peak demand re...
Conference Paper
Full-text available
Smart meters are becoming more ubiquitous as governments aim to reduce the risks to the energy supply as the world moves toward a low carbon economy. The data they provide could create a wealth of information to better understand customer behaviour. However at the household, and even the low voltage (LV) substation level, energy demand is extremely...
Article
Full-text available
Implementations of incremental variational data assimilation require the iterative minimization of a series of linear least-squares cost functions. The accuracy and speed with which these linear minimization problems can be solved is determined by the condition number of the Hessian of the problem. In this study, we examine how different components...
Article
Numerical weather prediction (NWP) centres use numerical models of the atmospheric flow to forecast future weather states from an estimate of the current state. Variational data assimilation (VAR) is used commonly to determine an optimal state estimate that miminizes the errors between observations of the dynamical system and model predictions of t...
Article
Full-text available
Variational data assimilation schemes are commonly used in major numerical weather prediction (NWP) centres around the world. The convergence of the variational scheme and the sensitivity of the analysis to perturbations are dependent on the conditioning of the Hessian of the linearized least-squares variational equation. The problem is ill-conditi...

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Projects

Projects (2)
Project
I am working on the development of a network of a crane with three phase energy storage based on load forecasting. The aim of this work is to design a control strategy for a three phase energy storage located at the substation side to reduce peak demand for a network of crane.