Control methodologies: Peak reduction algorithms for DNO owned storage devices on the Low Voltage network

The Distribution Network Operators (DNOs) role is becoming more difficult as electric vehicles and electric heating penetrate the network, increasing the demand. As a result it becomes harder for the distribution networks infrastructure to remain within its operating constraints. Energy storage is a potential alternative to conventional network reinforcement such as upgrading cables and transformers. The research presented here in this paper shows that due to the volatile nature of the LV network, the control approach used for energy storage has a significant impact on performance. This paper presents and compares control methodologies for energy storage where the objective is to get the greatest possible peak demand reduction across the day from a pre-specified storage device. The results presented show the benefits and detriments of specific types of control on a storage device connected to a single phase of an LV network, using aggregated demand profiles based on real smart meter data from individual homes. The research demonstrates an important relationship between how predictable an aggregation is and the best control methodology required to achieve the objective.

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.

The impact of location and type on the performance of Low-Voltage network connected Battery Energy Storage Systems

This paper assesses the impact of the location and configuration of Battery Energy Storage Systems (BESS) on Low-Voltage (LV) feeders. BESS are now being deployed on LV networks by Distribution Network Operators (DNOs) as an alternative to conventional reinforcement (e.g. upgrading cables and transformers) in response to increased electricity demand from new technologies such as electric vehicles. By storing energy during periods of low demand and then releasing that energy at times of high demand, the peak demand of a given LV substation on the grid can be reduced therefore mitigating or at least delaying the need for replacement and upgrade. However, existing research into this application of BESS tends to evaluate the aggregated impact of such systems at the substation level and does not systematically consider the impact of the location and configuration of BESS on the voltage profiles, losses and utilisation within a given feeder. In this paper, four configurations of BESS are considered: single-phase, unlinked three-phase, linked three-phase without storage for phase-balancing only, and linked three-phase with storage. These four configurations are then assessed based on models of two real LV networks. In each case, the impact of the BESS is systematically evaluated at every node in the LV network using Matlab linked with OpenDSS. The location and configuration of a BESS is shown to be critical when seeking the best overall network impact or when considering specific impacts on voltage, losses, or utilisation separately. Furthermore, the paper also demonstrates that phase-balancing without energy storage can provide much of the gains on unbalanced networks compared to systems with energy storage.

Power distribution network design aided by an expert system

The next couple of years will see the need for replacement of a large amount of life-expired switchgear on the UK 11 kV distribution system. Latest technology and alternative equipment have made the choice of replacement a complex task. The authors present an expert system as an aid to the decision process for the design of the 11 kV power distribution network.

The real-time optimisation of DNO owned storage devices on the LV network for peak reduction

Energy storage is a potential alternative to conventional network reinforcementof the low voltage (LV) distribution network to ensure the grid’s infrastructure remainswithin its operating constraints. This paper presents a study on the control of such storagedevices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, wherethe objective is to achieve the greatest peak reduction in demand, for a given storagedevice 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-pointcontroller and bench marked against a control algorithm with a perfect forecast. A specificcase study, using storage on the LV network, is presented, and the results of each algorithmare 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.

Mathematical solutions for electricity networks in a low carbon future

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 volatile, irregular and noisy compared to the demand at the high voltage (HV) substation level. Novel analytical methods will be required in order to optimise the use of household level data. In this paper we briefly outline some mathematical techniques which will play a key role in better understanding the customer's behaviour and create solutions for supporting the network at the LV substation level.

A new error measure for forecasts of household-level, high resolution electrical energy consumption

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 single household-level, or of small aggregations of households, can improve the peak demand reduction brought about through such devices by helping to plan the appropriate charging and discharging cycles. However, before such methods can be developed, validation measures are required which can assess the accuracy and usefulness of forecasts of volatile and noisy household-level demand. In this paper we introduce a new forecast verification error measure that reduces the so called “double penalty” effect, incurred by forecasts whose features are displaced in space or time, compared to traditional point-wise metrics, such as Mean Absolute Error and p-norms in general. The measure that we propose is based on finding a restricted permutation of the original forecast that minimises the point wise error, according to a given metric. We illustrate the advantages of our error measure using half-hourly domestic household electrical energy usage data recorded by smart meters and discuss the effect of the permutation restriction.

Long term individual load forecast under different electrical vehicles uptake scenarios

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 must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences - that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at day-time, night-time, or a mix of both. The model is implemented on a neighbourhood in south-east England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected.

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested.

Can microgrids make a major contribution to UK energy supply?

Almost all the electricity currently produced in the UK is generated as part of a centralised power system designed around large fossil fuel or nuclear power stations. This power system is robust and reliable but the efficiency of power generation is low, resulting in large quantities of waste heat. The principal aim of this paper is to investigate an alternative concept: the energy production by small scale generators in close proximity to the energy users, integrated into microgrids. Microgrids—de-centralised electricity generation combined with on-site production of heat—bear the promise of substantial environmental benefits, brought about by a higher energy efficiency and by facilitating the integration of renewable sources such as photovoltaic arrays or wind turbines. By virtue of good match between generation and load, microgrids have a low impact on the electricity network, despite a potentially significant level of generation by intermittent energy sources. The paper discusses the technical and economic issues associated with this novel concept, giving an overview of the generator technologies, the current regulatory framework in the UK, and the barriers that have to be overcome if microgrids are to make a major contribution to the UK energy supply. The focus of this study is a microgrid of domestic users powered by small Combined Heat and Power generators and photovoltaics. Focusing on the energy balance between the generation and load, it is found that the optimum combination of the generators in the microgrid- consisting of around 1.4 kWp PV array per household and 45% household ownership of micro-CHP generators- will maintain energy balance on a yearly basis if supplemented by energy storage of 2.7 kWh per household. We find that there is no fundamental technological reason why microgrids cannot contribute an appreciable part of the UK energy demand. Indeed, an estimate of cost indicates that the microgrids considered in this study would supply electricity at a cost comparable with the present electricity supply if the current support mechanisms for photovoltaics were maintained. Combining photovoltaics and micro-CHP and a small battery requirement gives a microgrid that is independent of the national electricity network. In the short term, this has particular benefits for remote communities but more wide-ranging possibilities open up in the medium to long term. Microgrids could meet the need to replace current generation nuclear and coal fired power stations, greatly reducing the demand on the transmission and distribution network.

Purpose built teaching equipment for a laboratory based course in three phase machines and drives

The hazards associated with high voltage three phase inverters and the rotating shafts of large electrical machines have resulted in most of the engineering courses covering these topics to be predominantly theoretical. This paper describes a set of purpose built, low voltage and low cost teaching equipment which allows the "hands on" instruction of three phase inverters and rotating machines. By using low voltages, the student can experiment freely with the motors and inverter and can access all of the current and voltage waveforms, which until now could only be studied in text books or observed as part of laboratory demonstrations. Both the motor and the inverter designs are optimized for teaching purposes cost around $25 and can be made with minimal effort.

Three-phase machines and drives - equipment for a laboratory-based course

The hazards associated with high-voltage three-phase inverters and high-powered large electrical machines have resulted in most of the engineering courses covering three-phase machines and drives theoretically. This paper describes a set of purpose-built, low-voltage, and low-cost teaching equipment that allows the hands-on instruction of three-phase inverters and rotating machines. The motivation for moving towards a system running at low voltages is that the students can safely experiment freely with the motors and inverter. The students can also access all of the current and voltage waveforms, which until now could only be studied in textbooks or observed as part of laboratory demonstrations. Both the motor and the inverter designs are for teaching purposes and require minimal effort and cost

Purpose built teaching equipment for a laboratory based course in three phase machines and drives

The hazards associated with high voltage three phase inverters ond the rotating sha@s of large electrical machines have resulted in most of the engineering courses covering these topics to be predominantly theoretical. This paper describes a set of purpose built, low voltage and low cost teaching equipment which allows the “hands on I’ instruction of three phase inverters and rotating machines. By using low voltages, the student can experiment freely with the motors and inverter and can access all of the current and voltage waveforms, which until now could only be studied in text books or observed as part of laboratory demonstrations. Both the motor and the inverter designs are optimized for teaching purposes, cost around $25 and can be made with minimal effort.

Three-phase machines and drives - Equipment for a laboratory-based course

The hazards associated with high-voltage three-phase inverters and high-powered large electrical machines have resulted in most of the engineering courses covering three-phase machines and drives theoretically. This paper describes a set of purpose-built, low-voltage, and low-cost teaching equipment that allows the hands-on instruction of three-phase inverters and rotating machines. The motivation for moving towards a system running at low voltages is that the students can safely experiment freely with the motors and inverter. The students can also access all of the current and voltage waveforms, which until now could only be studied in textbooks or observed as part of laboratory demonstrations. Both the motor and the inverter designs are for teaching purposes and require minimal effort and cost.

Defining user perception of distributed multimedia quality

This article presents the results of a study that explored the human side of the multimedia experience. We propose a model that assesses quality variation from three distinct levels: the network, the media and the content levels; and from two views: the technical and the user perspective. By facilitating parameter variation at each of the quality levels and from each of the perspectives, we were able to examine their impact on user quality perception. Results show that a significant reduction in frame rate does not proportionally reduce the user's understanding of the presentation independent of technical parameters, that multimedia content type significantly impacts user information assimilation, user level of enjoyment, and user perception of quality, and that the device display type impacts user information assimilation and user perception of quality. Finally, to ensure the transfer of information, low-level abstraction (network-level) parameters, such as delay and jitter, should be adapted; to maintain the user's level of enjoyment, high-level abstraction quality parameters (content-level), such as the appropriate use of display screens, should be adapted.