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.

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.

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. The proposed multi-agent energy management system consists of single-type agents who control one or more gird entities, which are represented as generic sub-agent elements. The agent applies one control algorithm across all elements and uses a cost function to evaluate the suitability of the element as a supplier. The behavior set by the agent's user defines which parameters of an element have greater weight in the cost function, which allows the user to specify the preference on suppliers dynamically. This study shows the ability of the multi-agent energy management system to select suppliers according to the selection behavior given by the user. The optimality of the supplier for the required demand is ensured by the cost function based on the parameters of the element.

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 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.

Using standby generators in support of an electricity network: logistics and experience

Electricity load shifting is becoming a big topic in the world of ‘green’ retail. Marks & Spencer (M&S) aim to become the world’s most sustainable retailer (1) and part of that commitment means contributing to the future electricity network. While intelligent operation of fridges and Heating, Ventilation and Air Conditioning (HVAC) systems are a wide area of research, standby generators should be considered too, as they are the most widely adopted form of distributed generation. In this paper, the experience of using standby generators in Northern Ireland to support the grid is shared and the logistics of future projects are discussed. Interactions with maintenance schedules, electricity costs, grid code, staffing and store opening times are discussed as well as the financial implications associated with running generators for grid support.

Financial implications for companies running standby generators in support of a smart grid

Integrating renewable energy into built environments requires additional attention to the balancing of supply and demand due to their intermittent nature. Demand Side Response (DSR) has the potential to make money for organisations as well as support the System Operator as the generation mix changes. There is an opportunity to increase the use of existing technologies in order to manage demand. Company-owned standby generators are a rarely used resource; their maintenance schedule often accounts for a majority of their running hours. DSR encompasses a range of technologies and organisations; Sustainability First (2012) suggest that the System Operator (SO), energy supply companies, Distribution Network Operators (DNOs), Aggregators and Customers all stand to benefit from DSR. It is therefore important to consider impact of DSR measures to each of these stakeholders. This paper assesses the financial implications of organisations using existing standby generation equipment for DSR in order to avoid peak electricity charges. It concludes that under the current GB electricity pricing structure, there are several regions where running diesel generators at peak times is financially beneficial to organisations. Issues such as fuel costs, Carbon Reduction Commitment (CRC) charges, maintenance costs and electricity prices are discussed.

Systematic selection of analogue redesign method for forward-type digital power converters

This article proposes a systematic approach to determine the most suitable analogue redesign method to be used for forward-type converters under digital voltage mode control. The focus of the method is to achieve the highest phase margin at the particular switching and crossover frequencies chosen by the designer. It is shown that at high crossover frequencies with respect to switching frequency, controllers designed using backward integration have the largest phase margin; whereas at low crossover frequencies with respect to switching frequency, controllers designed using bilinear integration with pre-warping have the largest phase margins. An algorithm has been developed to determine the frequency of the crossing point where the recommended discretisation method changes. An accurate model of the power stage is used for simulation and experimental results from a Buck converter are collected. The performance of the digital controllers is compared to that of the equivalent analogue controller both in simulation and experiment. Excellent closeness between the simulation and experimental results is presented. This work provides a concrete example to allow academics and engineers to systematically choose a discretisation method.

Bond graph models of DC-DC converters operating in both CCM and DCM

In this paper, Bond Graphs are employed to develop a novel mathematical model of conventional switched-mode DC-DC converters valid for both continuous and discontinuous conduction modes. A unique causality bond graph model of hybrid models is suggested with the operation of the switch and the diode to be represented by a Modulated Transformer with a binary input and a resistor with fixed conductance causality. The operation of the diode is controlled using an if-then function within the model. The extracted hybrid model is implemented on a Boost and Buck converter with their operations to change from CCM to DCM and to return to CCM. The vector fields of the models show validity in a wide operation area and comparison with the simulation of the converters using PSPICE reveals high accuracy of the proposed model, with the Normalised Root Means Square Error and the Maximum Absolute Error remaining adequately low. The model is also experimentally tested on a Buck topology.

Orange juice–derived flavanone and phenolic metabolites do not acutely affect cardiovascular risk biomarkers: a randomized, placebo-controlled, crossover trial in men at moderate risk of cardiovascular disease

Background: Epidemiological data suggest inverse associations between citrus flavanone intake and cardiovascular disease (CVD) risk. However, insufficient randomized controlled trial (RCT) data limit our understanding of mechanisms by which flavanones and their metabolites potentially reduce cardiovascular (CV) risk factors. Objective: We examined the effects of orange juice or a dose-matched hesperidin supplement on plasma concentrations of established and novel flavanone metabolites and their effects on CV risk biomarkers in men at moderate CVD risk. Methods: In an acute, randomized, placebo-controlled crossover trial, 16 fasted participants (aged 51-69 y) received orange juice or a hesperidin supplement (both providing 320 mg hesperidin) or control (all matched for sugar and vitamin C content). At baseline and 5 h post-intake, endothelial function (primary outcome), further CV risk biomarkers (i.e. blood pressure, arterial stiffness, cardiac autonomic function, platelet activation and NADPH oxidase gene expression) and plasma flavanone metabolites were assessed. Prior to each intervention, a diet low in flavonoids, nitrate/nitrite, alcohol and caffeine was followed and a standardized low-flavonoid evening meal was consumed. Results: Orange juice intake significantly elevated mean (± SEM) plasma concentrations of 8 flavanone (1.75 ± 0.35 µmol/L, P < 0.0001) and 15 phenolic metabolites (13.27 ± 2.22 µmol/L, P < 0.0001) compared with control at 5 h post-consumption. Despite increased plasma flavanone and phenolic metabolite concentrations, CV risk biomarkers were unaltered. Following hesperidin supplement intake, flavanone metabolites were not different to control, suggesting altered absorption/metabolism compared with the orange juice matrix. Conclusions: Following single-dose flavanone intake within orange juice, we detected circulating flavanone and phenolic metabolites collectively reaching a concentration of 15.20 ± 2.15 µmol/L but observed no effect on CV risk biomarkers. Longer-duration RCTs are required to further examine the previous associations between higher flavanone intakes and improved cardiovascular health and to ascertain the relative importance of food matrix and flavanone-derived phenolic metabolites.

Diet rich in high glucoraphanin broccoli reduces plasma LDL cholesterol: evidence from randomised controlled trials

Cruciferous-rich diets have been associated with reduction in plasma LDL-cholesterol (LDL-C), which may be due to the action of isothiocyanates derived from glucosinolates that accumulate in these vegetables. This study tests the hypothesis that a diet rich in high glucoraphanin (HG) broccoli will reduce plasma LDL-C. METHODS AND RESULTS: One hundred and thirty volunteers were recruited to two independent double-blind, randomly allocated parallel dietary intervention studies, and were assigned to consume either 400 g standard broccoli or 400 g HG broccoli per week for 12 weeks. Plasma lipids were quantified before and after the intervention. In study 1 (37 volunteers), the HG broccoli diet reduced plasma LDL-C by 7.1% (95% CI: -1.8%, -12.3%, p = 0.011), whereas standard broccoli reduced LDL-C by 1.8% (95% CI +3.9%, -7.5%, ns). In study 2 (93 volunteers), the HG broccoli diet resulted in a reduction of 5.1% (95% CI: -2.1%, -8.1%, p = 0.001), whereas standard broccoli reduced LDL-C by 2.5% (95% CI: +0.8%, -5.7%, ns). When data from the two studies were combined the reduction in LDL-C by the HG broccoli was significantly greater than standard broccoli (p = 0.031). CONCLUSION: Evidence from two independent human studies indicates that consumption of high glucoraphanin broccoli significantly reduces plasma LDL-C

Investigation of high-efficiency Wireless Power Transfer criteria of resonantly-coupled loops and dipoles through analysis of the figure of merit

The efficiency of a Wireless Power Transfer (WPT) system is greatly dependent on both the geometry and operating frequency of the transmitting and receiving structures. By using Coupled Mode Theory (CMT), the figure of merit is calculated for resonantly-coupled loop and dipole systems. An in-depth analysis of the figure of merit is performed with respect to the key geometric parameters of the loops and dipoles, along with the resonant frequency, in order to identify the key relationships leading to high-efficiency WPT. For systems consisting of two identical single-turn loops, it is shown that the choice of both the loop radius and resonant frequency are essential in achieving high-efficiency WPT. For the dipole geometries studied, it is shown that the choice of length is largely irrelevant and that as a result of their capacitive nature, low-MHz frequency dipoles are able to produce significantly higher figures of merit than those of the loops considered. The results of the figure of merit analysis are used to propose and subsequently compare two mid-range loop and dipole WPT systems of equal size and operating frequency, where it is shown that the dipole system is able to achieve higher efficiencies than the loop system of the distance range examined.

Using proxies to calculate the carbon impact of investment into electricity network assets

Replacement and upgrading of assets in the electricity network requires financial investment for the distribution and transmission utilities. The replacement and upgrading of network assets also represents an emissions impact due to the carbon embodied in the materials used to manufacture network assets. This paper uses investment and asset data for the GB system for 2015-2023 to assess the suitability of using a proxy with peak demand data and network investment data to calculate the carbon impacts of network investments. The proxies are calculated on a regional basis and applied to calculate the embodied carbon associated with current network assets by DNO region. The proxies are also applied to peak demand data across the 2015-2023 period to estimate the expected levels of embodied carbon that will be associated with network investment during this period. The suitability of these proxies in different contexts are then discussed, along with initial scenario analysis to calculate the impact of avoiding or deferring network investments through distributed generation projects. The proxies were found to be effective in estimating the total embodied carbon of electricity system investment in order to compare investment strategies in different regions of the GB network.