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.

A new study on the power distribution of OFDMA, SC-FDMA and CP-CDMA signals

This paper explores a new technique to calculate and plot the distribution of instantaneous transmit envelope power of OFDMA and SC-FDMA signals from the equation of Probability Density Function (PDF) solved numerically. The Complementary Cumulative Distribution Function (CCDF) of Instantaneous Power to Average Power Ratio (IPAPR) is computed from the structure of the transmit system matrix. This helps intuitively understand the distribution of output signal power if the structure of the transmit system matrix and the constellation used are known. The distribution obtained for OFDMA signal matches complex normal distribution. The results indicate why the CCDF of IPAPR in case of SC-FDMA is better than OFDMA for a given constellation. Finally, with this method it is shown again that cyclic prefixed DS-CDMA system is one case with optimum IPAPR. The insight that this technique provides may be useful in designing area optimised digital and power efficient analogue modules.

CASCADE: An Agent Based Framework For Modeling The Dynamics Of Smart Electricity Systems

The Complex Adaptive Systems, Cognitive Agents and Distributed Energy (CASCADE) project is developing a framework based on Agent Based Modelling (ABM). The CASCADE Framework can be used both to gain policy and industry relevant insights into the smart grid concept itself and as a platform to design and test distributed ICT solutions for smart grid based business entities. ABM is used to capture the behaviors of diff erent social, economic and technical actors, which may be defi ned at various levels of abstraction. It is applied to understanding their interactions and can be adapted to include learning processes and emergent patterns. CASCADE models ‘prosumer’ agents (i.e., producers and/or consumers of energy) and ‘aggregator’ agents (e.g., traders of energy in both wholesale and retail markets) at various scales, from large generators and Energy Service Companies down to individual people and devices. The CASCADE Framework is formed of three main subdivisions that link models of electricity supply and demand, the electricity market and power fl ow. It can also model the variability of renewable energy generation caused by the weather, which is an important issue for grid balancing and the profi tability of energy suppliers. The development of CASCADE has already yielded some interesting early fi ndings, demonstrating that it is possible for a mediating agent (aggregator) to achieve stable demandfl attening across groups of domestic households fi tted with smart energy control and communication devices, where direct wholesale price signals had previously been found to produce characteristic complex system instability. In another example, it has demonstrated how large changes in supply mix can be caused even by small changes in demand profi le. Ongoing and planned refi nements to the Framework will support investigation of demand response at various scales, the integration of the power sector with transport and heat sectors, novel technology adoption and diffusion work, evolution of new smart grid business models, and complex power grid engineering and market interactions.

Expert system aid for design of overload line distribution networks

The use of expert system techniques in power distribution system design is examined. The selection and siting of equipment on overhead line networks is chosen for investigation as the use of equipment such as auto-reclosers, etc., represents a substantial investment and has a significant effect on the reliability of the system. Through past experience with both equipment and network operations, most decisions in selection and siting of this equipment are made intuitively, following certain general guidelines or rules of thumb. This heuristic nature of the problem lends itself to solution using an expert system approach. A prototype has been developed and is currently under evaluation in the industry. Results so far have demonstrated both the feasibility and benefits of the expert system as a design aid.

An investigation of the impact of time of generation on carbon savings from PV systems in Great Britain

PV only generates electricity during daylight hours and primarily generates over summer. In the UK, the carbon intensity of grid electricity is higher during the daytime and over winter. This work investigates whether the grid electricity displaced by PV is high or low carbon compared to the annual mean carbon intensity using carbon factors at higher temporal resolutions (half-hourly and daily). UK policy for carbon reporting requires savings to be calculated using the annual mean carbon intensity of grid electricity. This work offers an insight into whether this technique is appropriate. Using half hourly data on the generating plant supplying the grid from November 2008 to May 2010, carbon factors for grid electricity at half-hourly and daily resolution have been derived using technology specific generation emission factors. Applying these factors to generation data from PV systems installed on schools, it is possible to assess the variation in the carbon savings from displacing grid electricity with PV generation using carbon factors with different time resolutions. The data has been analyzed for a period of 363 to 370 days and so cannot account for inter-year variations in the relationship between PV generation and carbon intensity of the electricity grid. This analysis suggests that PV displaces more carbon intensive electricity using half-hourly carbon factors than using daily factors but less compared with annual ones. A similar methodology could provide useful insights on other variable renewable and demand-side technologies and in other countries where PV performance and grid behavior are different.

Energy-efficient resource allocation in OFDM systems with distributed antennas

In this paper, we develop an energy-efficient resource-allocation scheme with proportional fairness for downlink multiuser orthogonal frequency-division multiplexing (OFDM) systems with distributed antennas. Our aim is to maximize energy efficiency (EE) under the constraints of the overall transmit power of each remote access unit (RAU), proportional fairness data rates, and bit error rates (BERs). Because of the nonconvex nature of the optimization problem, obtaining the optimal solution is extremely computationally complex. Therefore, we develop a low-complexity suboptimal algorithm, which separates subcarrier allocation and power allocation. For the low-complexity algorithm, we first allocate subcarriers by assuming equal power distribution. Then, by exploiting the properties of fractional programming, we transform the nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form, which includes a tractable solution. Next, an optimal energy-efficient power-allocation algorithm is developed to maximize EE while maintaining proportional fairness. Through computer simulation, we demonstrate the effectiveness of the proposed low-complexity algorithm and illustrate the fundamental trade off between energy and spectral-efficient transmission designs.

Controlling market power of vertically integrated firms in electricity networks: demand response of aggregator agents

This article reports the results of an experiment that examined how demand aggregators can discipline vertically-integrated firms - generator and distributor-retailer holdings-, which have a high share in wholesale electricity market with uniform price double auction (UPDA). We initially develop a treatment where holding members redistribute the profit based on the imposition of supra-competitive prices, in equal proportions (50%-50%). Subsequently, we introduce a vertical disintegration (unbundling) treatment with holding-s information sharing, where profits are distributed according to market outcomes. Finally, a third treatment is performed to introduce two active demand aggregators, with flexible interruptible loads in real time. We found that the introduction of responsive demand aggregators neutralizes the power market and increases market efficiency, even beyond what is achieved through vertical disintegration.

Using reanalysis data to quantify extreme wind power generation statistics : a 33 year case study in Great Britain

With a rapidly increasing fraction of electricity generation being sourced from wind, extreme wind power generation events such as prolonged periods of low (or high) generation and ramps in generation, are a growing concern for the efficient and secure operation of national power systems. As extreme events occur infrequently, long and reliable meteorological records are required to accurately estimate their characteristics. Recent publications have begun to investigate the use of global meteorological “reanalysis” data sets for power system applications, many of which focus on long-term average statistics such as monthly-mean generation. Here we demonstrate that reanalysis data can also be used to estimate the frequency of relatively short-lived extreme events (including ramping on sub-daily time scales). Verification against 328 surface observation stations across the United Kingdom suggests that near-surface wind variability over spatiotemporal scales greater than around 300 km and 6 h can be faithfully reproduced using reanalysis, with no need for costly dynamical downscaling. A case study is presented in which a state-of-the-art, 33 year reanalysis data set (MERRA, from NASA-GMAO), is used to construct an hourly time series of nationally-aggregated wind power generation in Great Britain (GB), assuming a fixed, modern distribution of wind farms. The resultant generation estimates are highly correlated with recorded data from National Grid in the recent period, both for instantaneous hourly values and for variability over time intervals greater than around 6 h. This 33 year time series is then used to quantify the frequency with which different extreme GB-wide wind power generation events occur, as well as their seasonal and inter-annual variability. Several novel insights into the nature of extreme wind power generation events are described, including (i) that the number of prolonged low or high generation events is well approximated by a Poission-like random process, and (ii) whilst in general there is large seasonal variability, the magnitude of the most extreme ramps is similar in both summer and winter. An up-to-date version of the GB case study data as well as the underlying model are freely available for download from our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/.

Assessing the impact of occupant behaviour on the electricity consumption for lighting and small power in office buildings

Lighting and small power will typically account for more than half of the total electricity consumption in an office building. Significant variations in electricity used by different tenants suggest that occupants can have a significant impact on the electricity demand for these end-uses. Yet current modelling techniques fail to represent the interaction between occupant and the building environment in a realistic manner. Understanding the impact of such behaviours is crucial to improve the methodology behind current energy modelling techniques, aiming to minimise the significant gap between predicted and in-use performance of buildings. A better understanding of the impact of occupant behaviour on electricity consumption can also inform appropriate energy saving strategies focused on behavioural change. This paper reports on a study aiming to assess the intent of occupants to switch off lighting and appliances when not in use in office buildings. Based on the Theory of Planned Behaviour, the assessment takes the form of a questionnaire and investigates three predictors to behaviour individually: 1) behavioural attitude; 2) subjective norms; 3) perceived behavioural control. The paper details the development of the assessment procedure and discusses preliminary findings from the study. The questionnaire results are compared against electricity consumption data for individual zones within a multi-tenanted office building. Initial results demonstrate a statistically significant correlation between perceived behavioural control and energy consumption for lighting and small power

Constructing the frequency and wave normal distribution of whistler-mode wave power

We introduce a new methodology that allows the construction of wave frequency distributions due to growing incoherent whistler-mode waves in the magnetosphere. The technique combines the equations of geometric optics (i.e. raytracing) with the equation of transfer of radiation in an anisotropic lossy medium to obtain spectral energy density as a function of frequency and wavenormal angle. We describe the method in detail, and then demonstrate how it could be used in an idealised magnetosphere during quiet geomagnetic conditions. For a specific set of plasma conditions, we predict that the wave power peaks off the equator at ~15 degrees magnetic latitude. The new calculations predict that wave power as a function of frequency can be adequately described using a Gaussian function, but as a function of wavenormal angle, it more closely resembles a skew normal distribution. The technique described in this paper is the first known estimate of the parallel and oblique incoherent wave spectrum as a result of growing whistler-mode waves, and provides a means to incorporate self-consistent wave-particle interactions in a kinetic model of the magnetosphere over a large volume.

An aggregated wind power generation model based on MERRA reanalysis data: MATLAB model and example data for the April 2014 wind farm distribution of Great Britain.

The MATLAB model is contained within the compressed folders (versions are available as .zip and .tgz). This model uses MERRA reanalysis data (>34 years available) to estimate the hourly aggregated wind power generation for a predefined (fixed) distribution of wind farms. A ready made example is included for the wind farm distribution of Great Britain, April 2014 ("CF.dat"). This consists of an hourly time series of GB-total capacity factor spanning the period 1980-2013 inclusive. Given the global nature of reanalysis data, the model can be applied to any specified distribution of wind farms in any region of the world. Users are, however, strongly advised to bear in mind the limitations of reanalysis data when using this model/data. This is discussed in our paper: Cannon, Brayshaw, Methven, Coker, Lenaghan. "Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain". Submitted to Renewable Energy in March, 2014. Additional information about the model is contained in the model code itself, in the accompanying ReadMe file, and on our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/

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.

Identifying behavioural predictors of small power electricity consumption in office buildings

It is widely accepted that there is a gap between design energy and real world operational energy consumption. The behaviour of occupants is often cited as an important factor influencing building energy performance. However, its consideration, both during design and operation, is overly simplistic, often assuming a direct link between attitudes and behaviour. Alternative models of decision making from psychology highlight a range of additional influential factors and emphasise that occupants do not always act in a rational manner. Developing a better understanding of occupant decision making could help inform office energy conservation campaigns as well as models of behaviour employed during the design process. This paper assesses the contribution of various behavioural constructs on small power consumption in offices. The method is based upon the Theory of Planned Behaviour (TPB) which assumes that intention is driven by three factors: attitude, subjective norms, and perceived behavioural control, but we also consider a fourth construct: habit measured through the Self- Report Habit Index (SRHI). A questionnaire was issued to 81 participants in two UK offices. Questionnaire results for each behavioural construct were correlated against each participant’s individual workstation electricity consumption. The intentional processes proposed by TPB could not account for the observed differences in occupants’ interactions with small power appliances. Instead, occupants were interacting with small power “automatically”, with habit accounting for 11% of the variation in workstation energy consumption. The implications for occupant behaviour models and employee engagement campaigns are discussed.