After Diversity Maximum Demand Research Articles

The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial

Previous studies on the effect of mass uptake of heat pumps on the capability of local or national electricity grids have relied on modelling or small datasets to create the aggregated heat pump load profile. This article uses the UK Renewable Heat Premium Payment dataset, which records the electricity consumption of nearly 700 domestic heat pump installations every 2minutes, to create an aggregated load profile using an order of magnitude more sites than previously available. The aggregated profile is presented on cold and medium winter weekdays and weekends and is shown to contain two peaks per day, dropping overnight to around 40% of its peak. After Diversity Maximum Demand (ADMD) for the population of heat pumps is calculated as 1.7kW per site; this occurs in the morning, whereas the peak national grid demand occurs in the evening. Analysis is carried out on how heat pump ADMD varies with number of heat pumps in the sample. A simple upscaling exercise is presented to give a first order approximation of the increase in GB peak electricity demand with mass deployment of heat pumps. It is found that peak grid demand increases by 7.5GW (14%) with 20% of households using heat pumps. The effect of the same heat pump uptake on grid ramp rate is also discussed; this effect is found to be minor. Finally, a comparison of heat pump and gas boiler operation is given, discussing day and night time operation and mean and peak power at different external temperatures.

Efficient Planning of Electrical Distribution System for Consumers in Sarawak, Malaysia

This study presents a method to determine power consumption pattern for several types of consumers in Sarawak, Malaysia. The power consumption data for consumers has been recorded using EDMI Mk.6 Genius polyphase electronic (E3) meters installed at their premises. The multistage cluster sampling is used to design the sample size to determine the sufficient amount of meters required. The data obtained from the meters has been analysed to obtain the pattern of power consumption for different types of consumers. This power consumption pattern has been applied to determine load factor, diversity factor for the calculation of After Diversity Maximum Demand (ADMD). ADMD is also used to determine the optimal amount of load, distribution transformer size and 11kV cable size. Temperature sensitivity analysis related to the demand has been investigated as well. It is found that power consumption pattern model is beneficial in finding the total electrical load, distribution transformer size and 11kV cable size needed by the consumers. Thus through this study the load characteristics had been determined to support utility operation and planning efficiently.

A Practical Probabilistic Design Procedure for LV Residential Distribution Systems

Two basic types of low voltage (LV) distribution exist in practice. The systems based on the European approach have long LV feeders whose sizing requires more careful design consideration than systems based on North American practice with shorter LV feeders. The most important criterion in conductor sizing is the correct estimation of the design loads. This paper describes the development and application of a probabilistic design method based on the Beta probability density function (pdf). The paper investigates an approximate approach for estimating load parameters in countries where a large load database is not available. This approach estimates the Beta parameters from the group after-diversity-maximum-demand (ADMD) for the customers.

Application of a Monte Carlo Simulation Method for Predicting Voltage Regulation on Low-Voltage Networks

Estimating voltage regulation on Low-Voltage (LV) networks is bread and butter work for many electricity network engineers. Despite a universal appreciation that voltage quality, to the consumer, is a statistical problem driven by uncertainty in consumer coincident demands, general industry practice still uses empirical formulae based on the concept of an After Diversity Maximum Demand (ADMD). Lack of understanding of the distributional nature of consumer demand and the impact of this on four-wire LV networks restricts innovative and least-cost solutions to voltage regulation problems. Solving these problems will also lead to better understanding of the impacts of fuel switching and consumer side distributed generation. This paper summarizes research work on Monte Carlo modeling of residential electricity demand and its application to LV regulation problems. Validity of the method is demonstrated by comparison of predicted distributions of delivered voltage to actual voltage recordings for three case studies. The relevance of this modeling technique is discussed, and its application to fuel switching and distributed generation problems are promoted.

Monte Carlo Simulation of Residential Electricity Demand for Forecasting Maximum Demand on Distribution Networks

The prevalent engineering practice (PEP) for maximum demand estimation in low-voltage (LV) electricity networks is based on an After Diversity Maximum Demand (ADMD) modified by a diversity factor. This method predicts the maximum likely voltage drop accounting for consumer diversity. However, this approach does not take into account the stochastic nature of the demand and is inconsistent with international power quality standards. We present a Monte Carlo simulation model of consumer demand taking into account the statistical spread of demand in each half hour using data sampled from a gamma distribution. The parameters of the gamma distribution are based on data metered at a number of residential properties fed by one transformer. The simulated demand is corrected for temperature and total consumption. The simulated profiles at the residential properties are aggregated and the simulated maximum demand is compared with actual maximum demand at a given transformer and an entire distribution network showing good agreement in both cases.

Simulation of Power Quality in Residencial Electricity Networks

The prevalent engineering practice (PEP) for demand estimation in Low Voltage electricity networks is based on an After Diversity Maximum Demand (ADMD) modified by a Diversity Factor. This method predicts the maximum likely voltage drop, accounting for diversity. However, it is inconsistent with both the nature of demand, which can be represented as a stochastic process, and with the wording of power quality standards such as BS EN 50160. This paper develops a numerical method that is then applied to a case study. A programme is undertaken to meter residential electricity demand. Using this data a generalised mathematical model is developed. The model is applied in a Monte Carlo simulation of the three-phase Low Voltage network. A house that was the subject of a power quality complaint, which resulted in network reparation, is chosen for the case study. A retrospective analysis is performed to hindcast the power quality received before the reparation, using both the Monte Carlo method and PEP; this is compared with a voltage trace recorded at the time of the complaint. The analysis is then used to model the power quality for the present network design and compared with measurements. The model is also used to examine phase unbalance, and the effect of sampling interval on measured demand.

Modelling of maximum temperatures in aerial bundled cables feeding domestic loads using the beta probability distribution function

A method for the thermal sizing of aerial bundled cables (ABCs) feeding stochastic domestic electrical loads is presented in this paper. This method is based on an algorithm from an International Electrotechnical Commission (IEC) publication, but uses an adapted form of Morgan's method for calculating the external thermal resistance. The beta distribution of grouped loads is incorporated in the thermal sizing algorithm. A beta thermal model is calculated, from which size selections of ABCs can be made. The resulting approach is an improvement on previous methods which use the after-diversity maximum demand for describing the load.

The statistical description of grouped domestic electrical load currents

The requirements for supplying a growing population with electricity in a developing country demand a thorough knowledge of the statistical properties of the anticipated domestic loads. The cardinal issue is the behaviour of the stochastic, coincidental load demands of small groups of consumers. Statistical descriptions of these loads are necessary for the rigorous theoretical analyses of conductor currents as well as the resulting voltage drops. This paper is based on load data which were captured during the winter peak of 1991. The issues which are emphasized are the sensitivity of the sampling interval, the correlations between energy and ambient temperature on after-diversity maximum demand (ADMD) and particularly the choice of an appropriate frequency distribution function.