The sociodemographic diversity of residential customers can affect the level of financial risk that an electricity provider experiences in the retail market. To demonstrate the relationship between sociodemographic diversity and financial risk, electricity consumption data drawn from the United Kingdom Power Networks 'Low Carbon London' project was analyzed to explore the relationship between sociodemographic diversity and financial risk experienced by electricity retailers. The results show that when increasing the sociodemographic diversity amongst a group of residential customers the effect on financial risk depends on the electricity consumption patterns of individual customers and the relationship of consumption patterns between residential customers. Increasing sociodemographic diversity amongst residential customers with very distinct energy consumption patterns can decrease the overall financial risk associated with the aggregated revenue received from these customers. However, the results showed that adding customers to a customer base without consideration for their sociodemographic background can cause the overall financial risk associated with the aggregated revenue received to change erratically. Whilst previous studies have considered customer diversity and its influence on peak electricity demand, this research advances the stateof-the-art by showing the importance of customer diversity to the financial quantity risk experienced by electricity retailers. This finding has serious implications for electricity providers seeking to mitigate financial risk in the retail electricity market.
Summary To forecast the demand response of a residential customer, forecasted load profiles must first be calculated and the demand response produced by residential energy management devices determined. Finding an efficient method to select an optimal forecasted load profile to assess the demand response of these devices is a fundamental issue currently overlooked. Poorly forecasted load profiles can affect customer compensation for demand reduction, estimation of retail profits and allocation and reconciliation processes for electricity markets. This research resolved to develop an economic model for selecting an optimal forecasted load profile from which the demand response of residential energy management devices can be determined. Using 2 years of half‐hourly electricity consumption data for 5379 households obtained from the “Low Carbon” London project (UK), a novel economic model was created to assess the economic advantage of nine different forecast methods. Results suggest that when creating forecasted load profiles to measure demand response of these devices the sociodemographic of customers, tariff type and the operational objectives of the device must be taken into account to maximize the economic benefit of demand response initiatives for the customer and retailer.
Most mathematician, have accepted that a constant divided by zero is undefined. However, accepting this situation is an unsatisfactory solution to the problem as division by zero has arisen frequently enough in mathematics and science to warrant some serious consideration. The aim of this paper was to propose and prove the existence of a new number set in which division by zero is well defined. To do this, the paper first uses set theory to develop the idea of unstructured numbers and uses this new number to create a new number set called "Semi-structured Complex Number set" (Ś). It was then shown that a semi-structured complex number is a three-dimensional number which can be represented in the xyz-space with the x-axis being the real axis, the y-axis the imaginary axis and the z-axis the unstructured axis. A unit of rotation p was defined that enabled rotation of a point along the xy-, xz-and yz-planes. The field axioms were then used to show that the set is a "complete ordered field" and hence prove its existence. Examples of how these semi-structured complex numbers are used algebraically are provided. The successful development of this proposed number set has implications not just in the field of mathematics but in other areas of science where division by zero is essential.
Disaggregated data is often used to model the cost-benefit of residential energy management systems. However, obtaining such data is time-intensive and monetarily expensive. This hinders the depth of analysis that can be done on these systems and negatively influences their large-scale uptake. This study proposes a novel generalised model of these systems that uses smart meter load profile data to model their cost-benefit. Using two years of half-hourly electricity consumption data from 5379 households in London, the model was used to examine how sociodemographic, tariff structures, and the choice of operational objectives of these systems, interact to influence their cost-benefit. The results showed that the proposed model produced reliable cost-benefit results within what is normally obtained in literature. The model demonstrated that applying one set of objectives to different customers leads to an inequitable distribution in benefits; rather, an optimal set of objectives for a given customer under a specific tariff structure can be found to produce a more equitable distribution in benefits across all customers. The proposed model is replicable and uses data that can be obtained easily and cheaply from smart meters, making it versatile for large-scale cost-benefit analysis by any electricity retailer.
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