Coincidence-Factor Reationships of Electric-Service-Load Characteristics

Bary, Constantine

doi:10.1109/t-aiee.1945.5059190

Cited by 16 publications

(3 citation statements)

References 3 publications

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“…The BARY curve is a curve that describes the relationship between the simultaneity rate and the load rate and is characterized by the empirical equation of the BARY curve. 21 The empirical equation for the BARY curve is as follows:…”

Section: ) An Empirical Equation For the Bary Curvementioning

confidence: 99%

Research on the capacity cost allocation and the electricity capacity price optimization method for a power system based on the BARY‐GA algorithm

Liu,

et al. 2023

Energy Science & Engineering

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Under the new power system, a high proportion of new energy is widely connected to the power grid, and it is necessary to increase investment in centralized and distributed energy storage, flexible resource regulation, and transmission and distribution grids, resulting in an increase in power system costs. The current electricity capacity price does not reflect the economic value of the added system adequately, and flexible capacity is needed to ensure the safety of the power grid under the new power system. In the context of the transformation of the new power system and investment function, it is increasingly difficult to recover the costs according to the traditional electricity price. It is necessary to establish a coordinated development mechanism for the electricity capacity price and the electricity price that recovers the fixed costs according to the investment function to ensure a reasonable return on investment in flexible resources and to provide effective investment signals. Therefore, this work first studied and proposed a mechanism for the formation of capacity and electricity prices based on the proportion of allowed income and fixed costs at different voltage levels and calculates the level of electricity capacity (demand) prices and electricity consumption prices. Then, using the BARY curve, genetic algorithm, and clustering of user load rates, a collaborative mechanism between the electricity capacity price and the electricity price was designed. With the goal of optimizing the electricity capacity price and considering constraints such as the flexibility and reliability of the new power system, the ratio of the capacity cost allocated to the electricity capacity price by voltage level users to the capacity cost allocated to the electricity price was calculated, and the optimal combination of electricity capacity price and electricity price for different load‐rate intervals was determined. Finally, the effectiveness of the model was verified through numerical simulations. On the basis of the investment characteristics of the new power system, suggestions for a capacity pricing mechanism under the new power system were proposed from the perspectives of transfer capacity function, transfer electricity function, grid reliability, and environmental and social benefits.

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Section: ) An Empirical Equation For the Bary Curvementioning

confidence: 99%

Research on the capacity cost allocation and the electricity capacity price optimization method for a power system based on the BARY‐GA algorithm

Liu,

et al. 2023

Energy Science & Engineering

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“…The asynchronous nature of the individual peak demands leads to a lower aggregated peak demand, meaning that network assets can be sized differently depending on the expected amount of connected consumers. Various properties have since been introduced to study and quantify the simultaneity of peak demands in distribution grids, such as the after-diversity maximum demand (ADMD) [2], the coincidence factor (CF) [3], or the diversity factor (DF) [4].…”

Section: Related Researchmentioning

confidence: 99%

Peak Demand Dynamics of Low-Voltage Consumers Under Aggregation and Its Impact on Upstream Pv Injection

Claeys¹,

Azaioud²,

Desmet³

2021

CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution

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Renewable Energy Communities will allow consumers on the low-voltage grid to actively participate in the energy market. This work explores the dynamics of the daily peak demands of these communities under aggregation, as multiple countries are introducing capacity-based grid tariffs for residential consumers. Both the aggregation level and yearly consumption of the households comprising it are shown to have a significant impact on the timing of the daily peak demands. While considering both the size of the PV installation and its orientation as variables, it is subsequently shown that this effect of the aggregation level is extended to the PV self-consumption and self-sufficiency indices.

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“…Studies have been conducted to calculate and predict domestic electricity load diversity since the 1930s [31]. The diversity factor was introduced as an index that offers insight regarding the probability that one household will consume energy coincidentally to another.…”

Section: Literature Review: Energy Load Profiles and Energy Demand Diversitymentioning

confidence: 99%

Sizing of district heating systems based on smart meter data: Quantifying the aggregated domestic energy demand and demand diversity in the UK

Wang

Crawley

et al. 2020

Energy

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The sizing of district energy systems involves a trade-off between reliability and continuity of service, and avoidance of capital and running costs associated with oversizing. Finding the most appropriate sizing requires a thorough understanding of energy demand. However, empirical data necessary to support such an understanding is scarce, and district energy systems are typically oversized. This study uses smart meter data from the largest field trial to analyse residential energy consumption in the UK. It presents graphically the seasonal and daily variations in energy consumption patterns, the weather dependence of energy loads, and peak hourly demand during particularly cold weather conditions. It also explores the diversity effect in residential energy consumption and computes the after diversity maximum demand at different levels of aggregations. Results show that, peak hourly gas consumption was nearly seven times higher than electricity consumption during the cold spells, while diversity reduced gas and electricity maximum demand per dwelling up to 33% and 47%, respectively. This empirical quantitative analysis of energy demand and diversity can support improved design and operation of district energy, and in particular, enable reduced capital and running costs, and an improved understanding of economies of scale for district heating networks.

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Coincidence-Factor Reationships of Electric-Service-Load Characteristics

Cited by 16 publications

References 3 publications

Research on the capacity cost allocation and the electricity capacity price optimization method for a power system based on the BARY‐GA algorithm

Research on the capacity cost allocation and the electricity capacity price optimization method for a power system based on the BARY‐GA algorithm

Peak Demand Dynamics of Low-Voltage Consumers Under Aggregation and Its Impact on Upstream Pv Injection

Sizing of district heating systems based on smart meter data: Quantifying the aggregated domestic energy demand and demand diversity in the UK

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