Modeling of demand response programs based on market elasticity concept

Jalili, Hassan; Eslami, Mohammad H.; Moghaddam, Mohsen Parsa; Siano, Pierluigi

doi:10.1007/s12652-018-0821-4

Cited by 18 publications

(9 citation statements)

References 24 publications

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“…Secondly, it was assumed that once the residential energy management system is installed it begins to work immediately and continuously to reduce household electricity demand. Generally, it is important to make distinctions between single period, multiperiod and continuous demand response events as the techniques used to model these events are different [61] and would require extra modelling considerations that are beyond the scope of this paper. However, further research can focus on how the model can be used to assess the behaviour of different load types.…”

Section: Discussionmentioning

confidence: 99%

A Novel Generalised Model for Residential Energy Management System

Jean-Paul¹,

Lie²,

Anderson³

et al. 2022

Highlights Sustain.

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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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Section: Discussionmentioning

confidence: 99%

A Novel Generalised Model for Residential Energy Management System

Jean-Paul¹,

Lie²,

Anderson³

et al. 2022

Highlights Sustain.

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“…In [76], the authors propose a price-based DR model for residential customers who can change their consumption profile, maximizing profits. Although the model proposal responds to an incentive-based design, it is complemented by the strategy of shifting load between hourly blocks, from peak to valley and from shoulder to valley periods, through a process of iterations.…”

Section: Model Scheme Proposed Based On the Literature Reviewmentioning

confidence: 99%

“…The work carried out by the authors of [74] simulates the impact of demand price elasticity in programs based on real-time pricing (RTP) and peak-time rebate (PTR). Other works that have modeled demand price elasticity in the market can also be consulted [75][76][77][78][79][80]. Equation (1) represents the price elasticity of demand (ε) and is explained in [81].…”

mentioning

confidence: 99%

A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market

et al. 2022

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The energy transition has shown that fossil generation can be complemented with renewable energy and other resources capable of providing flexibility to the energy system’s operation, in compliance with the wholesale electricity market’s rules. This paper proposes a market-based methodology for introducing flexible demand in the energy dispatch , optimizing the scheduling of electricity system operation in the short-term, and considers the challenge of implementing an incentive scheme for participants in demand-response programs. The scheme includes the criteria of the elasticity of substitution and a renewable energy quota. This methodology is focused on a strategic demand shift to minimize the cost of supply; increase the dispatch of renewable energy; control CO2 emissions; and satisfy the generation, demand, and transmission operating constraints. These conditions encourage the development of a simulation tool that allows a sensitivity analysis to aid decision-making by operators and agents. The proposed methodology optimizes the operational cost of generation supply and specific performance indicators to determine the percentages of demand shift, the amount of CO2 emissions, the ratio of unserved power, the demand benefits obtained from an incentive scheme, and the natural market behavior.

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“…e idea was first developed by Schweppe et al [47], reported that a customer would react differently in adjusting demand following the spot prices. To reflect responsiveness between demand and price, a parameter known as price elasticity of demand derived through equation ( 1) is used: the model has been chosen as most of the developing countries' customers are price sensitive [48]: Price elasticity can be divided into two, namely, crosselasticity (E(i, j)) and self-elasticity when (i � j), (E(i, i)). Cross-elasticity measures the effect of the price of a specific time interval on electricity consumption during another time interval, whereas self-elasticity measures the demand reduction in a specified time interval due to the price of that interval [26,49].…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…e main reasons for peak reduction, in this case, are customer welfare and price elasticity as the authors demonstrated it in Ref. [48]. e remaining amount of load has been shifted to other off-peak hours due to the effect of cross-elasticity [19].…”

Section: E Impacts Of Ls and Slr On The Technical And Economical Indicesmentioning

confidence: 99%

Demand-Side Management of Solar Microgrid Operation: Effect of Time-of-Use Pricing and Incentives

Philipo

Jande

Kivevele

2020

Journal of Renewable Energy

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Over 17% of the world’s population lack access to electricity, the majority being in rural areas of sub-Saharan Africa and South Asia. Microgrid technologies are a promising solution towards rural and remote area electrification; however, ever-increasing electricity demand remains a big challenge leading to pronounced power outages. Demand-side management is an indispensable tool towards addressing the challenges. This paper employs a mathematical model based on incentives and time-of-use rates to simulate daily power usage pattern of residential customers using data collected from an isolated village Ngurdoto solar microgrid, Arusha, Tanzania. Customer responsiveness on the increase in price was evaluated based on the concept of price elasticity of demand. Using two demand response strategies, namely, load shifting (LS) and scheduled load reduction (SLR), the results reveal that LS can achieve up to 4.87% energy-saving, 19.23% cost-saving, and about 31% and 19% peak reduction and power factor improvement, respectively. SLR method resulted in about 19% energy-saving, 49% cost-saving, and 24% power factor improvement. Thus, the results presented in this study may lead to a more efficient and stable system than the current state in developing countries’ utility.

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Modeling of demand response programs based on market elasticity concept

Cited by 18 publications

References 24 publications

A Novel Generalised Model for Residential Energy Management System

A Novel Generalised Model for Residential Energy Management System

A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market

Demand-Side Management of Solar Microgrid Operation: Effect of Time-of-Use Pricing and Incentives

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