Demand Response: Social Welfare Maximization in an Unbundled Energy Market Case Study for the Low-Voltage Networks of a Distribution Network Operator in The Netherlands

Nijhuis, Michiel; Babar, Muhammad; Gibescu, Madeleine; Cobben, Sjef

doi:10.1109/tia.2016.2608783

Cited by 28 publications

(11 citation statements)

References 9 publications

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“…• Promoting social welfare: Increasing customer satisfaction after implementing DR programs can also be considered as one of the DR benefits. Issues such as reducing consumption costs and managing power outages can be considered as factors affecting the promotion of the social welfare of customers, which can be sought by various methods of pricing [124][125].…”

Section: B)mentioning

confidence: 99%

An Overview of Demand Response: From its Origins to the Smart Energy Community

Honarmand¹,

Hosseinnezhad

Hayes

et al. 2021

IEEE Access

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The need to increase network efficiency, enhance reliability, and reduce environmental effects, as well as advances in communication infrastructures, have led to demand response (DR) becoming an essential part of smart grid operation. DR can provide power system operators with a range of flexible resources through different schemes. From the operational decision-making viewpoint, in practice, each scheme can affect the system performance differently. Therefore, categorizing different DR schemes based on their potential impacts on the power grid, operational targets, and economic incentives can embed a pragmatic and practical perspective into the selection approach. In order to provide such insights, this paper presents an extensive review of DR programs. A goal-oriented classification based on the type of market, reliability, power flexibility and the participants' economic motivation is proposed for DR programs. The benefits and barriers based on new classes are presented. Every involved party, including the power system operator and participants, can utilize the proposed classification to select a proper plan in the DR-related ancillary service ecosystem. The various enabling technologies and practical strategies for the application of DR schemes in various sectors are reviewed. Following this, changes in the procedure of DR schemes in the smart community concept are studied. Finally, the direction of future research and development in DR is discussed and analyzed.

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Section: B)mentioning

confidence: 99%

An Overview of Demand Response: From its Origins to the Smart Energy Community

Honarmand¹,

Hosseinnezhad

Hayes

et al. 2021

IEEE Access

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“…RDRAs have modeled various dynamic tariffs to improve the market decision-making and pricing scheme designs. For instance, the RDRA's objective function proposed in [110] was to maximize the end-user's surplus Social Welfare Maximization [116, 117, 118, 119, 120] [119, 121, 122, 123, 124, 125, 126, 127, 128] [ 110,116,117,119,120,123,125,127,129,130,131,132,133] Electricity Market [134] [122, 135, 136, 137, 138, 139] [139, 140, 141, 142, 143] which was calculated by the differentiation between the total agreement and the actual payment of households. The simulation results indicated an approximate 20% reduction in consumer bills as well as flatter load profiles over time.…”

Section: Table 1 Classification Of Papers Based On Rdras' Objectivesmentioning

confidence: 99%

Residential Demand Response Strategies and Applications in Active Distribution Network Management

Davarzani

Pisică

Munisami

2021

Renewable and Sustainable Energy Reviews

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“…In optimally designing any aspect of a demand response program, one of the considerations that must be accounted is maximizing the total social welfare ( U ) derived from the program 47 (generally known as the social welfare maximization problem 48 ). This is shown in Expression ().

Maximize U = ([], β_{1} . b_{c} + β_{2} . b_{r})

…”

Section: Development Of Mathematical Modelsmentioning

confidence: 99%

A generalized economic model for optimally selecting forecasted load profiles for measuring demand response in residential energy management system

et al. 2021

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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.

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Demand Response: Social Welfare Maximization in an Unbundled Energy Market Case Study for the Low-Voltage Networks of a Distribution Network Operator in The Netherlands

Cited by 28 publications

References 9 publications

An Overview of Demand Response: From its Origins to the Smart Energy Community

An Overview of Demand Response: From its Origins to the Smart Energy Community

Residential Demand Response Strategies and Applications in Active Distribution Network Management

A generalized economic model for optimally selecting forecasted load profiles for measuring demand response in residential energy management system

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