Dynamic pricing in electricity and natural gas distribution networks: An EPEC model

Chen, Sheng; Sun, Guoqiang; Wei, Zhinong; Wang, Dan

doi:10.1016/j.energy.2020.118138

Cited by 32 publications

(15 citation statements)

References 27 publications

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“…Several works of literature also evaluated interdependency between power and gas distribution networks through modelling the physical constraints of both systems. A dynamic pricing problem in power and gas distribution networks is provided in [20], where power and gas companies act as leaders in the upper level and obtain their optimal dynamic prices via modelling the demand response presented by integrated aggregators in the lower level. A two-step data-driven DRO is proposed in [21] to model the optimization problem of combined PDN and gas distribution network (GDN), in which the minimum voltage deviation and minimum operation cost are determined through active power management and reactive power provision in both day-ahead and real-time step.…”

Section: Literature Reviewmentioning

confidence: 99%

“…2) Some of the literature, such as [14][15][16][17][18][19][20][21][22][23][24][25][26][27], mainly focused on the cooperation of PDN and HDN or PDN and GDN and did not consider the interaction of all three distribution networks simultaneously. Since the interdependence of energy networks increases with the rise of GF-CHP units, it is necessary to consider a network-constrained integrated energy management structure.…”

Section: Gaps and Contributionsmentioning

confidence: 99%

“…3) In [14][15][16][17][18][19][20], the authors mainly modelled the HDN based on constant mass flow and variable temperature (CF-VT) control strategy, which has less flexibility compared with variable mass flow and variable temperature (VF-VT) control strategy.…”

Section: Gaps and Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

Robust network-constrained energy management of a multiple energy distribution company in the presence of multi-energy conversion and storage technologies

Mirzaei

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Mohammadi‐Ivatloo

et al. 2021

Sustainable Cities and Society

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Section: Literature Reviewmentioning

confidence: 99%

Section: Gaps and Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Robust network-constrained energy management of a multiple energy distribution company in the presence of multi-energy conversion and storage technologies

Mirzaei

Zare

Mohammadi‐Ivatloo

et al. 2021

Sustainable Cities and Society

View full text Add to dashboard Cite

“…In contrast to the flow control strategy, dynamic pricing has a long history and a wide range of applications. The use of dynamic pricing is well established across industries, such as hospitality, entertainment and retailing, allowing flexible pricing rules for products or services tied to current market demands (Abrate et al 2019;Chen et al 2020;Jørgensen and Zaccour 2019). The literature also details extensive applications of dynamic pricing to public transport systems (Kamandanipour et al 2020;Saharan et al 2020;Zhang et al 2019).…”

Section: Fig 1 Evolution Of Mileage and Ridership Of The Guangzhou Me...mentioning

confidence: 99%

Understanding the effects of travel demand management on metro commuters’ behavioural loyalty: a hybrid choice modelling approach

2021

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As part of efforts to promote sustainable mobility, many cities are currently experiencing the rapid expansion of their metro network. The consequent growth in ridership motivates a broad range of travel demand management (TDM) policies, both in terms of passenger flow control and dynamic pricing strategies. This work aims to reveal the impact of TDM on metro commuters' behavioural loyalty using stated preference data collected in Guangzhou, China. Commuters' behavioural response to TDM strategies is investigated in terms of the possible shift in departure time and travel mode. A hybrid choice model framework is used to incorporate four latent variables of interest, i.e., service quality, overall impression, external attractiveness and switching cost, into the discrete choice model and thereby capture the relationships between the attitudinal factors and observed variables. The model estimation results indicate that the four latent variables all prove useful in interpreting commuters' behavioural loyalty. Commuters' perceived service quality and overall impression both show a positive effect on their willingness to continue travelling by metro and are thus instructive for ridership retention. External attractiveness is found to be significant only in the case of the tendency to shift to a private car. Switching costs reveal commuters' emotional attachment to their already developed commuting habit. These insights into commuters' behavioural change intention enable metro operators to enhance commuters' loyalty to their service and develop more effective TDM strategies in future practice. Keywords behavioural change • nested logit model • attitude • factor analysis • SP-off-RP survey • urban rail transitSection 5 reports the model estimation results and discusses the policy implications. In the last section, the conclusions drawn from the above content are summarised, and directions for future work are discussed.

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“…Dynamic pricing is defined as setting a time-varying price for a certain product or service [2], [3]. Recently, dynamic pricing is broadly applicable in various domains such as: hotel revenue management [4], airline industry [5], [6], mobile data services [7], electricity [8], [9] and [10], and e-services [11]. However, there is a fundamental challenge when applying dynamic pricing, which is how to set prices optimally in order to maximize revenue returns.…”

Section: Introductionmentioning

confidence: 99%

Multi-Step Look-Ahead Optimization Methods for Dynamic Pricing With Demand Learning

2021

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Dynamic pricing is a beneficial strategy for firms seeking to achieve high revenues. It has been widely applied to various domains such as the airline industry, the hotel industry, and e-services. Dynamic pricing is basically the problem of setting time-varying prices for a certain product or service for the purpose of optimizing revenue. However, a major challenge encountered when applying dynamic pricing is the lack of knowledge of the demand-price curve. The demand-price curve defines the customer's response towards changing the price. In this work, we address the dynamic pricing problem in case of unknown demandprice relation. This work introduces a less myopic pricing approach based on looking ahead for one or several future steps in our quest to optimize revenue. Specifically, the proposed formulation maximizes the summation of the immediate revenue and the expected future revenues of one or multiple look-ahead steps.A key benefit of the proposed approach is that it automatically strikes a balance between the conflicting goals of revenue maximization and demand learning, by producing less myopic and profitable prices. We provide a formulation for the presented look-ahead pricing approach, and we implement two variants of it: one-step and two-step look-ahead methods. Experiments are conducted on synthetic and real datasets to compare the proposed pricing methods to other pricing strategies in literature. The experimental results indicate that the proposed look-ahead methods outperform their counterparts in terms of the achieved revenue gain.

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Dynamic pricing in electricity and natural gas distribution networks: An EPEC model

Cited by 32 publications

References 27 publications

Robust network-constrained energy management of a multiple energy distribution company in the presence of multi-energy conversion and storage technologies

Robust network-constrained energy management of a multiple energy distribution company in the presence of multi-energy conversion and storage technologies

Understanding the effects of travel demand management on metro commuters’ behavioural loyalty: a hybrid choice modelling approach

Multi-Step Look-Ahead Optimization Methods for Dynamic Pricing With Demand Learning

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