A literature review on dynamic pricing of electricity

Dutta, Goutam; Mitra, Krishnendranath

doi:10.1057/s41274-016-0149-4

Cited by 224 publications

(117 citation statements)

References 81 publications

Supporting

Mentioning

114

Contrasting

Unclassified

Order By: Relevance

“…Fuel cost Cost F g;t to procure energy fromg th thermal generating unit in hour t is given by Equation (6), subject to constraints (6) to (14). The piece-wise linearization of the quadratic cost function is given in Appendix A.…”

Section: Thermal Generationmentioning

confidence: 99%

“…Market prices are dynamic in nature and vary throughout the day. 6 Consumer response to offered sale prices depends on its elasticity, indicating consumption change in response to price change. 3 A popularly evolving dynamic pricing mechanism, Time of Use (ToU) pricing motivates consumers to shift their demand from high to low price periods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Risk‐based retailer profit maximization: Time of Use price setting for elastic demand

Chawda

Mathuria

Bhakar

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Retailers in electricity markets face challenges of hourly varying energy prices and consumers' elastic demand. Besides these challenges, utilization of high renewable energy (RE) self‐generation such as solar energy could significantly affect energy procurement and sale prices decisions of a retailer. A retailer with RE self‐generation makes decisions targeting best utilization of RE availability, to maximize its profit. However, a retailer without RE makes decisions considering energy prices and elastic demand. In this perspective, this paper determines optimal Time of Use (ToU) sale prices to be offered and optimal energy procurement portfolio, for a retailer with and without RE, considering elastic demand and wholesale market price uncertainty, aiming to maximize retailer's profit. Case study considers three consumer classes to examine risk neutral and risk averse behavior of a retailer. Results illustrate impact of multiple consumer class behavior on ToU price, procurement strategy, and risk preferences of a retailer.

show abstract

Section: Thermal Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk‐based retailer profit maximization: Time of Use price setting for elastic demand

Chawda

Mathuria

Bhakar

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…However, fluctuation in wholesale electricity prices has increased because of the intermittent nature of generation from RESs [1]. By contrast, the retail market generally offers flat pricing or block pricing, because customers cannot continuously respond to price fluctuations [2]. Therefore, retailers who secure energy in the wholesale market to serve the demand of customers face price risks caused by the difference between wholesale prices and retail rates.…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Critical Peak Pricing for an Energy Retailer Considering Balancing Costs

2019

View full text Add to dashboard Cite

Recently, there have been frequent fluctuations in the wholesale prices of electricity following the increased penetration of renewable energy sources. Therefore, retailers face price risks caused by differences between wholesale prices and retail rates. As a hedging against price risk, retailers can utilize critical peak pricing (CPP) in a price-based program. This study proposes a novel multi-stage stochastic programming (MSSP) model for a retailer with self-generation photovoltaic facility to optimize both its bidding strategy and the CPP operation, in the face of several uncertainties. Using MSSP, decisions can be determined sequentially with realization of the uncertainties over time. Furthermore, to ensure a global optimum, a mixed integer non-linear programming is transformed into mixed integer linear programming through three linearization steps. In a numerical simulation, the effectiveness of the proposed MSSP model is compared with that of a mean-value deterministic model based on a rolling horizon method. We also investigate the optimal strategy of a retailer by changing various input parameters and perform a sensitivity analysis to assess the impacts of different uncertain parameters on the retailer’s profit. Finally, the effect of the energy storage system on the proposed optimization problem is investigated.

show abstract

“…Such tariff has been used in utility companies in the USA [10,11]. Furthermore, a dedicated review of dynamic electricity pricing can be found in [12].…”

Section: Introductionmentioning

confidence: 99%

Optimal dynamic pricing for smart grid having mixed customers with and without smart meters

Meng

Zeng

2018

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

This paper investigates an optimal day-ahead dynamic pricing problem in an electricity market with one electricity retailer and multiple customers. The main objective of this paper is to support the retailer to make the best day-ahead dynamic pricing decision, which maximizes its profit under the realistic assumption that mixed types of customers coexist in the electricity market where some customers have installed smart meters with the embedded home energy management system in their home whereas other customers have not installed smart meters. To this end, we propose a hybrid demand modelling framework which firstly uses an optimal energy management algorithm with bill minimization to model the behavior of customers with smart meters and secondly use a data-driven demand modelling method to model the behavior of customers without smart meters. Such a hybrid demand model can not only schedule usages of home appliances to the interests of customers with smart meters but also be used to understand electricity usage behaviors of customers without smart meters. Based on the established hybrid demand model for all customers, a profit maximization algorithm is developed to achieve optimal prices for the retailer under relevant market constraints. Under the condition of no growth of the revenue (i.e. no increase of total bill from all customers), simulation results indicate our optimization algorithm can improve the profit for around 5% on average.

show abstract

A literature review on dynamic pricing of electricity

Cited by 224 publications

References 81 publications

Risk‐based retailer profit maximization: Time of Use price setting for elastic demand

Risk‐based retailer profit maximization: Time of Use price setting for elastic demand

Optimal Operation of Critical Peak Pricing for an Energy Retailer Considering Balancing Costs

Optimal dynamic pricing for smart grid having mixed customers with and without smart meters

Contact Info

Product

Resources

About