Real‐time pricing in smart community with constraint from the perspective of advertising game

Dai, Yeming; Li, Lü; Zhao, Pei; Duan, Jinpeng

doi:10.1002/2050-7038.12043

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…It should be a non-decreasing and concave function (Fahrioglu andAlvarado, 2007). The same as Dai et al (2019), Deng et al (2015) and Tan et al (2015), weconsider the following quadratic utility function with linear decreasing marginal benefit: …”

Section: System Modelmentioning

confidence: 99%

Distributed real-time pricing for smart grid considering sparse constraints and integration of distributed energy and storage devices

Li¹,

Gao²,

Lei³

et al. 2021

COMPEL

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Purpose The purpose of this paper is to seek an efficient method to tackle the energy provision problem for smart grid with sparse constraints and distributed energy and storage devices. Design/methodology/approach A complex smart grid is first studied, in which sparse constraints and the complex make-up of different energy consumption due to the integration of distributed energy and storage devices and the emergence of multisellers are discussed. Then, a real-time pricing scheme is formulated to tackle the demand response based on sparse bilevel programming. And then, a bilevel genetic algorithm (BGA) is further designed. Finally, simulations are conducted to evaluate the performance of the proposed approach. Findings The considered situation is widespread in practice, and meanwhile, the other cases including traditional model without the sparse constraints can be seen as its extensions. The BGA based on sparse bilevel programming has advantages of “no need of convexity of the model.” Moreover, it is feasible without the need to disclose the private information to others; therefore, privacies are protected and system scalability is kept. Simulation results validate the proposed approach has good performance in maximizing social welfare and balancing system energy distribution. Research limitations/implications In this paper, the authors consider the sparse constraints due to the fact that each user can only choose limited utility companies per time slot. In reality, there exist some other sparse cases, which deserve further study in the future. Originality/value To the best of the authors’ knowledge, this is one of the very first studies to address pricing problems for the smart grid with consideration of sparse constraints and integration of distributed energy and storage devices.

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Section: System Modelmentioning

confidence: 99%

Distributed real-time pricing for smart grid considering sparse constraints and integration of distributed energy and storage devices

Li¹,

Gao²,

Lei³

et al. 2021

COMPEL

View full text Add to dashboard Cite

show abstract

“…In [20], the TOU price optimization for users is modeled as a bi-level Stackelberg game between the retailer and users. In [21], a Stackelberg game model with one leader and multiple followers between the retailers and the users is established to study the real-time pricing scheme in a smart community. In [22], a one-leader and multi-follower game is developed to characterize the interactions between the demand-side management (DSM) center and users.…”

Section: Introductionmentioning

confidence: 99%

Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization

Gao,

Pan,

et al. 2024

Journal of Modern Power Systems and Clean Energy

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In the competitive energy market, energy retailers are facing the uncertainties of both energy price and demand, which requires them to formulate reasonable energy purchasing and selling strategies for improving their competitiveness in this market. Particularly, the attractive multi-energy retail packages are the key for retailers to increase their benefit. Therefore, combined with incentive means and price signals, five types of multi-energy retail packages such as peak-valley time-of-use (TOU) price package and day-night bundled price package are designed in this paper for retailers. The iterative interactions between retailers and end-users are modeled using a bi-level model of stochastic optimization based on multi-leader multi-follower (MLMF) Stackelberg game, in which retailers are leaders and end-users are followers. Retailers make decisions to maximize the profit considering the conditional value at risk (CVaR) while end-users optimize the satisfaction of both energy comfort and economy. Besides, a distributed algorithm is proposed to obtain the Nash equilibrium of above MLMF Stackelberg game model while the particle swarm optimization (PSO) algorithm and CPLEX solver are applied to solve the optimization model for each participant (retailer or end-user). Numeral results show that the designed retail packages can increase the overall profit of retailers, and the overall satisfaction of industrial users is the highest while that of residential users is the lowest after game interaction.

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“…To maximize the benefits of the main grid and MGs, the strategy is provided considering multiple demands according to the dynamic price scheme by the game theory method 15 . In Reference 16, an advertising differential game is developed in a smart community, and the real‐time electricity price scheme is obtained. In Reference 17, for the maximization of social welfare, the noncooperative game is studied between the suppliers and all users with spatiotemporal coupling constraints, and DE and storage are considering in the system.…”

Section: Introductionmentioning

confidence: 99%

A bilevel programming approach for real‐time pricing strategy of smart grid considering multi‐microgrids connection

Yuan

Gao

et al. 2021

Int J Energy Res

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Summary As an important method for demand‐side management, the real‐time pricing can not only adjust the power balance between the supply and demand‐sides, but also effectively promote the internal energy dispatching behavior of the microgrid. In this article, a bilevel programming model is proposed based on the demand response to coordinate the master‐slave hierarchical relationship in the electricity market. At the upper level, the main grid determines the prices and generation for the supplier's maximum profit. At the lower level, we consider multiple microgrids, which operate in isolated‐island or grid‐connected mode, with photovoltaic generation system, energy storage system, and loads. Then, the optimal energy strategy is emerged for the user's maximum welfare according to the dynamic prices. Furthermore, we solve the upper and lower models separately to avoid user privacy being violated. The upper problem uses the genetic algorithm (GA), while the lower problem is handled by the branch and bound algorithm (BBA). A novel distributed algorithm named GA‐BBA is developed accordingly. Finally, taking an example of a smart grid system with three microgrids, the model and algorithm are simulated and compared. The results show that the peak‐to‐average ratio under the real‐time pricing is reduced from 1.6547 (fixed electricity price) and 1.4513 (time‐of‐use) to 1.2545. The peak‐to‐valley difference is also decreased by approximately 14.33%. In addition, the total social welfare of the system is increased by 145.53 kCNY (Chinese Yuan) through comparison with the existing model. The conclusions verify that the proposed bilevel programming model not only effectively reduces peak load but also increases the social welfare, which is more reasonable and realistic. Novelty Statement A novel bilevel optimization energy management system is proposed to optimize the hierarchical market structure between the main grid and multi‐microgrids (MMGs). A RTP mechanism based on demand response is formulated to balance the benefits between the main grid and MMGs. Moreover, the optimal energy strategy is obtained along with the dynamic price. Besides the operating cost, users' utility in microgrids are taken to our model. A hybrid distributed GA‐BBA is developed to protect the user's utility privacy.

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Real‐time pricing in smart community with constraint from the perspective of advertising game

Cited by 10 publications

References 26 publications

Distributed real-time pricing for smart grid considering sparse constraints and integration of distributed energy and storage devices

Distributed real-time pricing for smart grid considering sparse constraints and integration of distributed energy and storage devices

Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization

A bilevel programming approach for real‐time pricing strategy of smart grid considering multi‐microgrids connection

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