Real-time pricing method for smart grids based on complementarity problem

Wang, Hongjie; Gao, Yan

doi:10.1007/s40565-019-0508-7

Cited by 26 publications

(15 citation statements)

References 25 publications

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“…In [29], the focus is on PV and DR, while in [30], the focus is on retailers. Still focusing on statistical simulations, in [31], consumers react to prices in real time to define the optimal consumption, price, and production, aiming to maximize social welfare. From the perspective of a distribution network reliability, in [32], several scenarios are tested regarding the positive impacts of DR and electric vehicles, where these resources are prioritized.…”

Section: Ctn Dgn Fimentioning

confidence: 99%

Distributed Energy Resource Scheduling with Focus on Demand Response Complex Contracts

Faria

Vale

2021

Journal of Modern Power Systems and Clean Energy

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Demand response (DR) is a flexible way to improve distributed energy resource scheduling. The innovative contribution of this paper is to include complex contracts in the model, which can accommodate the constraints according to the special expectations of each player. Such contracts are included in the optimization of distributed energy resource scheduling to dispatch DR according to the expectations of consumers. Multi-period DR events are considered. In this way, consumers can specify the limits on the time, power, and remuneration regarding participation in DR events, which has not been considered in the literature. The state of the art treats these aspects separately or uses a statistical approach, without providing consumers with options to combine their preferences regarding different aspects of their flexibility deployment. The model has been validated for 218 consumers using several scenarios and different types of distributed generation, showing that it is possible to increase DR with respect to the preferences of consumers.

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Section: Ctn Dgn Fimentioning

confidence: 99%

Distributed Energy Resource Scheduling with Focus on Demand Response Complex Contracts

Faria

Vale

2021

Journal of Modern Power Systems and Clean Energy

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“…If Equation ( 5) is a convex function, we can apply Lagrange relaxation [6,26,27], and Lagrange multipliers correspond to shadow prices. Lagrangian function and Karush-Kuhn-Tucker conditions are represented as:…”

Section: Formulation Of Power Supply-demand-balancing Operation Under Swmmentioning

confidence: 99%

Calculation Method for Electricity Price and Rebate Level in Demand Response Programs

et al. 2021

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Demand response programs (DRs) can be implemented with less investment costs than those in power plants or facilities and enable us to control power demand. Therefore, they are highly expected as an efficient option for power supply–demand-balancing operations. On the other hand, DRs bring new difficulties on how to evaluate the cooperation of consumers and to decide electricity prices or rebate levels with reflecting its results. This paper presents a theoretical approach that calculates electricity prices and rebate levels in DRs based on the framework of social welfare maximization. In the authors’ proposal, the DR-originated changes in the utility functions of power suppliers and consumers are used to set a guide for DR requests. Moreover, optimal electricity prices and rebate levels are defined from the standpoint of minimal burden in DRs. Through numerical simulations and discussion on their results, the validity of the authors’ proposal is verified.

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“…Many works have made study on RTP. In Samadi et al (2010) and Zhu et al (2018), a distributed RTP scheme based on the maximization of social welfare was formulated for the grid operator to tackle the DR. By the Karush–Kuhn–Tuker condition, Wang and Gao (2019) transformed the social welfare maximization model into a series of nonsmooth equations, and then used smoothing method and the quasi-Newton method to obtain the price. Qian et al (2013) formulated RTP scheme based on two-stage optimization.…”

Section: Introductionmentioning

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

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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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Real-time pricing method for smart grids based on complementarity problem

Cited by 26 publications

References 25 publications

Distributed Energy Resource Scheduling with Focus on Demand Response Complex Contracts

Distributed Energy Resource Scheduling with Focus on Demand Response Complex Contracts

Calculation Method for Electricity Price and Rebate Level in Demand Response Programs

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

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