The incentive announcement effect of demand response on market power mitigation in the electricity market

yoo, tae-hyun; Ko, Woong; Rhee, Chang-Ho; Park, Jong–Keun

doi:10.1016/j.rser.2017.03.035

Cited by 21 publications

(10 citation statements)

References 33 publications

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“…DR programs can affect market mechanisms. By participating in the schemes, participants can have the opportunity to influence the market by managing their consumption, thereby preventing market power and rising prices [111]. • Reduction in price volatility: In electricity markets, price volatility happened from time to time due to uncertainty in prices, can be triggered for various reasons at peak loads [112].…”

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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“…After that, they adopt a reinforcement learning method which is similar to balanced stratified sampling to estimate the equilibrium solution. Yoo et al [29] focus on the transaction between power generation companies that also sell power and residential users. Under the condition of maximizing the overall interests of both sides, they study how the power grid company formulates the optimal demand response compensation.…”

Section: Literature Reviewmentioning

confidence: 99%

An Analysis From the Perspective of Direct Power-Purchase for Industrial Users: Should the Power Grid Company Implement Incentive-Based Demand Response Management?

Wang

Jia

et al. 2021

IEEE Access

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As a means for the power grid company to guide industrial users to reduce power consumption through economic compensation, incentive-based demand response management (IBDRM) plays an important role in the process that industrial users purchase power directly from power plants (direct power-purchase for short). Therefore, this paper studies the impacts of IBDRM on the power grid company, power plants, and industrial users from the perspective of industrial users' direct power-purchase, so as to analyze whether the power grid company should implement IBDRM in direct power-purchase. First, we model the interactions of the power grid company, the power plant, and the industrial user under different scenarios, including Case N (the power grid company does not implement IBDRM) and Case R (the power grid company implements IBDRM) by using Stackelberg game. Then, we solve the models to get equilibrium results. The primary contributions of this article include following parts: 1) Compared with Case N, the power plant's power selling price and the industrial user's power-purchase quantity are higher under Case R, and both are negatively correlated with the compensation for the power plant. 2) The power grid company should implement IBDRM in direct power-purchase. Because under Case R, the industrial user reduces the power consumption, the profits of the power grid company and the power plant are higher, and the social welfare is also greater. 3) Under Case R, we also find that the social welfare is positively correlated with the compensation for the industrial user.

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“…The intermittent nature of renewable energy had a very large impact on the way the negotiations operated since the energy supply directly influences the market prices. At the market level, the competition has increased, bringing a higher number of sellers to participate (Yoo et al 2017). This also leads to the emergence of aggregators to represent (and even manage) groups of small player (Vale et al 2011a), in order to increase their impact on the market.…”

Section: Introductionmentioning

confidence: 99%

Hybrid approach based on particle swarm optimization for electricity markets participation

et al. 2019

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In many large-scale and time-consuming problems, the application of metaheuristics becomes essential, since these methods enable achieving very close solutions to the exact one in a much shorter time. In this work, we address the problem of portfolio optimization applied to electricity markets negotiation. As in a market environment, decision-making is carried out in very short times, the application of the metaheuristics is necessary. This work proposes a Hybrid model, combining a simplified exact resolution of the method, as a means to obtain the initial solution for a Particle Swarm Optimization (PSO) approach. Results show that the presented approach is able to obtain better results in the metaheuristic search process.

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The incentive announcement effect of demand response on market power mitigation in the electricity market

Cited by 21 publications

References 33 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

An Analysis From the Perspective of Direct Power-Purchase for Industrial Users: Should the Power Grid Company Implement Incentive-Based Demand Response Management?

Hybrid approach based on particle swarm optimization for electricity markets participation

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