Dynamic pricing for decentralized energy trading in micro-grids

Liu, Youbo; Zuo, Kunyu; Liu, Xueqin; Kennedy, Jason

doi:10.1016/j.apenergy.2018.06.124

Cited by 89 publications

(66 citation statements)

References 33 publications

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“…where a CGT , b CGT , and c CGT are the coefficients of carbon emissions of the CGT; Q GB,t is the heat supply of the GB over time t; ψ CO 2 GB is the carbon emissions per unit from the GB; E UG,t is the energy supply of the UPG over time t, including power, heat, cooling, and gas; and ψ CO 2 UG is the unit carbon emissions of the UPG. Currently, coal-fired power generation in China, for example, has accounted for 70% of overall power generation, and the coefficient of carbon emissions per unit power generation is approximately 0.997.…”

Section: Minimizing the Carbon Emissionsmentioning

confidence: 99%

“…The concept of the "energy Internet" has integrated different types of independent systems, such as power, heat, and gas, thus solving the problems of low utilization efficiency due to the independence [1]. However, technical barriers and closure of traditional energy industries are current challenges for the large energy Internet [2]. A micro energy system (MES), as an extension of a micro power grid, will be a terminal energy supply system of the energy Internet [3], which is conducive to 2 of 27 achieving multi-energy complementation and energy utilization efficiency improvement.…”

Section: Introductionmentioning

confidence: 99%

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Coordinated Energy Management for Micro Energy Systems Considering Carbon Emissions Using Multi-Objective Optimization

et al. 2019

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To promote the utilization of distributed resources, this paper proposes a concept of a micro energy system (MES) and its core structure with energy production, conversion, and storage devices. In addition, the effect of demand–response on the operation of a MES is studied. Firstly, based on uncertainties of a MES, a probability distribution model is introduced. Secondly, with the objectives of maximizing operating revenue, and minimizing operational risk and carbon emissions, a multi-objective coordinated dispatching optimization model was constructed. To solve this model, this paper linearizes objective functions and constraints via fuzzy satisfaction theory, then establishes the input–output matrix of the model and calculates the optimal weight coefficients of different objective functions via the rough set method. Next, a comprehensive dispatching optimization model was built. Finally, data from a MES in Longgang commercial park, Shenzhen City, were introduced for a case study, and the results show that: (1) A MES can integrate different types of energy, such as wind, photovoltaics, and gas. A multi-energy cycle is achieved via energy conversion and storage devices, and different energy demands are satisfied. Demand–response from users in a MES achieves the optimization of source–load interaction. (2) The proposed model gives consideration to the multi-objectives of operating revenue, operational risk, and carbon emissions, and its optimal strategy is obtained by using the proposed solution algorithm. (3) Sensitivity analysis results showed that risks can be avoided, to varying degrees, via reasonable setting of confidence. Price-based demand–response and maximum total emission allowances can be used as indirect factors to influence the energy supply structure of a MES. In summary, the proposed model and solution algorithm are effective tools for different decision makers to conceive of dispatching strategies for different interests.

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Section: Minimizing the Carbon Emissionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coordinated Energy Management for Micro Energy Systems Considering Carbon Emissions Using Multi-Objective Optimization

et al. 2019

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“…In [15], a new dynamic pricing method is proposed to offer decentralized power trading and optimize financial incentives for the owners of RESs. A price-responsive model for every type of RES is investigated and then an adaptive three-tiered framework is developed that considers the aggregator scheduling, microgrid balancing and power trading optimization.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid

et al. 2018

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An unprecedented opportunity is presented by smart grid technologies to shift the energy industry into the new era of availability, reliability and efficiency that will contribute to our economic and environmental health. Renewable energy sources play a significant role in making environments greener and generating electricity at a cheaper cost. The cloud/fog computing also contributes to tackling the computationally intensive tasks in a smart grid. This work proposes an energy efficient approach to solve the energy management problem in the fog based environment. We consider a small community that consists of multiple smart homes. A microgrid is installed at each residence for electricity generation. Moreover, it is connected with the fog server to share and store information. Smart energy consumers are able to share the details of excess energy with each other through the fog server. The proposed approach is validated through simulations in terms of cost and imported electricity alleviation.

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“…In recent years, some scholars have examined the optimization of microgrids under the electrical market environment, which can be categorized as follows: (1) The objective of the optimal operation strategy of microgrids is different. The optimal operation strategies of some researches are single objective functions, such as minimizing the operation cost [10][11][12] or maximizing the benefits [13,14] of microgrids in the power market. Considering the limitations of single-objective optimization, other scholars use multi-objective optimization to explore this problem [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response

Zhao

et al. 2020

Energies

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More and more attention has been paid to the development of renewable energy in the world. Microgrids with flexible regulation abilities provide an effective way to solve the problem of renewable energy connected to power grids. In this article, an optimization strategy of a microgrid participating in day-ahead market operations considering demand responses is proposed, where the uncertainties of distributed renewable energy generation, electrical load, and day-ahead market prices are taken into account. The results show that, when the microgrid implements the demand response, the operation cost of the microgrid decreases by 4.17%. Meanwhile, the demand response program can transfer the peak load of the high-price period to the low-price period, which reduces the peak valley difference of the load and stabilizes the load curve. Finally, a sensitivity analysis of three factors is carried out, finding that, with the increase of the demand response adjustable ratio or the maximum capacity of the electrical storage devices, the operation cost of the microgrid decreases, while, with the increase of the demand response cost, the operation cost of the microgrid increases and, finally, tends to the cost without the demand response. The sensitivity analysis reveals that the demand response cost has a reasonable pricing range to maximize the value of the demand response.

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Dynamic pricing for decentralized energy trading in micro-grids

Cited by 89 publications

References 33 publications

Coordinated Energy Management for Micro Energy Systems Considering Carbon Emissions Using Multi-Objective Optimization

Coordinated Energy Management for Micro Energy Systems Considering Carbon Emissions Using Multi-Objective Optimization

An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid

Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response

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