Auction Mechanisms for Energy Trading in Multi-Energy Systems

Zhong, Weifeng; Xie, Kan; Liu, Yi; Yang, Chao; Xie, Shengli

doi:10.1109/tii.2017.2787751

Cited by 88 publications

(36 citation statements)

References 20 publications

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“…For the learning mechanism, we have considered the discount factor γ = 0.9, the step-size α = 0.2, and the exploitation-exploration rate = 0.1 for iteration (19). As we can see, the prosumer tries to consume low power at slots with high effectual prices (14) and self-generated powers, and consume more power at slots with low effectual prices and high self-generated powers. Further, the prosumer discharges the DS unite to sell power at the peak load demand at which the selling price is high, and charge them at the low-demand slots at which the buy price is low.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Bahrami et al studied the users' long-term load scheduling problem in [13] developing an online load scheduling learning algorithm based on the actor-critic method to determine the users' Markov perfect equilibrium (MPE) policy. The authors of [14] investigated auction mechanisms for energy trading in a smart multi-energy district, in which the district manager sells electricity, natural gas, and heating energy to users as well as trading with outer energy networks. According to feed-in-tariff of photovoltaic (PV) energy, a system model of energy sharing management (ESM) was introduced in [15], which included the profit model of micro-grid operator (MGO) and the utility model of PV prosumers.…”

Section: A Related Workmentioning

confidence: 99%

“…Accordingly, we can define the DSM objective function of each prosumer k at slot h as: (15) where S k (h) ∈ S h k is the state of prosumer k at slot h determined according to [s τ k,a (r k,a (τ ), d k,a (τ ))]a∈A k and x net −k (h), the state of the charge of the DS units, the power produced from the power resources, and the prosumer attitude (e.g., belief on the worthiness of price ρmcp(h)). The feasible state set S h k = A k × J k × M k × P k is constructed of A k (feasible state of appliances according to constraints in (1)), J k (feasible state of the DS units according to constraints in (2)), M k (feasible state of the power resources according to constraint (3), (11), and P rer k (h) ∈ P rer k (τ )), and P k (feasible state of the effectual price (14) according to the personal historical data of the customer k's payment). To take the best response, the constraints in (1) and (2) temporarily couple the prosumer decision through the scheduling horizon H. So, the challenge is to understand how a current action/state will affect the future profits, meaning that, for scheduling the equipment, the prosumer must infer (trade-off) that consuming/buying/selling power in the current slot is more profitable or the next slots.…”

Section: A Optimal Dsmmentioning

confidence: 99%

See 2 more Smart Citations

A Self-Governed Online Energy Management and Trading for Smart Micro/Nano-Grids

Latifi

Rastegarnia

Khalili

et al. 2020

IEEE Trans. Ind. Electron.

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Joint energy consumption and trading management is still a major challenge in smart (micro-) grids. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. Here, an inclusive formulation for energy management and trading of a Micro/Nano-grid (M/NG) is proposed. Subsequently, a holistic solution to jointly optimizing the internal energy consumption management and external local energy trading for a smart grid including several M/NGs is provided. As the problem is computationally intractable, the proposed approach involves three hierarchical stages. Firstly, a game-theoretic online stochastic energy management model is provided with a reinforcement learning solution by which the M/NGs can schedule their power consumptions. Secondly, an effective incentive-compatible doubleauction is formulated by which the M/NGs can directly trade with each other. Thirdly, the central controller develops an optimal power allocation program to reduce the power transmission loss and the destructive effects of local energy trading. The simulation results validate the efficiency of the proposed framework.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: A Optimal Dsmmentioning

confidence: 99%

See 1 more Smart Citation

A Self-Governed Online Energy Management and Trading for Smart Micro/Nano-Grids

Latifi

Rastegarnia

Khalili

et al. 2020

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

show abstract

“…[65], [84], [38], [53], [26], [60], [37], [39], [85], [41], [42], [51], [52], [55], [56], [66], [77], [79] Evolutionary Technology Derivatives, Ecosystems.…”

Section: Cmentioning

confidence: 99%

“…[6], [8], [20], [21], [28], [25] [37], [56], [57], [62], [64], [63], [74], [10], [45], [46], [80], [81], [9], [82], [58], [59], [78] Disruptive Technology Structure, Organisation, Governance, Disintermediation.…”

Section: Cmentioning

confidence: 99%

A Three-Tier Framework for Understanding Disruption Trajectories for Blockchain in the Electricity Industry

Villiers¹,

Cuffe²

2020

Preprint

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Ever since the invention of Bitcoin by the pseudonymous Satashi Nakamoto, cryptocurrency has provoked debate in banking and finance sectors, and is sometimes considered a potential successor to fiat currency. Blockchain, the new technology underpinning decentralised and immutable databases, has seen much discussion as a potentially game-changing development. Although many industries are exploring its value, the technology has thus far made only minor impacts. A rapidly expanding base of research has emerged on blockchain's role as a potential disruptor in the electrical energy industry. However, it may be difficult to distinguish hype from more imminently plausible impacts. This paper attempts to serve as a guide for engineering management wishing to make sense of blockchain's potential in electricity. This is accomplished by formulating a novel blockchain industry disruption framework, which exists across three tiers. These tiers extend from ideas with the least effect on an industry to total revolutionary concepts that could completely transform an industry. This taxonomy is constructed by examining existing research into disruption hierarchies and blockchain classification methods. Through the lens of this taxonomy, a literature review is performed on blockchain's role in energy to draw out themes and ideas characterising each tier. The potential likelihood of real-world application of various ideas are discussed, giving consideration to how established industries may be affected or disrupted. The authors provide some conjecture here. Finally, courses of action are suggested for those whose sector may be affected by blockchain.

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An IOT ‐based efficient energy management in smart grid using SMACA technique

Ponlatha¹,

Umasankar²,

Vadivu³

et al. 2021

Int Trans Electr Energ Syst

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Summary Energy management system (EMS) for distribution system with internet of things (IoT) using hybrid method is proposed in this paper. The proposed hybrid system is joined implementation of slime mould optimization algorithm (SMA) and chimp optimization algorithm (CA) and thus it is known as SMACA technique. The key point of the proposed scheme is to optimally direct the power and resources of the distribution system through persistent display of data as IoT‐based communication system. At proposed scheme, every home device is interconnected using data acquisition module with an internet protocol (IP) address, which generates an enormous wireless network of working devices. For encouraging improved demand response for the distribution system to take care of energy, IoT‐based communication system is utilized. To simply treat energy, optimal load requirement forecast and energy control processes are deal with SMACA system. In addition, the optimal utilization of the available resources and flexibility of these networks is provided and prolonged with IoT‐based distribution system. In addition, the proposed system is capable for satisfying the common supply and energy requirement. Finally, the proposed model is performed on MATLAB/Simulink platform, and the performance of proposed system is compared with different techniques.

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Auction Mechanisms for Energy Trading in Multi-Energy Systems

Cited by 88 publications

References 20 publications

A Self-Governed Online Energy Management and Trading for Smart Micro/Nano-Grids

A Self-Governed Online Energy Management and Trading for Smart Micro/Nano-Grids

A Three-Tier Framework for Understanding Disruption Trajectories for Blockchain in the Electricity Industry

An IOT ‐based efficient energy management in smart grid using SMACA technique

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