Fully Distributed Demand Response Using the Adaptive Diffusion–Stackelberg Algorithm

Latifi, Milad; Khalili, Azam; Rastegarnia, Amir; Sanei, Saeid

doi:10.1109/tii.2017.2703132

Cited by 49 publications

(36 citation statements)

References 31 publications

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“…For evaluating the learning capability of the prosumers, we have considered 10 M/NGs each having 100 appliances randomly chosen between low/mid/high-flexible appliances and market clearing price of Pennsylvania-New Jersey-Maryland Interconnection (PJM) electricity market similar to the data in [49]. Each low/mid-flexible appliance has two possible actions (on and off) and the power consumption of each high-flexible appliance is quantized into 10 consumption (action) levels.…”

Section: Numerical Resultsmentioning

confidence: 99%

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%

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

Latifi

Rastegarnia

Khalili

et al. 2020

IEEE Trans. Ind. Electron.

Self Cite

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“…In liberalized electricity markets, the retailers buy electricity from the wholesale market and sell it to their customers in a way to maximize social welfare [18]. Besides, in the liberalized ancillary service market, to facilitate PEVs participation and satisfy the minimum regulation capacity (e.g., 0.1 MW), the aggregators are served as an interface between the independent system operator (ISO) and a fleet of PEVs [12].…”

Section: System Modelmentioning

confidence: 99%

“…As in the price-participant scenarios, the consumption patterns affect the price signal, there is some oscillation in the convergence process around the optimal point. This is because, as much as the customers shift their PEVs consumption to time slots with lower prices, the spot price increases at those slots to prevent creation of sub-peaks [18]. The performance of the centralized method in the price taking (PT) scenario shows that the aggregate system payment is the lowest.…”

Section: B Performance Comparisonmentioning

confidence: 99%

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Latifi

Rastegarnia

Khalili

et al. 2019

IEEE Trans. Ind. Electron.

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Abstract-This paper presents a game theoretic decentralized electric vehicle charging schedule for minimizing the customers' payments, maximizing the grid efficiency, and providing maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers' privacy, we take advantages of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real time dynamic pricing model, and supports the vehicle-to-grid (V2G) and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short term uncertainties.Index Terms-Plug-in electric vehicles (PEVs), decentralized charging, Nash Folk strategy, regret matching.

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“…A few works aiming at centralized optimization have been done to incorporate DR programs into the optimal dispatch of the microgrids . For mostly existing distributed DR programs, they are generally fully distributed rather than distributed multiarea dispatch; ie, each device (DES and load) must be configured with an agent, which makes fully distributed DR programs unrealistic in MGC. In other words, it is more adoptable to do a multiarea scheduling by deploying agents according to the number of microgrids.…”

Section: Introductionmentioning

confidence: 99%

A distributed dispatch method for microgrid cluster considering demand response

Zhou

Wang

2018

Int Trans Electr Energ Syst

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Summary On the basis of model predictive control and auxiliary problem principle, this paper proposes a practical distributed framework to solve the economic dispatch of microgrid clusters (MGCs); specifically, each microgrid solves its local model in a parallel way and cooperates with adjacent microgrids in an iterative way. Besides, incentive‐based demand response is incorporated in optimal dispatch problem in order to enhance the flexibility of demand side. The proposed method aims to make a balance between the profit of microgrid system operator and customer's experiences for a typical MGC by setting a proper trade‐off parameter. Specifically, the framework is able to make sub‐microgrids plug in and plug out freely and has fast iterative speed. The simulation results show that a few of the iterations are needed to obtain the optimum by using the proposed framework and, meanwhile, verify the effectiveness of plug‐and‐play functionality.

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Fully Distributed Demand Response Using the Adaptive Diffusion–Stackelberg Algorithm

Cited by 49 publications

References 31 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

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

A distributed dispatch method for microgrid cluster considering demand response

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