A Cooperative Demand Response Scheme Using Punishment Mechanism and Application to Industrial Refrigerated Warehouses

Ma, Kai; Hu, Guoqiang; Spanos, Costas J.

doi:10.1109/tii.2015.2431219

Cited by 91 publications

(40 citation statements)

References 46 publications

(74 reference statements)

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“…Renewable energy generation Stochastic optimization Handling date uncertainties of renewable energy [10][11][12] Robust optimization [14][15][16][17] Wind power forecasting Linear methods Increasing the accuracy of prediction model [19,20] Nonlinear methods [24][25][26][27] Microgrid management Ordinary decision theory Optimizing energy-scheduling strategies [28][29][30] Noncooperative games [33][34][35][36] Cooperative games [37][38][39][40] and robust optimization [9]. On the one hand, stochastic optimization provides an effective framework to optimize statistical objective functions while the uncertain numerical data are assumed to follow a proverbial probability distribution.…”

Section: Application Scenarios Solution Methods Optimization Goals LImentioning

confidence: 99%

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Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

Zhou¹,

Xiong²,

Xu³

et al. 2017

Development and Integration of Microgrids

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Energy internet provides an open framework for integrating every piece of equipment involved in energy generation, transmission, transformation, distribution, and consumption with novel information and communication technologies. In this chapter, the authors adopt a combination of game theory and big data to address the coordinated management of renewable and traditional energy, which is a typical issue on energy interconnections. The authors formulate the energy management problem as a three-stage Stackelberg game and employ the backward induction method to derive the closed-form expressions of the optimal strategies. Next, we study the big data-based power generation forecasting techniques and introduce a scheme of the wind power forecasting, which can assist the microgrid to make strategies. Simulation results show that more accurate prediction results of wind power are conducive to better energy management.

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Section: Application Scenarios Solution Methods Optimization Goals LImentioning

confidence: 99%

“…For microgrid energy management schemes based on cooperative games, the authors proposed a cooperative demand response scheme for reducing the electricity bills of users in Ref. [37]. In Ref.…”

Section: Application Scenarios Solution Methods Optimization Goals LImentioning

confidence: 99%

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

Zhou¹,

Xiong²,

Xu³

et al. 2017

Development and Integration of Microgrids

View full text Add to dashboard Cite

show abstract

“…RE UC NCG SG [3,4] √ × × × [5][6][7] × √ × × [8][9][10][11][12][13][14] × × √ × [15][16][17][18][19][20][21] × × × √…”

Section: Indexesmentioning

confidence: 99%

“…In scenario A, there is no competition among the price-taking consumers, i.e., the consumers' energy consumption cannot affect the price announced by the energy provider. In scenario B, the interactions among the price-anticipating consumers are formulated into a non-cooperative game, i.e., the consumers' energy consumption can change the price announced by the energy provider [9]. …”

Section: Problem Formulationmentioning

confidence: 99%

“…A non-cooperative game (NCG) was formulated among the consumers in [8][9][10][11][12][13][14]. In [9], the price-taking consumers and the price-anticipating consumers were considered. The price-taking consumers assume that their energy consumption cannot affect the electricity price, whereas the price-anticipating consumers believe that their energy consumption can change the electricity price.…”

Section: Introductionmentioning

confidence: 99%

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Energy Trading and Pricing in Microgrids with Uncertain Energy Supply: A Three-Stage Hierarchical Game Approach

Yang

et al. 2017

Energies

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This paper studies an energy trading and pricing problem for microgrids with uncertain energy supply. The energy provider with the renewable energy (RE) generation (wind power) determines the energy purchase from the electricity markets and the pricing strategy for consumers to maximize its profit, and then the consumers determine their energy demands to maximize their payoffs. The hierarchical game is established between the energy provider and the consumers. The energy provider is the leader and the consumers are the followers in the hierarchical game. We consider two types of consumers according to their response to the price, i.e., the price-taking consumers and the price-anticipating consumers. We derive the equilibrium point of the hierarchical game through the backward induction method. Comparing the two types of consumers, we study the influence of the types of consumers on the equilibrium point. In particular, the uncertainty of the energy supply from the energy provider is considered. Simulation results show that the energy provider can obtain more profit using the proposed decision-making scheme.

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Medical Freezers as Flexible Load for Demand Response in a Business Park Microgrid with Local Solar Power Generation

Gonzalez

Gibescu

Cobben

et al. 2019

Communications in Computer and Information Science

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This work presents a day-ahead demand response (DR) scheduling framework that quantifies the flexibility in non-residential buildings by using thermodynamic modeling, and assesses the benefits of DR in terms of three separate optimization variants: net payment minimization, energy self-sufficiency, and peak power reduction. We test the framework in a case study of a medical research facility located in a business park with local solar power generation. The flexible loads are four groups of independently-controlled medical freezers. Our DR framework generates optimal freezer operation and solar power production/curtailment schedules that are compared against a business-as-usual scenario with no DR. We perform simulations for cases with and without end-ofhorizon temperature constraints. Results show that the flexibility harnessed from the freezers' thermal mass for DR actions improves the price-responsiveness, energy independence, and peak power consumption of the system with respect to the business-as-usual scenario. Furthermore, adding end-of-horizon constraints ensures that the thermal buffer of the flexible load will be full for the next simulation time window. Keywords: Demand response • Genetic algorithm • Smart grid • Local RES integration • Physical systems modeling This work was supported by Alliander N.V.

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A Cooperative Demand Response Scheme Using Punishment Mechanism and Application to Industrial Refrigerated Warehouses

Cited by 91 publications

References 46 publications

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

Energy Trading and Pricing in Microgrids with Uncertain Energy Supply: A Three-Stage Hierarchical Game Approach

Medical Freezers as Flexible Load for Demand Response in a Business Park Microgrid with Local Solar Power Generation

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