Network-Constrained Stackelberg Game for Pricing Demand Flexibility in Power Distribution Systems

Aguiar, Nayara; Dubey, Anamika; Gupta, Vijay

doi:10.1109/tsg.2021.3078905

Cited by 25 publications

(9 citation statements)

References 26 publications

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“…This literature typically adopts the concept of Stackelberg equilibrium. For instance, in [22], a Stackelberg game is formulated through which a Distribution Company maximizes its profit. The objective-maximizing equilibrium can be calculated via a bi-level program, by exploiting the fact that the optimal responses of participants can be expressed as a set of KKT conditions.…”

Section: B Related Workmentioning

confidence: 99%

Correlated Equilibrium Power Flow in Distribution Networks Using Graphical Game Theory

Τσαούσογλου

2023

IEEE Trans. Smart Grid

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Section: B Related Workmentioning

confidence: 99%

Correlated Equilibrium Power Flow in Distribution Networks Using Graphical Game Theory

Τσαούσογλου

2023

IEEE Trans. Smart Grid

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“…The game model was solved by the combination of heuristic algorithm (differential evolution algorithm) and MIP method. (Aguiar et al, 2021) proposed a Stackelberg game model to determine the trading prices of flexible users in distribution network.…”

Section: Introductionmentioning

confidence: 99%

A multi-stakeholder potential game model in active distribution network considering the bounded rationality of small users

Wang

Ligao

et al. 2022

Front. Energy Res.

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The increase in the penetration rate of distributed renewable energy sources has brought unprecedented challenges to the economic and stable operation of the active distribution network (ADN). To improve the operating efficiency and total benefits of the ADN, it is necessary to establish an optimization model considering the stakeholders’ market behavior and the game relationship among them under the market environment. In this paper, a multi-stakeholder potential game model in the ADN considering the bounded rationality of small users is proposed. The game relationships among five stakeholders, including the distributed photovoltaic generation aggregators, the distributed wind power aggregators, the energy storage operators, the large users and the load aggregators, are modeled on an hourly time scale. Existing research has proved that the game equilibrium of the potential game model must exist. Based on the information transfer and the strategy update mode on the social network, a demand response evolutionary game model for small users is established. Moreover, a distributed algorithm is designed to obtain the pure strategy equilibrium. Finally, a case with five stakeholders and 160 small users is used to verify the rationality and feasibility of the proposed model.

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“…Although virtual-battery-based control and coordination approaches do not require the modelling of each TCL, the physical parameters of the TCLs are still needed to develop a virtual battery model. Finally, some studies (e.g., [22][23][24][25][26]) used utility functions to quantify the level of satisfaction with thermal comfort. Despite their simplicity, such functions are very generic and often do not guarantee the actual satisfaction of end users.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven, Distributed Game-Theoretic Transactional Control Approach for Hierarchical Demand Response

2022

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Modern power systems require flexible demand-side resources to maintain the balance between electricity supply and demand. Building thermostatically controlled loads (TCLs) are great flexible assets, as they account for a significant portion of electricity consumption in buildings. In this regard, demand response (DR) programs have long been used by grid operators to enable the coordination of TCLs to reveal and utilize this demand flexibility. Existing efforts in DR are mostly model-based, which is not scalable because gathering the physical parameters and developing an accurate model for each participating load is impractical. To address this limitation, this paper proposes a data-driven, distributed hierarchical transactional control approach, which leverages concepts from machine learning, game theory, and model-free control. In this approach, the interactions between a distribution system operator (DSO) and load aggregators (LAs) in the upper level are designed as a Stackelberg game, where the DSO is the leader and the LAs are the followers. The DSO aims to maximize its profit and minimize the total demand peak-to-average ratio (PAR) of the system, whereas the LAs aim to minimize their electricity cost while maintaining the quality of service provided. In the lower level, the LAs control the individual loads of end users while tracking the optimal aggregated load profile passed from the upper level. The performance of this approach is evaluated using a case study with a single DSO, three LAs, and 300 TCLs. The results show that the total demand PAR is reduced from 1,38 to the range of 1.

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Network-Constrained Stackelberg Game for Pricing Demand Flexibility in Power Distribution Systems

Cited by 25 publications

References 26 publications

Correlated Equilibrium Power Flow in Distribution Networks Using Graphical Game Theory

Correlated Equilibrium Power Flow in Distribution Networks Using Graphical Game Theory

A multi-stakeholder potential game model in active distribution network considering the bounded rationality of small users

A Data-Driven, Distributed Game-Theoretic Transactional Control Approach for Hierarchical Demand Response

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