A Multi-Timescale Allocation Algorithm of Energy and Power for Demand Response in Smart Grids: A Stackelberg Game Approach

Jiang, Tingyu; Chung, C. Y.; Ju, Ping; Gong, Yitao

doi:10.1109/tste.2022.3166954

Cited by 14 publications

(7 citation statements)

References 31 publications

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“…Two sides in this game are a distribution market operator and virtual power plants. A multi-timescale algorithm is proposed to solve the allocation problem of available energy and power, which can reduce the power imbalance [12]. To achieve low-carbon economic goals, ref.…”

Section: Literature Survey Of Game Theorymentioning

confidence: 99%

Pricing Strategies for Distribution Network Electric Vehicle Operators Considering the Uncertainty of Renewable Energy

Yuan,

Jiao,

Wang

et al. 2024

Processes

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In the future, the active load of the distribution network side will be dominated by electric vehicles (EVs), showing that the charging power demand of electric vehicles will change with the change in charging electricity price. With the popularity of electric vehicles in the distribution network, their aggregation operators will play a more prominent role in pricing management and charging behavior, and setting an appropriate charging price can achieve a win–win situation for operators and electric vehicle users. At the same time, the proportion of scenery in the distribution network is relatively high, and the uncertainty of self-output has a certain impact on the pricing strategy of operators and the charging behavior of electric vehicle users, which has become an important research topic. Based on the above background, an EV operator pricing strategy considering the landscape uncertainty is proposed, a Stackelberg game model is established to maximize the respective benefits of operators and EV users, and the two-layer model is further transformed into a single-layer model through the Karush–Kuhn–Tucker (KKT) condition and duality theorem. Finally, the IEEE 33 system is simulated with the CPLEX solver, and the global optimal pricing strategy is obtained. Simulation results prove that electric vehicle operators experience a maximum profit increase of 2.6% due to the impact of maximum capacity of energy storage equipment and the uncertainty of renewable energy output can result in electric vehicle operators losing approximately 20% of their profits at most.

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Section: Literature Survey Of Game Theorymentioning

confidence: 99%

Pricing Strategies for Distribution Network Electric Vehicle Operators Considering the Uncertainty of Renewable Energy

Yuan,

Jiao,

Wang

et al. 2024

Processes

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“…In this paper, the energy purchased by the system from the power grid is generated by thermal power units, so the main carbon emission sources of the system are MT, GB, and purchased energy. The model of the initial carbon quota is [7]: (10) where E F,MT , E F,GB , and E F,grid are the initial carbon quotas of MT, GB, and electricity purchased from the power grid, respectively. P grid t is the electric power purchased from the grid.…”

Section: Carbon Trading Modelmentioning

confidence: 99%

“…Therefore, it is more appropriate to study the distributed optimization of MAIES. The game theory is a method of studying how multiple decisionmakers make appropriate decisions based on their information and abilities when there are interests or conflicts between them [10]. In the process of energy transaction, the energy manager first formulates the price strategy according to the energy demand, and then the other agents respond according to the price information.…”

Section: Introductionmentioning

confidence: 99%

Master–Slave Game Optimal Scheduling for Multi-Agent Integrated Energy System Based on Uncertainty and Demand Response

Zhu,

Wang

2024

Sustainability

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With the transformation of the energy market from the traditional vertical integrated structure to the interactive competitive structure, the traditional centralized optimization method makes it difficult to reveal the interactive behavior of multi-agent integrated energy systems (MAIES). In this paper, a master–slave game optimal scheduling strategy of MAIES is proposed based on the integrated demand response. Firstly, a master–slave game framework of MAIES is established with an energy management agent as leader, an energy operation agent, an energy storage agent, and a user aggregation agent as followers. Secondly, in view of the wind and solar uncertainty, the Monte Carlo method is used to generate random scenarios, and the k-means clustering method and pre-generation elimination technology are used for scenario reduction. Then, according to different flexible characteristics of loads, a multi-load and multi-type integrated demand response model including electric, thermal, and cold energy is built to fully utilize the regulation role of flexible resources. On this basis, the transaction decision-making models of each agent are constructed, and the existence and uniqueness of the Stackelberg equilibrium solution are proved. Finally, the case simulations demonstrate the effectiveness of the proposed optimal scheduling strategy of MAIES. Compared to the scenario without considering the wind and solar uncertainty and the integrated demand response, the rate of renewable energy curtailment was reduced by 6.03% and the carbon emissions of the system were reduced by 1335.22 kg in the scenario considering the proposed method in this paper.

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“…However, the main limitation of these proposals is that they do not address the sequential market clearing as all of them includes market with different timescales in the same level of the problem. To overcome this, Stackelberg games were used in [27] and [28] for multi-time scale allocation of energy and reserves. Equilibrium models were proposed in [29] and [30] to determine the optimal bidding strategy of virtual power plants participating in national day-ahead and real-time markets.…”

Section: Introductionmentioning

confidence: 99%

Stacking Revenues From Flexible DERs in Multi-Scale Markets Using Tri-Level Optimization

Paredes

Aguado

Essayeh

et al. 2024

IEEE Trans. Power Syst.

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Rapid proliferation of flexible Distributed Energy Resources (DERs) as a result of Net Zero Emissions objectives entails a profound shift in the paradigm of local and national energy systems. Currently, DERs' simultaneous participation in multiple markets is generally restricted, which undermines their profitability. With the aim of increasing the number of business cases for them, a tri-level optimization problem that seeks the maximisation of revenues from DERs is proposed. The optimization problem considers simultaneous participation of different flexible DERs, such as, Electric Vehicles (EVs), Battery Energy Storage Systems (BESSs) and Heating, Ventilation and Air Conditioning (HVACs), in national and local markets. Markets are cleared sequentially, and the model is recast into a tractable single-level problem using its dual formulation and strong duality condition. Results from a case study based on the IEEE 14 bus transmission network, a realistic distribution network and SimBench dataset demonstrate the effectiveness of the proposed approach in increasing profits compared with a baseline scenario.

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A Multi-Timescale Allocation Algorithm of Energy and Power for Demand Response in Smart Grids: A Stackelberg Game Approach

Cited by 14 publications

References 31 publications

Pricing Strategies for Distribution Network Electric Vehicle Operators Considering the Uncertainty of Renewable Energy

Pricing Strategies for Distribution Network Electric Vehicle Operators Considering the Uncertainty of Renewable Energy

Master–Slave Game Optimal Scheduling for Multi-Agent Integrated Energy System Based on Uncertainty and Demand Response

Stacking Revenues From Flexible DERs in Multi-Scale Markets Using Tri-Level Optimization

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