Sizing of centralized shared energy storage for resilience microgrids with controllable load: A bi-level optimization approach

Du, Xili; Li, Xiaozhu; Hao, Yibo; Chen, Laijun

doi:10.3389/fenrg.2022.954833

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…Fattaheian et al (2022) applied the Stackelberg game to model incentivizing resource scheduling optimization in post-contingency conditions, and a strong duality condition is used to re-cast the preliminary bilevel model into a one-level mathematical problem. The pricing strategies in existing research studies mainly belong to fixed pricing mechanisms (Du et al, 2022). In the future, as more SMIPs participate in EM trading, the fixed pricing mechanisms will not be satisfied to the flexible EM model.…”

Section: Introductionmentioning

confidence: 99%

Stackelberg game-based optimal electricity trading method for distribution networks with small-micro industrial parks

Chen,

Ye,

et al. 2024

Front. Energy Res.

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In order to improve the operating benefits of the distribution network (DN) and reduce the energy consumption costs of small-micro industrial parks (SMIPs), a two-layer optimal electricity trading method for DN with SMIPs is proposed. First, based on the Stackelberg game, a multi-objective two-layer optimal trading model for DN and SMIP is established. In the upper layer, the DN agent is regarded as the leader, and a trading model is established with the goal of maximizing the profits of agents. In the lower layer, an energy optimization model is proposed for the SMIP operators, which are regarded as the followers, with the goal of minimizing the operating costs. According to the buying and selling electricity prices at the upper and lower layers, a dynamic pricing strategy is formulated. The Karush–Kuhn–Tucker condition (KKT) is introduced to transform the two-layer model into a single-layer model, and based on linear transformations, the model is further converted into a mixed-integer linear programming model. The transformations aim to address the non-linear issues arising from multivariable coupling between the upper and lower-layer trading models. The simulation results show that the trading strategy proposed in this paper can effectively increase the profit of DNs while reducing the operating costs of SMIPs and can provide a reference for decision-making in the electricity market (EM) with the participation of SMIP.

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Section: Introductionmentioning

confidence: 99%

Stackelberg game-based optimal electricity trading method for distribution networks with small-micro industrial parks

Chen,

Ye,

et al. 2024

Front. Energy Res.

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“…Finding a point of balanced benefits has become a key factor for the stability of the alliances in EM trading (Cao et al, 2021). Stackelberg game theory is frequently employed as an effective tool in solving optimization problems within the electricity market (Du et al, 2022). To solve the inherent conflict among the players, a Stackelberg game-based technique is proposed by Haghifam et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Electricity market clearing for multiple stakeholders based on the Stackelberg game

Wu,

Ye,

Chen

et al. 2024

Front. Energy Res.

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In order to improve the operating benefits of the distribution network and reduce the energy consumption costs of small–micro-industrial parks, an electricity market clearing considering small–micro-industrial parks is proposed based on the Stackelberg game. First, an optimal operating model of multiple stakeholders is established for integrated energy suppliers, the electricity market, and small–micro-industrial parks. In this model, an optimal electricity supply model for integrated energy suppliers is established with the goal of maximizing the operating benefits. A market clearing optimization model is established for the electricity market with the goal of maximizing the social surplus profit. In addition, an energy utilization optimization model is established for the small–micro-industrial parks with the goal of minimizing the energy procurement costs. Second, with the electricity market as the leader, the integrated energy suppliers and the small–micro-industrial parks as the followers, a leader–follower game strategy is proposed based on the Stackelberg game theory to achieve the maximizing benefits for multiple stakeholders. Finally, the simulation indicates that the proposed strategy can find the best profit point during the game process and achieve a balance between supply and demand.

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“…In the coming years, shared energy storage (SES) is expected to experience rapid growth [14]. Introducing a sharing model for energy exchange among multiple MGs and fully leveraging the collaborative advantages of MGs is an effective means with which to improve the energy utilization efficiency of energy storage systems [15]. Utilizing SES can enhance the operational flexibility, security, and power quality of renewable energy-based MGs [16,17].…”

Section: Introductionmentioning

confidence: 99%

Shared Energy Storage Capacity Configuration of a Distribution Network System with Multiple Microgrids Based on a Stackelberg Game

Zhang,

Huang

2024

Energies

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With the ongoing development of new power systems, the integration of new energy sources is facing increasingly daunting challenges. The collaborative operation of shared energy storage systems with distribution networks and microgrids can effectively leverage the complementary nature of various energy sources and loads, enhancing energy absorption capacity. To address this, a shared energy storage capacity allocation method based on a Stackelberg game is proposed, considering the integration of wind and solar energy into distribution networks and microgrids. In this approach, a third-party shared energy storage investor acts as the leader, while distribution networks and microgrids serve as followers. The shared energy storage operator aims to maximize annual revenue, plan shared energy storage capacity, and set unit capacity leasing fees. Upon receiving pricing, distribution networks and microgrids aim to minimize annual operating costs, determine leased energy storage capacity, and develop operational plans based on typical daily scenarios. Distribution networks and microgrids report leasing capacity, and shared energy storage adjusts leasing prices, accordingly, forming a Stackelberg game. In the case study results, the annual cost of MGs decreased by 29.63%, the annual operating cost of the ADN decreased by 11.25%, the cost of abandoned light decreased by 60.77%, and the cost of abandoned wind decreased by 27.79% to achieve the collaborative optimization of operations. It is proven that this strategy can improve the economic benefits of all parties and has a positive impact on the integration of new energy.

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Sizing of centralized shared energy storage for resilience microgrids with controllable load: A bi-level optimization approach

Cited by 11 publications

References 22 publications

Stackelberg game-based optimal electricity trading method for distribution networks with small-micro industrial parks

Stackelberg game-based optimal electricity trading method for distribution networks with small-micro industrial parks

Electricity market clearing for multiple stakeholders based on the Stackelberg game

Shared Energy Storage Capacity Configuration of a Distribution Network System with Multiple Microgrids Based on a Stackelberg Game

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