A Prosumer-Based Energy Sharing Mechanism of Active Distribution Network Considering Household Energy Storage

Yang, Shaobo; Hu, Xuekai; Wang, Hongli; Ligao, Junjie; Meng, Liang; Zhou, Wen; Zhou, Hao

doi:10.1109/access.2022.3217540

Cited by 15 publications

(3 citation statements)

References 28 publications

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“…In parallel, research on the complementary integration of new energy production and consumption in multi-microgrid systems has been growing. Scholars in [16], [17] proposed energy management strategies for multi-microgrids, emphasizing their interconnection and mutual benefit, which enhances system reliability and overall benefits. Furthermore, [18] introduced a cooperative optimization model for multimicrogrids, employing the Shapley method for benefit distribution.…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimizing Grid-Connected Multi-Microgrid Systems With Shared Energy Storage for Enhanced Local Energy Consumption

Lu,

Zheng,

et al. 2024

IEEE Access

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In response to the growing demand for sustainable and efficient energy management, this paper introduces an innovative approach aimed at enhancing grid-connected multi-microgrid systems. The study proposes a strategy that involves the leasing of shared energy storage (SES) to establish a collaborative micro-grid coalition (MGCO), enabling active participation in the dispatching operations of active distribution networks (ADNs). A hierarchical framework wherein the ADN assumes the role of the central orchestrator is developed, while the MGCO and SES operators (SESO) function as responsive entities within a single-leader multi-follower game optimization model. The ADN leader implements timeof-use (TOU) pricing mechanisms as a strategic incentive to encourage active engagement from the MGCO and SESO followers in peak regulation dispatch activities. In response to these TOU policies, the followers implement a two-stage dispatch optimization strategy. The first stage is dedicated to optimizing energy storage utilization. Within this phase, each microgrid meticulously fine-tunes its energy storage charging and discharging strategies with the primary objectives of mitigating power fluctuations, reducing load variance, minimizing energy storage costs, and facilitating on-demand leasing of energy storage resources. Subsequently, energy storage resources are pooled and shared to harness collective benefits and enhance alliance-wide energy utilization. Emphasis is placed on prioritizing the integration of renewable energy sources, promptly responding to peak demand regulation and scheduling, maximizing overall benefits, and equitably distributing cooperative advantages among alliance members. To substantiate the effectiveness of this comprehensive methodology, the paper presents illustrative results that provide compelling evidence of its potential to facilitate the seamless integration of microgrids, shared energy storage, and active distribution networks.

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Section: B Literature Reviewmentioning

confidence: 99%

Optimizing Grid-Connected Multi-Microgrid Systems With Shared Energy Storage for Enhanced Local Energy Consumption

Lu,

Zheng,

et al. 2024

IEEE Access

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“…Traditional power [3] lines are being changed by smart energy technologies, which are bringing in a new age of efficiency, sustainability, and robustness. Adding new sensors, Internet of Things (IoT) devices, and communication networks is what makes this change possible.…”

Section: Introductionmentioning

confidence: 99%

Core Systems Reinvented: Smart Energy Technologies Transforming Electrical Grids

2024

Acta Energetica

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In the ever-changing world of modern energy systems, adding smart technologies has caused major changes in power lines around the world. This new idea came about because of the need to make things more efficient, reliable, and environmentally friendly while also meeting the growing demand for energy. Smart energy technologies, which are smart and flexible, are changing the way standard grid systems work by adding tracking, analytics, and control features that can be used in real time.Advanced monitors and meters that allow for fine-grained data collection across the grid network are at the heart of this change. These monitors make it easy for information to flow between devices, which gives grid workers new information about how the grid works, how much power is being used, and what problems might happen. At the same time, the widespread use of communication networks and Internet of Things (IoT) devices makes it easier to connect and send data. This makes it easier to connect distributed energy resources (DERs) like solar panels, wind mills, and energy storage systems.Also, artificial intelligence (AI) and machine learning techniques are very important for making use of the huge amounts of data that smart grids produce. AI-driven analytics improve projection accuracy, make grid operations more efficient, and find repair needs or possible problems before they happen. They do this by looking at past trends and predicting future demand patterns. This ability to predict repair needs cuts down on downtime and running costs, making the grid more reliable and able to handle new problems like hacking threats or extreme weather.Additionally, blockchain technology now provides independent options for managing transactions and enabling peer-to-peer energy trade within microgrids. This promotes energy democracy and gives prosumers the power to actively join in the energy market. Grid-edge computing also makes it possible to make decisions in real time at the network's edge, which makes the system more flexible and eases the load on central infrastructure. I.

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“…However, the effective utilization of flexible resources requires a well-regulated market trading mechanism. Several studies have examined centralized trading mechanisms in ADNs, establishing distribution network trading models based on Stackelberg games [3] [4]. Safety constraints were considered when constructing a market clearing model for ADNs [5].…”

Section: Introductionmentioning

confidence: 99%

Demand-side energy sharing mechanism with residential energy storage in active distribution networks

Jin,

Zhou,

Che

et al. 2024

J. Phys.: Conf. Ser.

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With the widespread adoption of distributed new energy and residential energy storage (RES), demand-side users in active distribution networks (ADNs) are transitioning from traditional consumers to prosumers. The flexible resources of demand-side prosumers require an appropriate trading mechanism to achieve optimal resource allocation. A demand-side energy sharing mechanism based on residential energy storage is proposed. Firstly, a trading decision model for prosumer units is established by integrating the charge-discharge utility of RES devices with production-consumption characteristics. Then, a decentralized social welfare maximization energy sharing problem for ADNs is constructed using the Alternating Minimization Algorithm (AMA). Furthermore, a 10-node active distribution network is analyzed as an example. The proposed mechanism can maximize social welfare and increase the adaptation level of new energy from 64.58% to 66.83%.

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A Prosumer-Based Energy Sharing Mechanism of Active Distribution Network Considering Household Energy Storage

Cited by 15 publications

References 28 publications

Optimizing Grid-Connected Multi-Microgrid Systems With Shared Energy Storage for Enhanced Local Energy Consumption

Optimizing Grid-Connected Multi-Microgrid Systems With Shared Energy Storage for Enhanced Local Energy Consumption

Core Systems Reinvented: Smart Energy Technologies Transforming Electrical Grids

Demand-side energy sharing mechanism with residential energy storage in active distribution networks

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