Abstract The Integrated Energy System (IES) facilitates the synergistic operation of diverse energy forms through flexible energy conversion and management strategies,
Research on two‐level energy management based on tiered demand response and energy storage systems IET Renewable Power Generation DOI: 10.1049/rpg2.13010
A collaborative multi-energy multi-microgrid optimization model based on hierarchical multi-agent deep reinforcement learning is established.
This chapter introduces an energy storage system controlled by a reinforcement learning agent for smart grid households. It optimizes electricity trading in a variable tariff
The coordinated operation and management of energy and carbon emissions in an integrated energy system (IES) can effectively promote overall energy ef
In this era of global low-carbon development, an integrated energy system (IES) is full of prospects for reducing carbon emissions by coordinating and optimizing various
Considering the load balance constraints of electricity, gas, heat, and cooling, a low-carbon economic dispatch model of the multi-regional integrated energy system is established, and a multi-regional
To fill the research gaps, we propose a multi-agent energy management model with the global carbon emission constraint using the attention-based multi-agent deep
This research proposes a two‐level energy management model leveraging flexible load tiered demand response and energy storage systems. It optimizes economic benefits while ensuring
The study highlights the need for multi-agent cooperation. To address energy waste and conflicts of interest among multiple park-integrated energy systems (PIES), a bi
Therefore, the study on the scheduling strategy of multi-agent P-IES with multiple energy interactions under the dynamic pricing strategy is of great significance. In terms of low carbonisation of P-IES, the
Therefore, this paper proposes a generalised shared energy storage and integrated energy system transaction optimisation method based on a two-stage game model,
To realize the win-win benefits and resource coordination of the multilevel operating entities of a "microgrid cluster (MGC), microgrid (MG) and user" and improve the self-consumption of new
Research on two‐level energy management based on tiered demand response and energy storage systems IET Renewable Power Generation DOI: 10.1049/rpg2.13010 License CC BY-NC-ND 4.0
This paper presents a novel decentralized bi-level stochastic optimization approach based on the progressive hedging algorithm for multi-agent systems (MAS) in multi
Multi-agent reinforcement learning for energy management in microgrids with shared hydrogen storage David Toquica, Kodjo Agbossou, Nilson Henao Show more Add to
Multi-Agent Schedule OptimizationMethod for Regional Energy InternetConsidering the Improved TieredReward and Punishment CarbonTrading
In order to solve the problems of environmental pollution and the conflict of interests of multi-market players in the regional integrated energy system, a collaborative optimization method of
A novel peer-to-peer (P2P) energy sharing model incorporating shared energy storage (SES) is proposed in order to effectively utilize renewable energy sources and facilitate
Shared energy storage has the potential to decrease the expenditure and operational costs of conventional energy storage devices. However, studies on shared energy
摘要：With the increasing shortage of energy in today''s society, the rapid development of the multi-energy complementary microgrid system came into being, at the same time, the industry
To achieve low-carbon economic dispatch within the regional integrated energy system and fully exploit the carbon assets of multiple agents, a combined peer-to-peer (P2P)
These actions collectively aim to maximize the virtual power plant''s overall performance. The upper-tier model then communicates the power output to the lower-tier
By analyzing data on the cost of operating distribution networks, voltage stability, and distributed power consumption, we investigate the potential advantages of the
To address the insufficient flexibility of multi-energy coupling in the integrated energy system and the overall strategic demand of low-carbon development, a multi-storage
This paper proposes an agent-based framework to support the development of an energy storage system with standardized communications. This framework can be utilized with different power
Furthermore, Ref. [29] constructed a tiered reward and penalty carbon trading mechanism that considers the impact of carbon capture equipment on carbon emissions, while also proposing
In this paper, we consider a group of building users in the community with SESS, and each user can schedule power injection from the grid as well as SESS according to
In this context, this paper introduces a novel two-layer energy management strategy for microgrid clusters, utilizing demand-side flexibility and the capabilities of shared
In contrast to centralized management approaches, recent studies proposed multi-agent reinforcement learning (MARL) configurations to deal with privacy concerns and
In this paper, we propose a multi-tiered framework for controlling distributed energy resources (DERs) such as elastic and non-elastic loads, electric vehicles (EV s), and Battery Energy
Multi-agent energy storage service pattern Shared energy storage is an economic model in which shared energy storage service providers invest in, construct, and operate a storage system with the involvement of diverse agents. The model aims to facilitate collaboration among stakeholders with varying interests.
Case 1: In a multi-agent configuration of energy storage, the DNO can generate revenue by selling excess electricity to the energy storage device. This helps to smooth and increase the flexibility of DER output, resulting in a reduction in abandoned energy.
The results indicate that the multi-agent shared energy storage mode offers the most flexible scheduling, the lowest configuration cost among all distributed energy storage alternatives, the best cost-saving effect for DNOs, and enables promotion of DER consumption, voltage stability regulation and backup energy resource.
In summary, configuring and sharing an energy storage device among multiple agents, in consideration of their respective interests, can lead to more efficient utilization of the device. Moreover, such a setup can determine the most suitable configuration and operation mode under the influence of various factors.
Analysis of the graph reveals that the energy storage cycles and energy storage utilization are significantly higher in Case 1 when contrasted with Case 3. These results suggest that the multi-agent configuration method is more adaptable in scheduling tasks, leading to a more optimized utilization of energy storage devices.
In this mathematical model, the energy storage unit can exchange power directly with other agents without being limited by the distribution network topology. This example serves to demonstrate the importance of topology considerations. 5.2. Convergence analysis for algorithms
