Real-Time Multi-Home Energy Management with EV Charging Scheduling Using Multi-Agent Deep Reinforcement Learning Optimization

Kaewdornhan, Niphon; Srithapon, Chitchai; Liemthong, Rittichai; Chatthaworn, Rongrit

doi:10.3390/en16052357

Cited by 12 publications

(7 citation statements)

References 38 publications

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“…Optimization Model/Method [97] Decentralized Non-cooperative game/backward induction-based [78] An iterative solution using B&B [93] Mixed integer quadratic conic/ADMM [89] Multi-agent NLP/MIPS solver in Math power [105] MIQP/ADMM [107] Reinforcement learning [94] Hybrid…”

Section: Number Coordinated Managementmentioning

confidence: 99%

A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management

2023

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The transition from internal combustion engines to electric vehicles (EVs) has received significant attention and investment due to its potential in reducing greenhouse gas emissions. The integration of EVs into electric and transport systems presents both benefits and challenges in energy management. The scheduling of EV charging can alleviate congestion in the electric system and reduce waiting times for EV owners. The use of renewable energy sources (RESs) for EV charging and supporting the grid can help mitigate the uncertainty of these energy resources. Vehicle-to-grid (V2G) technology can be used as an alternative approach in the event of sudden high consumption of the grid. Additionally, cost minimization through large-scale coordinated planning is crucial for the future of e-mobility systems. This review paper focuses on the latest trends considering the various approaches and features in coordinated EV scheduling, as well as the influence of different stakeholders, categorized as single- and multiple-charging stations (CS) and aggregator levels. By implementing coordinated EV scheduling, various methods are presented to better manage the needs and satisfaction of EV owners as well as the profit of CS and the market trends of e-mobility systems. In this regard, EV charging strategies considering V2G, uncertainty evaluation of parameters, coordinated charging management, congestion of CSs and electrical lines, route mapping, and technical and economic aspects of the system hierarchy, including consumers, CSs and aggregators, are reviewed and discussed.

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Section: Number Coordinated Managementmentioning

confidence: 99%

A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management

2023

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“…Van Binh and Long Bao considered the cost of battery degradation and EV charging optimization [23]. Niphon K et al described several energy management methods to reduce the impact of EV entry on the grid [24]. Luo et al mainly focussed on the influence of large-scale EVs on the distribution grid and proposed a multistage sequential load recovery method to ensure the safe and stable operation of the grid [25].…”

Section: Introductionmentioning

confidence: 99%

Integrating Electric Vehicles to Power Grids: A Review on Modeling, Regulation, and Market Operation

Jia,

Ma,

et al. 2023

Energies

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Fossil energy consumption and environmental protection issues have pushed electric vehicles (EVs) to become one of the alternatives to traditional fossil-fuel vehicles. EV refers to a vehicle that uses electric energy as power and is driven by an electric motor. The electric energy of EVs is stored in batteries. When the EV is not traveling, the battery can provide power for other loads. Therefore, with the increase in the number of EVs and the load of the power grid, the EV-to-grid (V2G) mode, which uses EVs to supply power to the power grid, has gradually entered the field of vision of researchers. The physical connection mode, charge and discharge technology, and energy management strategy are the main topics of the current review papers; however, there is a lack of systematic research on V2G modeling, framework, and business models. This paper describes the concepts of the spatio-temporal distribution model and the adjustable capacity of EVs. In addition, common constraints and methods in optimization are introduced. Moreover, this paper introduces the interactive relationship among power grids, load aggregators, and EV users. Furthermore, the business model of V2G is introduced and analyzed from various perspectives. Finally, the future development of V2G is pointed out. This paper’s goal is to provide an overview of the present V2G application scenarios and to identify any challenges that must be overcome.

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“…In [28], deep learning mod els were applied to solve EV demand forecasting. A real-time HEMS with an EV charg ing/discharging model was proposed in [29] and was solved by means of a deep reinforce ment learning algorithm. The objective of the optimization problem was to improve the EV customer's reward.…”

Section: Introductionmentioning

confidence: 99%

“…Energies 2023, 16, x FOR PEER REVIEW mechanisms [24][25][26]. The authors of [27] prop stations in frequency regulation by means of V els were applied to solve EV demand forecast ing/discharging model was proposed in [29] an ment learning algorithm. The objective of the EV customer's reward.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Proximal Policy Optimization for Energy Management of Electric Vehicles and PV Storage Units

Alonso,

Amaris,

Martin

et al. 2023

Energies

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Connected autonomous electric vehicles (CAEVs) are essential actors in the decarbonization process of the transport sector and a key aspect of home energy management systems (HEMSs) along with PV units, CAEVs and battery energy storage systems. However, there are associated uncertainties which present new challenges to HEMSs, such as aleatory EV arrival and departure times, unknown EV battery states of charge at the connection time, and stochastic PV production due to weather and passing cloud conditions. The proposed HEMS is based on proximal policy optimization (PPO), which is a deep reinforcement learning algorithm suitable for continuous complex environments. The optimal solution for HEMS is a tradeoff between CAEV driver’s range anxiety, batteries degradation, and energy consumption, which is solved by means of incentives/penalties in the reinforcement learning formulation. The proposed PPO algorithm was compared to conventional methods such as business-as-usual (BAU) and value iteration (VI) solutions based on dynamic programming. Simulation results indicate that the proposed PPO’s performance showed a daily energy cost reduction of 54% and 27% compared to BAU and VI, respectively. Finally, the developed PPO algorithm is suitable for real-time operations due to its fast execution and good convergence to the optimal solution.

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Real-Time Multi-Home Energy Management with EV Charging Scheduling Using Multi-Agent Deep Reinforcement Learning Optimization

Cited by 12 publications

References 38 publications

A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management

A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management

Integrating Electric Vehicles to Power Grids: A Review on Modeling, Regulation, and Market Operation

Proximal Policy Optimization for Energy Management of Electric Vehicles and PV Storage Units

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