Coordination of the Charging of Electric Vehicles Using a Multi-Agent System

Papadopoulos, Panagiotis; Jenkins, Nick; Cipcigan, Liana; Grau, I.; Zabala, Eduardo

doi:10.1109/tsg.2013.2274391

Cited by 66 publications

(45 citation statements)

References 17 publications

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“…Nevertheless, the notion of what constituted a distributed algorithms still differs a lot: Whereas early publications focussed on the atomic units (mainly named agents) as a gateway to distributed energy units, many hierarchical approaches have been presented for both energy market aspects [20], [21], [22] as balancing approaches [23]. In recent years, some fully distributed algorihms have been presented, with the individual agents being capable of solution generation and evaluation without a centralized components [24], [25].…”

Section: Distributed Energy Schedulingmentioning

confidence: 99%

On local minima in distributed energy scheduling

Nieße

Bremer

Lehnhoff

2017

Annals of Computer Science and Information Systems

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Abstract-Distributed energy scheduling constitutes a tough task for optimization algorithms, as the underlying problem structure is highdimensional, multimodal and non-linear. For this reason, metaheuristics and especially distributed algorithms have been in the focus of research for several years with promising results. The modeling of the distributed energy units' flexibility is a specific research task, with different concepts like comfort-level based approaches, enumeration of possible schedules, and continuous schedule representation using machine learning and decoder techniques. Although a continuous representation of flexibility has shown better results regarding the global optimization goal, there have been hints that the susceptibility to local minima traps enlarges compared to the enumeration of distinct schedules. In this contribution, we present an exemplary system for predictive scheduling of distributed energy units consisting of a continuous flexibility modelling approach and a fully distributed planning heuristic. A prestudy is presented, where we analyze the problem structure regarding local minima and describe planned work to reduce the heuristic's susceptibility to be kept in these.

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Section: Distributed Energy Schedulingmentioning

confidence: 99%

On local minima in distributed energy scheduling

Nieße

Bremer

Lehnhoff

2017

Annals of Computer Science and Information Systems

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“…The decentralized approach only collects PEV's own information, which can relieve computation burdens for the control centre. However, a central external signal, i.e., a coordination control signal [17] or a real-time pricing signal [18], is required to send to an aggregator. A fully decentralized strategy only needs local information that is presented in [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Distributed Initialization-Free Cost-Optimal Charging Control of Plug-In Electric Vehicles for Demand Management

Zhao

Ding

2017

IEEE Trans. Ind. Inf.

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Abstract-This paper considers the optimal charging problem of plug-in electric vehicles (PEVs) on demand side management. PEVs provide a promising alternative solution to reduction of environmental pollution and fuel emission. With a large number of PEVs connected to the grid, a well-designed charging coordination approach is needed to release the impacts on the power system. A distributed cooperative control strategy of PEVs is proposed to meet system interests while respecting each PEV's charging constraint. The proposed strategy is distributed, which only needs to be interacted with the neighbouring agents. Our analysis shows that the proposed strategy solves the optimal charging problem of PEVs in an initialization-free approach, which avoids any procedure for initialization during PEVs' charging process. Furthermore, the proposed strategy is robust to the time-varying available charging power and plug-and-play operations. The simulation studies validate the effectiveness of the proposed distributed strategy.

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“…4 MAS are classified as a distributed control architecture and have been proposed as a promising technology for 5 addressing control and co-ordination issues in the power industry, including DER [15], [16]. In addition, MAS have 6 been proposed for controlling CHP unit clusters [17] and for co-ordinating electric vehicle charging [18], [19], [20], [21]. 7 8 The concept presented in this paper combines the multiple energy carrier modelling approach of energy hubs, with 9 the distributed control approach of multi-agent systems.…”

Section: Introductionmentioning

confidence: 99%

“…Agent-based implementations are by definition market oriented, provided 18 that they use cooperative or competitive negotiation and coordination techniques, and normally use market-like 19 structures to reach an optimal or near-optimal solution [29], [30]. Such market-oriented approaches have also been 20 developed by the authors in previous work [18], [19], [20], [21]. [20], [21], [29], [31].…”

Section: Introductionmentioning

confidence: 99%

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Multiple energy carrier optimisation with intelligent agents

et al. 2016

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Multiple energy carrier systems stem from the need to evolve traditional electricity, gas and other energy systems to 10 more efficient, integrated energy systems. An approach is presented, for controlling multiple energy carriers, 11 including electricity (AC or DC), heat, natural gas and hydrogen, with the objective to minimise the overall cost 12 and/or emissions, while adhering to technical and commercial constraints, such as network limits and market 13 contracts. The technique of multi-agent systems (MAS) was used. The benefits of this approach are discussed and 14 include a reduction of more than 50% in the balancing costs of a potential deviation. An implementation of this 15 methodology is also presented. In order to validate the operation of the developed system, a number of experiments 16 were performed using both software and hardware. The results validated the efficient operation of the developed 17 system, proving its ability to optimise the operation of multiple energy carrier inputs within the context of an energy 18 hub, using a hierarchical multi-agent system control structure. It is necessary to evolve traditional electricity, gas and other energy systems to more flexible, integrated energy 34 systems [11], referred to as multiple energy carrier, or multi-carrier systems. The points of interaction between 35 different energy carriers have been described as "energy hubs" [5], [12], which present an integrated approach for 36 optimizing systems with multiple energy carriers, such as electricity, hydrogen, or natural gas networks [13]. Devices 37 are incorporated in an energy hub with the purpose of converting from one carrier to another, e.g. a CHP unit 38 converting natural gas to electricity and heat. Storage elements such as batteries or thermal storage may also be 39 considered. The energy carrier inputs to the energy hub are optimised and controlled in order to supply a given set of 40 energy carrier loads / outputs, thus achieving whole-system optimization [12], [14]. In (1), the backward coupling 41 matrix (Dnm) which links the inputs (Pm) with the outputs (Ln) is shown, as this is a formality that is used in Section 2.2. 42The elements of the Dnm matrix are constructed using the conversion efficiencies of individual devices in the energy 43 hub [12]. Matrix dimensions are × 1, × 1 and × , for Pm, Ln and Dnm respectively. 44

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Coordination of the Charging of Electric Vehicles Using a Multi-Agent System

Cited by 66 publications

References 17 publications

On local minima in distributed energy scheduling

On local minima in distributed energy scheduling

Distributed Initialization-Free Cost-Optimal Charging Control of Plug-In Electric Vehicles for Demand Management

Multiple energy carrier optimisation with intelligent agents

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