Impact of wide-scale EV charging on the power supply network

Heider, Anya; Haubrich, H.-J.

doi:10.1049/ic:19980361

Cited by 15 publications

(14 citation statements)

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“…The SOC lies between 20% and 100% and battery capacities are 10 kWh. The availability, though simplified, is consistent with earlier findings showing that most secondary cars in Germany are parked at the working location from approximately 6:00 a.m. until 6:00 p.m. [20]. Balancing power can only be offered during this time frame.…”

Section: Phevs As Energy Storage Devicessupporting

confidence: 88%

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Investigating PHEV wind balancing capabilities using heuristics and model predictive control

Galus

Fauci

Andersson

2010

IEEE PES General Meeting

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The strive for sustainable energy systems will most likely lead to an increased use of renewable energy sources which to a large extent are fluctuating. Therefore, a larger demand for balancing capacity will be required to maintain system security. Plug-in Hybrid Electric Vehicles (PHEV) represent a potential storage which could offer additional balancing power. Here, the applicability of PHEVs for this purpose is investigated. The study is performed in a system including a wind farm. The PHEV storage management approaches are based on a heuristic-and on a model predictive control scheme. They are compared in case studies. It is shown that the heuristic scheme is scalable while both schemes are able to balance the forecast error of renewable sources to an acceptable level.Index Terms-PHEV, V2G, balancing energy, MPC, wind power forecast, energy hub, heuristic

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Section: Phevs As Energy Storage Devicessupporting

confidence: 88%

“…Now, the control problem including electricity-and gas network as well as hub equations can be formulated for the system in figure 5 according to (18) with the objective function in (20). Variables indicated with a tilde represent vectors with variable values over the prediction horizon, e.g.…”

Section: U(t) = [P Es (T) P N (T) ]mentioning

confidence: 99%

Investigating PHEV wind balancing capabilities using heuristics and model predictive control

Galus

Fauci

Andersson

2010

IEEE PES General Meeting

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“…From Figure 7, one might think that since similar utility is being obtained by all agents when bidding a quantity in a range from social optimal value to maximum battery capacity, the agent of any particular type is better off by bidding the maximum quantity. However, precise simulations considering all bids fixed at 23 kWh shows that at such a bid profile, agents of the four types (1,2,3,4) respectively get utilities worth $7.0759, $0, $9.4506 and $0 whereas, if any one agent (depending on it being of type 2, 3 or 4) changes its bid to 22 kWh, it ends up increasing its utility unilaterally to $6.37, $10.2150 or $4.5761 respectively. This clearly shows that in this case, bidding at the maximum quantities is not a Nash equilibrium.…”

Section: Numerical Studymentioning

confidence: 99%

“…Effective management of the PEV based electricity demand will be crucial for maintaining the stability and the operational efficiency of the power grid in the near future [1], [2], [3]. Fortunately, PEVs also provide significant flexibility in terms of their energy consumption rates and schedules, which can be utilized towards reducing the variability of the aggregate demand over time.…”

Section: Introductionmentioning

confidence: 99%

Extended second price auctions for Plug-in Electric Vehicle (PEV) charging in smart distribution grids

Bhattacharya

Kar

Chow

et al. 2014

2014 American Control Conference

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Abstract-Large scale deployment of Plug-in Electric Vehicles (PEVs) in the smart grid environment necessitates the use of appropriate charging control algorithms that handle the additional PEV based load effectively. While ensuring grid stability, these PEV charging control algorithms must ensure that the available energy is delivered to where it is needed the most. In this paper, we explore the question of efficient allocation of energy (charging rates and schedules) to PEVs by the aggregator (electricity utility) through an auction mechanism. Recognizing the practical limitations of the Vickrey-Clark-Groves (VCG) mechanism which would be natural to apply in this context, we investigate two practical mechanisms that can be viewed as extensions of second price auction mechanisms, and have limited message (bid) complexity. In the first mechanism, the multi-level second price (MSP), each PEV agent submits a number of price bids, one for each of a given set of energy levels (energy quantities). In the second mechanism, the progressive second price (PSP), the PEV agents submit a two-dimensional bid indicating the price as well as the desired energy quantity. Taking into account differences across PEV-owners in terms of their willingnessto-pay values and charging time constraints, we analyze the social optimality and incentive compatibility properties of the two auction mechanisms.

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“…Based on survey data [3], [4], if every household owns just one PEV in the near future, the peak demand of the grid load from charging the PEVs can increase the peak load by 2.5 times the current peak load. Therefore efficient management of the this excess demand is important for the overall efficiency and stability of the grid [5], [6], [7]. This has led researchers to study the question of coordinating/optimizing the charging of PEVs [8], who have demonstrated that the undesirable effects of PEV charging on the distribution grid can be largely avoided through this coordination.…”

Section: Introductionmentioning

confidence: 99%

Price-driven charging of Plug-in Electric Vehicles: Nash equilibrium, social optimality and best-response convergence

Ghavami

Kar

Bhattacharya

et al. 2013

2013 47th Annual Conference on Information Sciences and Systems (CISS)

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As the number of charging Plugged-in Electric Vehicles (PEVs) increase, it is crucial to control the charging of PEVs in order to minimize energy generation and transmission costs, and ensure grid stability. In this work, we analyze the equilibrium properties of a natural price-driven charging control game in the distribution grid, between the utility (that sets the time-dependent energy usage price) and selfish PEVs (that choose their own charging schedules to minimize individual cost). We demonstrate through analysis and simulations that individual best-response strategies converge to socially optimal charging profiles (also equilibrium solutions) under fairly weak assumptions on the (asynchronous) charging profile update processes. We also discuss how the framework can be extended to consider the topology of the distribution tree and associated transmission line capacity constraints.

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Impact of wide-scale EV charging on the power supply network

Cited by 15 publications

References 0 publications

Investigating PHEV wind balancing capabilities using heuristics and model predictive control

Investigating PHEV wind balancing capabilities using heuristics and model predictive control

Extended second price auctions for Plug-in Electric Vehicle (PEV) charging in smart distribution grids

Price-driven charging of Plug-in Electric Vehicles: Nash equilibrium, social optimality and best-response convergence

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