Optimal control of PEVs for energy cost minimization and frequency regulation in the smart grid accounting for battery state-of-health degradation

Cui, Tiansong; Wang, Yanzhi; Chen, Shuang; Zhu, Qi; Nazarian, Shahin; Pedram, Massoud

doi:10.1145/2744769.2744882

Cited by 17 publications

(7 citation statements)

References 20 publications

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“…The reason for considering storage price is that the stored power and bought power are homogeneous commodities. Present decisions on charging or discharging will influence the future purchases of power [28], so that the storage price is a kind of potential cost. Let ∆ i denote the variation of the energy stored in the batteries during the (dis)charging procedure.…”

Section: B Large-slot Layer: Capacity Provisioning Of Dcsmentioning

confidence: 99%

“…Line 3 is used to update the value of F PC i according to the revised q i . Every time we solve a new version of q i , we plug the corresponding F PC i into (28) and obtain a new version of problem P1, which can be solved by standard convex programming methods, such as the SQP method shown in Line 4. In Appendix A, we give mathematical proofs that our proposed SCP algorithm can always find the globally optimal solution of problem P. Besides, the SCP algorithm converges fast, as it can find the optimal solution of problem P by solving problem P1 no more than 2 × I i=1 N i times .…”

Section: A Solutions Of Problem Pmentioning

confidence: 99%

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Eco-Friendly Powering and Delay-Aware Task Scheduling in Geo-Distributed Edge-Cloud System: A Two-Timescale Framework

Sun

Wen

et al. 2020

IEEE Access

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Power consumption and task latency are two crucial issues in edge-cloud computing. This paper mainly aims to promote the use of clean power in geo-distributed data centers (DCs) in a deregulated electricity market where customers are allowed to buy power from multiple suppliers, combined with the guarantee of task latency. To alleviate the conflict between frequent switches of servers and the uncertainty of task arrivals in DCs, this paper proposes a two-timescale framework consisting of the long-term capacity planning of geo-distributed DCs and the real-time task dispatching from edge gateways (EGs) to DCs. First, DCs make long-term plans on the number of active servers aiming at the eco-friendly and delay-aware power cost minimization, which is formulated as problem P. Specifically, we introduce a convex pollution indicator function (PIF) to measure the pollution cost of the various types of powers sold by different suppliers, which can encourage the use of cleaner power and improve power savings. Second, in each sub-slot, each EG separately optimizes its individual mixed strategies of task dispatching to DCs with the knowledge of the planned capacities and the real-time queue backlogs of DCs, where a Lyapunov optimization framework is applied. Finally, we give the corresponding distributed algorithm design. Simulation results reveal that our method can realize the trade-off between the power cost and the delay cost of requests, and improve the clean power usage by up to 50%-60% of the total power usage in DCs. Additionally, comparisons with other schemes show that our approach can provide more stable guarantees of task latency in different situations of workload density, which benefits from the diverse-timescale optimizations of capacities of DCs and task routing from EGs to DCs. INDEX TERMS distributed task scheduling; edge-cloud system; two-timescale framework; pollution indicator function; eco-friendly and delay-aware; Lyapunov optimization

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Section: B Large-slot Layer: Capacity Provisioning Of Dcsmentioning

confidence: 99%

Section: A Solutions Of Problem Pmentioning

confidence: 99%

Eco-Friendly Powering and Delay-Aware Task Scheduling in Geo-Distributed Edge-Cloud System: A Two-Timescale Framework

Sun

Wen

et al. 2020

IEEE Access

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show abstract

“…Firstly, we consider the energy conversion efficiency of battery charging and discharging [34], denoted as η i (δ i ) or η i for short, where η i ∈ (0, 1). The relationship between Q bat i and ∆ i is given by Q bat i = g(∆ i ), where g(∆ i ) is defined as…”

Section: Model Of Power-storage Schedulingmentioning

confidence: 99%

“…It is observed that η i may be well fitted with a power function or an exponential function, which will be further analyzed in our simulations. Secondly, another issue that we should be concerned about is that present decisions on charging or discharging will influence the future cost [34], which is denoted as potential cost. For instance, the more the power that the battery discharges at present, the more the power it will have to charge in the future, which will increase the future power cost.…”

Section: Model Of Power-storage Schedulingmentioning

confidence: 99%

Eco-friendly Power Cost Minimization for Geo-distributed Data Centers Considering Workload Scheduling

Sun,

Wen,

et al. 2018

Preprint

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The rapid development of renewable energy in the energy Internet is expected to alleviate the increasingly severe power problem in data centers, such as the huge power costs and pollution. This paper focuses on the eco-friendly power cost minimization for geo-distributed data centers supplied by multisource power, where the geographical scheduling of workload and temporal scheduling of batteries' charging and discharging are both considered. Especially, we innovatively propose the Pollution Index Function to model the pollution of different kinds of power, which can encourage the use of cleaner power and improve power savings. We first formulate the eco-friendly power cost minimization problem as a multi-objective and mixedinteger programming problem, and then simplify it as a singleobjective problem with integer constraints. Secondly, we propose a Sequential Convex Programming (SCP) algorithm to find the globally optimal non-integer solution of the simplified problem, which is non-convex, and then propose a low-complexity searching method to seek for the quasi-optimal mixed-integer solution of it. Finally, simulation results reveal that our method can improve the clean energy usage up to 50%-60% and achieve power cost savings up to 10%-30%, as well as reduce the delay of requests.

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“…However, few studies have actively incorporated battery aging as part of regulation operation or bidding optimization objectives. References [20], [21] take into account the battery lifespan in regulation control optimization, using an aging model that is too simple to reflect properly the complex battery cycle aging mechanisms. The results in [22] incorporate the battery aging cost into regulation bidding strategies, but the proposed method does not optimize real-time operations.…”

Section: Introductionmentioning

confidence: 99%

Optimal Battery Participation in Frequency Regulation Markets

Shi

Kirschen

et al. 2018

IEEE Trans. Power Syst.

142

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Battery participants in performance-based frequency regulation markets must consider the cost of battery aging in their operating strategies to maximize market profits. In this paper we solve this problem by proposing an optimal control policy and an optimal bidding policy based on realistic market settings and an accurate battery aging model. The proposed control policy has a threshold structure and achieves near-optimal performance with respect to an offline controller that has complete future information. The proposed bidding policy considers the optimal control policy to maximize market profits while satisfying the market performance requirement through a chance-constraint. It factors the value of performance and supports a trade-off between higher profits and a lower risk of violating performance requirements. We demonstrate the optimality of both policies using simulations. A case study based on the PJM regulation market shows that our approach is effective at maximizing operating profits. Index Terms-Battery energy storage, degradation, frequency regulation, power system economics NOMENCLATURE A. Parameters and Variables B Battery energy storage power rating in MW b t Battery dispatch power during t in MW b The set of all battery dispatch power b = {b t } C Regulation capacity in MW C Maximum regulation capacity E Battery energy storage capacity in MWh E, E Upper and lower energy limit for the battery energy storage in MWh E g t , E g t Upper and lower energy limit enforced by the control policy g during interval t in MWh e t Battery energy level during t in MWh e The set of all battery energy levels e = {e t } e max t , e min t Maximum and minimum energy level up to interval t in MWh g Control policy for the battery energy storage i Index of identified cycles j Index of regulation capacity bid segments J Number of total bid segments M Duration of one dispatch interval in hours R Battery cell replacement price in $/MWh r t Normalized regulation instruction during t r The set of all normalized regulation instructions r = {r t } The authors are with the

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Optimal control of PEVs for energy cost minimization and frequency regulation in the smart grid accounting for battery state-of-health degradation

Cited by 17 publications

References 20 publications

Eco-Friendly Powering and Delay-Aware Task Scheduling in Geo-Distributed Edge-Cloud System: A Two-Timescale Framework

Eco-Friendly Powering and Delay-Aware Task Scheduling in Geo-Distributed Edge-Cloud System: A Two-Timescale Framework

Eco-friendly Power Cost Minimization for Geo-distributed Data Centers Considering Workload Scheduling

Optimal Battery Participation in Frequency Regulation Markets

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