Modeling, identification, and optimal control of batteries for power system applications

Fortenbacher, Philipp; Mathieu, Johanna L.; Andersson, Göran

doi:10.1109/pscc.2014.7038360

Cited by 48 publications

(54 citation statements)

References 12 publications

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“…The function of this block is to calculate the value of . The calculation block is defined by referring to (4) and (5). Figure 3 shows the detail calculation of the additional block.…”

Section: Battery Control Methodsmentioning

confidence: 99%

“…If there are many changes of battery along the whole system lifetime, it can increase total investment cost. Degradation of battery depends on its operation which can be influenced by the operational management [5]. Several studies proposed method to extend battery lifetime, such as optimization based on life loss cost [6] and replacement cost [7].…”

Section: Introductionmentioning

confidence: 99%

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Battery Charge Control by State of Health Estimation

Hadi

Fujita

2017

IJEECS

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“…The function of this block is to calculate the value of . The calculation block is defined by referring to (4) and (5). Figure 3 shows the detail calculation of the additional block.…”

Section: Battery Control Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Battery Charge Control by State of Health Estimation

Hadi

Fujita

2017

IJEECS

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“…The operation cost contains the energy transaction cost with the main grid and the degradation cost of the DESS, where the degradation cost depends on charging power

(P_{j, t}^{italicch})

, discharging power

(P_{j, t}^{italicdch})

, and SoE ( E j , t ). The parameter a is the electricity price, and parameters b , c , and d are cost coefficients obtained from another study …”

Section: Problem Formulationmentioning

confidence: 99%

“…Specifically, charging/discharging power limit combined with time‐coupling stat‐of‐energy (SoE) constraint have been adopted in the related optimization model . Moreover, the financial indicators based on life‐cycle analysis including upfront capital cost and operation cost can effectively support the decision‐making process . On the basis of this point, sizing and allocating DESSs in optimization problems have been widely studied in the literature from the perspective of different stakeholders.…”

Section: Introductionmentioning

confidence: 99%

Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

Wen

et al. 2019

Int Trans Electr Energ Syst

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To counterbalance the significant challenges imposed by renewable distributed generations penetration, this paper discusses the need of distributed energy storage system investment in distribution networks and proposes a robust optimization based storage investment model. The operational constraints of distribution network (e.g., voltage profile and substation capacity limitation) and storage device (e.g., state of energy and charging/discharging limit) are considered to guarantee the technical operation requirements. The proposed model is mathematically formulated as a two‐stage robust optimization with uncertainty of renewable distributed generator that is quantified by a polyhedral uncertainty set. The investment‐decision variables are optimized in the first stage, and the feasibility in the real‐time worst‐case scenario is checked in the second stage. A column‐and‐constraint generation (C&CG) algorithm and the big‐M linearization method are employed to solve the associated optimization problem. Numerical experiments on IEEE‐37‐node and IEEE‐123‐node distribution networks demonstrate the effectiveness of the proposed model.

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“…Q s ii (ξ i ) is the cost associated with storage being close to empty or full, where capacity degradation tends to be the most severe [22]. Similarly, R ii (ξ i ) is the cost associated with injecting or extracting power from the storage.…”

Section: A Energy Storage Placement and Sizing For Frequency Regulationmentioning

confidence: 99%

Allocating Sensors and Actuators via Optimal Estimation and Control

Taylor

Luangsomboon

Fooladivanda

2017

IEEE Trans. Contr. Syst. Technol.

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We consider the problem of planning the location and size of sensors and actuators to achieve optimal dynamic performance. Using basic results from control and convex optimization, we formulate mixed-integer semidefinite programs for the actuator placement and sizing to obtain the linear quadratic regulator with the lowest cost, and the sensor placement to obtain the Kalman filter with the lowest error. The two formulations are nearly identical due to the duality of optimal linear control and estimation. We also pose similar problems in terms of observability and controllability, which result in smaller mixed-integer semidefinite programs. Since the mixedinteger semidefinite programing is not yet a mature technology, we also use greedy heuristics in conjunction with continuous semidefinite programming. The approach is demonstrated on two modern applications from power systems: the placement and sizing of energy storage for regulation and the placement of phasor measurement units for estimation.Index Terms-Energy storage, Kalman filter (KF), linear quadratic regulator (LQR), mixed-integer semidefinite programming, phasor measurement unit (PMU), sensor and actuator placement.

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Modeling, identification, and optimal control of batteries for power system applications

Cited by 48 publications

References 12 publications

Battery Charge Control by State of Health Estimation

Battery Charge Control by State of Health Estimation

Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

Allocating Sensors and Actuators via Optimal Estimation and Control

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