Real-Time Self-Dispatch of a Remote Wind-Storage Integrated Power Plant Without Predictions: Explicit Policy and Performance Guarantee

Guo, Zhongjie; Wei, Wei; Chen, Laijun; Chen, Yue; Mei, Shengwei

doi:10.1109/oajpe.2021.3089583

Cited by 14 publications

(11 citation statements)

References 37 publications

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“…A novel operation-planning model is presented in [10] to increase the participation of wind-battery systems in both the gas market and the day-ahead electricity market. A real-time approach for integrated wind-battery systems is provided in [11] to increase wind farm revenue; however, the battery degradation process (BDP) and its impact on the battery lifetime are ignored. It is worth noting that the BDP is one of the significant factors affecting the battery degradation cost (BDC) and the battery performance [12].…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling and Cost-Benefit Analysis of Lithium-Ion Batteries Based on Battery State of Health

2023

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This paper presents a novel battery degradation cost (BDC) model for lithium-ion batteries (LIBs) based on accurately estimating the battery lifetime. For this purpose, a linear cycle counting algorithm is devised to estimate the battery cycle aging. In this algorithm, the local maximum and minimum values of the profile of the battery state of charge are identified by the proposed linear formulations. Then, the battery cycle aging due to the complete and incomplete cycles is determined. In this step, the battery cycle aging during an incomplete cycle is calculated by converting it to two complete cycles. After that, the calendar aging process of the LIB is linearly formulated based on the semi-empirical model to estimate the BDC accurately. After linearizing the LIB degradation process, a mechanism for computing the BDC during the scheduling horizon is designed by modeling the BDC as a series of equal payments over the LIB lifetime. In order to incorporate the BDC in the battery energy management problem, an iterative algorithm is presented for efficiently calculating the BDC associated with the adopted charging/discharging strategy. The numerical simulation results indicate that integrating the battery degradation process into the battery scheduling problem can reduce the amount of the battery capacity fading by 32.81%, as well as increase the profit of battery owners by 1.21%. Moreover, the conducted analyses highlight the importance of considering the LIB calendar aging process in determining the optimal LIB capacity.INDEX TERMS Battery degradation cost (BDC), cycle aging process, calendar aging process, battery scheduling problem, wind energy.

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Section: Introductionmentioning

confidence: 99%

Optimal Scheduling and Cost-Benefit Analysis of Lithium-Ion Batteries Based on Battery State of Health

2023

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“…E t is inherently a queue; introducing the anxiliary Q t is to restrict E t within the stable range [E l , E u ] with the collaboratively chosen perturbance γ and weight coefficient V. This choice depends on specific problems; in this case, γ = E l + τP U + Vπ max and the allowable range of V is given in Theorem 1 in Ref. [79]. Further, a larger V can improve the performance; the reason is that a larger V means a heavier weight on penalty term; however, if V is too large, the constraint E l ≤ E t ≤ E u cannot be satisfied because the weight on drift term is too minimal.…”

Section: Lyapunov Optimisationmentioning

confidence: 99%

“…This technique is first developed in the renowned textbook [78] for stochastic network optimisation with application to communication and queueing systems. To better explain how this method is applied in energy storage dispatch and control, we will use a wind‐storage integrated plant operation problem for illustration [79]. Considering the system structure in Figure 7, the wind‐storage system participates in real‐time energy market for arbitrage in a self‐scheduling manner; the amount of electricity sold to the grid in each τ ‐duration slot t is

f_{t} = τ ((), P_{t}^{w g} + P_{t}^{s g} η_{d})

where η d is the discharging efficiency of energy storage.…”

Section: Online Optimisationmentioning

confidence: 99%

“…For instance, in Ref. [79], multiparametric programming techniques are employed to extract the analytically dispatch policy, which maps the electricity price π t , wind resources

P_{t}^{w}

, and the current queue Q t to the actions to be taken. Such analytical relations are piecewise affine functions, and each piece corresponds to one subdivision of the allowable range of

((), π_{t}, P_{t}^{w}, Q_{t})

, as seen in Figure 8 with eight subdivisions in total; to obtain the dispatch action is to find which subdivision the observation of

((), π_{t}, P_{t}^{w}, Q_{t})

falls into.…”

Section: Online Optimisationmentioning

confidence: 99%

“…A valid choice of V is given in Theorem 2 in Ref. [79] The Lyapunov optimisation with drift-plus-penalty algorithm has been reported in many storage-related scenarios. A typical application is dynamic or real-time energy management, as in Ref.…”

Section: Lyapunov Optimisationmentioning

confidence: 99%

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Optimisation methods for dispatch and control of energy storage with renewable integration

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Renewable energy integration is an effective measure to resolve environmental problems and implement sustainable development, yet the volatility of wind and solar generation has a profound impact on power system operation, calling for sufficient backup capacity. Energy storage can shift demand over time and mitigate real‐time power mismatch and thus help integrate renewable energy resources into power grids. However, the unit capacity price of energy storage is still relatively high, and the capacity of energy storage is usually limited. Given the prominent uncertainty and finite capacity of energy storage, it is crucially important to take full advantage of energy storage units by strategic dispatch and control. From the mathematical point of view, energy storage dispatch and control give rise to a sequential decision‐making process involving uncertain parameters and inter‐temporal constraints. Multitudinous optimisation methods have been developed for such a problem while they differ in two aspects: the modelling of uncertainty and the mechanism to handle uncertainty. This review aims to draw a methodological picture of historical developments and state‐of‐the‐art advances in this research field. These methods are subsumed into three major categories: multistage optimisation, online optimisation, and multi‐timescale optimisation. In addition to introducing the advancements and applications, the authors also explain the motivations and theoretical foundations of these methodologies. The authors hope interested readers can attain a holistic outlook of these sophisticated approaches and choose the most appropriate one for their specific applications.

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Real-time self-scheduling of Jintan AA-CAES plant in energy and reactive power markets

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Wei,

Wang

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Real-Time Self-Dispatch of a Remote Wind-Storage Integrated Power Plant Without Predictions: Explicit Policy and Performance Guarantee

Cited by 14 publications

References 37 publications

Optimal Scheduling and Cost-Benefit Analysis of Lithium-Ion Batteries Based on Battery State of Health

Optimal Scheduling and Cost-Benefit Analysis of Lithium-Ion Batteries Based on Battery State of Health

Optimisation methods for dispatch and control of energy storage with renewable integration

Real-time self-scheduling of Jintan AA-CAES plant in energy and reactive power markets

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