Multistage Transmission-Constrained Unit Commitment With Renewable Energy and Energy Storage: Implicit and Explicit Decision Methods

Zhou, Yuzhou; Zhai, Qiaozhu; Wu, Lei

doi:10.1109/tste.2020.3031054

Cited by 49 publications

(28 citation statements)

References 26 publications

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“…(2B-I) Non-anticipativity constraints of energy storage-Type I [65]: this type generates specific bounds for the SoC of energy storage. To this end, consider the following non-anticipativity constraints for storage j in any t:…”

Section: Implicit Policymentioning

confidence: 99%

“…f(⋅) is the function that connects the real SoC change of storage to the grid-side power with energy exchange efficiency; please see (39) in Ref. [65].…”

Section: Implicit Policymentioning

confidence: 99%

“…Consequently, the maximum generation rise between two adjacent slots is

P_{i, t}^{G, \max} - P_{i, t - 1}^{G, \min}

, so we add (31b) to make such a rise reachable. Similarly, the maximum generation drop between two adjacent slots is

P_{i, t - 1}^{G, \max} - P_{i, t}^{G, \min}

, so we add (31b) to make such a drop reachable. Non‐anticipativity constraints of energy storage‐Type I [65]: this type generates specific bounds for the SoC of energy storage. To this end, consider the following non‐anticipativity constraints for storage j in any t :

{\underset{true̲}{E}}_{j} \leq E_{j, t}^{\min} \leq E_{j, t, s} \leq E_{j, t}^{\max} \leq {\overline{E}}_{j}, \forall s \in S V S

- {\overline{P}}_{j}^{d i s} \leq f ((), E_{j, t}^{\max} - E_{j, t - 1}^{\min}) \leq {\overline{P}}_{j}^{c h}

- {\overline{P}}_{j}^{d i s} \leq f ((), E_{j, t}^{\min} - E_{j, t - 1}^{\max}) \leq {\overline{P}}_{j}^{c h}

where

{\underset{true̲}{E}}_{j} / {\overline{E}}_{j}

are the given physical lower/upper bounds of SoC and

{\overline{P}}_{j}^{d i s} / {\overline{P}}_{j}^{c h}

are the given physical upper bounds of...…”

Section: Multistage Optimisationmentioning

confidence: 99%

“…Please note that the non‐anticipativity constraints in implicit policy do not literally guarantee time causality. At the beginning of Section 2.3.3, it is mentioned that the implicit policy method is to identify a specific feasible region for dispatch variables, which is finally described by those ranges. They are sufficient conditions for the multistage robust feasibility of economic dispatch while the necessity is not clear [65]. The explicit policy restricts the redispatch function, and the implicit policy restricts the feasible region of decision variables.…”

Section: Multistage Optimisationmentioning

confidence: 99%

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

et al. 2022

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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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Section: Implicit Policymentioning

confidence: 99%

“…f(⋅) is the function that connects the real SoC change of storage to the grid-side power with energy exchange efficiency; please see (39) in Ref. [65].…”

Section: Implicit Policymentioning

confidence: 99%

“…Consequently, the maximum generation rise between two adjacent slots is

P_{i, t}^{G, \max} - P_{i, t - 1}^{G, \min}

, so we add (31b) to make such a rise reachable. Similarly, the maximum generation drop between two adjacent slots is

P_{i, t - 1}^{G, \max} - P_{i, t}^{G, \min}

{\underset{true̲}{E}}_{j} \leq E_{j, t}^{\min} \leq E_{j, t, s} \leq E_{j, t}^{\max} \leq {\overline{E}}_{j}, \forall s \in S V S

- {\overline{P}}_{j}^{d i s} \leq f ((), E_{j, t}^{\max} - E_{j, t - 1}^{\min}) \leq {\overline{P}}_{j}^{c h}

- {\overline{P}}_{j}^{d i s} \leq f ((), E_{j, t}^{\min} - E_{j, t - 1}^{\max}) \leq {\overline{P}}_{j}^{c h}

where

{\underset{true̲}{E}}_{j} / {\overline{E}}_{j}

are the given physical lower/upper bounds of SoC and

{\overline{P}}_{j}^{d i s} / {\overline{P}}_{j}^{c h}

are the given physical upper bounds of...…”

Section: Multistage Optimisationmentioning

confidence: 99%

Section: Multistage Optimisationmentioning

confidence: 99%

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

et al. 2022

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show abstract

“… are the pre-specified allowable discharging range, and they are the optimal solution to problem (6). It can be proved that if the non-anticipative day-ahead scheduling problem (6) has feasible solutions, then there must be at least one feasible scheduling strategy for the real-time dispatch under all the uncertainty realizations that reside in the pre-specified sets (1) [10][11]. Different from the models of multi-period dispatch with energy storage, the time-couping SoC constraints are relaxed, but the bounds of SoC will not be violated under any uncertainty realizations due to the pre-defined bounds of charging/discharging power and SoC level.…”

Section: Online Dispatchmentioning

confidence: 99%

Online Dispatch of Shared Energy Storage with Remote Renewable Generations

Wang

Xie

et al. 2021

E3S Web Conf.

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The sustained penetration of wind and solar generation is conducive to alleviating the energy crisis and environmental pollution. However, the finite capacity of transmission corridor limits the delivery of remote renewables and leads to curtailment. Energy storage plays an important role in renewables accommodation and improving equipment utilization, and shared energy storage can magnify the benefits through a temporal and spatial complementary. This paper proposes an online dispatch approach of energy storage shared by multiple renewable plants. The uncertainty and non-anticipativity issue are addressed via a two-stage decision-making process. The day-ahead scheduling determines the allowable energy and power levels of shared energy storage; the real-time online dispatch decides the wait-and-see strategies of charging and discharging power based on a single-period optimization model constrained by the pre-scheduled bounds. The case study verifies the effectiveness of the proposed method.

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Multistage robust optimization for the day-ahead scheduling of hybrid thermal-hydro-wind-solar systems

Zhong,

Fan,

2023

J Glob Optim

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Multistage Transmission-Constrained Unit Commitment With Renewable Energy and Energy Storage: Implicit and Explicit Decision Methods

Cited by 49 publications

References 26 publications

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

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

Online Dispatch of Shared Energy Storage with Remote Renewable Generations

Multistage robust optimization for the day-ahead scheduling of hybrid thermal-hydro-wind-solar systems

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