Study on Peak Shaving Strategy of Pumped Storage Power Station Combined with Wind and Photovoltaic Power Generation

Duan, Cong; Ma, Shihua; Wu, Jun; Li, Fuqiang; Hu, Yuou

doi:10.1109/iccsec.2017.8446981

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…Power Type Peak Shaving Method Applicable Conditions [12] centralized optimal control model small quantity [14][15][16] centralized optimization operation model small quantity [17] centralized mathematical model small quantity this paper decentralized two-layer methods large quantity…”

Section: Referencementioning

confidence: 99%

“…The authors in [14][15][16] analyze the output characteristics of wind power and PV, and establish a peak shaving optimization operation model in the wind-solar-storage hybrid power generation system. The authors in [17] propose a mathematical model of peak shaving strategy, and the pumped storage power stations combined with wind power and PV are utilized.…”

Section: Introductionmentioning

confidence: 99%

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Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism

Li¹,

Wang³

et al. 2022

Energies

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As one of the power auxiliary services, peak shaving is the key problem to be solved in the power grid. With the rapid development of DGs, the traditional peak shaving scheduling method for centralized adjustable energy is no longer applicable. Thus, this paper proposes two-layer optimization methods of allocating the peak shaving task for DGs. Layer 1 mainly proposes four evaluation indexes and the peak shaving priority sequence can be obtained with modified TOPSIS, then the DG cluster’s task is allocated to the corresponding DGs. On the basis of dynamic evaluation and the self-renewal mechanism, layer 2 proposes a peak shaving optimization model with dynamic constraints which assigns peak shaving instructions to each cluster. Finally, the effectiveness of the method is verified by using the real DGs data of a regional power grid in China based on the MATLAB simulation platform. The results demonstrate that the proposed methods can simply the calculation complexity by ranking the DGs in the peak shaving task and update the reliable aggregate adjustable power of each cluster in time to allocate more reasonably.

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Section: Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism

Li¹,

Wang³

et al. 2022

Energies

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“…Deterministic optimization as a suitable method for the short-term planning of energy systems is described in [5,6] and in combination with a rolling horizon strategy in [7][8][9]. Furthermore, in [10], a deterministic peak shaving strategy is studied, and in [11], an energy optimization scheduling strategy for an integrated energy system based on multi-time-scale coordination is proposed. When the deviation of the realized data from the nominal one is too high, it can happen that the inherent flexibility of the energy system is not sufficient to keep the previously calculated schedule.…”

Section: Introductionmentioning

confidence: 99%

Adjustable Robust Energy Operation Planning under Uncertain Renewable Energy Production

Eingartner,

Naumann,

Schmitz

et al. 2024

Energies

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In this paper, the application of the method of affinely adjustable robust optimization to a planning model of an energy system under uncertain parameters is presented, and the total scheduling costs in comparison with the deterministic model are evaluated. First, the basics of optimization under uncertain data are recapped, and it is described how these methods can be used in different applications for energy systems. This is followed by the methodology of adjustable robust optimization by defining the affinely adjustable robust counterpart. Finally, a numerical case study is conducted to compare the adjustable robust method with a rolling deterministic scheduling method. Both are implemented on a model of an energy system and compared with each other by simulation using real-world data. By calculating the total operating costs for both methods, it can be concluded that the adjustable robust optimization provides a significantly more cost-effective solution to the scheduling problem.

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“…In [16], A multi-objective unit commitment model considering different demand between the operating cost and the negative peak load regulation ability was proposed, adequate scheduling scheme could be performed via this new model. A mathematical model of peak regulation strategy of pumped storage power station combined with wind-photovoltaic power generation was given in [17], which aimed at the target of complete consumption of wind and photovoltaic power generation. In [18], an optimization model based on unit commitment was presented from the perspective of both the power demand and supply side, which took special load as a reserve in peak regulation process.…”

Section: Introductionmentioning

confidence: 99%

Unit Commitment Comprehensive Optimal Model Considering the Cost of Wind Power Curtailment and Deep Peak Regulation of Thermal Unit

Yang¹,

Cao²,

Cai

et al. 2020

IEEE Access

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Clean renewable energy is given high priority to generate power in power system dispatching. Unfortunately, the objective to minimize wind power curtailment may results in additional costs, since the inverse peak regulation characteristic of wind power probably result in redundant power qualities curtailed to ensure the balance of the electric supply and demand in real time. In this paper, considering the cost of deep peak regulation of thermal unit and wind power curtailment, unit commitment comprehensive optimal model is proposed to minimize total cost by searching the optimal wind power curtailment. In order to achieve an economic strategy among the wind power curtailment and the deep peak regulation, two indexes including the amount of hourly optimal wind power curtailment and the critical value of comprehensive wind power curtailment are developed. Particle swarm optimization algorithm is employed for solving the proposed model. Afterwards, the relationship between the degree of deep peak regulation and the amount of wind power curtailment is researched, the derived conclusions can provide valuable information for system operators. INDEX TERMS Wind power curtailment, deep peak regulation, unit commitment comprehensive optimal model, particle swarm optimization algorithm, system operators.

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Study on Peak Shaving Strategy of Pumped Storage Power Station Combined with Wind and Photovoltaic Power Generation

Cited by 6 publications

References 1 publication

Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism

Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism

Adjustable Robust Energy Operation Planning under Uncertain Renewable Energy Production

Unit Commitment Comprehensive Optimal Model Considering the Cost of Wind Power Curtailment and Deep Peak Regulation of Thermal Unit

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