Integrated energy system scheduling considering the correlation of uncertainties

Huang, Shangjiu; Lu, Hao; Chen, Maozhi; Zhao, Wenjun

doi:10.1016/j.energy.2023.129011

Cited by 14 publications

(8 citation statements)

References 37 publications

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“…It is assumed that the output of the WT is P WT i,t and the output of the PV cell is P PV i,t . Obviously, both P WT i,t and P PV i,t are time-varying parameters [31]. Therefore, the expression of the optimization problem with P WT i,t and P PV i,t parameters is as follows:…”

Section: Problem Descriptionmentioning

confidence: 99%

“…For the multi-district RIES shown in Figure 1, the constraints can be categorized into CCHP constraints, SESS constraints and coupling constraints between the CCHP and SESS, as shown in Table 1. The constraints of the CCHP and SESS can be found in references [26,31] and Section 2.2, respectively. At the same time, according to Table 1, the direct energy coupling constraints of the CCHP and SESS should be considered.…”

Section: Optimization Constraintsmentioning

confidence: 99%

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Dynamic Robust Optimization Method Based on Two-Stage Evaluation and Its Application in Optimal Scheduling of Integrated Energy System

Zhou,

2024

Applied Sciences

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As an emerging energy allocation method, shared energy storage devices play an important role in modern power systems. At the same time, with the continuous improvement in renewable energy penetration, modern power systems are facing more uncertainties from the source side. Therefore, a robust optimization algorithm that considers both shared energy storage devices and source-side uncertainty is needed. Responding to the above issues, this paper first establishes an optimal model of a regional integrated energy system with shared energy storage. Secondly, the uncertainty problem is transformed into a dynamic optimization problem with time-varying parameters, and a modified robust optimization over time algorithm combined with scenario analysis is proposed to solve such optimization problems. Finally, an optimal scheduling objective function with the lowest operating cost of the system as the optimization objective is established. In the experimental part, this paper first establishes a dynamic benchmark test function to verify the validity of proposed method. Secondly, the multi-mode actual verification of the proposed algorithm is carried out through a regional integrated energy system. The simulation results show that the modified robust optimization over time (ROOT) algorithm could find solutions with better robustness in the same dynamic environment based on the two-stage evaluation strategy. Compared with the existing algorithms, the average fitness and survival time of the robust solution obtained by the modified ROOT algorithm are increased by 94.41% and 179.78%. At the same time, the operating cost of the system is reduced by 11.65% by using the combined optimization scheduling method proposed in this paper.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Optimization Constraintsmentioning

confidence: 99%

Dynamic Robust Optimization Method Based on Two-Stage Evaluation and Its Application in Optimal Scheduling of Integrated Energy System

Zhou,

2024

Applied Sciences

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“…This paper mainly studies the interaction between the CCHP, EV and SESS. The detailed modeling work of the equipment in CCHP can be found in reference [22], which will not be introduced in this paper.…”

Section: Overall Framework Of Riesmentioning

confidence: 99%

Multi-Regional Integrated Energy Economic Dispatch Considering Renewable Energy Uncertainty and Electric Vehicle Charging Demand Based on Dynamic Robust Optimization

Zhou,

2024

Energies

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Aiming at the problem of source-load uncertainty caused by the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs) into modern power system, a robust optimal operation scheduling algorithm for regional integrated energy systems (RIESs) with such uncertain situations is urgently needed. Based on this background, aiming at the problem of the irregular charging demand of EV, this paper first proposes an EV charging demand model based on the trip chain theory. Secondly, a multi-RIES optimization operation model including a shared energy storage station (SESS) and integrated demand response (IDR) is established. Aiming at the uncertainty problem of renewable energy, this paper transforms this kind of problem into a dynamic robust optimization with time-varying parameters and proposes an improved robust optimization over time (ROOT) algorithm based on the scenario method and establishes an optimal scheduling mode with the minimum daily operation cost of a multi-regional integrated energy system. Finally, the proposed uncertainty analysis method is verified by an example of multi-RIES. The simulation results show that in the case of the improved ROOT proposed in this paper to solve the robust solution of renewable energy, compared with the traditional charging load demand that regards the EVs as a whole, the EV charging load demand based on the trip chain can reduce the cost of EV charging by 3.5% and the operating cost of the multi-RIES by 11.7%. With the increasing number of EVs, the choice of the starting point of the future EV trip chain is more variable, and the choice of charging methods is more abundant. Therefore, modeling the charging demand of EVs under more complex trip chains is the work that needs to be studied in the future.

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“…However, the participation of flexible load on the customer side in the demand response has a direct impact on the customer's comfort with energy [11]. References [12–14] studied the operational optimization of CIES in different contexts but did not consider the interaction between the integrated energy system and the customer side. In CIES, customers have different demands and preferences for cooling, heat, and electricity loads, so it is important to realize a scheduling method that takes into account economic efficiency and customer dissatisfaction [15].…”

Section: Introductionmentioning

confidence: 99%

A Two‐Stage Multi‐Objective Optimal Scheduling Model for Community Integrated Energy System

Liu,

Huang,

Sun

et al. 2024

IEEJ Transactions Elec Engng

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Community integrated energy system coupled with renewable energy generation provides an effective solution to improve economy and reduce carbon emissions. This paper establishes a two‐stage multi‐objective optimal scheduling model for community integrated energy system. During the day‐ahead scheduling stage, a comprehensive customer dissatisfaction model based on Kano model is established, and the model takes total operating cost, comprehensive customer dissatisfaction, and carbon emissions as multi‐objectives. The Non‐dominate Sorting Genetic Algorithmic‐II (NSGA‐II) and the CRITIC‐TOPSIS evaluation model are used to develop a day‐ahead scheduling scheme. On the premise of ensuring customer dissatisfaction, the intra‐day scheduling stage evens out the uncertainty of wind power and photovoltaic power through rolling optimization and improves the reliability of system operation. Simulation results show that the proposed model reduces the total operating cost and carbon emissions by 8.1% and 12.8%, respectively, which validates the effectiveness of the proposed model. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Integrated energy system scheduling considering the correlation of uncertainties

Cited by 14 publications

References 37 publications

Dynamic Robust Optimization Method Based on Two-Stage Evaluation and Its Application in Optimal Scheduling of Integrated Energy System

Dynamic Robust Optimization Method Based on Two-Stage Evaluation and Its Application in Optimal Scheduling of Integrated Energy System

Multi-Regional Integrated Energy Economic Dispatch Considering Renewable Energy Uncertainty and Electric Vehicle Charging Demand Based on Dynamic Robust Optimization

A Two‐Stage Multi‐Objective Optimal Scheduling Model for Community Integrated Energy System

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