Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations

Mei, Fei; Zhang, Jiatang; Lu, Jixiang; Jin-jun, LU; Jiang, Yuhan; Gu, Jiaqi; Yu, Kun; Gan, Lei

doi:10.1016/j.energy.2020.119629

Cited by 62 publications

(25 citation statements)

References 29 publications

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“…where constraint (23) defines the net output power of ES; constraints ( 24) and ( 25) specify the range of charging and discharging power of ES, respectively; constraint (26) indicates that the ES cannot be charged and discharged at the same time; constraint (27) ensures the energy balance of ES; constraint (28) ensures that the remaining energy state in the first time period is the same as that in the final time period; constraint (29) illustrates the upper and lower bounds of the remaining energy states of ES.…”

Section: Esmentioning

confidence: 99%

“…To deal with the uncertainties of renewable energy sources in PIESs, various methods have been employed in the previous studies, such as stochastic optimization (SO) [27,28] and robust optimization (RO) [29]. In [27], a multiple-scenario based SO model was proposed for the day-ahead economic dispatch of PIESs, where the uncertainties of load prediction and renewable energy sources were fully considered. In [28], a stochastic optimal operation model was presented for PIESs with dynamic heat and gas network characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data‐driven distributionally robust economic dispatch for park integrated energy systems with coordination of carbon capture and storage devices and combined heat and power plants

Wang¹,

Gao²,

Jia

et al. 2022

IET Renewable Power Gen

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Park integrated energy system (PIES) can utilize multiple energy resources complementarily and promote comprehensive energy efficiency. However, the uncertainty of renewable energy generation poses significant challenges to the optimal operation of PIES. This paper proposes a data-driven distributionally robust optimization (DDRO) model for the day-ahead scheduling of PIESs with coordination of carbon capture and storage devices (CCS) and combined heat and power plants (CHP). First, a deterministic economic dispatch model of PIES was presented with the aim at minimizing the total operating costs of PIES and promoting the photovoltaic (PV) power accommodation. Then, a DDRO model was developed based on the historical data to yield the optimal solution in the worst PV output scenario, where the confidence set is established with comprehensive consideration of norm-1 and norm-inf constraints. Furthermore, an efficient solving framework was proposed for the DDRO based on the combination of the column-and-constraint generation (C&CG) algorithm and a duality-free decomposition method. Finally, case studies are carried out to demonstrate the effectiveness of the proposed model.

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Section: Esmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data‐driven distributionally robust economic dispatch for park integrated energy systems with coordination of carbon capture and storage devices and combined heat and power plants

Wang¹,

Gao²,

Jia

et al. 2022

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…Limits to finding the optimal solution are computational resources and depend on the problem size. Due to the uncertain nature of the multi-energy supply and demand system, a series of stochastic optimisation methods have been developed to achieve stochastic matching of system supply and demand, among which multi-scenario-based methods (Mei et al, 2021), Latin hypercube sampling (Ju, Tan, Zhao, Gu, & Wang, 2019), capture the uncertainties by constructing reasonable scenarios. Optimisation-based methods are developed to ensure the robustness of the system operation (Liu et al, 2017), and other types of the methods such as chance constrained planning (van Ackooij, Finardi, & Ramalho, 2018) and heuristic algorithms (Mayer, Szilágyi, & Gróf, 2020) are also applied to the stochastic optimisation of the multi-energy supply and demand systems.…”

Section: Smart Multi-energy Supply and Demand Systemmentioning

confidence: 99%

Control for smart systems: Challenges and trends in smart cities

Jia

Panetto

Macchi

et al. 2022

Annual Reviews in Control

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“…Units with uncertain output in the park will seriously affect the economy of system operation. At present, stochastic optimization (SO) (Mei et al, 2021;Wang et al, 2015) and robust optimization (RO) (Zhang et al, 2017;Shen et al, 2020) are popular optimization methods to solve uncertainty. However, both optimization methods have their own defects: the SO requires a large amount of data to generate the scene with a deterministic probability density function (PDF), so the accuracy of probability density is reduced due to the lack of data (Ioannou et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A master–slave game optimal scheduling strategy for park-integrated energy systems based on adjustable robust optimization

et al. 2022

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As the bridge between power companies and users, the integrated energy system has become one of the carriers of energy reform, energy-saving, and emission reduction. Based on this, a master–slave game bilevel optimization model considering power company–park-integrated energy system (PIES)–user is established. In the upper game, the power company, as the leader, takes the maximization of its interests as the goal to consider and formulate the price of purchasing and selling electricity with the park. As a follower, combined with the fluctuation of electricity price and the electricity demand of its equipment, the park determines the relationship between purchasing and selling electricity with the power company. In the lower-level game, the park becomes the leader, taking into account the energy needs of users and formulating a reasonable price for selling energy. Users, as followers, intend to maximize consumer surplus and adjust their energy demand strategies to achieve the best energy consumption experience. Analyzing the properties of the game, it is verified that there is a unique Nash equilibrium solution in the game model. At the same time, the idea of solving the distribution of the model is adopted, and the equilibrium solution of the model is obtained by using limited information. In addition, the output uncertainty of renewable energy in the park is dealt with by adjustable robust optimization. Finally, aiming at achieving a win–win situation among all stakeholders, the proposed game model is verified to effectively solve the equilibrium strategy problem among the PIES, the power company, and users through simulation analysis of an example.

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Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations

Cited by 62 publications

References 29 publications

Data‐driven distributionally robust economic dispatch for park integrated energy systems with coordination of carbon capture and storage devices and combined heat and power plants

Data‐driven distributionally robust economic dispatch for park integrated energy systems with coordination of carbon capture and storage devices and combined heat and power plants

Control for smart systems: Challenges and trends in smart cities

A master–slave game optimal scheduling strategy for park-integrated energy systems based on adjustable robust optimization

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