Data‐driven distributionally robust economic dispatch for distribution network with multiple microgrids

Wang, Lingling; Jiang, Chuanwen; Gong, Kai; Si, Ruihua; Shao, Hongbo; Liu, Wanxun

doi:10.1049/iet-gtd.2020.0861

Cited by 24 publications

(10 citation statements)

References 37 publications

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“…Non-controllable loads are forecasted on an hourly basis and uncertainty is applied, but controllable loads are provided on a perbus basis. Each bus of the MG has an amount of controllable load to be allocated in any period of the day, and a percentage of that amount is allocated in each period, as in (16).…”

Section: Bess Costs (𝐶 𝑡 𝐵𝐸𝑆𝑆mentioning

confidence: 99%

“…𝐶𝐿 𝑡,𝑏 𝑎𝑙 = 𝑑𝑟 𝑡,𝑏 * 𝐶𝐿 𝑏 (16) where 𝐶𝐿 𝑡,𝑏 𝑎𝑙 represents the amount of controllable load allocated in bus b in period t, 𝐶𝐿 𝑏 represents the total amount of controllable load to be allocated in bus b in the 24-hour timespan, and 𝑑𝑟 𝑡,𝑏 represents the percentage of the total controllable load for bus b that has been allocated in period t. Equation (17) guarantees that the values of 𝑑𝑟 𝑡,𝑏 are between 0 and 100% and (18) establishes that no more than 100% of the total controllable load for bus b is allocated in the 24-hour period.…”

Section: Bess Costs (𝐶 𝑡 𝐵𝐸𝑆𝑆mentioning

confidence: 99%

“…On the other side of the spectrum, some works, including (8)(9)(10), consider the most accurate network models with non-linear constraints that heavily increase the complexity and computational burden of the model. Finally, some works, such as (11)(12)(13)(14)(15)(16) consider multiple-bus systems with linearized constraints to reduce complexity while maintaining important network aspects such as voltage control and line power flow constraints.…”

Section: Introductionmentioning

confidence: 99%

“…This work, although not focused on MGs, but in traditional networks, offers an interesting possibility for works in the MG field. In (16), historical data is also used, but in a different approach compared to stochastic programming, called distributionally DDRO, where the past scenarios are used to estimate an empirical probability distribution function that is utilized to perform stochastic optimization.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Data-driven Robust Model for Day-ahead Operation Planning of Microgrids Considering Distributed Energy Resources and Demand Response

Filho

Pinto

Aquino

et al. 2023

Braz. arch. biol. technol.

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The optimization of microgrids present challenges such as managing distributed energy resources (DERs) and the high reliance on intermittent generation such as PV and wind turbines, which present an aleatory behavior. The most popular techniques to deal with the uncertainties are stochastic optimization, which comes with a high computational burden, and adaptive robust optimization (ARO), which is often criticized for the conservativeness of its solutions. In response to these drawbacks, this work proposes a mixed-integer linear programming (MILP) model using a data-driven robust optimization approach (DDRO) solved by a two-stage decomposition using the column-and-constraint generation algorithm (C&CG). The DDRO model uses historic data to create the bounds of its uncertainty set, eliminating the conservativeness created by the arbitrary definition of the uncertainty set that is seen in ARO while maintaining a low computational burden. The DDRO model applied was not previously utilized in MGs, only in bulk power HIGHLIGHTS• Novel data-driven approach to uncertainties in microgrid resources• Faster convergence that stochastic optimization• Reduced conservativeness compared to robust optimization• Microgrid system with comprehensive selection of distributed energy resources

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Section: Bess Costs (𝐶 𝑡 𝐵𝐸𝑆𝑆mentioning

confidence: 99%

Section: Bess Costs (𝐶 𝑡 𝐵𝐸𝑆𝑆mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Data-driven Robust Model for Day-ahead Operation Planning of Microgrids Considering Distributed Energy Resources and Demand Response

Filho

Pinto

Aquino

et al. 2023

Braz. arch. biol. technol.

View full text Add to dashboard Cite

show abstract

“…In recent years, research on microgrids has begun to focus on the interaction and coordinated interconnection between multiple microgrids [21][22][23]. Organic interconnection of microgrid can overcome the shortcomings of a single microgrid, improve the stability and reliability of overall operation, and reduce the impact of renewable energy fluctuations on the distribution network [24][25][26]. Literature [27] proposed a hierarchical optimization method for the energy scheduling of multiple microgrids in the distribution network of power grids.…”

Section: Introductionmentioning

confidence: 99%

Bi-Level Energy Management Model of Grid-Connected Microgrid Community

Cao¹,

Dong²,

Ren³

et al. 2022

Energy Engineering

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As the proportion of renewable energy power generation continues to increase, the number of grid-connected microgrids is gradually increasing, and geographically adjacent microgrids can be interconnected to form a Micro-Grid Community (MGC). In order to reduce the operation and maintenance costs of a single micro grid and reduce the adverse effects caused by unnecessary energy interaction between the micro grid and the main grid while improving the overall economic benefits of the micro grid community, this paper proposes a bi-level energy management model with the optimization goal of maximizing the social welfare of the micro grid community and minimizing the total electricity cost of a single micro grid. The lower-level model optimizes the output of each equipment unit in the system and the exchange power between the system and the external grid with the goal of minimizing the operating cost of each microgrid. The upper-level model optimizes the goal of maximizing the social welfare of the microgrid. Taking a microgrid community with four microgrids as an example, the simulation analysis shows that the proposed optimization model is beneficial to reduce the operating cost of a single microgrid, improve the overall revenue of the microgrid community, and reduce the power interaction pressure on the main grid.

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On Distributionally Robust Generalized Nash Games Defined over the Wasserstein Ball

Fabiani,

Franci

2023

J Optim Theory Appl

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Data‐driven distributionally robust economic dispatch for distribution network with multiple microgrids

Cited by 24 publications

References 37 publications

A Data-driven Robust Model for Day-ahead Operation Planning of Microgrids Considering Distributed Energy Resources and Demand Response

A Data-driven Robust Model for Day-ahead Operation Planning of Microgrids Considering Distributed Energy Resources and Demand Response

Bi-Level Energy Management Model of Grid-Connected Microgrid Community

On Distributionally Robust Generalized Nash Games Defined over the Wasserstein Ball

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