Distributionally robust dynamic economic dispatch model with conditional value at risk recourse function

Dai, Li; You, Dahai; Yin, Xianggen; Wang, Gang; Zou, Qi

doi:10.1002/etep.2775

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…The process of narrowing the generator limit continues until it becomes tiny, namely, 𝒳 𝑠𝑚𝑎𝑙𝑙 . For example, in the k th step, the space of the feasible area, 𝒮 𝑘 , can already be considered to represent the solution point, where the value of 𝒳 𝑠𝑚𝑎𝑙𝑙 is calculated by (26).…”

Section: P Opmentioning

confidence: 99%

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A Method for Completing Economic Load Dispatch Using the Technique of Narrowing Down Area

2022

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Section: P Opmentioning

confidence: 99%

“…The complicated methods used to solve optimal power flow were studied by [30][31]. The Newton-Rapson method determines power flows based on PSO, which has been satisfactorily developed [26]. These methods are also artificial; they will face problems if applied to large systems.…”

Section: Introductionmentioning

confidence: 99%

A Method for Completing Economic Load Dispatch Using the Technique of Narrowing Down Area

2022

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“…Furthermore, the computation time of MDRO was larger than DDRO with W D 1 . This is because the MDRO method used delayed constraint generation algorithm and the alternate convex search algorithm to solve this model [35]. In each iteration, this algorithm solved semidefinite programming (solved by SDP3), quadratic programming (solved by KNITRO), and linear programming.…”

Section: Case Study On An Ieee-118 Bus Systemmentioning

confidence: 99%

Data Driven Robust Energy and Reserve Dispatch Based on a Nonparametric Dirichlet Process Gaussian Mixture Model

Dai¹,

You²,

Yin³

2020

Energies

Self Cite

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Traditional robust optimization methods use box uncertainty sets or gamma uncertainty sets to describe wind power uncertainty. However, these uncertainty sets fail to utilize wind forecast error probability information and assume that the wind forecast error is symmetrical and independent. This assumption is not reasonable and makes the optimization results conservative. To avoid such conservative results from traditional robust optimization methods, in this paper a novel data driven optimization method based on the nonparametric Dirichlet process Gaussian mixture model (DPGMM) was proposed to solve energy and reserve dispatch problems. First, we combined the DPGMM and variation inference algorithm to extract the GMM parameter information embedded within historical data. Based on the parameter information, a data driven polyhedral uncertainty set was proposed. After constructing the uncertainty set, we solved the robust energy and reserve problem. Finally, a column and constraint generation method was employed to solve the proposed data driven optimization method. We used real historical wind power forecast error data to test the performance of the proposed uncertainty set. The simulation results indicated that the proposed uncertainty set had a smaller volume than other data driven uncertainty sets with the same predefined coverage rate. Furthermore, the simulation was carried on PJM 5-bus and IEEE-118 bus systems to test the data driven optimization method. The simulation results demonstrated that the proposed optimization method was less conservative than traditional data driven robust optimization methods and distributionally robust optimization methods.

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“…LiDai et.al proposed solution to DED problem along with effective control of load shedding and wind curtailment with the help of conditional value at risk recourse function [32]. Reference [33] is using improved firework algorithm for solving DED problems incorporating stochastic behavior of wind and solar. Literature is also available for optimal scheduling of thermal generators along with hydro units along with wind production considering their probabilistic behavior [34].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Economic Dispatch of Power System Network Having Thermal Units and Electric Vehicles using IGWO

Jain

Mani

Siddiqui

2021

mjee

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Grey Wolf optimizer is a well-known metaheuristic technique but it faces the problem of premature convergence, trapping in local optima and poor balance between exploration and exploitation. Hence, various modifications have been proposed over past few years. One such modification is modelling the prey position as dynamic in nature as shown in GWOLF. However, still it has been observed that to get better solution, GWOLF needs to be modified too, hence an improved version of GWO (IGWO) is proposed. IGWO has the advantage of both GWO and GWOLF. In this paper, IGWO is used to solve Dynamic Economic Dispatch (DED) problem. IGWO is having a better balance between exploitation and exploration for the complex problem such as Dynamic Economic Dispatch (DED) taking into account of valve-point effect, transmission losses and ramp-rate limits with and without Electric Vehicles (EVs). The efficiency of the algorithm is demonstrated on solving different DED problems for 5 generator and 15 generator test systems with and without losses along with different charging profile distribution of electric vehicles. The results showcased by IGWO is compared with the other algorithm. The results obtained by IGWO algorithm adopted in solving dynamic economic dispatch problem is giving competitive results as compared to the results given by other algorithm present in literature.

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Distributionally robust dynamic economic dispatch model with conditional value at risk recourse function

Cited by 4 publications

References 24 publications

A Method for Completing Economic Load Dispatch Using the Technique of Narrowing Down Area

A Method for Completing Economic Load Dispatch Using the Technique of Narrowing Down Area

Data Driven Robust Energy and Reserve Dispatch Based on a Nonparametric Dirichlet Process Gaussian Mixture Model

Dynamic Economic Dispatch of Power System Network Having Thermal Units and Electric Vehicles using IGWO

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