Nonconvex Economic Dispatch by Integrated Artificial Intelligence

Lin, Whei-Min; Cheng, Fu-Sheng; Tsay, Ming-Tong

doi:10.1109/mper.2001.4311392

Cited by 48 publications

(30 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dynamic programming [3] can treat such types of problems, but it suffers from the curse of dimensionality. Over the past decade, many salient methods have been developed to solve these problems, such as the hierarchical numerical method [4], genetic algorithm (GA) [5][6][7], evolutionary programming [8][9][10], Tabu search [11], neural network approaches [12,13], differential evolution [14], particle swarm optimization [15][16][17][18], hybrid artificial intelligence (AI) method [19], improved PSO [20] and Immune-PSO(IPSO) [21].…”

Section: Introductionmentioning

confidence: 99%

An Improved Mean-Variance Optimization for Nonconvex Economic Dispatch Problems

Kim¹,

Song²,

Park³

et al. 2013

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-This paper presents an efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions using a 'Mean-Variance Optimization (MVO)' algorithm with KuhnTucker condition and swap process. The aim of the ED problem, one of the most important activities in power system operation and planning, is to determine the optimal combination of power outputs of all generating units so as to meet the required load demand at minimum operating cost while satisfying system equality and inequality constraints. This paper applies Kuhn-Tucker condition and swap process to a MVO algorithm to improve a global minimum searching capability. The proposed MVO is applied to three different nonconvex ED problems with valve-point effects, prohibited operating zones, transmission network losses, and multi-fuels with valve-point effects. Additionally, it is applied to the large-scale power system of Korea. The results are compared with those of the state-of-the-art methods as well.

show abstract

Section: Introductionmentioning

confidence: 99%

An Improved Mean-Variance Optimization for Nonconvex Economic Dispatch Problems

Kim¹,

Song²,

Park³

et al. 2013

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

show abstract

“…The remaining units will contribute with regulating reserves [17]. This system supplies a 2650MW load demand with 200MW as spinning reserve.…”

Section: B 15-unit Test System Without Transmission Lossmentioning

confidence: 99%

Improved Immune Algorithm for Power Economic Dispatch Considering Units with Prohibited Operating Zones and Spinning Reserve

Chiang¹

2014

IJET

View full text Add to dashboard Cite

Abstract-This study proposes an improved immune algorithm (IIA) with multiplier updating (MU) for power economic dispatch (PED) considering units with both of prohibited operating zones (POZ) and spinning reserve. The IIA equipped with an accelerated operation and a migration operation can efficiently search and actively explore solutions. The MU is introduced to handle the equality and inequality constraints of the power system. To show the advantages of the proposed algorithm, three realistic examples are investigated, and computational results of the proposed method are compared with that of previous methods. The proposed approach (IIA-MU) integrates the IIA and the MU, revealing that the proposed approach has the following merits -ease of implementation; applicability to non-convex fuel cost functions; better effectiveness than previous methods; better efficiency than immune algorithm with the MU (IA-MU), and the requirement for only a small population in applying the optimal PED problem of generators with POZ and spinning reserve.

show abstract

“…The remaining units will contribute with regulating reserves [16]. This system supplies a 2650MW load demand with 200MW as spinning reserve.…”

Section: B Examplementioning

confidence: 99%

Artificial Immune Algorithm for Practical Power Economic Dispatch Problems

Chiang¹

2013

IJMLC

View full text Add to dashboard Cite

Abstract-This paper proposes an artificial immune algorithm with a multiplier updating method (AIA-MU) for practical power economic dispatch (PED) considering units with prohibited operating zones (POZ). The AIA equipped with a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the system constraints. To show the advantages of the proposed algorithm, two examples are investigated, and the computational results of the proposed method are compared with that of the previous methods. The proposed approach integrates the AIA and the MU, revealing that the proposed approach has the following merits -ease of implementation; applicability to non-convex fuel cost functions of the POZ; better effectiveness than previous methods, and the requirement for only a small population in applying the optimal PED problem of generators with POZ.Index Terms-Power economic dispatch, artificial immune algorithm, prohibited operating zones.

show abstract

Nonconvex Economic Dispatch by Integrated Artificial Intelligence

Cited by 48 publications

References 22 publications

An Improved Mean-Variance Optimization for Nonconvex Economic Dispatch Problems

An Improved Mean-Variance Optimization for Nonconvex Economic Dispatch Problems

Improved Immune Algorithm for Power Economic Dispatch Considering Units with Prohibited Operating Zones and Spinning Reserve

Artificial Immune Algorithm for Practical Power Economic Dispatch Problems

Contact Info

Product

Resources

About