Decision Making for Environmental/Economic Dispatch Based on Optimal Power Flow

Xiao, Chixin; Sutanto, Danny; Muttaqi, Kashem M.; Zhang, Minjie

doi:10.1109/pedes.2018.8707528

Cited by 1 publication

(5 citation statements)

References 14 publications

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“…Secondly, the successful number-groups, which are corresponding to the successful control parameters, are survived in the next generation, while elements in the unsuccessful ones have to be updated respectively by using (14), , ,…”

Section: Self-adaptive Strategy For the Control Parametersmentioning

confidence: 99%

“…If the AOF is set as the Tchebyscheff AOF as given in (10), i.e., ⃗ ⃗ , ⃗ # , where ⃗ # # , … , # is the ideal point, and # ⃗ . Meanwhile, the corresponding control parameters have been updated as shown in (13), (13.1) and (14).…”

Section: F the Proposed Algorithmmentioning

confidence: 99%

“…(3) are selected as the cost function and the emission function respectively. For comparison, the sub-problems are initially solved by the traditional Newton method [14] and the results are used as the standard benchmark to compare with those from the proposed method using evolutionary programming.…”

Section: A Case Studymentioning

confidence: 99%

“…The fuel cost and the emission coefficients corresponding to each generation unit (i.e., , … , ) are extracted from [13] and listed in Table I . Each sub-problem is based on the AOF of the weighted sum as given in (12), and the linear combination of the cost and the emission via the weighted sum AOF, as well as constraints can be combined into a Lagrange function which can be solved by optimal power flow of Newton method [14]. Consequently, not only the algorithm can provide the PF and the POSs, but it can also provide the corresponding weight vector for each POS and each point on the PF.…”

Section: A Case Studymentioning

confidence: 99%

“…Reference [ 14 ] uses an aggregate function and the Lagrangian optimization model to solve a 2-objective MOPF that simultaneously minimizes the fuel cost and the emission of pollutants (EED) [15]- [16] from the power system. The method has a good performance on both the computing time and the convergence rate [ 17 ], but it becomes less effective when applied to non-convex problems [18] [19].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Judicious Decision-Making Approach for Power Dispatch in Smart Grid Using a Multiobjective Evolutionary Algorithm Based on Decomposition

Xiao

Sutanto

Muttaqi

et al. 2020

IEEE Trans. on Ind. Applicat.

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Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is an efficient mathematical strategy for solving multiobjective optimization problems. However, the MOEA/D algorithm has not yet been widely used on the multiobjective optimal power flow (MOPF) problems, which consider several conflicting objectives with varying tradeoff levels. This article proposes a novel differential evolution (DE) strategy based on the MOEA/D framework to quickly determine a set of optimal solutions of MOPFs, in the objective space formed from the different objectives, such as the most optimal economic dispatch, the least environmental emission objectives, and the minimum transmission losses, while considering the power system constraints. A judicious decision can be made by the user from the set of optimal solutions of the MOPF associated with the weight vectors representing the tradeoff levels of the different objectives. For improved performance, two aggregate objective functions, a load flow operator and a selfadaptive DE strategy work cooperatively: 1) to improve the weak convergence of the MOEA/D and to achieve a better decision speed; 2) to obtain more accurate optimal solutions even under non-convex conditions; 3) to ensure that the power system constraints are taken into account; 4) to integrate the above features into a fast and efficient algorithm. The proposed algorithm has been validated using the IEEE 30-bus system and a revised 33-bus radial system added to one node of the 30-bus system. The simulation results show that the proposed algorithm can provide a good accuracy and can converge to a set of optimal solutions of the MOPFs. Disciplines Disciplines Engineering | Science and Technology StudiesAbstract-Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is an efficient mathematical strategy for solving multi-objective optimization problems. However, the MOEA/D algorithm has not yet been widely used on the multiobjective optimal power flow (MOPF) problems, which considers several conflicting objectives with varying trade-off levels. This paper proposes a novel differential evolution (DE) strategy based on the MOEA/D framework to quickly determine a set of optimal solutions of MOPFs, in the objective space formed from the different objectives, such as the most optimal economic dispatch, the least environmental emission objectives, and the minimum transmission losses, while considering the power system constraints. A judicious decision can be made by the user from the set of optimal solutions of the MOPF associated with the weight vectors representing the trade-off levels of the different objectives. For improved performance, two aggregate objective functions, a load flow operator and a self-adaptive DE strategy work cooperatively: 1) to improve the weak convergence of the MOEA/D and to achieve a better decision speed; 2) to obtain more accurate optimal solutions even under non-convex conditions; 3) to ensure that the power system constraints are taken into account; 4) to integrate the ab...

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Section: Self-adaptive Strategy For the Control Parametersmentioning

confidence: 99%

Section: F the Proposed Algorithmmentioning

confidence: 99%

Section: A Case Studymentioning

confidence: 99%