Enhanced Multiobjective Evolutionary Algorithm Based on Decomposition for Solving the Unit Commitment Problem

Tsourdos

et al. 2021

IEEE Trans. Cybern.

Constrained autonomous vehicle overtaking trajectories are usually difficult to be generated due to certain practical requirements and complex environmental limitations. This problem becomes more challenging when multiple contradicting objectives are required to be optimized and the on-road objects to be overtaken are irregularlyplaced. In this paper, a novel swarm intelligence-based algorithm is proposed for producing the multi-objective optimal overtaking trajectory of autonomous ground vehicles. The proposed method solves a multiobjective optimal control model in order to optimize the maneuver time duration, the trajectory smoothness, and the vehicle visibility, while taking into account different types of mission-dependent constraints. However, one problem that could have an impact on the optimization process is the selection of algorithm control parameters. To desensitize the negative influence, a novel fuzzy adaptive strategy is proposed and embedded in the algorithm framework. This allows the optimization process can dynamically balance the local exploitation and global exploration, thereby exploring the trade-off between objectives more effective. The performance of using the designed fuzzy adaptive multi-objective method is analyzed and validated by executing a number of simulation studies. The results confirm the effectiveness of applying the proposed algorithm to produce multi-objective optimal overtaking trajectories for the autonomous ground vehicles. Moreover, the comparison to other stateof-the-art multi-objective optimization schemes shows that the designed strategy tends to be more capable in terms of producing a set of widespread and high-quality pareto-optimal solutions.

Section: Comparison Against Other Optimization Methodsmentioning

confidence: 99%

Multiobjective Overtaking Maneuver Planning for Autonomous Ground Vehicles

Tsourdos

et al. 2021

IEEE Trans. Cybern.

“…The objective function of UC as a well-known problem in power system operation is commonly modeled as (1a) which includes the energy production costs presented in (1b) and start-up/shut-down costs provided in (1c) and (1d) [2]- [6], [13], [16]. …”

Section: A Operational Cost Objective Functionmentioning

confidence: 99%

“…System operator needs to have sufficient up-anddown SR to properly manage power systems' uncertainties and contingencies. To the best of our knowledge, the previous works in the area of unit commitment (UC) are related to solution methodologies for UC [2]- [6], risk-constrained UC and modeling related uncertainties [6]- [9], and scheduling of sufficient reserve [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Bavafa

Niknam

Azizipanah‐Abarghooee

et al. 2017

IEEE Trans. Ind. Inf.

Abstract-Large penetration of wind generating units in power systems necessitates a flexible unit commitment tool to handle the intermittent nature of these units as well as demand. Moreover, power system operators face not only the risks of wind power curtailments, but also probable unit outages. Therefore, assessing a trade-off between operational costs and such risks is very important. In the proposed approach, the probability of the residual demand falling within the up-and-down spinning reserve imposed by n-1 security criterion is converted into a risk index. A new bi-objective probabilistic risk/cost based unit commitment model is proposed to simultaneously minimize both the operational costs and risk. The novel formulation presented provides a new power re-dispatch process to satisfy up-and-down ramp rate constraints. A new operational cycles based unit commitment algorithm is developed. The approach profits from a new non-dominated sorting backtracking search optimization algorithm for extracting the Pareto optimal set. The proposed approach is shown to provide superior results when applied to two test systems: a) 10-unit and b) IEEE 118-bus, 54-unit system.

“…The multi-objective version of the UCP has not been the subject of extensive research and most of the works reported in the literature either considers the emissions as constraints [4,5] or transforms the problem into a single objective one, see, e.g., [6][7][8]. A recent review on the use of multi-objective optimization (MOO) in the energy sector, namely in the electricity sector, can be found in [9].…”

Section: Introductionmentioning

confidence: 99%

A Metaheuristic Approach to the Multi-Objective Unit Commitment Problem Combining Economic and Environmental Criteria

Roque

Fontes

2017

Energies

Abstract:We consider a Unit Commitment Problem (UCP) addressing not only the economic objective of minimizing the total production costs-as is done in the standard UCP-but also addressing environmental concerns. Our approach utilizes a multi-objective formulation and includes in the objective function a criterion to minimize the emission of pollutants. Environmental concerns are having a significant impact on the operation of power systems related to the emissions from fossil-fuelled power plants. However, the standard UCP, which minimizes just the total production costs, is inadequate to address environmental concerns. We propose to address the UCP with environmental concerns as a multi-objective problem and use a metaheuristic approach combined with a non-dominated sorting procedure to solve it. The metaheuristic developed is a variant of an evolutionary algorithm, known as Biased Random Key Genetic Algorithm. Computational experiments have been carried out on benchmark problems with up to 100 generation units for a 24 h scheduling horizon. The performance of the method, as well as the quality, diversity and the distribution characteristics of the solutions obtained are analysed. It is shown that the method proposed compares favourably against alternative approaches in most cases analysed.