Reliability Constrained Unit Commitment Using Simulated Annealing

Simopoulos, D.N.; Kavatza, S.D.; Vournas, Costas

doi:10.1109/tpwrs.2006.881128

Cited by 121 publications

(99 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…24 hours) [1]. The classical single-objective UCP aims at minimizing the total operational costs of all generation units, given a number of equality constraints (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Memetic Evolutionary Multi-Objective Optimization Method for Environmental Power Unit Commitment

Pedroni

Zio

2013

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

Abstract--A multi-objective power unit commitment problem is framed to consider simultaneously the objectives of minimizing the operation cost and minimizing the emissions from the generation units. To find the solution of the optimal schedule of the generation units, a memetic evolutionary algorithm is proposed, which combines the non-dominated sorting genetic algorithm-II (NSGA-II) and a local search algorithm. The power dispatch sub-problem is solved by the weighed-sum lambdaiteration approach. The proposed method has been tested on systems composed by 10 and 100 generation units for a 24 hour demand horizon. The Pareto-optimal front obtained contains solutions of different trade off with respect to the two objectives of cost and emission, which are superior to those contained in the Pareto-front obtained by the pure NSGA-II. The solutions of minimum cost are shown to compare well with recent published results obtained by single-objective cost optimization algorithms.Index Terms--power unit commitment, environmental/ economic dispatch, multi-objective optimization, evolutionary algorithm, memetic algorithm, non-dominated sorting genetic algorithm, local search, lambda-iteration approach.

show abstract

“…24 hours) [1]. The classical single-objective UCP aims at minimizing the total operational costs of all generation units, given a number of equality constraints (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Li and E. Zio cost [1,12,[14][15][16]19]. On the other hand, the increasing awareness of environmental protection is pushing the utilities to improve their design and operational strategies, for reducing the emissions from the power plants [20].…”

Section: Introductionmentioning

confidence: 99%

A Memetic Evolutionary Multi-Objective Optimization Method for Environmental Power Unit Commitment

Pedroni

Zio

2013

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

show abstract

“…The conventional generating units can be modeled as a two state model for the reliability evaluation [14]. But, two state model is not suitable to reliability evaluation of solar cell generator (SDG) due to unstable solar radiation.…”

Section: Solar Radiation Modelmentioning

confidence: 99%

Optimal Sizing and Placement of Solar Cell Distributed Generator Suitable for Integrated Power System Environment

2018

Teh. vjesn.

View full text Add to dashboard Cite

A novel fuzzified Clustered Gravitational Search Algorithm (CGSA) has been employed for solving multi-objective problem formulated for solar based distributed generation. Optimal sizing and placement of solar distributed generation is considered. High solar penetration can lead to high-risk level in power system reliability. In order to maintain the system reliability, solar power dispatch is usually restricted based on the reliability level of the system. Two conflicting objective functions such as power loss and reliability level of the system are also considered for solving optimal placement of solar distributed generation (SDG). Binary coded CGSA is employed for solving optimal placement of SDG and sizing is determined using real coded CGSA. The fuzzy membership function for each objective is designed and multi-objective optimal placement problem has been presented. The proposed method is validated on IEEE standard 69-bus radial distribution networks. The efficiency of the proposed optimization technique is validated by comparing the results with other results available in the existing articles.

show abstract

“…The proposed algorithm is relatively fast and provides feasible nearoptimal solutions. A new method for the incorporation of the unit unavailability and the uncertainty of the load forecast in the solution of the short-term UC solved by the SA was presented in (Simopoulos et al, 2006b). The required spinning reserve capacity was conducted by imposing reliability constraints, based on the expected unserved energy and the loss of load probability indices.…”

Section: Generator Maintenance Scheduling and Unit Commitmentmentioning

confidence: 99%