A genetic algorithm based approach to thermal unit commitment of electric power systems

Ma, Xingwang; El-Keib, A.A.; Smith, Robert E.; Ma, H.

doi:10.1016/0378-7796(95)00954-g

Cited by 58 publications

(12 citation statements)

References 16 publications

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“…Deterministic approaches include priority list (PL), dynamic programming (DP), Lagrangian Relaxation (LR), integer/ mixed-integer programming method and branch-and-bound method. Meta-heuristic approaches include expert systems (ES), fuzzy logic (FL), ANNs, genetic algorithm (GA), evolutionary programming (EP), simulated annealing (SA), tabu search (TS) and particle swarm optimizer (PSO) [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Risk‐based self‐scheduling for GenCos in the day‐ahead competitive electricity markets

Yamin

Altawil

Al-Ajlouni

et al. 2008

Int Trans Elec Energy Syst

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SUMMARYThis paper addresses the risk-based self-scheduling problem using a hybrid technique between Lagrangian relaxation (LR) and particle swarm optimizer (PSO). The paper analyses a self-scheduling model that accounts for profit and risk simultaneously. The effect of risk is explicitly modelled in the self-scheduling problem taking into account the variance of the market-clearing prices. The forecasted hourly probabilities that spinning and non-spinning reserves are called and generated are also considered in the formulation to simulate the reserve uncertainty. Artificial neural network (ANN) is applied for forecasting the hourly reserve probability. The proposed approach is applied to a 36 unit test system.

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Section: Introductionmentioning

confidence: 99%

Risk‐based self‐scheduling for GenCos in the day‐ahead competitive electricity markets

Yamin

Altawil

Al-Ajlouni

et al. 2008

Int Trans Elec Energy Syst

View full text Add to dashboard Cite

show abstract

“…It has good capability for local optimization searches. The genetic algorithm (GA) [22][23][24][25][26][27][28][29] is a general-purpose stochastic and parallel search method based on the mechanics of natural selection and natural genetics. As a search method it has a potential to obtain near-global minimum and a capability to obtain accurate results within a short period of time with constraints easily included.…”

Section: Introductionmentioning

confidence: 99%