Enhanced techniques on sequential unit commitment with interchange transactions

Fan, Jiyuan; Zhang, L.; McDonald, John D.

doi:10.1109/59.485990

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…Furthermore, the limiting conditions, system limiting conditions, and power generator limiting conditions generated from the relationship with previous time periods must be satisfied for every state of every hour, the curse of dimensionality frequently occurs, in which the dimensions are too large to calculate the solution to the optimal unit commitment problem. In other words, the problem of unit commitment planning is an optimization problem in the form of large-scale combined non-convex, which includes numerous equation/inequation limiting conditions and integer/real number variables [52,55].…”

Section: Model Descriptionmentioning

confidence: 99%

Integrated Model of Economic Generation System Expansion Plan for the Stable Operation of a Power Plant and the Response of Future Electricity Power Demand

Kim

2018

Sustainability

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The current study aims to establish an optimal Generation System Expansion Plan that can satisfy the increasing electricity demand while maintaining operational elements and the stability of the energy supply. The architecture is composed of plan-level and operation-level models, which are basically based on optimization. In the first step, we estimated future power demand data through time series analysis. In addition, power plant data were defined and verified data were collected. In the next step, the previous Generation System Expansion Plan methodology was used to deduce a feasible solution and construction costs that satisfy the reserve rate. In the third step, mixed integer programming (MIP)-based power generation system operation plan methodology was used to deduce numbers on the operation of power generation system. In addition, power plants with similar characteristics were grouped to reduce the calculation complexity of unit commitment. In the last step, a feasible solution for the duration of the plan (deduced in Stage II) and operations and maintenance cost information were combined to produce the optimal solution that minimizes the total cost. Experiments were conducted to demonstrate the proposed integrated generation system expansion planning architecture for establishing the optimal generation system expansion planning. This study has academic implications for the establishment of optimal power plant expansion plans to meet future increasing power demand while maintaining operational considerations and supply stability. The effectiveness of the proposed methodology is also illustrated through comparison and verification with the National Plan for Electricity Supply and Demand.

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Section: Model Descriptionmentioning

confidence: 99%

Integrated Model of Economic Generation System Expansion Plan for the Stable Operation of a Power Plant and the Response of Future Electricity Power Demand

Kim

2018

Sustainability

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“…Dynamic programming was adopted in literature (Lee, 1991), and the average full-load consumption combined with the unit operation factor was selected for its reliability and validity. To better improve the accuracy of the solution, reference (Fan et al, 1996) undertook the sequential input method. Later, the exit commitment algorithm appeared.…”

Section: Introductionmentioning

confidence: 99%

Multi–dimensional firefly algorithm based on local search for solving unit commitment problem

2023

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The Unit Commitment problem (UC) is a complex mixed-integer nonlinear programming problem, so the main challenge faced by many researchers is obtaining the optimal solution. Therefore, this dissertation proposes a new methodology combining the multi-dimensional firefly algorithm with local search called LS-MFA and utilizes it to solve the UC problem. In addition, adaptive adjustment, tolerance mechanism, and pit-jumping random strategy help to improve the optimal path and simplify the redundant solutions. The experimental work of unit commitment with the output of 10–100 machines in the 24-hour period is carried out in this paper. And it shows that compared with the previous UC artificial intelligence algorithms, the total cost obtained by LS-MFA is less and the results are excellent.

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“…The UC problem will become very difficult and complicated when the scale of the problem increases. For many years, many researchers have developed a variety of solution methodologies from early approaches in the basis of Priority List (PL) [14][15][16], Dynamic Programming (DP) [5,7,[17][18][19] and Lagrangian Relaxation (LR) [20][21][22] to the advanced approaches in the basis of Mixed Integer Programming (MIP) [23][24][25] which is the most commonly adopted. Furthermore, artificial intelligence is being used to solve the UC problem, i.e., Tabu Search (TS) [26,27], Genetic Algorithm (GA) [28][29][30], Simulated Annealing (SA) [31][32][33], Particle Swarm Optimization (PSO) [34][35][36], and Ant Colony Optimization (ACO) [37,38].…”

Section: Chapter Introduction 11 Backgroundmentioning

confidence: 99%

Scheduling strategy design for unit commitment with energy storage system and solar energy resource

Qin¹

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Unit commitment means the pre-setting scheduling and arrangement of the electrical generator operation. It plays a critical role in the power system optimization problem which aims to utilize power resources rationally and enhance the efficiency of operational economy under the condition of safe operation of power system. With the high penetration of renewable energy, which increases deregulation when renewable energy is fed into the traditional power system and attention of safety operation in power system, there is a growing focus on optimization with uncertainties. This thesis proposes a unit commitment model to minimize the impact of uncertainty. The scheduling strategy is composed of two main cases which correspond to the two intervals of the probability distribution of the solar power output. The energy storage system is coordinated to guarantee the power system security when the margin of error is beyond the confidence interval of solar power probability. In order to deal with both volatile load demand and the solar power, we apply a normal probability distribution to define net load demand. The scheduling strategy is divided into two intervals based on its confidence interval, and interval optimization is adopted to reduce the complexity of this optimization problem in the confidence interval. Energy storage system is flexible to maintain the power balance with an acceptable cost and maximize the utilization of the renewable energy in the non-confidence interval. The numeri

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Enhanced techniques on sequential unit commitment with interchange transactions

Cited by 8 publications

References 8 publications

Integrated Model of Economic Generation System Expansion Plan for the Stable Operation of a Power Plant and the Response of Future Electricity Power Demand

Integrated Model of Economic Generation System Expansion Plan for the Stable Operation of a Power Plant and the Response of Future Electricity Power Demand

Multi–dimensional firefly algorithm based on local search for solving unit commitment problem

Scheduling strategy design for unit commitment with energy storage system and solar energy resource

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