F. Zhuang

Bio: F. Zhuang is an academic researcher from McGill University. The author has contributed to research in topics: Simulated annealing & Subgradient method. The author has an hindex of 2, co-authored 2 publications receiving 824 citations.

Towards a more rigorous and practical unit commitment by Lagrangian relaxation

Abstract: A Lagrangian relaxation algorithm for power system generator unit commitment is proposed. The algorithm proceeds in three phases. In the first phase, the Lagrangian dual of the unit commitment is maximized by standard subgradient techniques. The second phase finds a reserve-feasible dual solution, followed by a third phase of economic dispatch. A mathematically based, systematic and generally applicable procedure to search for a reserve-feasible dual solution is presented. The algorithm has been tested on systems of up to 100 units to be scheduled over 168 hours, giving reliable performance and low execution times. Both spinning and time-limited reserve constraints are treated. >

Unit commitment by simulated annealing

Abstract: A general optimization method, known as simulated annealing, is applied to generation unit commitment. By exploiting the resemblance between a minimization process and the cooling of a molten metal, simulated annealing generates feasible solutions randomly and moves among these solutions using a strategy leading to a global minimum with high probabilities. The method assumes no specific problem structures and is highly flexible in handling unit commitment constraints. A concise introduction to the method is given. Numerical results on test systems of up to 100 units are reported. >

A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem

Abstract: This paper presents a new mixed-integer linear formulation for the unit commitment problem of thermal units. The formulation proposed requires fewer binary variables and constraints than previously reported models, yielding a significant computational saving. Furthermore, the modeling framework provided by the new formulation allows including a precise description of time-dependent startup costs and intertemporal constraints such as ramping limits and minimum up and down times. A commercially available mixed-integer linear programming algorithm has been applied to efficiently solve the unit commitment problem for practical large-scale cases. Simulation results back these conclusions

A genetic algorithm solution to the unit commitment problem

Abstract: This paper presents a genetic algorithm (GA) solution to the unit commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple GA algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the varying quality function technique and adding problem specific operators, satisfactory solutions to the unit commitment problem were obtained. Test results for power systems of up to 100 units and comparisons with results obtained using Lagrangian relaxation and dynamic programming are also reported.

Unit commitment-a bibliographical survey

Abstract: With the fast-paced changing technologies in the power industry, new power references addressing new technologies are coming to the market. So there is an urgent need to keep track of international experiences and activities taking place in the field of modern unit-commitment (UC) problem. This paper gives a bibliographical survey, mathematical formulations, and general backgrounds of research and developments in the field of UC problem for past 35 years based on more than 150 published articles. The collected literature has been divided into many sections, so that new researchers do not face any difficulty in carrying out research in the area of next-generation UC problem under both the regulated and deregulated power industry.

Power system observability with minimal phasor measurement placement

Abstract: The placement of a minimal set of phasor measurement units (PMUs) so as to make the system measurement model observable, and thereby linear, is investigated. A PMU placed at a bus measures the voltage as well as all the current phasors at that bus, requiring the extension of the topological observability theory. In particular, the concept of spanning tree is extended to that of spanning measurement subgraph with an actual or a pseudomeasurement assigned to each of its branches. The minimal PMU set is found through a dual search algorithm which uses both a modified bisecting search and a simulated-annealing-based method. The former fixes the number of PMUs while the latter looks for a placement set that leads to an observable network for a fixed number of PMUs. In order to accelerate the procedure, an initial PMU placement is provided by a graph-theoretic procedure which builds a spanning measurement subgraph according to a depth-first search. From computer simulation results for various test systems it appears that only one fourth to one third of the system buses need to be provided with PMUs in order to make the system observable. >

A stochastic model for the unit commitment problem

Abstract: The authors develop a model and a solution technique for the problem of generating electric power when demands are not certain. They also provide techniques for improving the current methods used in solving the traditional unit commitment problem. The solution strategy can be run in parallel due to the separable nature of the relaxation used. Numerical results indicate significant savings in the cost of operating power generating systems when the stochastic model is used instead of the deterministic model.