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

In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.

Multiple criteria decision making

Designing microgrid energy markets : A case study : The Brooklyn Microgrid

Traditional electric power systems are designed in large part to utilize large baseload power plants, with limited ability to rapidly ramp output or reduce output below a certain level. The increase in demand variability created by intermittent sources such as photovoltaic (PV) presents new challenges to increase system flexibility. This paper aims to investigate and emphasize the importance of the grid-connected PV system regarding the intermittent nature of renewable generation, and the characterization of PV generation with regard to grid code compliance. The investigation was conducted to critically review the literature on expected potential problems associated with high penetration levels and islanding prevention methods of grid tied PV. According to the survey, PV grid connection inverters have fairly good performance. They have high conversion efficiency and power factor exceeding 90% for wide operating range, while maintaining current harmonics THD less than 5%. Numerous large-scale projects are currently being commissioned, with more planned for the near future. Prices of both PV and balance of system components (BOS) are decreasing which will lead to further increase in use. The technical requirements from the utility power system side need to be satisfied to ensure the safety of the PV installer and the reliability of the utility grid. Identifying the technical requirements for grid interconnection and solving the interconnect problems such as islanding detection, harmonic distortion requirements and electromagnetic interference are therefore very important issues for widespread application of PV systems. The control circuit also provides sufficient control and protection functions like maximum power tracking, inverter current control and power factor control. Reliability, life span and maintenance needs should be certified through the long-term operation of PV system. Further reduction of cost, size and weight is required for more utilization of PV systems. Using PV inverters with a variable power factor at high penetration levels may increase the number of balanced conditions and subsequently increase the probability of islanding. It is strongly recommended that PV inverters should be operated at unity power factor.

Grid-connected photovoltaic power systems: Technical and potential problems—A review

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

/pdf/a-comprehensive-survey-on-particle-swarm-optimization-1d380v5xx1.pdf

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

This paper proposes an enhanced adaptive Lagrangian relaxation (ELR) for a unit commitment (UC) problem. ELR consists of adaptive LR (ALR) and heuristic search. The ALR algorithm is enhanced by new on/off decision criterion, new initialization of Lagrangian multipliers, unit classification, identical marginal unit decommitment, and adaptive adjustment of Lagrangian multipliers. After the ALR best feasible solution reached is obtained, the heuristic search consisting of unit substitution and unit decommitment is used to fine tune the solution. The proposed ELR is tested and compared to conventional Lagrangian relaxation (LR), genetic algorithm (GA), evolutionary programming (EP), Lagrangian relaxation and genetic algorithm (LRGA), and genetic algorithm based on unit characteristic classification (GAUC) on the systems with the number of generating units in the range of 10 to 100. ELR total system production costs are less expensive than the others especially for the large number of generating units. Furthermore, the computational times of ELR are much less than the others and increase linearly with the system size, which is favorable for large-scale implementation.

Unit commitment by enhanced adaptive Lagrangian relaxation

This article proposes an improved evolutionary programming (IEP) for solving optimal power flow (OPF) with nonsmooth and nonconvex generator fuel cost curves. Initially, the whole population is divided into multiple subpopulations, which are used to perform the parallel search in divided solution space. IEP includes Gaussian and Cauchy mutation operators in different subpopulations to enhance the search diversity, selection operators with probabilistic updating strategy to avoid entrapping in local optimum, and reassignment operator for every subpopulation to exchange search information. The proposed IEP was tested on the IEEE 30 bus system with three different types of generator fuel cost curves. It is shown that IEP total generator fuel cost is less expensive than those of evolutionary programming, tabu search, hybrid tabu search and simulated annealing, and improved tabu search, leading to substantial generator fuel cost savings. Moreover, IEP can easily facilitate parallel implementation to reduce the...

Optimal Power Flow by Improved Evolutionary Programming

Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients

This study proposes a cuckoo search algorithm (CSA) for solving non-convex economic dispatch (ED) considering generator and system characteristics including valve-point effects, multiple fuels, prohibited zones, spinning reserve and power loss. CSA is a new meta-heuristic optimisation method inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species. When the host birds discover an alien egg in their nest, they can either throw it away or simply abandon their nest and build a new one elsewhere. The CSA idealised such breeding behaviour in combination with Levy flights behaviour of some birds and fruit flies for applying to various constrained optimisation problems. The effectiveness of the proposed method has been tested on different non-convex ED problems. Test results have indicated that the proposed method can obtain less expensive solutions than many other methods reported in the literature. Accordingly, the proposed CSA is a promising method for solving the practical nonconvex ED problems.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2012.0142

Weerakorn Ongsakul

Papers

Unit commitment by enhanced adaptive Lagrangian relaxation

Optimal Power Flow by Improved Evolutionary Programming

Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients

Cuckoo search algorithm for non-convex economic dispatch

A simulation model for predicting the performance of a solar photovoltaic system with alternating current loads