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Summary: This paper establishes a probabilistic scenario–based framework for the stochastic dynamic economic emission dispatch with unit commitment (SDEED‐UC) problem, taking into account wind power uncertainty. To solve the stochastic UC problem, this paper presents a probabilistic scenario analysis approach to find the unit scheduling solution considering all the original scenarios under a predetermined probability level. And a reduced scenario set can be obtained by the simultaneous backward method. Consequently, the SDEED problem is decomposed into a number of deterministic multiobjective dispatch problems. For each scenario, an enhanced multiobjective particle swarm optimization (EMOPSO) algorithm integrated with the handling strategy for system operation constraints is proposed to produce the Pareto optimal solutions. Furthermore, the available reserve capacity by the UC solution is verified. The simulation results demonstrate that the proposed probabilistic scenario–based and EMOPSO approach is practicable for solving SDEED‐UC from the perspectives of system operation economy, emission, and reliability simultaneously. [ABSTRACT FROM AUTHOR]

Until recently, transmission network constraints have been incorporated into the unit commitment (UC) problem (security-constrained UC [SCUC]) using the classical direct current (DC)-based equation. This paper proposes a new formulation to model network constraints in the UC problem by using the power transfer distribution factor (PTDF). The classical formulation uses the binary commitment status, the power generation, and the voltage-phase angles as decision variables. For the proposed formulation, the nodal balance equality constraints are modeled using one equality constraint, and the transmission network constraints have been modeled using the PTDF and the active power generation (decision variables). The main advantage of this formulation is the reduction of decision variables in the operation problem in comparison with the classical DC formulation. Transmission losses, using a piecewise linear approximation, are included in the SCUC problem as well. Both formulations are validated and tested on the Institute of Electrical and Electronics Engineers (IEEE) 14-bus, the IEEE 30-bus, the IEEE 57-bus, and the IEEE 118-bus. The IEEE 14-bus and IEEE 57-bus systems are used to compare the loss and the lossless models. The results show that the PTDF-based formulation, which requires fewer continuous variables and equality constraints than the classical DC formulation, reduces the simulation time without sacrificing optimality. The results also demonstrate the feasibility of the proposed formulation to address the SCUC problem and its potential applicability to medium-scale and large-scale power systems. [ABSTRACT FROM AUTHOR]

Because wind power spillage is barely considered, the existing robust unit commitment cannot accurately analyze the impacts of wind power accommodation on on/off schedules and spinning reserve requirements of conventional generators and cannot consider the network security limits. In this regard, a novel double-level robust security-constrained unit commitment formulation with optimizable interval of uncertain wind power output is firstly proposed in this paper to obtain allowable interval solutions for wind power generation and provide the optimal schedules for conventional generators to cope with the uncertainty in wind power generation. The proposed double-level model is difficult to be solved because of the invalid dual transform in solution process caused by the coupling relation between the discrete and continuous variables. Therefore, a two-stage iterative solution method based on Benders Decomposition is also presented. The proposed double-level model is transformed into a single-level and two-stage robust interval unit commitment model by eliminating the coupling relation, and then this two-stage model can be solved by Benders Decomposition iteratively. Simulation studies on a modified IEEE 26-generator reliability test system connected to a wind farm are conducted to verify the effectiveness and advantages of the proposed model and solution method. [ABSTRACT FROM AUTHOR]

Unit commitment, one of the significant tasks in power system operations, faces new challenges as the system uncertainty increases dramatically due to the integration of time-varying resources, such as wind. To address these challenges, we propose the formulation and solution of a generalized unit commitment problem for a system with integrated wind resources. Given the prespecified interval information acquired from real central wind forecasting system for uncertainty representation of nodal wind injections with their correlation information, the proposed unit commitment problem solution is computationally tractable and robust against all uncertain wind power injection realizations. We provide a solution approach to tackle this problem with complex mathematical basics and illustrate the capabilities of the proposed mixed integer solution approach on the large-scale power system of the Northwest China Grid. The numerical results demonstrate that the approach is realistic and not overly conservative in terms of the resulting dispatch cost outcomes. [ABSTRACT FROM AUTHOR]

This paper attempts to provide an optimum loading schedule of power generating units with the least cost by solving a unit commitment (UC) problem and to present good estimates of cost differences when UC problem is not applied. UC is a fundamental optimization problem of power generation systems which determines the optimum schedule of generating units which minimizes generation costs. However, for small power generation firms which are situated in developing countries, UC-based problems are poorly understood if not implemented and the scheduling of generating units is based on some methodologies which may provide results that are not optimal. Thus, a case study in a small power generation firm in central Philippines is carried out to elucidate these objectives. The case requires a solution of the mixed-integer nonlinear programming (MINLP) problem. Results show that the proposed UC-based problem yields optimal costs and the cost disparity from the current scheduling scheme is approximately at 27% which may be considered as potential cost savings. This shows that UC-based problem provides a reliable platform in achieving minimal generation costs. These results are significant to decision-makers particularly in small power generation firms and to engineering practitioners in the Philippines and in some developing countries as these provide an overview of the disparity of cost figures of not implementing UC. [ABSTRACT FROM AUTHOR]

This article proposes the use of a heuristic method based on the ant colony optimization algorithm (MACO) to solve the dynamic economic dispatch (DED) problem with taking into consideration the valve-point effects. The suggested method takes into account both static and dynamic constraints of generator operation, the transmission losses and non-smooth cost functions that shape the valve-point effects. Also, a strategy for the DED imposed equality constraint handling is suggested. The proposed method's feasibility is proven for three different systems that have 10, 13 and 30 thermal generating units. The simulation results show that the suggested approach is very efficient having a capacity to obtain quality solutions in solving the DED problem with valve-points effects. The numerical results obtained through the suggested method are superior to other results recently reported in literature, for the same systems. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper describes the use of Chambers-Mallows-Stuck method for simulating stable random variables in the generation of test systems for economic analysis in power systems. A study that focused on generating test electrical systems through fat tail model for unit commitment problem in electrical power systems is presented. Usually, the instances of test systems in Unit Commitment are generated using normal distribution, but in this work, simulations data are based on a new method. For simulating, we used three original systems to obtain the demand behavior and thermal production costs. The estimation of stable parameters for the simulation of stable random variables was based on three generally accepted methods: (a) regression, (b) quantiles, and (c) maximum likelihood, choosing one that has the best fit of the tails of the distribution. Numerical results illustrate the applicability of the proposed method by solving several unit commitment problems. [ABSTRACT FROM AUTHOR]

Integrating wind power generation into bulk power grids imposes significant challenging to system operations due to the stochastic variation and limited predictability of wind power generation. One of the key issues is how to optimally schedule various units and determine a proper spinning reserve level to guarantee a reliable operation of the integrated system in a robust and economic manner. In this paper, we propose a two-level unit commitment (TLUC) for power systems facing large uncertainties in the context of adjustable robust optimization. The commitment decision acts as first-stage decisions that need to be robust against uncertainty, whereas the output decision acts as adjustable decisions that is decided after uncertainty is revealed. This decision mechanism naturally decomposes a conventional unit commitment (UC) problem into two levels, the upper level provides unit working status, whereas the lower level checks if there is at least one dispatch solution under uncertainty subject to the status. Unlike conventional UC, where the reserve level is fixed from operating experiences, in TLUC, the reserve level also acts as a decision variable in the upper level that controls the robustness of UC decisions. A heuristic algorithm is developed to solve this TLUC problem, providing the robust UC decision against uncertainty as well as the minimal spinning reserve level required to maintain reliability. Numerical experiments are carried out on the IEEE 39-bus system and large-scale realistic Guangdong power grid in China and demonstrate the effectiveness of our approach. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An implicit reserve constraint unit commitment (IRCUC) model is presented in this paper. Different from the traditional unit commitment (UC) model, the constraint of spinning reserve is not given explicitly but implicitly in a trade-off between the production cost and the outage loss. An analytical method is applied to evaluate the reliability of UC solutions and to estimate the outage loss. The stochastic failures of generating units and uncertainties of load demands are considered while assessing the reliability. The artificial fish swarmalgorithm(AFSA) is employed to solve this proposedmodel. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated from10 to 100 units system, and the testing results are compared with those obtained by genetic algorithm (GA), particle swarmoptimization (PSO), and ant colony optimization (ACO) in terms of total production cost and computational time. The simulation results show that the proposed method is capable of obtaining higher quality solutions. [ABSTRACT FROM AUTHOR]