Solving unit commitment problem using a novel version of harmony search algorithm

In this context, a novel structure was proposed for improving harmony search (HS) algorithm to solve the unit comment (UC) problem. The HS algorithm obtained optimal solution for defined objective function by improvising, updating and checking operators. In the proposed improved self-adaptive HS (SGHS) algorithm, two important control parameters were adjusted to reach better solution from the simple HS algorithm. The objective function of this study consisted of operation, start-up and shut-down costs. To confirm the effectiveness, the SGHS algorithm was tested on systems with 10, 20, 40 and 60 generating units, and the obtained results were compared with those of the simple HS algorithm and other related works.     

A new decomposition approach for the thermal unit commitment problem

In this paper, we propose a new formulation based on benders decomposition approach to solve the thermal unit commitment (UC) problem. In the proposed approach, the UC problem is decomposed into a master problem, which is an integer optimization problem, and a subproblem, which is a nonlinear optimization problem. The proper on/off states of the generating units are found by solving the master problem using the mixed-integer programming method. The subproblem utilizes the solution of the master problem to form appropriate cuts and returns the cuts to the master problem for running the next iteration of the UC problem. In both optimization problems, corresponding constraints are exactly modeled. To demonstrate the effectiveness of the proposed approach, simulation results are compared with the results obtained by other methods.     

智能算法求解机组组合的可行解检验方法

针对智能算法求解电力系统机组组合时存在的经济调度无解问题,提出了一种简便、高效率的检验方法.提出的算法可用于检验智能算法中较难处理的爬坡约束和直流潮流安全约束,有效地避免对不可行机组状态组合的经济调度.算例表明,该方法检验效率高,可有效节省计算时间,具有较强的实用意义.     

A solution to stochastic unit commitment problem for a wind-thermal system coordination

Unit commitment (UC) problem is one of the most important decision making problems in power system. In this paper the UC problem is solved by considering it as a real time problem by adding stochasticity in the generation side because of wind-thermal co-ordination system as well as stochasticity in the load side by incorporating the randomness of the load. The most important issue that needs to be addressed is the achievement of an economic unit commitment solution after solving UC as a real time problem. This paper proposes a hybrid approach to solve the stochastic UC problem considering the volatile nature of wind and formulating the UC problem as a chance constrained problem in which the load is met with high probability over the entire time period.     

A novel decentralized method based on the system engineering concept for reliability‐security constraint unit commitment in restructured power environment

In the deregulated power environment, including Central operator (CO) and Micro Grids (MGs), different parts of the network are dedicated to the private sector, and each of them seeks to increase their profits independently. The CO and MGs should cooperate and collaborate in terms of operating, security and reliability in the whole power system. This article proposes a new method based on a System of System (SoS) concept for the secure and economic hourly generation scheduling to find optimal operational point. The main methodology includes three steps. In the first step, the power system is divided into several subsystems by using a spectral clustering partitioning technique to reduce converge time by decentralizes methods. And also load forecasting based on a Gaussian probability distribution function to avoid conventional calculation and considering uncertainty of the loads has been presented. To find a similar scenario, and reduction scenario with low probability, the probabilistic method has been addressed. The main contribution of this method is removing scenarios with low value of probabilities and scenarios which are similar to each other. In fact, the reduced set must include scenarios which are scattered appropriately in the uncertain space while holding high probabilities. In order to estimate the similarity (distance) between two scenarios the Kantorovich distance is implemented. In the second step, the hierarchical Bi‐level optimization approach is used to execute the decentralized decision making to solve the Security Constraints Unit Commitment (SCUC) problem between CO and MGs. Regarding all physical relations and shared data among CO and MGs, the SoS concept and Bi‐level optimization are presented to find the optimal operating point of autonomous systems. In the third step, a random number of generators will be select. Hence, the initial iteration number is set. In this step, sampling from state space to classifying reliability object achieved (The expected energy not supplied and loss of load probability are the reliability criterion). The presented method is evaluated using a 6‐bus, the RTS 24‐bus, 118‐bus, and 4672‐bus as an IEEE test systems. The suggested structure has been implemented by GAMS, and the results illustrate the usefulness of the presented methodology. To comparing proposed approach with the centralized method, the results illustrate improving total operational costs and security (in RTS‐24($603,209), 118 bus ($2 562 154) and 4672‐bus ($9 185 168)) in scenario 3 near to 9%, 10% and 8% respectively. Similarly, by comparison (in three test systems) with genetic algorithm these improvements are near to 23%, 22% and 13% respectively.     

A new technique for solving the multi-objective optimization problem using hybrid approach

Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO₂ emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a bi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach (firefly algorithm-real genetic algorithm, FFA-RGA) has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi-objective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.     

An accurate method for the PV model identification based on a genetic algorithm and the interior-point method

Due to the PV module simulation requirements as well as recent applications of model-based controllers, the accurate photovoltaic (PV) model identification method is becoming essential to reduce the PV power losses effectively. The classical PV model identification methods use the manufacturers provided maximum power point (MPP) at the standard test condition (STC). However, the nominal operating cell temperature (NOCT) is the more practical condition and it is shown that the extracted model is not well suited to it. The proposed method in this paper estimates an accurate equivalent electrical circuit for the PV modules using both the STC and NOCT information provided by manufacturers. A multi-objective global optimization problem is formulated using only the main equation of the PV module at these two conditions that restrains the errors due to employing the experimental temperature coefficients. A novel combination of a genetic algorithm (GA) and the interior-point method (IPM) allows the proposed method to be fast and accurate regardless the PV technology. It is shown that the overall error, which is defined by the sum of the MPP errors of both the STC and the NOCT conditions, is improved by a factor between 5.1% and 31% depending on the PV technology.     

Metamodel based design optimization approach in promoting the performance of proton exchange membrane fuel cells

This research presents a systematization and effectiveness approach in promoting the performance of the power density of a Proton Exchange Membrane Fuel Cell (PEMFC) by Metamodel-Based Design Optimization (MBDO). The proposed methodology of MBDO combines the design of experiment (DoE), metamodeling choice and global optimization. The fractional factorial experimental design method can screen important factors and the interaction effects in DoE, and obtain optimal design of the robust performance parameters by Taguchi method. Metamodeling then adopts the ability to establish a non-linear model of a complex PEMFC system configuration of an artificial neural network (ANN) based on the back-propagation network (BPN). Finally, on the many parameters (factors) of optimization, a genetic algorithm (GA) with a high capability for global optimization is used to search the best combination of the parameters to meet the requirement of the quality characteristics. Experimental results confirmed by the test equipment demonstrate that the MBDO approach is effective and systematic in promoting PEMFC performance of power density.     

Reducing the fuel consumption and emissions with the use of an external fuel cell hybrid power unit for electric taxiing at airports

Airport ground operations have a great impact on the environment. Various innovative solutions have been proposed for aircraft to perform taxi movements by deactivating their main engines. Although these solutions are environmentally beneficial, onboard and external electric taxiing solutions that are actively used and planned to be used in airports are not completely carbon-free. The disadvantages of the existing solutions can be alleviated by using an external fuel cell hybrid power unit to meet the energy required for taxiing that does not put additional weight on the aircraft. To reveal the power and energy required by the system, Airbus A320-200, which is a narrow-body aircraft and frequently used in airports, has been considered in this study. To determine the physical requirements of the aircraft for taxiing, a total of 900 s taxi-out movement consisting of four different periods with different runway slope, headwind, and maximum speeds were examined. According to the determined physical requirements, the conceptual design of the proposed fuel cell battery system was created and the physical data of the system for each period were obtained using the Matlab Simulink environment. As a result of the simulation, it is seen that the system consumes approximately 1.96 g of hydrogen per second. In addition, it has been calculated that 578.34 kg of CO₂ is emitted during the taxi-out movement. The results also show that as a result of using the proposed system, approximately 14.6 million tons of CO₂ emission per year can be prevented.