A new memetic algorithm approach for the price based unit commitment problem

Unit commitment (UC) is a very important optimization task, which plays a major role in the daily operation planning of electric power systems that is why UC is a core research topic attracting a lot of research efforts. An innovative method based on an advanced memetic algorithm (MA) for the solution of price based unit commitment (PBUC) problem is proposed. The main contributions of this paper are: (i) an innovative two-level tournament selection, (ii) a new multiple window crossover, (iii) a novel window in window mutation operator, (iv) an innovative local search scheme called elite mutation, (v) new population initialization algorithm that is specific to PBUC problem, and (vi) new PBUC test systems including ramp up and ramp down constraints so as to provide new PBUC benchmarks for future research. The innovative two-level tournament selection mechanism contributes to the reduction of the required CPU time. The method has been applied to systems of up to 110 units and the results show that the proposed memetic algorithm is superior to other methods since it finds the optimal solution with a high success rate and within a reasonable execution time.     

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.     

考虑机组组合问题的机组检修计划优化新模型

以经济费用最小为目标函数,建立了发电机组检修计划优化问题（UMS）新模型。由于生产费用在经济费用中占有的比例最大,因此在计算新模型的生产费用时考虑了发电机组组合优化问题（UC）。鉴于考虑UC问题的UMS问题为双层优化问题,其中UMS问题为上层优化问题,UC问题为下层优化问题,提出了一种改进离散粒子群算法（MDPSO）,并将其用于搜索UMS问题的最优解向量,即解决上层优化问题;而由于拉格朗日松弛法在解决UC问题上具有计算速度快、结果精度高等优点,将其用于解决下层优化问题。利用该新模型和MDPSO算法对IEEE-RTS系统的机组的年检修计划进行优化,并与离散粒子群算法（DPSO）比较,结果表明DPSO算法在解决UMS问题上具有精度高、收敛速度快等优点。     

A grouping-based global harmony search algorithm for modeling of proton exchange membrane fuel cell

In recent years, accurate identification of voltage versus current (V-I) characteristics of proton exchange membrane fuel cell (PEMFC) has attracted significant attention in the literature. However, the main drawback in accurate modeling is the lack of information about the precise values of the model parameters. In this paper, in order to overcome this drawback a grouping-based global harmony search algorithm, named GGHS, is proposed for parameter identification issue. The proposed algorithm attempts to provide an efficient way in which a new harmony can be properly improvised. In order to study the capability of the proposed algorithm, the results obtained by GGHS are compared with those obtained by two versions of harmony search (HS) algorithms, three versions of particle swarm optimization (PSO) algorithms, as well as seeker optimization algorithm (SOA). Simulation results accentuate the superiority of the GGHS over the other methods.     

Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm

This paper proposes the generation scheduling approach for a microgrid comprised of conventional generators, wind energy generators, solar photovoltaic (PV) systems, battery storage, and electric vehicles. The electrical vehicles (EVs) play two different roles: as load demands during charging, and as storage units to supply energy to remaining load demands in the MG when they are plugged into the microgrid (MG). Wind and solar PV powers are intermittent in nature; hence by including the battery storage and EVs, the MG becomes more stable. Here, the total cost objective is minimized considering the cost of conventional generators, wind generators, solar PV systems and EVs. The proposed optimal scheduling problem is solved using the hybrid differential evolution and harmony search (hybrid DE-HS) algorithm including the wind energy generators and solar PV system along with the battery storage and EVs. Moreover, it requires the least investment.     

Optimal economic operation of microgrids considering combined heat and power unit,reserve unit,and demand-side management using developed adolescent identity search algorithm

In the present study, a method is proposed to solve the problem of economic load distribution in MGs, meet the challenges arising from the use of renewable sources periodically, ensure the stable performance of MGs, and minimize the operating cost of MGs considering combined heat and power (CHP) units and reserve system. Moreover, demand-side management (DSM) as a tool is employed to reduce the operating cost of the power system. Therefore, the proposed model for optimal operation of MGs using DSM is formulated as an optimization problem. Load shifting is considered as an effective solution in DSM. Minimizing the total operating cost of the system is considered as the objective function of this problem. Problem constraints include operating and executive constraints for load shifting. Finally, the model is solved using the developed adolescent identity search algorithm (AISA). In the developed model, Powell's local search operator is employed to improve the efficiency of searching for the optimal solution. Due to the existing uncertainties in load consumption and day-ahead market price, the method is presented as a scenario-based stochastic energy management problem. The results reveal the proposed method is highly efficient in solving the problem, and load management can improve economic indicators.     

A solution to unit commitment problem using invasive weed optimization algorithm

Unit commitment (UC) is one of the most important aspect of power generation in the world today. Though, there is no method to find the exact optimized solution, there exists several meta-heuristic algorithms to determine the close to exact solution. This paper proposes a novel solution to effectively determine UC and generation cost using the technique of invasive weed optimization (IWO). The existing technique distributes the load demand among all the generating units. The method proposed here utilizes the output of UC obtained by using the Lagrangian relaxation (LR) method and calculates the required generation from only the plants that are ON discarding the OFF generator units and thereby giving a faster and more accurate response. Moreover, the results show the comparison between the LR-particle swarm optimization (PSO) and LR-IWO, and prove that the cost of generation for a 4 unit, 8 hour schedule is much less in the case of IWO when compared to PSO.     

Parameter extraction of fuel cells using hybrid interior search algorithm

Precise modelling of fuel cells is very important for understanding their functioning. In this work, an application of hybrid interior search algorithm (HISA) is proposed to extract the parameters of fuel cells for their electromechanical equations based on nonlinear current‐voltage characteristics. Proposed hybridised algorithm has been developed using evolutionary mutation and crossover operators so as to enhance the modelling capability of interior search algorithm (ISA). To assess the modelling performance of HISA, parameter extraction of two types of fuel cell models, namely, proton exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) have been considered. Modelling performance of HISA, assessed using mean squared error between computed and experimental data, is found to be superior to ISA and several other recently reported prominent optimisation methods. Based on the presented intensive simulation investigations, it is concluded that HISA improves the performance of the basic ISA in terms of fitter solutions, robustness, and convergence rate and therefore offers a promising optimisation technique for parameter extraction of fuel cells.     

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.     

Smart house-based optimal operation of thermal unit commitment for a smart grid considering transmission constraints

This paper presents a smart house-based power system for thermal unit commitment programme. The proposed power system consists of smart houses, renewable energy plants and conventional thermal units. The transmission constraints are considered for the proposed system. The generated power of the large capacity renewable energy plant leads to the violated transmission constraints in the thermal unit commitment programme, therefore, the transmission constraint should be considered. This paper focuses on the optimal operation of the thermal units incorporated with controllable loads such as Electrical Vehicle and Heat Pump water heater of the smart houses. The proposed method is compared with the power flow in thermal units operation without controllable loads and the optimal operation without the transmission constraints. Simulation results show the validation of the proposed method.     

基于和声搜索算法的水库发电优化调度研究

为提高水电厂发电效益,综合运用和声搜索算法,提出单级水库的优化调度方法,即首先分析单级水库优化调度约束条件,然后以月末水位为自变量建立基于年发电量最大原则的水库优化调度模型,最后运用和声搜索算法模拟水库调度。实例应用结果表明,基于和声搜索算法的优化结果较多年实际最大发电量及遗传算法的优化结果分别提高了8.69%、1.55%。     

A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem

The increasing costs of fuel and operation of thermal power generating units warrant development of optimization methodologies for economic dispatch (ED) problems. Optimization methodologies that are based on meta-heuristic procedures could assist power generation policy analysts to achieve the goal of minimizing the generation costs. In this context, the objective of this study is to present a novel approach based on harmony search (HS) algorithm for solving ED problems, aiming to provide a practical alternative for conventional methods. To demonstrate the efficiency and applicability of the proposed method and for the purposes of comparison, various types of ED problems are examined. The results of this study show that the new proposed approach is able to find more economical loads than those determined by other methods.     

Performance prediction of the PEMFCs based on gate recurrent unit network optimized by improved version of prairie dog optimization algorithm

In this article, the black box dynamic model is presented for forecasting the performance of the PEM (Proton-exchange membrane) fuel cell (FC). An optimized deep artificial neural network has been used to build the experimental nonlinear model of the polymer membrane FC series that functions with hydrogen and oxygen. This research investigates predictability for a gate recurrent unit (GRU) optimized by a modified Prairie Dog Optimizer in PEMFCs. The results obtained have been validated by applying a case study and then a comparison is conducted among the outcomes of the offered technique and 2 other published methods: modified relevance vector machine and Lattice Gated Recurrent Unit (LGRU). The voltage clearly changes significantly, as demonstrated by simulations, even though the FC is handled with a low starting temperature and current. Also, the voltage point distribution has become more concentrated when the current and temperature are high. In both the training and prediction phases, the MAPE is reduced to approximately 0.0043 and 0.0047, respectively, showing that the proposed GRU technique produces superior prediction results when the operational settings approach the optimum operating conditions. According to simulations, the proposed IPDO/GRU with a 0.004 root mean square has the least error, followed by the mRVM and GRU with 0.009 and 0.010 root mean square values. The outcomes show that using the offered procedure does provide the finest verification of the empirical data.     

Power quality enhancement of grid-connected fuel cell using evolutionary computing techniques

Fuel Cell (FC), as a type of new renewable energy sources grid-connected at Point of Common Coupling (PCC), is introduced in this study. This article presents the power quality improvement of the FC integrated to the power network through a chopper and an inverter using the conventional PI controller. Two PI controllers, tuned by three recent different evolutionary computing techniques namely Harmony Search (HS), Modified Flower Pollination Algorithm (MFPA) and Electromagnetic Field Optimization (EFO) methods are considered. The two PI controllers are used for driving the inverter connected the on-grid FC in order to govern the PCC voltage between the FC and the power network. These two controllers are exploited to drive the power and the current regulators at different voltage sag and swell conditions. The three optimization methods are compared to the Particle Swarm Optimization (PSO) with regards to voltage profile, power quality and execution time.Simulation results, using Matlab/Simulink^?, show the significance of the three optimization techniques in regulating the voltage at PCC with reduced harmonics during the system voltage sag and swell conditions when compared to the PSO. Through the numerical analysis, the superiority of MFPA method among the different optimization metaheuristic techniques is highlighted particularly for enhanced dynamic voltage response purposes.     

Energy-saving generation dispatch toward a sustainable electric power industry in China

Energy shortages, climate change and environmental pollution are critical issues that the entire world is faced with currently. To tackle the challenge and realize sustainable development, the Chinese government launched the Energy-Saving Generation Dispatch (ESGD) in 2007. In the ESGD scheme, generating units are dispatched based on fuel consumption rates and pollutant emission intensities from low to high. However, annual generation quotas still widely exist. With the mandatory shutdown of small-capacity and low-efficiency thermal generating units in 2006–2010, most of the currently running thermal generating units are large-capacity and highly efficient units. The additional improvement of the overall energy efficiency under this situation is a key problem for the Chinese electric power industry. To this end, a new type of ESGD framework is designed in this paper. Sequential coordination among yearly, monthly, day-ahead and real-time generation schedules is proposed. Based on the framework, the corresponding models are formulated. Empirical analysis is conducted using the realistic data obtained from the Guangdong Power Grid Corporation. Four generation dispatch modes are compared. The results indicate that the proposed ESGD mode can further reduce energy consumption and pollutant emissions. Hopefully, this paper can provide a valuable reference for policy making in the Chinese power sector.     

Optimal management of microgrids including renewable energy scources using GPSO-GM algorithm

Environmentally friendly energy sources with high power quality or reliability and low costs are regarded as an effective solution for energy supply problems arising from use of conventional methods. Presented in this paper, gives an optimal management strategy of PV/wind/diesel independent hybrid systems for supplying required energy in autonomous microgrids. A new optimization problem is formulated for minimizing the capital investment and fuel costs of the system. To solve the proposed optimization problem a novel algorithm, named Guaranteed convergence Particle Swarm Optimization with Gaussian Mutation (GPSO-GM), is developed. Two operators, namely mutation and guaranteed convergence, are added to PSO in order to help finding more accurate results and increasing the speed of calculations. The performance of the proposed strategy is evaluated in two case studies.     

An image processing algorithm to estimate the melt fraction and energy storage of a PCM enclosed in a spherical capsule

The experimental investigation of the melting behavior of a phase change material (PCM) inside a spherical container is reported. PCM considered for the study is lauric acid whose melting point is about 43°C. Unconstrained melting experiments are carried out at two different temperatures, ie, at 60°C and 80°C. The initial temperature of the PCM is taken as 30°C. To carry out a detailed analysis of the heat transfer, the transient variations in the melt fraction and amount of energy stored are derived. In order to estimate the liquid/solid volumes, a novel image processing technique is developed. In this approach, a circle‐fitting algorithm is employed to obtain the amount of PCM melted inside a spherical container. The total instantaneous amount of heat transport to the PCM is measured with an uncertainty of about 6.1%. The results obtained by the introduced circle‐fitting algorithm have been compared qualitatively and quantitatively with the solid modeling method. The present technique could capture the asymmetric behavior of the solid PCM. The experimental findings show that the rate of melt fraction is high at the beginning of the experiment due to a larger area of contact between the spherical container and solid PCM. Further, parametric analysis has been performed and reported in terms of Fourier, Stefan, and Grashof numbers.     

基于双目标节能调度的可再生能源配额制福利效果评估方法研究

我国目前对配额制的福利效果,即配额制造成的福利再分配的评估方法的研究并不多。针对这一问题,文章提出一个基于双目标节能调度模型的可再生能源配额制福利效果分析方法。建立了在煤耗最小化的前提下成本最小的双目标节能调度模型;选取了27台火电机组、1台水电机组和1台风电机组,在此基础上依次扩大可再生能源的机组容量比例,利用和声搜索算法对不同可再生能源比例下的双目标模型进行试算;分析比较不同比例下机组的负荷、煤耗、机组收益、电网购电成本以及降耗成本等。得出结论:在可再生能源配额制度下,变动可再生能源的比例,会导致福利的再分配,即负荷量、煤耗、成本和收益都会在不同的机组之间进行转移和再分配;可再生能源比例从10%扩大到20%的单位降耗成本最低。