A genetic evolving ant direction DE for OPF with non-smooth cost functions and statistical analysis

This paper proposes an evolving ant direction differential evolution (EADDE) algorithm for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics. The EADDE employs ant colony search to find a suitable mutation operator for differential evolution (DE) whereas the ant colony parameters are evolved using genetic algorithm approach. The Newton–Raphson method solves the power flow problem. The feasibility of the proposed approach was tested on IEEE 30-bus system with three different cost characteristics. Several cases were investigated to test and validate the robustness of the proposed method in finding the optimal solution. Simulation results demonstrate that the EADDE provides superior results compared to a classical DE and other methods recently reported in the literature. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.     

Multiobjective clearing of reactive power market in deregulated power systems

This paper presents a day-ahead reactive power market which is cleared in the form of multiobjective context. Total payment function (TPF) of generators, representing the payment paid to the generators for their reactive power compensation, is considered as the main objective function of reactive power market. Besides that, voltage security margin, overload index, and also voltage drop index are the other objective functions of the optimal power flow (OPF) problem to clear the reactive power market. A Multiobjective Mathematical Programming (MMP) formulation is implemented to solve the problem of reactive power market clearing using a fuzzy approach to choose the best compromise solution according to the specific preference among various non-dominated (pareto optimal) solutions. The effectiveness of the proposed method is examined based on the IEEE 24-bus reliability test system (IEEE 24-bus RTS).     

Interactive DE for solving combined security environmental economic dispatch considering FACTS technology

This paper presents an efficient interactive differential evolution (IDE) to solve the multi-objective security environmental/economic dispatch (SEED) problem considering multi shunt flexible AC transmission system (FACTS) devices. Two sub problems are proposed.The first one is related to the active power planning to minimize the combined total fuel cost and emissions, while the second is a reactive power planning (RPP) using multi shunt FACTS device based static VAR compensator (SVC) installed at specified buses to make fine corrections to the voltage deviation, voltage phase profiles and reactive power violation. The migration operation inspired from biogeography-based optimization (BBO) algorithm is newly introduced in the proposed approach, thereby effectively exploring and exploiting promising regions in a space search by creating dynamically new efficient partitions. This new mechanism based migration between individuals from different subsystems makes the initial partitions to react more by changing experiences. To validate the robustness of the proposed approach, the proposed algorithm is tested on the Algerian 59-bus electrical network and on a large system, 40 generating units considering valve-point loading effect. Comparison of the results with recent global optimization methods show the superiority of the proposed IDE approach and confirm its potential for solving practical optimal power flow in terms of solution quality and convergence characteristics.     

Market-clearing for pricing system security based on voltage stability criteria

This paper presents a novel technique for representing system security in the operation of decentralized electricity markets, with special emphasis on voltage stability. The market-clearing algorithm is modeled as voltage stability constrained-optimal power flow (VSC-OPF) problem for providing market solutions by means of a function of complying with the required voltage stability criteria. Benders’ decomposition is applied for solving the VSC-OPF incorporating post-contingency control actions, which is motivated by the improvement of computational efficiency using parallel processing. The proposed VSC-OPF framework also takes into consideration the bilateral contract information, which is integrated into the market-clearing process and, at the same time, the optimal pricing expressions through computing local marginal prices (LMPs) with respect to ensuring voltage stability are derived. VSC-OPF is tested on the IEEE 14-bus benchmark system and the results obtained, when compared to those obtained by means of a conventional OPF, show that the proposed technique is able to improve system security while yielding better market solutions and total transaction levels.     

基于图论的电力系统PMU布点优化算法

基于最小支配集理论和电力系统线性量测模型．提出了可观测节点集合、WAMS可观测矩阵两个概念以及一种新的节点可观测性计算规则。以保证系统的完全可观测性和以系统图的最小支配集为搜索范围构成约束条件,以电力系统状态完全可观测和相量测量装置（PMU）配置数目最小为目标,形成了PMU配置优化问题。并应用禁忌搜索（TS）方法求解该问题,保证了全局寻优。最后采用IEEE14、30、57、118节点系统和新英格兰39节点系统对该方法进行了验证,仿真结果表明该方法的有效性和可行性。     

Parameter identification of an SOFC model with an efficient,adaptive differential evolution algorithm

An efficient, adaptive differential evolution (DE) algorithm is proposed in which DE parameter adaptation is implemented. A ranking-based vector selection and crossover rate repairing technique are also presented. The method is referred to as IJADE (Improved Jingqiao Adaptive DE). To verify the performance of IJADE, the parameters of a simple SOFC electrochemical model that is used to control the output performance of an SOFC stack are identified and optimized. The SOFC electrochemical model is built to provide the simulated data. The results indicate that the proposed method is able to efficiently identify and optimize model parameters while showing good agreement with both simulated and experimental data. Additionally, when compared to other DE variants and other evolutionary algorithms, IJADE obtained better results in terms of the quality of the final solutions, robustness, and convergence speed.     

A novel power system reconfiguration for a distribution system with minimum load balancing index using bacterial foraging optimization algorithm

In this paper, the objective of minimum load balancing index (LBI) for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm (BFOA). The feeder reconfiguration problem is formulated as a non-linear optimization problem and the optimal solution is obtained using BFOA. With the proposed reconfiguration method, the radial structure of the distribution system is retained and the burden on the optimization technique is reduced. Test results are presented for the 16-bus sample network, the proposed reconfiguration method has effectively decreased the LBI, and the BFOA technique is efficient in searching for the optimal solution.     

An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE)

To accurately model the PV module, it is crucial to include the effects of irradiance and temperature when computing the value of the model parameters. Considering the importance of this issue, this paper proposes an improved modeling approach using differential evolution (DE) method. Unlike other PV modeling techniques, this approach enables the computation of model parameters at any irradiance and temperature point using only the information provided by the manufacturer’s data sheet. The key to this improvement is the ability of DE to simultaneously compute all the model parameters at different irradiance and temperature. To validate the accuracy of the proposed model, three PV modules of different types (multi-crystalline, mono-crystalline and thin-film) are tested. The performance of the model is evaluated against the popular single diode model with series resistance R_s. It is found that the proposed model gives superior results for any irradiance and temperature variations. The modeling method is useful for PV simulator developers who require comprehensive and accurate model for the PV module.     

混合微分进化算法应用于换热网络优化

针对微分进化(Differential Evolution,DE)算法应用于换热网络优化存在局部搜索能力不足、收敛速度慢和求解精度低等问题,提出一种混合微分进化(Hybrid Differential Evolution,HDE)算法。当DE算法的变异、交叉和选择操作不再使种群的最优值继续进化时,加入梯度操作使当前种群的最优个体趋向更好的解。为了防止算法早熟收敛,当种群的多样性低于设定的阈值时,引入迁移操作,在最优个体附近区域重新生成新的个体并以此替换旧的个体,增强算法的种群多样性。通过算例验证了该算法可以有效适用于换热网络的优化过程,具有更强的局部搜索能力,更快的收敛速度和更高的优化效率。     

Probabilistic multiple distribution static compensator placement and sizing based on the two-point estimate method

This paper deals with the stochastic placement and sizing of the distribution static compensator in the distribution systems considering uncertainty. The proposed stochastic framework utilises the point estimate method to consider the forecasting uncertainty of the active and reactive loads in the load flow equations. The objective functions to be investigated are total active power losses and the voltage profile simultaneously. In order to reach a proper compromise to satisfy all the objective functions, an interactive fuzzy satisfying approach is employed. Also, since the problem investigated is a type of discrete, nonlinear, multi-objective optimisation problem, a new modified optimisation technique is proposed to escape from the local optima as well as the premature convergence. Finally, the satisfying performance of the proposed method is examined on the 86-bus IEEE distribution system.     

地区电网无功电压最优控制

本文提出了地区电网在满足母线电压、线路及变压器不超载的安全约束下,以有载调压变分接头和母线无功补偿量为控制变量,用线性规划获得计入无功电压静态特性的无功电压最优控制策略的方法.本文根据实时采样数据辨识负荷母线的无功电压静态特性,并有网损最小、无功补偿量最小、控制效益最大、三种不同的目标函数可供不同需要选用.在IEEE试验和实际系统的试验表明:能快速获得不同目标函数的最优控制策略.     

双有源桥DC-DC变换器H∞混合灵敏度控制及优化

以双有源桥DC-DC变换器（DAB）为研究对象,针对交直流微网中因可再生能源的波动性和随机性、测量误差、运行工况变化等原因所导致的参数摄动问题,提出基于差分进化算法（DE）优化的H_∞混合灵敏度鲁棒控制（H_∞-MSC）策略。通过选取合适的加权函数对系统不同频段特性进行规划,得到理想的动态性能及鲁棒性能,并针对加权函数难以选取的缺点,采用DE算法对选择过程进行优化,通过迭代寻优匹配最佳控制器参数,改进H_∞-MSC算法。最后,通过半实物实验对比例积分（PI）单移相控制算法和DE-H_∞-MSC算法进行对比分析。研究结果表明,DE-H_∞-MSC算法能有效增强DAB系统的动态性能和鲁棒性能,可提高微电网系统运行的稳定性。     

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.     

Lift-and-project MVEE based convex hull for robust SCED with wind power integration using historical data-driven modeling approach

This paper presents an adjustable robust security constrained economic dispatch (SCED) model with wind power uncertainties. First, the scenario based adjustable robust SCED model is presented. It considers multiple scenarios from historical data as well as the spatial correlation among wind farms. Then, the proposed SCED model becomes an optimization problem with a large amount of constraints which is skillfully solved using a lift-and-project minimum volume enclosing ellipsoid (MVEE) based convex hull. Furthermore, the proposed model is transformed into a second order cone programming (SOCP) model by the use of participation factors to generate adjustable generation outputs and thus guarantee the energy balance. In order to further reduce the computational complexity, the inactive constraints reduction strategy is proposed to quickly eliminate inactive SOC security constraints before solving the model. Numerical results of IEEE 14-bus and 118-bus test systems as well as the practical Polish power systems with several wind farms show that the proposed model can achieve better economies. Moreover, more than 82% of security constraints are identified as inactive in various cases of the simulation, and the proposed inactive constraints reduction strategy is promising for improving the computational performance.     

基于改进的多目标差分演化算法求解电力系统清洁经济优化调度问题

利用改进的多目标差分演化算法求解考虑安全运行约束的电力系统清洁经济优化调度问题,对相互冲突的发电费用函数和污染气体排放量函数同时进行优化,采用非支配排序和拥挤距离排序对种群进行选择操作,并引入基于平均熵的种群初始化和基于混沌Logistic映射的控制参数调整策略来改善算法收敛性能。在IEEE 30节点6机系统上获得的仿真结果验证了该算法的优越性。     

基于LS-FID策略与差分进化算法相结合的含DG的配电网重构

为提高电力系统运行的经济性和可靠性,并考虑分布式电源的接入,建立了以系统网损和电压偏差最小为目标函数的配电网重构模型。考虑到传统算法效率低、不可行解量大的缺陷,提出环路搜索—快速孤岛检测(LS-FID)策略,并将该策略与差分进化算法结合求解网络重构方案。简化网络拓扑,减少了部分不可行解的产生;然后利用环路搜索方法分解搜索空间,针对迭代中出现的不可行解,利用快速孤岛检测方法进行检测以减少计算次数。最后对IEEE33、IEEE69和119节点系统进行仿真分析,并与多种智能算法进行比较,仿真结果表明所提方法在提高搜索速度、减少搜索空间和提高寻优成功率等方面有较大优势,验证了该方法的有效性和优越性。     

Impact of heat,power and hydrogen generation on optimal placement and operation of fuel cell power plants

In this paper, a stochastic model is proposed for planning the location and operation of Fuel Cell Power Plants (FCPPs) as Combined Heat, power, and Hydrogen (CHPH) units. Total cost, emissions of FCPPs and substation, and voltage deviation are the objective functions to be minimized. Location and operation of FCPPs as CHPH are considered in this paper while their investment cost is not taken into account. In the proposed model, indeterminacy refers to electrical and thermal loads forecasting, pressure of oxygen and hydrogen, and the nominal temperature of FCPPs. In this method, scenarios are produced using roulette wheel mechanism and probability distribution function of input random variables. Using this method, the probabilistic problem is considered to be distributed as some scenarios and consequently probabilistic problem is considered as combination of some deterministic problems. Considering the nature of objective functions, the problem of locating and operating FCPPs as CHPH is considered as a mixed integer nonlinear problem. A Self Adaptive Charged System Search (SACSS) algorithm is employed for determining the best Pareto optimal set. Furthermore, a set of non-dominated solutions is saved in repository during simulation procedure. A 69-bus distributed system is used for verifying the beneficiary proposed method.     

Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization

This paper presents a novel modified interactive honey bee mating optimization (IHBMO) base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.     

Genetic single objective optimisation for sizing and allocation of renewable DG systems

The optimal design of renewable-based distributed generations (DGs) is a challenging issue in order to maximise their benefits and to overcome power quality problems. Therefore, this paper proposes a methodology for optimal allocation and sizing of renewable DG units to minimise total power losses over radial distribution systems. The planning problem is formulated as a single objective nonlinear mixed integer-constrained optimisation problem and is solved by using the augmented Lagrangian genetic algorithm (ALGA) by combining the objective function and the nonlinear constraints. In that case, the ALGA solves a sequence of sub-problems where the objective function penalises the constraints violation in order to obtain the best solution. The proposed technique is applied to IEEE radial test systems including 33-bus, 69-bus and 119-bus and is implemented with different scenarios including all possible combinations and various types of renewable DG units to prove the effectiveness of the proposed methodology.     

Parameter optimization of the biohydrogen real time power generating system using differential evolution algorithm

By comparing the differential evolution and genetic algorithms, this study attempts to optimize estimation of a biohydrogen real time power generating system in which circuit parameters fluctuate with operating temperature and current density. Based on uses of the differential evolution algorithm method, optimal estimation of the circuit parameters is achieved by data from a V–I characteristic experiment on the proposed biohydrogen real time power generating system. The circuit feature is then solved by formulating the estimated circuit parameters based on Kirchhoff’s law to elucidate its feature of the biohydrogen real time power generating system and results show that DE is faster than GA and more accurate. Next, the estimated V–I characteristics are compared with measurement results to demonstrate the feasibility of the proposed method.