Determination of acceptable operating cost level of nuclear energy for Turkey's power system

Generally, it is very difficult to assess the true operating cost of an electrical power unit in the countries where there is little or no operational experience. Since Turkey has no experience on operating a nuclear unit, operating costs of a nuclear unit is uncertain for use in generation expansion planning (GEP). Furthermore, there is a disagreement of whether it is cheap or not. In this study, an acceptable level of operating cost of nuclear units is determined for Turkey's power system. It is aimed to find a numerical value for nuclear operating cost at which nuclear is able to compete with other energy sources. Seven types of units are chosen as candidate units to the power system. Mixed-integer programming (MIP) is used as a mathematical model of generation expansion planning. The model consists of the cost function that minimizes the construction and operating costs and the reliability constraints. Adaptive simulated annealing genetic algorithm (ASAGA) is used for optimization algorithm to determine the types, times, and number of candidate units which meet forecasted demand within a pre-specified reliability criterion over the planning horizon from 2006 to 2025. In the case studies, a high level of nuclear energy operating cost is taken and then the cost is gradually lowered. Optimizations are made for each level of nuclear operating costs within four different scenarios and the quantities of nuclear capacity selected by optimizations are recorded. It is determined that, nuclear energy is able to compete with other energy sources when the operating cost is less than 210$/kWh yr or 2.4cent/kWh.     

基于SA-PSO的风电消纳经济性动态规划分析

  [目的]  电网运行规划研究在电力系统中具有十分重要的地位,对电源布局具有决策意义,其本质上是一个多约束组合优化问题,重点研究了风电运行成本的计算模型。  [方法]  该模型对电源布局规划中需要重点考虑的约束问题进行了分析,采用粒子群算法用于解决此类目标优化问题,并结合模拟退火理论对传统粒子群算法进行了优化处理。  [结果]  通过仿真计算,与遗传算法及粒子群算法进行了对比分析,证实了这种算法的优越性,可以找到满足各种约束条件的最优电网出力方案。最后,进一步分析了在不同风速下对风电并网运行成本影响。  [结论]  该模型应用于电网规划分析是可行且有效的。     

Statistical wind direction modeling for the analysis of large scale wind power generation

Understanding the effects of large‐scale wind power generation on the electric power system is growing in importance as the amount of installed generation increases. In addition to wind speed, the direction of the wind is important when considering wind farms, as the aggregate generation of the farm depends on the direction of the wind. This paper introduces the wrapped Gaussian vector autoregressive process for the statistical modeling of wind directions in multiple locations. The model is estimated using measured wind direction data from Finland. The presented methodology can be used to model new locations without wind direction measurements. This capability is tested with two locations that were left out of the estimation procedure. Through long‐term Monte Carlo simulations, the methodology is used to analyze two large‐scale wind power scenarios with different geographical distributions of installed generation. Wind generation data are simulated for each wind farm using wind direction and wind speed simulations and technical wind farm information. It is shown that, compared with only using wind speed data in simulations, the inclusion of simulated wind directions enables a more detailed analysis of the aggregate wind generation probability distribution. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques

This paper proposes a new hybrid algorithm Meta-heuristic for the problem of network planning systems. The main goal of this paper is, to develop an efficient optimization tool which will minimise the cost functions of the stated optimization problems in network planning systems. The following are the objectives of the research: to investigate the capabilities of genetic algorithm, simulated annealing and tabu search for the defined optimization tasks; to develop a hybrid optimization algorithm which will produce improved iterations compared to those found by GA, SA, and TS algorithms. The performance of the hybrid algorithm is illustrated and six hybrid algorithms are developed, to improve the iterations obtained. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. It is advantageous to use exact DC load flow constraint equations based on the modified form of Kirchhoff's Second Law because the iterative process for line addition is not required. Hence, the computation time is decreased. Finally, the hybrid VI shows to be a very good option for network planning systems given that it obtains much accentuated reductions of iteration, which is very important for network planning.     

基于最大投资回报率的风光联合并网情况下输电网规划

江苏省电力设计院, 江苏 南京 211102     

Power generation expansion planning with emission control: a nonlinear model and a GA‐based heuristic approach

This paper presents an application of genetic algorithms (GA) for solving the long‐term power generation expansion planning (PGEP) problem, a highly constrained nonlinear discrete optimization problem. The problem is formulated into a mixed integer nonlinear programming (MINLP) program that determines the most economical investment plan for additional thermal power generating units over a planning horizon, subject to the requirements of power demands, power capacities, loss of load probability (LOLP) levels, locations, and environmental limitations. Computational results show that the GA‐based heuristic method can solve the PGEP problem effectively and more efficiently at a significant saving in runtime, when compared with a commercial optimization package. Copyright © 2005 John Wiley & Sons, Ltd.     

Integration of wind power in the liberalized Dutch electricity market

Wind power is becoming a large‐scale electricity generation technology in a number of European countries, including the Netherlands. Owing to the variability and unpredictability of wind power production, large‐scale wind power can be foreseen to have large consequences for balancing generation and demand in power systems. As an essential aspect of the Dutch market design, participants are encouraged to act according to their energy programs, as submitted day‐ahead to the system operator. This program responsibility shifts the burden of balancing wind power away from the system operator to the market. However, the system operator remains the responsible party for balancing any generation/load imbalances that may still be arising in real time. In this article, features that are unique for the Dutch market design are presented and their implications on the system integration of wind power are investigated. It is shown that the Dutch market design penalizes the intermittent nature of wind power. A discussion of opportunities and threats of balancing wind power by use of market forces is provided. Last, an outline is given of future work. Copyright © 2006 John Wiley &Sons, Ltd.     

Wind generation modelling to help the managerial process of modern transmission systems

In this paper, a strategy is proposed in order to introduce in a realistic way wind generation into a transmission power system non sequential Monte Carlo adequacy study with economic dispatch. Thanks to the implemented solution, wind generation is consequently confronted to operational constraints related to high powered thermal units, nuclear parks or thermal machines with technical minimum value. Moreover, during each simulated system state, a DC load flow is also calculated in order to evaluate reinforcements optimizing the large scale integration of wind power production. The simulation tool modified during the present work is called Scanner© and is the property of Tractebel Engineering (Gaz de France – Suez) company. It has been here applied to an academic test system: the Roy Billinton Test System (RBTS).     

基于混合遗传——模拟退火算法的日前购电策略研究

采用混合遗传—模拟退火算法求解基于经济性与系统安全性为目的的日前购电优化数学模型。通过IEEE-30系统,实例验证混合遗传—模拟退火算法用于日前购电决策问题是可行的,同时与传统遗传算法比较表明,混合遗传—模拟退火算法结果较优。     

Cost‐optimized allocation of wind power investments: a Nordic–German perspective

Using a linear cost minimization model with a 1 h time resolution, we investigated the influence of geographic allocation of wind power on large‐scale wind power investments, taking into account wind conditions, distance to load, and the nature of the power system in place (i.e. power generation and transmission capacities). We employed a hypothetical case in which a 20% wind power share of total electricity demand is applied to the Nordic–German power system. Free, i.e. geographically unrestricted, allocation of new wind power capacity is compared with a case in which national planning frameworks impose national limitations on wind power penetration levels. Given the cost assumptions made in the present work, the prospect of increasing the wind power capacity factor from 20 to 30% could motivate investments in transmission capacity from northern Scandinavia to continental Europe. The results obtained using the model show that the distribution of wind farms between regions with favorable wind conditions is dependent upon two factors: (i) the extent to which existing lines can be used to transmit the electricity that results from the new wind power and (ii) the correlation for wind power generation between the exporting region and the wind power generation already in place. In addition, the results indicate that there is little difference, i.e. just over 1%, in total yearly cost between the free allocation of new wind power and an allocation that complies with national planning frameworks. However, on a national level, there are significant differences with respect to investments in transmission and wind power capacities and the replacement of conventional power generation. Copyright © 2012 John Wiley & Sons, Ltd.     

基于数据挖掘的电力系统异常数据辨识与调整

为了保证电力系统的可靠运行,需要对系统中的异常数据进行检测辨识与调整。在数据挖掘领域,模糊C均值聚类法（FCM）在处理小量低维的数据挖掘时是有效的,但是面向电力系统的数据库的数据挖掘是要处理大量、高维的数据,这样FCM算法在时间性能上难以令人满意。文中基于采样技术对FCM算法进行改进,利用遗传算法对聚类结果进行优化,利用一种新的基于遗传优化的采样模糊C均值聚类算法FFGO（FuzzyFCMwithGeneticOptimization）,实现对异常数据的实时动态处理。     

A comprehensive state‐of‐the‐art survey on power generation expansion planning with intermittent renewable energy source and energy storage

Generation expansion planning (GEP) is a power plant mix problem that identifies what, where, when, and how new generating facilities should be installed and when old units be retired over a specific planning horizon. GEP ensures that the quantity of electricity generated matches the electricity demand throughout the planning horizon. This kind of planning is of importance because most production and service delivery is dependent on availability of electricity. Over the years, the traditional GEP approaches have evolved to produce more realistic models and new solution algorithms. For example, with the agitation for green environment, the inclusion of renewable energy plants and energy storage in the traditional GEP model is gradually gaining attention. In this regards, a handful of research has been conducted to identify the optimal expansion plans based on various energy‐related perspectives. The appraisal and classification of studies under these topics are necessary to provide insights for further works in GEP studies. This article therefore presents a comprehensive up‐to‐date review of GEP studies. Result from the survey shows that the integration of demand side management, energy storage systems (ESSs), and short‐term operational characteristics of power plants in GEP models can significantly improve flexibility of power system networks and cause a change in energy production and the optimal capacity mix. Furthermore, this article was able to identify that to effectively integrate ESS into the generation expansion plan, a high temporal resolution dimension is essential. It also provides a policy discussion with regard to the implementation of GEP. This survey provides a broad background to explore new research areas in order to improve the presently available GEP models.     

Short‐term hydropower planning coordinated with wind power in areas with congestion problems

In this paper, a day‐ahead planning algorithm for a multi‐reservoir hydropower system coordinated with wind power is developed. Coordination applies to real situations, where wind power and hydropower are owned by different utilities, sharing the same transmission lines, although hydropower has priority for transmission capacity. Coordination is thus necessary to minimize wind energy curtailments during congestion situations. The planning algorithm accounts for the uncertainty of wind power forecast. Only planning for the spot market is considered. Once the production bid is placed on the market, it cannot be changed. The solution of the stochastic optimization problem should, therefore, fulfill the transmission constraints for all wind power production scenarios. An evaluation algorithm is also developed to quantify the impact from the coordinated planning in the long run. The developed planning algorithm and the evaluation algorithm are applied in a case study. The results are compared with uncoordinated operation. The results of the case study show that coordination with wind power brings additional income to the hydropower utility and leads to significant reduction of wind energy curtailments. Copyright © 2007 John Wiley & Sons, Ltd.     

Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling

In this paper, a multi-stage stochastic optimization (MSO) method is proposed for determining the medium to long term power generation mix under uncertain energy demand, fuel prices (coal, natural gas and oil) and, capital cost of renewable energy technologies. The uncertainty of future demand and capital cost reduction is modelled by means of a scenario tree configuration, whereas the uncertainty of fuel prices is approached through Monte Carlo simulation. Global environmental concerns have rendered essential not only the satisfaction of the energy demand at the least cost but also the mitigation of the environmental impact of the power generation system. As such, renewable energy penetration, CO_2,eq mitigation targets, and fuel diversity are imposed through a set of constraints to align the power generation mix in accordance to the sustainability targets. The model is, then, applied to the Indonesian power generation system context and results are derived for three cases: Least Cost option, Policy Compliance option and Green Energy Policy option. The resulting optimum power generation mixes, discounted total cost, carbon emissions and renewable share are discussed for the planning horizon between 2016 and 2030.     

基于智能电网大数据发掘和模拟退火算法的配网重构

为了提高配网电路的经济性及故障情况下快速恢复供电的能力,提出将智能电网大数据发掘和模拟退火算法相结合,即通过模拟退火算法中Metropolis准则的设定来避免在寻优过程中陷入局部最优,基于智能电网大数据发掘,考虑实时环境温度、变压器绕组温度和输电电缆温度情况下进行配网重构,并以美国PG&E 69节点配电系统为例进行仿真验证。结果表明,所提方法可有效降低优化的计算量,能迅速找到全局最优解,并满足所有约束,且配电网络的网损最小。     

基于萤火虫优化算法的主动配电网广义电源规划模型

为进一步降低配电网系统网损、提高系统运行电压水平,将广义电源的有功和无功功率作为控制变量,考虑分布式电源出力随机波动特性对配电网广义电源优化配置的影响,从而合理规划广义电源接入,构建同时考虑配电网运行安全性和经济性的多目标无功优化规划模型,利用萤火虫优化算法解决广义电源优化规划问题,并通过改进的IEEE-33节点系统验证了所提模型和算法的可行性和优越性。     

Planning India's long-term energy shipment infrastructures for electricity and coal

The Purdue Long-Term Electricity Trading and Capacity Expansion Planning Model simultaneously optimizes both transmission and generation capacity expansions. Most commercial electricity system planning software is limited to only transmission planning. An application of the model to India's national power grid, for 2008–2028, indicates substantial transmission expansion is the cost-effective means of meeting the needs of the nation's growing economy. An electricity demand growth rate of 4% over the 20-year planning horizon requires more than a 50% increase in the Government's forecasted transmission capacity expansion, and 8% demand growth requires more than a six-fold increase in the planned transmission capacity expansion. The model minimizes the long-term expansion costs (operational and capital) for the nation's five existing regional power grids and suggests the need for large increases in load-carrying capability between them. Changes in coal policy affect both the location of new thermal power plants and the optimal pattern inter-regional transmission expansions.     

计及谐波指标影响的含分布式电源配电网多目标规划

以分布式电源给配电网带来的电能质量管理问题为研究对象,通过一种考虑谐波指标影响的配电网多目标规划方法,对分布式电源接入配电网的网损和节点谐波阻抗矩阵进行分析;然后以最小网络损耗指标和最大谐波管理指标为目标,通过构造隶属度函数,将多目标优化问题转化为对优化结果的满意度;最后采用乌鸦搜索算法进行求解,并通过对IEEE 33节点配电系统的仿真分析验证所提方法的有效性。     

基于逐次优化改进遗传算法的特高压穿越无功规划

与超高压线路相比,特高压线路无功大量富余,会与下级电网形成很大的穿越无功,从而影响无功的分层控制,甚至威胁电力系统的安全稳定运行。常规的优化算法存在维数灾问题,即使是智能算法,也由于解空间维度大而寻优效率低下。对此,提出了一种基于逐次优化改进遗传算法,该方法利用逐次优化的思想,对传统遗传算法的寻优方式进行了改进,并将该算法应用于某实际区域大电网中求解无功规划问题。结果表明,该方法不仅有效降低了解空间的维度,且在保证算法效率的同时使寻优的效果得到较大的改善。     

基于自适应遗传退火算法的配电网故障定位研究

针对标准遗传算法易早熟收敛以及收敛速度慢的问题,提出了一种混合遗传算法（自适应遗传退火算法）用于解决辐射状配电网故障定位问题。该算法采用轮盘赌和最优保存策略相结合的选择机制,使得当前最优个体始终保持在种群里,并结合自适应交叉、变异概率,扩大种群的搜索范围,继而引入模拟退火算法,加快迭代后期算法的收敛速度。最后,通过对IEEE-33节点配电系统进行仿真计算,结果表明,该算法能够对单点和多点故障进行实时、准确地定位,并在故障信息畸变的情况下,也能快速地得到准确结果。