Market-driven dynamic transmission expansion planning

We present a novel transmission expansion planning model that explicitly considers a multi-year planning horizon. The proposed formulation is a mixed-integer linear problem that can be solved using commercially available software. The algorithm determines the best overall transmission expansion plan, reflecting both investment and operational costs. To analyze the results obtained from the model, we use a set of efficiency metrics to appraise the effect of the expansion among generators and demands, as well as congestion metrics to measure changes in nodal prices and line congestions. This paper also provides a case study that considers the expansion plan for a transmission system based on the one of mainland Spain.     

Efficient method for AC transmission network expansion planning

A combinatorial mathematical model in tandem with a metaheuristic technique for solving transmission network expansion planning (TNEP) using an AC model associated with reactive power planning (RPP) is presented in this paper. AC-TNEP is handled through a prior DC model while additional lines as well as VAr-plants are used as reinforcements to cope with real network requirements. The solution of the reinforcement stage can be obtained by assuming all reactive demands are supplied locally to achieve a solution for AC-TNEP and by neglecting the local reactive sources, a reactive power planning (RPP) will be managed to find the minimum required reactive power sources. Binary GA as well as a real genetic algorithm (RGA) are employed as metaheuristic optimization techniques for solving this combinatorial TNEP as well as the RPP problem. High quality results related with lower investment costs through case studies on test systems show the usefulness of the proposal when working directly with the AC model in transmission network expansion planning, instead of relaxed models.     

A PSO based approach for multi-stage transmission expansion planning in electricity markets

This paper presents a particle swarm optimization (PSO) based approach to solve the multi-stage transmission expansion planning problem in a competitive pool-based electricity market. It is a large-scale non-linear combinatorial problem. We have considered some aspects in our modeling including a multi-year time horizon, a number of scenarios based on the future demands of system, investment and operating costs, the N − 1 reliability criterion, and the continuous non-linear functions of market-driven generator offers and demand bids. Also the optimal expansion plan to maximize the cumulative social welfare among the multi-year horizon is searched. Our proposed PSO based approach, namely modified PSO (MPSO), uses a diversity controlled PSO to overcome the problem of premature convergence in basic PSO (BPSO) plus an initial high diversity swarm to cover the search space efficiently. The MPSO model is applied to the Garver six-bus system and to the IEEE 24-bus test system and compared to the BPSO model and a genetic algorithm based model.     

Transmission surplus capacity based power transmission expansion planning

A power transmission expansion planning model with consideration of transmission surplus capacity and network load factor is presented. With traditional planning model, some transmission lines will operate on high load factors due to ignorance of the load levels of transmission lines. This may lead to network congestion or degrade the dispatch flexibility of future network. Traditional planning model has put more emphasis on investment cost rather than other aspects such as operation environment, transmission benefit, etc. The transmission expansion planning model in the paper aims to maximize network transmission surplus capacity and optimize network load factor distribution with least investment. Chaos Optimal Algorithm (COA) is introduced to solve this nonlinear integer planning optimization problem for its advantage of stochastic and ergodic searching characteristics. The effectiveness of proposed model and methodology is tested with two typical systems.     

Multi-objective transmission expansion planning considering minimization of curtailed wind energy

This paper proposes a new multi-objective transmission expansion planning (TEP) in order to find optimum location of lines/transformers by considering intermittent nature of wind power and variable load structure. To reveal the benefit of wind power in the context of TEP, curtailed wind energy is considered as one of the objective functions besides the sum of the investment costs and penalty for energy not supplied (ENS). By this way, a trade-off between investment cost and curtailed wind energy is established using fuzzy satisfying approach while maintaining the adequacy of power system. The multi-objective nature of the proposed method is handled by using the Non-dominated Sorting Genetic Algorithm-II combined with the DC-optimal power flow which is performed several times to obtain curtailed wind energy and ENS. The nature of load and wind power is incorporated into the methodology by using agglomerative hierarchical clustering to reduce computational effort. The proposed methodology is illustrated on the different configurations of modified IEEE-RTS 24 bus test system. Numerical case studies indicate the effectiveness of the proposed method for reduction of total investment cost in TEP of power systems with wind power integration.     

发电扩容与输电扩容的协调优化模型

以电厂和电网分离为主要特征的电力市场化改革给电力扩容规划带来了巨大的冲击。如何协调好发电与输电的扩容直接关系到电力系统的稳定运行和健康发展。通过分析市场化改革后电力扩容规划存在的主要问题,构筑了发电扩容与输电扩容的协调优化模型。该模型中提出了利用协调因子的思想来解决电力扩容的规划风险和保障竞争性电力市场的电价机制。协调因子计及了缺电成本和输电利用成本,这不仅能充分反映电力扩容的规划风险,而且能使双方共同承担其风险,从而促进两者扩容的协调。文章为厂网协调发展探索了新的途径。     

考虑连锁故障的输电网扩展规划方法

为提高电网抵御多重故障的能力,常在输电网规划中嵌入N-k安全网络约束.但该方式未考虑故障间关联性,致使故障场景数量庞大且部分场景与实际不符,可能导致规划方案投资冗余.在计及故障关联性的基础上,提出一种考虑连锁故障的输电网双层扩展规划模型.上层模型以线路投资、运行及切负荷成本之和最小为目标,考虑了故障链约束;下层模型以切负荷最小为目标,对上层模型得到的规划方案进行连锁故障校验.上下层模型以故障链约束为纽带,通过迭代添加新的约束,达到降低求解规模、加快算法收敛的目的 .以IEEE RTS-24系统为分析算例,仿真结果表明所提模型能够在抵御连锁故障的同时提升规划方案经济性.     

An approach for transmission expansion planning using neurocomputing hybridized with a genetic algorithm

The aim of transmission expansion planning is to determine which right-of-way to use when constructing new lines in order to meet a forecasted load in the most economical way. This problem has been solved previously by mathematical sensitivity analysis (which finds a single nonoptimal solution). It is difficult to plan for economical and reliable expansion due to its discrete and combinatorial nature. Although another method that has efficiency for combinatorial problems is neurocomputing, this approach saves computational efforts but obtains poor solutions. The most desirable approach for this planning problem is one in which many good solutions are found in reasonable time, because planning experts will then be able to plan economical and reliable expansion according to these solutions. This paper presents an approach for solving transmission expansion planning based on neurocomputing hybridized with a genetic algorithm. This approach generates suitable initial states, which include past information, of neural networks utilizing genetic algorithm. Mingling neurocomputing and a genetic algorithm, the proposed approach can find many good solutions in reasonable time making full use of their merits. Computational examples show the effectiveness of the proposed approach by comparison with conventional approaches.     

Capacity expansion in transmission networks using portfolios of real options

We adopt in this paper a perspective of portfolios of real options, to propose a mixed integer linear programming model for multistage Transmission Network Expansion Planning. The model is then used to analyze three fundamental network building blocks – an independent design, a radial design, and a meshed design – seeking to develop network design insights, in particular regarding the joint value of postponement and other sources of operational flexibility. The results clearly point to the importance of explicitly incorporating uncertainty, adopting a multistage perspective, and addressing complex interactions between different sources of flexibility, in the design of transmission networks.     

A flexible decision method for transmission system expansion planning considering power interruption cost

Under the current deregulation of the electric utility industry, power system planning has become more complex. System planners are required to study how to use available resources to meet customer's demand economically with acceptable reliability and quality. Integrated resource planning has emerged recently as one effective approach for this problem, and the approach has been expanded to include the value of reliability, that is, power interruption cost. This paper is aimed at developing a flexible decision method to select a favorable plan among various transmission system expansion alternatives, not only from a power supplier's viewpoint, but also from the customer's perspective, including cost of interruption. Bellman-Zadeh's maximizing decision using a fuzzy decision set is applied as a flexible decision method which is able to reflect the planner's intentions. Numerical examples are given to ascertain the effectiveness of the proposed method.     

输电网的规划综合成本法及其应用研究

针对电网发展日益受到土地、环境及风险评估等内外部条件约束的现状,提出一种输电网规划与项目决策的新方法.该方法基于全寿命周期管理的理念,以全社会成本为尺度,对输电网扩展方案的技术性和经济性进行综合评判,通过引入土地机会成本和风险评估方法实现了对电网征地和电网风险等关键因素的量化评估.与传统输电网的规划设计方法进行比对分析表明,该方法能够更加准确地计及输电网规划方案的长期性和全面性,为输电网规划设计技术经济比选方法的改进提供了参考依据.     

Sensitivity analysis based on transmission line susceptances for congestion management

Transmission line limits impede power transfers and cause congestion greatly reducing the effectiveness of systems and increasing the cost of power transmissions. Through several methods, congestion can be effectively eliminated either by building a new transmission line or by increasing the capacity of the original line between congested zones. Both methods cause susceptance change and line capacity increase between nodes. Therefore, the sensitivity of a system to variations of its parameters becomes important in terms of operation and planning. In this paper, congestion relief as a function of line susceptances and line capacities is investigated. Mathematical derivations for calculating sensitivities based on line susceptances are given systematically, and numerical studies were performed by using sequential quadratic optimization programming to determine congestion in terms of line susceptances. It is shown that the optimum susceptance range becomes critical when the line parameters are changed dynamically by using thyristor-controlled series-compensators. To relieve congestion in the operation of a network may require tracking this optimal parameter range whenever system states change.     

Transmission network expansion planning considering repowering and reconfiguration

Transmission expansion planning (TEP) is a classic problem in electric power systems. In current optimization models used to approach the TEP problem, new transmission lines and two-winding transformers are commonly used as the only candidate solutions. However, in practice, planners have resorted to non-conventional solutions such as network reconfiguration and/or repowering of existing network assets (lines or transformers). These types of non-conventional solutions are currently not included in the classic mathematical models of the TEP problem. This paper presents the modeling of necessary equations, using linear expressions, in order to include non-conventional candidate solutions in the disjunctive linear model of the TEP problem. The resulting model is a mixed integer linear programming problem, which guarantees convergence to the optimal solution by means of available classical optimization tools. The proposed model is implemented in the AMPL modeling language and is solved using CPLEX optimizer. The Garver test system, IEEE 24-busbar system, and a Colombian system are used to demonstrate that the utilization of non-conventional candidate solutions can reduce investment costs of the TEP problem.     

市场环境下计及阻塞集中度指标的输电网扩展规划

与传统的输电网规划相比,电力市场环境下的输电网扩展规划不仅要满足市场用户的需求,还对减缓输电系统阻塞、抑制市场力的滥用和提高社会效益起着重要作用。对于因网络充裕度不足造成的长期输电阻塞,首先参考市场集中度监管指标Lerner指数的定义,提出了新的阻塞集中度指标作为评估输电网络充裕度的指标;然后从市场运营的实际情况出发,以阻塞集中度指标作为约束条件来抑制网络的垂直市场力,构造了将阻塞收益用于回收投资成本的输电网扩展规划模型。IEEE-24节点算例系统分析表明该模型综合考虑了输电投资成本回收以及输电阻塞管理等实际问题,能有效减缓系统阻塞发生,降低投资风险,并为输电网监管提供可靠依据。     

A dynamic model for coordination of generation and transmission expansion planning in power systems

This paper presents a new dynamic approach on the expansion planning problem in power systems. First, the coordination between generation system expansion and transmission system expansion has been formulated as a mixed integer nonlinear programming (MINLP) problem. Then, it has been shown that this MINLP model cannot be efficiently solved by the traditional MINLP solvers. Since the nonlinear term comes from the multiplication of a binary variable by a continuous one, a Benders decomposition approach has been employed to convert the MINLP formulation into a mixed integer linear programming (MILP) master problem, and a linear programming (LP) sub-problem. Besides, different times of construction have been considered for different transmission and generation facilities. In addition, a clustering based algorithm has been proposed to evaluate the reliability of the system at hierarchical level II (HLII). Since this dynamic planning method is an upgraded version of a recent developed static model, the result from both methods have been also compared. A simple 6-bus test system and IEEE 30-bus system have been selected to confirm the effectiveness of the introduced method.     

基于系统与元件动态交互量化分析的电力系统连锁故障事故链识别方法

针对现有的连锁故障事故链搜索方法一般只考虑线路的过负荷保护或确定性模拟保护控制的动作等不足,提出了基于系统与元件动态交互量化分析的电力系统连锁故障事故链识别方法。该方法基于事故链模型,提出关键元件保护控制动作评估指标,用于量化关键元件保护控制的动作情况。进一步根据该指标计算临界情况下关键元件保护控制的动作概率,确定连锁故障事故链的后续事件。最后,基于风险指标筛选高风险连锁故障事故链。对2017夏高峰负荷情况下的某实际电网进行仿真,结果验证了所提方法的可靠性和有效性。     

An approach for simultaneous distribution,sub-transmission,and transmission networks expansion planning

Sub-transmission network, as an intermediate grid between the distribution and transmission systems, receives the electric energy from the transmission network at extra high voltage levels, and delivers it to the distribution network at medium or low voltage levels. The adequate design and operation of sub-transmission system will lead to an efficient design of transmission network from the technical and economic viewpoints on one hand and the adequacy of power delivery to the distribution loads on the other hand. Therefore, the design optimality of these three networks is highly dependent on each other. However, as the simultaneous design of distribution, sub-transmission, and transmission systems is highly complicated, very few researches have tried to model and solve such a difficult problem. In this paper, a new approach has been developed for simultaneous distribution, sub-transmission, and transmission networks expansion planning. The proposed approach has been formulated as an optimization problem where an efficient and improved genetic algorithm (GA) is employed to solve such a complex problem. The utilized GA has been equipped with different modifying operators in order to make sure of its appropriate performance in obtaining useful and optimal solutions for the coordinated planning problem. The conducted approach has been implemented on a real network of Zanjan Regional Electrical Company (ZREC), and the results are compared with those of conventional method, i.e. separate expansion planning of these networks. The simulation results demonstrate the effectiveness of the conducted approach.     

基于云平台三维数字化输电线路路径规划方法

三维数字化设计是近年来输电线路设计研究中的热点方向,为解决三维数字化输电线路路径规划中的海量数据挖掘问题,研究了一种基于云平台的输电线路三维数字化设计路径规划方法.首先使用云平台和三维GIS技术,建立了复杂地理信息的数字化成本评估模型,并将其成本评估模型等效为加权栅格矩阵,再使用数据挖掘中的Q-learning方法进行...     

计及碳交易成本及能效电厂的电源规划模型

在低碳经济背景下,新能源机组及碳交易机制的引入成为电力系统实现低碳减排重要的方法。同时,由于需求侧资源在降低系统碳排放量和增加系统可靠性方面有较大潜力,为此,将需求侧的能效电厂加入到规划方案中,建立了包含系统经济性及可靠性的低碳电源规划模型,目标函数中计及了机组建设成本、能效电厂建设成本、机组运行成本、碳交易成本,并将可靠性约束条件加入到模型中。采用离散细菌群体趋药性算法对所建模型进行求解,对两种电源规划模型进行了对比,并在不同碳排放限额、不同电量不足期望值限额两种情况下进行了灵敏度分析,仿真结果验证了模型和算法的有效性。     

A novel modified GBMO algorithm based static transmission network expansion planning

Due to the growing demand of electricity, transmission sector has become important part of the power sector. The penetration level of renewable energy resources has increased presently, which gives more challenges to the transmission expansion planner. To overcome this problem better transmission expansion planning (TEP) needs to be done. Hence, it is necessary to incorporate the impact of high wind power penetration in TEP problem. In this paper wind farm are considered as an alternative source for supplying the load to the transmission networks. The complex wind energy cost model is incorporated with the traditional transmission network expansion planning (TNEP) problem. Factors accounting for wind power utilization cost, underestimation, and overestimation cost model of wind power are included. Static transmission network expansion planning (STNEP) problem is modeled using the DC power flow model. The main objective function is to minimize the total cost of the system, which consists of transmission line investment cost, fuel cost of generators and wind energy cost. To solve this non-linear, non-convex optimization problem with a novel optimization algorithm i.e. Modified Gases Brownian Motion Optimization (MGBMO) algorithm is applied. To validate the capability of the proposed method is tested with modified Garver’s 6-bus system, IEEE 24-bus system and IEEE 25-bus system.