Congestion management with FACTS devices in deregulated electricity markets ensuring loadability limit

This paper addresses an important issue of transmission system congestion management in a pool electricity market environment with the consideration of voltage stability as loadability limit. The optimal generators’ rescheduling has been obtained for three block bid structure submitted to the ISO in a day-a-head market. The base case economic load dispatch has been obtained for generators ensuring the loadability limits and is taken as base case generation output data during the congestion management to obtain new generation scheduling. The generation pattern has been obtained for three bid blocks taking load variation for 24 h considering load scaling factor. The three block bid structure offered to the ISO has been modeled as a linear curve, function of up and down rescheduling within the upper and lower limits offered for congestion management. The impact of third generation FACTS devices has also been studied on the optimal rescheduling of generators’ outputs and thereby the congestion cost. The results have been obtained for IEEE 24 bus and IEEE 57 bus test systems.     

Transmission congestion management in bilateral markets: An interruptible load auction solution

This paper demonstrates that appropriate invocation of interruptible loads by the independent system operator (ISO) can aid in relieving transmission congestion in power systems. An auction model is proposed, for an ISO operating in a bilateral contract dominated market, for real-time selection of interruptible load offers while satisfying the congestion management objective. The proposed congestion management scheme using interruptible loads can specifically identify load buses where corrective measures are needed for relieving congestion on a particular transmission corridor. The N − 1 contingency criterion has been taken into account to simulate various cases, and hence, examine the effectiveness of the proposed method. It has been shown that the method can assist the ISO to remove the overload from lines in both normal and contingency conditions in an optimal manner.     

Comparison of Sen Transformer and UPFC for congestion management in hybrid electricity markets

Congestion management (CM) is one of the most important and challenging tasks of the Independent System Operator (ISO) in the deregulated environment. Recently, Sen Transformer (ST) has emerged as an important power flow control device which has the capability of power flow control over a wide range like UPFC. This device with a conventional transformer and tap changers has the capability of bidirectional control of active and reactive power and can play a very important role in future markets for mitigating the congestion problems. In this paper, the capability of ST has been utilized to manage transmission line congestion for hybrid based electricity market model. The main contribution of the paper is: (i) to develop an optimal rescheduling of generators strategy for real time congestion management and impact of ST for congestion management, (ii) the comparison of ST with unified power flow controller (UPFC) for congestion management, (iii) the secure bilateral transactions determination in a hybrid market model and congestion management with both power flow controllers in combined pool and bilateral market model. The optimal location of ST and UPFC has been obtained solving mixed integer non-linear programming model of congestion management. The proposed model has been applied for results on IEEE 24-bus RTS test system.     

Short-term hydrothermal generation scheduling by a modified dynamic neighborhood learning based particle swarm optimization

The main objective of the short-term hydrothermal generation scheduling (SHGS) problem is to determine the optimal strategy for hydro and thermal generation in order to minimize the fuel cost of thermal plants while satisfying various operational and physical constraints. Usually, SHGS is assumed for a 1 day or a 1 week planing time horizon. It is viewed as a complex non-linear, non-convex and non-smooth optimization problem considering valve point loading (VPL) effect related to the thermal power plants, transmission loss and other constraints. In this paper, a modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) is proposed to solve the SHGS problem. In the proposed approach, the particles in swarm are grouped in a number of neighborhoods and every particle learns from any particle which exists in current neighborhood. The neighborhood memberships are changed with a refreshing operation which occurs at refreshing periods. It causes the information exchange to be made with all particles in the swarm. It is found that mentioned improvement increases both of the exploration and exploitation abilities in comparison with the conventional PSO. The presented approach is applied to three different multi-reservoir cascaded hydrothermal test systems. The results are compared with other recently proposed methods. Simulation results clearly show that the MDNLPSO method is capable of obtaining a better solution.     

Particle swarm optimization algorithm for capacitor allocation problem in distribution systems with wind turbine generators

This paper aims at adopting the Particle Swarm Optimization (PSO) technique to find the near-optimal solutions for the capacitor allocation problem in distribution systems for the modified IEEE 16-bus distribution system connected to wind energy generation based on a cost function. The proper allocation and the optimized number of capacitors have led to adequate power losses reduction and voltage profile enhancement. Because of the wind power generation variations due to the nature of wind speed intermittency and the lack of reactive power compensation, the problem under study have been presented involving a nonlinear fitness function. In order to solve it, the corresponding mathematical tools have to be used. The formulated fitness cost function has consisted of four terms: cost of real power loss, capacitor installation cost, voltage constraint penalty, and capacitor constraint penalty. PSO technique has been used to obtain the near-optimum solution to the proposed problem. Simulation results demonstrate the efficiency of the proposed fitness cost function when applied to the system under study. Furthermore, the application of PSO to the modified IEEE 16-bus system has shown better results in terms of power losses cost and voltage profile enhancement compared to Genetic Algorithm (GA). In order to verify the successful adaptation of PSO toward attaining adequate near-optimal capacitor allocations in distribution systems, this metaheuristic technique has been employed to the large-scale IEEE 30-bus system. The proposed PSO technique has provided adequate results while modifying the objective function and constraints to include the power factor and transmission line capacities for normal and contingency (N-1) operating conditions.     

基于可中断负荷的阻塞调度方法研究

当系统发生输电阻塞时,仅通过调节发电机的出力来消除输电阻塞不是经济的调节方式。提出在电力市场环境下通过调节发电机组出力和中断可中断负荷共同消除输电阻塞的优化调度方法,即在日前市场中以发电成本最小为目标函数,在实时平衡市场中将可中断负荷应用于阻塞管理,并与发电机组出力再调度共同作为阻塞管理的手段,使阻塞管理费用最小;该优化方法采用基于跟踪中心轨迹的内点法来求解。用IEEE30节点系统验证的结果表明所提方法具有经济性。     

Multi-objective optimization operation with corrective control actions for meshed AC/DC grids including multi-terminal VSC-HVDC

This paper presents a multi-objective optimal operation of meshed AC/DC power grids including multi-terminal voltage-source-converter-based high-voltage direct current (VSC-MTDC) systems. The proposed approach is modeled as a corrective security-constrained optimal power flow (CSC-OPF) problem, with the minimization of both the operation cost and power loss as the objectives. Moreover, it provides a cost-effective solution to assist in decision-making, and improves the system security during operation. The N − 1 contingency security criterion is enforced for both AC and DC transmission networks, and corrective control is used to eliminate or alleviate post-contingency security violations. The corrective control actions used in this paper include not only secure operation control actions, but also economical post-contingency corrective control of the multi-terminal VSC-HVDC. To increase the computation speed, a contingency screening technique is applied to CSC-OPF by efficiently selecting the most severe case of the N − 1 contingency, as obtained using a voltage security index (VSI). The proposed approach uses the non-dominated sorting genetic algorithm (NSGA-II) to find multi-objective OPF solutions by checking the post-contingency state feasibility while taking into account post-contingency corrective actions. Simulation results confirm the validity and effectiveness of this approach.     

Security-based multi-objective congestion management for emission reduction in power system

Power system operation in the era of post-restructuring faces several challenges: transmission congestion frequently occurs, security is deterred more than in the past, emission reduction is becoming a matter of importance and intermittent renewable power generation resources (RPGR) have been widely promoted. This paper intends to solve these challenges in a multi-objective optimisation framework. The proposed procedure comprises two stages: in the a priori stage, transmission congestion management cost (TCMC) and emission are traded-off via a proposed stochastic augmented ε-constraint technique which yields a set of non-dominated solutions. In the a posteriori stage, a solution is selected by considering power system security. For this purpose, two strategies are proposed: in the first strategy, based on a proposed managerial vision, a combination of data envelopment analysis introduced by Charnes, Cooper, and Rhodes (CCR-DEA), cross-efficiency technique and robustness analysis is deployed to select the most robust super-efficient solution. The advantage of the proposed a posteriori approach is that selecting the final solution is not subjected to assigning weights to the objective functions and/or providing higher-level information. In the second strategy, first the effective scenarios due to outage of transmission components are identified using CCR-DEA and next, each scenarios’ degree of severity (DOS) is obtained using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The sums of the DOS of non-dominated solutions’ effective scenarios are evaluated for final decision making. The proposed approach is applied to IEEE 24 bus test system and the results are analysed.     

Congestion management considering hydro-thermal combined operation in a pool based electricity market

This paper proposes a novel congestion management strategy for a pool based electricity market considering combined operation of hydro and thermal generator companies. The proposed congestion management problem is formulated as mixed binary nonlinear programming problem to minimize the cost of re-dispatching the hydro and thermal generator companies to alleviate congestion subject to operational, line overloading and water availability constraints. A piecewise-linearized unit performance curve is used in this formulation, which takes into account its non-concave nature. The effectiveness of the proposed technique is demonstrated by solving the modified IEEE 57-bus and IEEE 118-bus systems for congestion management under line outages.     

Hydrothermal self-scheduling problem in a day-ahead electricity market

This paper addresses the self-scheduling problem of determining the unit commitment status for power generation companies before submitting the hourly bids in a day-ahead market. The hydrothermal model is formulated as a deterministic optimization problem where expected profit is maximized using the 0/1 mixed-integer linear programming technique. This approach allows precise modelling of non-convex variable cost functions and non-linear start-up cost functions of thermal units, non-concave power-discharge characteristics of hydro units, ramp rate limits of thermal units and minimum up and down time constraints for both hydro and thermal units. Model incorporates long-term bilateral contracts with contracted power and price patterns, as well as forecasted market hourly prices for day-ahead auction. Solution is achieved using the homogeneous interior point method for linear programming as state of the art technique, with a branch and bound optimizer for integer programming. The effectiveness of the proposed model in optimizing the generation schedule is demonstrated through the case studies and their analysis.     

Hydro unit commitment and loading problem for day-ahead operation planning problem

We describe a new model for the hydro unit commitment and loading (HUCL) problem that has been developed to be used as a support tool for day-ahead operation in the Brazilian system. The objective is to determine the optimal unit commitment and generation schedules for cascaded plants with multiple units and a head-dependent hydropower model. In this paper, we propose a new mathematical model for the hydropower function where the mechanical and electrical losses in the turbine-generator are included. We model the HUCL problem as a nonlinear mixed 0–1 programming problem and solve it with a strategy that includes a two-phase approach based on dual decomposition. The computational tool allows the model to effectively schedule hydro units for the problem in the Brazilian regulatory framework. Application of the approach is demonstrated by determining a 24-time step HUCL schedule for four cascaded plants with 4170 MW of installed capacity.     

Evaluation of voltage and current profiles and Joule losses for a half-wavelength power transmission line

Half-wavelength lines (HWLLs) have been studied as an option to be applied to power transmission regarding line lengths around 2500 km for a 60 Hz frequency. The main advantage of this type of transmission line, which has not yet been put in commercial operation in any country, is the elimination of reactive compensation, which contributes to cost reduction if compared to a conventional ac transmission. This paper presents a demonstration of voltage and current magnitude profiles along the line, using phasors and graphic calculations. In addition, an algebraic expression for Joule losses is proposed, to be used in economical evaluation of a HWLL solution.     

考虑静态电压安全的输电阻塞管理

在电力系统中,输电网络是电力市场的基础和载体,过负荷或违反静态电压安全指标是输电阻塞的主要表现.阻塞将降低电力市场的公平有序竞争,削弱输电系统稳定性,降低电能质量,增加能量成本.因此,研究由于违反静态电压安全标准引起的系统阻塞问题,对于保证电力市场的正常运行和提高电力系统的效率具有重要的现实意义.考虑有载调压变压器以及可投切电容器组在保证系统安全性方面的重要作用,本文把这两个控制方法引入阻塞管理的优化策略中,与发电机组再调度一起,共同优化以处理由于系统静态电压安全引起的阻塞问题.     

Security-constrained transmission expansion planning: A stochastic multi-objective approach

This article presents a stochastic multi-objective optimization framework for transmission expansion planning (TEP) with steady state voltage security management, using AC optimal power flow (AC-OPF). The objectives are to minimize the sum of transmission investment costs (ICs), minimize the Expected Operation Cost (EOC), minimize the Expected Load Shedding Cost (ELSC) and maximize the Expected Loading Factor (ELF). The system load uncertainty has been considered and the corresponding scenarios are generated employing the Monte Carlo (MC) simulations. A scenario reduction technique is applied to reduce the number of scenarios. A multi-objective mathematical programming (MMP) is formulated and the ε-constraint method is used to solve the formulated problem. The N − 1 contingency analysis is also considered for the proposed TEP problem.The proposed TEP model has been applied to the well-known IEEE 24-bus Reliability Test System. The detailed results of the case study are presented and thoroughly analyzed. The obtained TEP results show the efficiency of the proposed algorithm.     

Energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle based on fuzzy logic

In order to enhance the fuel economy of hybrid vehicle and increase the mileage of continuation of journey, a fuzzy logic control is utilized to design energy management strategies for fuel cell/battery (FC + B) hybrid vehicle and fuel cell/battery/ultra-capacitor (FC + B + UC) hybrid vehicle. The models of hybrid vehicle for FC + B and FC + B + UC structure are developed by electric vehicle simulation software ADVISOR which uses a hybrid backward/forward approach. The results demonstrate that the proposed control strategy can satisfy the power requirement for four standard driving cycles and achieve the power distribution among various power sources. The comprehensive comparisons with the power tracking control strategy which is wide adopted in ADVISOR verify that the proposed control strategy has better rationality and validity in terms of fuel economy and dynamic property in four standard driving cycles. Therefore, the proposed strategy will provide a novel approach for the advanced energy management system of hybrid vehicle.     

Experimental analysis and optimization of key parameters of ZVS mode and its application in the proposed LLC converter designed for distributed power system application

This paper deals with experimental analysis of zero-voltage switching mode targeting high-frequency operation of chosen MOSFET type. After selection of specific type of transistor (IPW60R165CP) the experimental investigation has been made by changing parameters (e.g. dead-time, auxiliary capacitance of MOSFET, transistor current), that are influencing the ZVS commutation process. For these purposes we constructed the universal testing device, which is capable to secure realistic conditions of various types of commutation modes (hard switching, zero-voltage switching, zero-current switching). Afterwards the best settings of commutation mode have been utilized in proposed LLC converter suited for distributed power system application. Prototype is operating in ZVS region with optimized parameters. Switching frequency is from 130 kHz (input voltage 325 Vdc) to 210 kHz (input voltage 415 Vdc) with the output power of 1500 W. It is clear from the results that experimental analysis of the ZVS commutation mode brings expectation of transistor behavior which was totally confirmed also in the case of experimental analysis of LLC resonant converter.     

Congestion Management Considering Wind Energy Sources Using Evolutionary Algorithm

It is necessary to maintain power system security within a reasonable limit without harming the electric power supply in an emerging electricity market. Power systems are said to be congested when power transmission reaches beyond its limits. Therefore, in a deregulated power system environment, the system operator must look after the power transactions made by market parties. In this article, a novel congestion management method considering wind energy sources is introduced. The proposed congestion management technique is constructed considering the calculation of bus sensitivity factor and generator sensitivity factor. The bus sensitivity factor is used for finding the optimal location of a wind farm. The most sensitive generators are identified to reschedule their output by using the generator sensitivity factor. The artificial bee colony algorithm and particle swarm optimization techniques are applied to compare the generator rescheduling with earlier literature for congestion management. The proposed method has been tested on the 39-bus New-England test system to investigate the effectiveness of the proposed approach of wind farm integration for congestion management.     

Stochastic mid-term generation scheduling incorporated with wind power

A challenge now facing system operator is how to schedule optimally the generation units in a wind integrated power system over a one year time horizon considering the effects of wind forecasting and variability; also, regarding the effects of load uncertainty. By the same token, this paper first develops a new formulation for Stochastic Mid-term Generation Scheduling (SMGS). In the formulation, 2m + 1 point estimate method is developed to accurately estimate the output variables of Mid-term Generation Scheduling (MGS) problem. Then, the formulation is combined with adaptive modified gravitational search algorithm and a novel self-adaptive wavelet mutation strategy for the establishment of new robust algorithm for the present problem. It is noteworthy to say that the classical methods considered certain wind information in the deterministic solution of the MGS problem which is not the realistic approach. However, this study improves modeling of wind–thermal system in the MGS problem by considering possible uncertainties when scheduling the generators of power system. The proposed model is capable of taking uncertainty of load and wind into account. The proposed method is applied on two test cases and the numerical results confirmed the efficiency and stability of the proposed algorithm.     

Congestion management in hybrid power markets

This paper proposes a one-step methodology for congestion management of a hybrid power market that consists of a power pool and bilateral contracts between market participants. The proposed method is based on a modified Optimal Power Flow (OPF) model to minimize the total cost of generation and ancillary services. Transmission losses, reactive power support and congestion relief are provided centrally in one optimization step. The benders decomposition technique is applied to solve the proposed OPF problem. The congestions are relieved through an iterative process, which makes the congestion management scheme transparent to all the market parties. The IEEE-RTS is analyzed to illustrate the proposed technique.     

Exchange market algorithm for economic load dispatch

This paper presents exchange market algorithm for solving economic load dispatch problems. Exchange market algorithm (EMA) is a new, robust, and strong algorithm to extract the optimal point for global optimization. Inspired by the stock exchange trading method, EMA strives to solve optimization problem. Meticulous investigation of the stock exchange methods employed by the elites in such markets has yielded to shape this algorithm. This algorithm has two searcher operators as well as two absorbent operators for individuals to be absorbed to the elite person, which leads to creation and organization of random numbers in the best way. In order to show the abilities of the EMA, this algorithm has been implemented on four test systems in different dimensions (3, 6, 15 and 40 units) with convex and nonconvex cost functions. The numerical results have been compared with the results of some new and strong algorithms. The results prove the robustness and effectiveness of the proposed algorithm and show that it could be used as a reliable tool for solving practical ELD problems.