Multi-objective congestion management incorporating voltage and transient stabilities

Congestion in a power system is turned up due to operating limits. To relieve congestion in a deregulated power market, the system operator pays to market participants considering their bids to alter their active powers. After relieving congestion, the network may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. The proposed multi-objective framework for congestion management in this paper simultaneously optimizes competing objective functions of congestion management cost, voltage security, and dynamic security. The voltage stability margin and corrected transient energy margin are employed as indices to be incorporated into the multi-objective congestion management. A fuzzy decision maker is proposed to derive the most efficient solution among Pareto-optimal solutions of multi-objective mathematical programming problem. Results of testing the proposed method on the New-England test system elaborate the efficiency of the proposed method.     

Multi-objective congestion management by modified augmented ε-constraint method

Congestion management is a vital part of power system operations in recent deregulated electricity markets. However, after relieving congestion, power systems may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. Therefore, power system stability margins should be considered within the congestion management framework. The multi-objective congestion management provides not only more security but also more flexibility than single-objective methods. In this paper, a multi-objective congestion management framework is presented while simultaneously optimizing the competing objective functions of congestion management cost, voltage security, and dynamic security. The proposed multi-objective framework, called modified augmented ε-constraint method, is based on the augmented ε-constraint technique hybridized by the weighting method. The proposed framework generates candidate solutions for the multi-objective problem including only efficient Pareto surface enhancing the competitiveness and economic effectiveness of the power market. Besides, the relative importance of the objective functions is explicitly modeled in the proposed framework. Results of testing the proposed multi-objective congestion management method on the New-England test system are presented and compared with those of the previous single objective and multi-objective techniques in detail. These comparisons confirm the efficiency of the developed method.     

Impact of wind generation control strategies, penetration level and installation location on electricity market prices

Integration of wind facilities into power system grids have several impact on power system related issues including; transmission congestion, optimum power flow, system stability, power quality, system economics and load dispatch. Consequently, wind farm control strategy, location across the distribution network and its penetration level could have an impact on electricity market prices. This paper addresses these issues, by developing a single auction market model. An optimal power flow problem was formulated for determining the close to real time electricity market-clearing price and the total cost of generation. Simulation results, considering different operational cases, are presented to highlight the impact.     

Convergence bids and market manipulation in the California electricity market

Financial instruments are designed to improve the efficiency of a physical market in theory. In practice, underlying physical relationships may prevent financial instruments from working as intended. This paper analyzes the relationship between two financial instruments in electricity markets: convergence bidding and congestion revenue rights. Convergence bids allow participants to trade virtual power, whereas congestion revenue rights capture the value of congestion on a transmission line. Both instruments were introduced to solve inefficiencies, however the interaction of these two instruments provides incentives for manipulation. In this work I test whether the financial instruments are being used as intended. The results show that electricity market participants are more likely to hold unprofitable convergence bids on a node where the convergence bid can benefit the value of the congestion revenue rights held at that node. This type of uneconomic convergence bid is consistent with manipulative behavior. I then perform a back of the envelope calculation that shows these potentially manipulative convergence bids create inefficiencies in the market.     

Supply curve bidding of electricity in constrained power networks

This paper presents a Supply Curve Bidding (SCB) approach that complies with the notion of the Standard Market Design (SMD) in electricity markets. The approach considers the demand-side option and Locational Marginal Pricing (LMP) clearing. It iteratively alters Supply Function Equilibria (SFE) model solutions, then choosing the best bid based on market-clearing LMP and network conditions. It has been argued that SCB better benefits suppliers compared to fixed quantity-price bids. It provides more flexibility and better opportunity to achieving profitable outcomes over a range of demands. In addition, SCB fits two important criteria: simplifies evaluating electricity derivatives and captures smooth marginal cost characteristics that reflect actual production costs. The simultaneous inclusion of physical unit constraints and transmission security constraints will assure a feasible solution. An IEEE 24-bus system is used to illustrate perturbations of SCB in constrained power networks within the framework of SDM. By searching in the neighborhood of SFE model solutions, suppliers can obtain their best bid offers based on market-clearing LMP and network conditions. In this case, electricity producers can derive their best offering strategy both in the power exchange and the long-term contractual markets within a profitable, yet secure electricity market.     

Stochastic reactive power market with volatility of wind power considering voltage security

While wind power generation is growing rapidly around the globe; its stochastic nature affects the system operation in many different aspects. In this paper, the impact of wind power volatility on the reactive power market is taken into account. The paper presents a novel stochastic method for optimal reactive power market clearing considering voltage security and volatile nature of the wind. The proposed optimization algorithm uses a multiobjective nonlinear programming technique to minimize market payment and simultaneously maximize voltage security margin. Considering a set of probable wind speeds, in the first stage, the proposed algorithm seeks to minimize expected system payment which is summation of reactive power payment and transmission loss cost. The object of the second stage is maximization of expected voltage security margin to increase the system loadability and security. Finally, in the last stage, a multiobjective function is presented to schedule the stochastic reactive power market using results of two previous stages. The proposed algorithm is applied to IEEE 14-bus test system. As a benchmark, Monte Carlo Simulation method is utilized to simulate the actual market of given period of time to evaluate results of the proposed algorithm, and satisfactory results are achieved.     

Optimal model of congestion management in deregulated environment of power sector with promotion of renewable energy sources

In the competitive electricity market it becomes very much important to give special consideration for development of renewable energy sources (RESs) due to environmental and other social problems related with conventional generations. So this paper presents an optimal model of congestion management with special emphasis for promotion of RES in competitive electricity market. This paper presents a generalized optimal model of congestion management for deregulated power sector that dispatches the pool in combination with privately negotiated bilateral and multilateral contracts while maximizing social benefit. This model determines the locational marginal pricing (LMP) based on marginal cost theory. It also determines the size of non-firm transactions as well as pool demand and generations. Both firms as well as non-firm transactions are considered in this model. The proposed model has been applied to IEEE-30 bus test system with addition of some RES for analysis of the proposed model. The RES supplies its power to load either through the firm transaction or through power pool. The power from RES is not subjected to any curtailment in proposed model of congestion management.     

An approach to locational marginal price based zonal congestion management in deregulated electricity market

Congestion of transmission line is a vital issue and its management pose a technical challenge in power system deregulation. Congestion occurs in deregulated electricity market when transmission capacity is not sufficient to simultaneously accommodate all constraints of power transmission through a line. Therefore, to manage congestion, a locational marginal price (LMP) based zonal congestion management approach in a deregulated electricity market has been proposed in this paper. As LMP is an economic indicator and its difference between two buses across a transmission line provides the measure of the degree of congestion, therefore, it is efficiently and reliably used in deregulated electricity market for congestion management. This paper utilizes the difference of LMP across a transmission line to categorize various congestion zones in the system. After the identification of congestion zones, distributed generation is optimally placed in most congestion sensitive zones using LMP difference in order to manage congestion. The performance of the proposed methodology has been tested on the IEEE 14-bus system and IEEE 57-bus system.     

Optimal generation and reserve dispatch in a multi-area competitive market using a hybrid direct search method

With restructuring of the power industry, competitive bidding for energy and ancillary services are increasingly recognized as an important part of electricity markets. It is desirable to optimize not only the generator’s bid prices for energy and for providing minimized ancillary services but also the transmission congestion costs. In this paper, a hybrid approach of combining sequential dispatch with a direct search method is developed to deal with the multi-product and multi-area electricity market dispatch problem. The hybrid direct search method (HDSM) incorporates sequential dispatch into the direct search method to facilitate economic sharing of generation and reserve across areas and to minimize the total market cost in a multi-area competitive electricity market. The effects of tie line congestion and area spinning reserve requirement are also consistently reflected in the marginal price in each area. Numerical experiments are included to understand the various constraints in the market cost analysis and to provide valuable information for market participants in a pool oriented electricity market.     

Derivation of Stability Limits Using Analytical Sensitivity of the Transient Energy Margin

In recent years considerable progress has been achieved in power system transient stability assessment using the transient energy function (TEF) method. Due to continued developments and enhancements, the TEF method has matured to a stage where reliable and accurate assessment of transient stability of a power system is now possible. An inherent advantage of the TEF method is the availability of a qualitative measure of the degree of stability (or instability) in terms of the transient energy margin (?V). This is an important step toward reducing the number of transient stability runs needed to obtain transient stability loading limits or to make changes to improve the system transient behavior, when subjected to large disturbances. In this paper, a procedure is presented to obtain quantitative answers for these limits by developing an analytical approach to energy margin sensitivity. The approach consists of determining sensitivity coefficients and the development of the dynamic sensitivity equations. A first order expression for the sensitivity of the energy margin is used to determine transient stability limits for the following parameter changes: i. Generation changes ii. Load changes iii. Network changes. The proposed procedure is tested on a 17-generator equivalent of the network of the State of Iowa. The results obtained are then compared with the limits obtained by repetitive application of the TEF method.     

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.     

Reactive power market management considering voltage control area reserve and system security

This paper presents a new algorithm to optimize reactive power procurement through commercial transactions considering system voltage security. The proposed algorithm minimizes reactive power provision and transmission loss costs in addition to maximizing system voltage security margin through a multiobjective function. In order to maintain the voltage profile of power system during sever contingencies or due to load uncertainty, all voltage control areas (VCA) of the system are detected and then optimal reactive power reserve is provided for each VCA during the market settlement. A four-stage multiobjective mathematical programming method is proposed to settle the reactive power market. The proposed algorithm has been applied on IEEE-RTS test system. The simulation results show the effectiveness of the proposed algorithm for reactive power market management.     

规模化光伏电站与电网暂态交互影响定量分析

在电力系统暂态安全定量分析软件平台FASTEST(Fast Analysis of Stability using the Extended equal areacriterion and Simulation Technologies)上以研究规模化光伏电站接入系统后的稳定性分析为目的,建立了一个完整而且适合系统安全稳定分析的并网光伏电站机电暂态模型。基于FASTEST特有的暂态稳定量化分析功能,对并网光伏电站被浮云遮挡和系统侧发生扰动这2种典型情况进行仿真分析,从对系统暂态功角稳定性、暂态电压稳定性和暂态频率稳定性的影响的角度,探讨光伏电站与常规电源在影响电网运行特性方面的区别。结果表明,光伏电站并网后对系统暂态频率稳定性影响最为严重,相比较于带有励磁和调速系统的同步发电机,光伏电站在网侧故障时的暂态响应特性更为剧烈。     

基于遗传算法并考虑稳定约束的最优潮流开发

功角稳定(暂态或振荡)是电力系统运行的一个重要约束.本文描述了一种基于遗传算法(GA)并考虑功角稳定性约束的最优潮流求解方法.在该GA算法中,染色体的控制参量为发电机组的出力.本文应用实例选择英国电力市场中的平衡机制发电机组(BMU).平衡机制机组的选择和优化由GA算法依据BMU的出价(竞价)高低或其出力变化对系统稳定性影响的程度来确定.本文使用灵敏度因子来代表BMU机组对系统稳定性的影响程度,而这些灵敏度因子则是通过开展系统扰动仿真得出的.为确保发电出力始终与用电负荷相匹配,本文采用一种新的映射方法来保持系统有功平衡.文中使用因停电而导致的罚金成本或费用来处理系统稳定性约束,且这些稳定约束对系统目标函数适切性的贡献也被单独评估,以使算法对最优潮流的搜索集中于切实可行的解.对一个化简的英国电力系统开展的仿真测试表明,本文提出的GA算法能够应付此类高度非线性的最优化问题,并给出了对测试系统的数值仿真结果.     

计及购电成本的电网交易能力研究

在电力市场环境下,考虑购电成本对电网交易能力的影响,建立兼顾电网交易能力和购电成本的多目标交易优化模型,运用模糊建模的方法将多目标优化问题转化为求解最大满意度的单目标非线性规划问题,采用原对偶内点法求解。通过对IEEE-30节点测试系统进行仿真计算,结果表明所提出的多目标交易优化模型,可以在提高交易能力的同时尽量减小购电成本,验证了模型及算法的有效性。     

Optimal electricity market for wind power

This paper is about electricity market operation when looking from the wind power producers’ point of view. The focus in on market time horizons: how many hours there is between the closing and delivering the bids. The case is for the Nordic countries, the Nordpool electricity market and the Danish wind power production. Real data from year 2001 was used to study the benefits of a more flexible market to wind power producer. As a result of reduced regulating market costs from better hourly predictions to the market, wind power producer would gain up to 8% more if the time between market bids and delivery was shortened from the day ahead Elspot market (hourly bids by noon for 12–36 h ahead). An after sales market where surplus or deficit production could be traded 2 h before delivery could benefit the producer almost as much, gaining 7%.     

考虑暂态电压稳定性的电力系统多目标无功备用优化

鉴于目前电力系统无功备用优化方法较少涉及暂态电压稳定性,提出一种同时提高电力系统静态/暂态电压稳定性的无功备用优化模型,该模型首先基于数据密度提出一种无功电压控制分区方法,分别在静态/暂态电压的薄弱区域内定义静态/暂态无功备用,然后结合所建立的两个目标建立多目标无功备用优化模型,最后提出一种基于多阶段的多目标优化求解方法。通过求解所建模型,获得能够同时提升系统静态/暂态电压稳定性的无功备用配置方案。对IEEE 39节点系统的仿真分析证明了所提方法的有效性。     

A new spinning reserve requirement forecast method for deregulated electricity markets

Ancillary services are necessary for maintaining the security and reliability of power systems and constitute an important part of trade in competitive electricity markets. Spinning Reserve (SR) is one of the most important ancillary services for saving power system stability and integrity in response to contingencies and disturbances that continuously occur in the power systems. Hence, an accurate day-ahead forecast of SR requirement helps the Independent System Operator (ISO) to conduct a reliable and economic operation of the power system. However, SR signal has complex, non-stationary and volatile behavior along the time domain and depends greatly on system load. In this paper, a new hybrid forecast engine is proposed for SR requirement prediction. The proposed forecast engine has an iterative training mechanism composed of Levenberg–Marquadt (LM) learning algorithm and Real Coded Genetic Algorithm (RCGA), implemented on the Multi-Layer Perceptron (MLP) neural network. The proposed forecast methodology is examined by means of real data of Pennsylvania–New Jersey–Maryland (PJM) electricity market and the California ISO (CAISO) controlled grid. The obtained forecast results are presented and compared with those of the other SR forecast methods.     

Adding wind power to a wind-rich grid: Evaluating secondary suitability metrics

As the quantity of renewable electricity generation from wind farms increases in a region, the costs associated with integrating it into the broader electricity system also grow. This is primarily due to the need for dispatchable generators that vary power output to compensate for wind farm power variations. Such “balancing services” are an economic cost to the system that is typically not passed on to wind farms. We propose including the use of technical merits other than capacity factor and cost of energy for evaluating new wind farm sites and present a new graphical geospatial method, with the intention of identifying sites that minimize the need for additional electricity balancing service and transmission congestion. Specifically, locations with low correlation to existing wind farms, locations with high correlation to load, locations with high characteristic power time-shift from existing wind farms, and locations that relieve or do not negatively impact electricity transmission congestion are identified. A geospatial Venn diagram-based method of visualization is presented. These methods will equip regional planners with new tools to encourage wind farm development in areas that benefit the electricity grid beyond the lowest bid price.     

Sustainable Generation Portfolio

The concept of congestion management is extended to include dynamic effects. Based on a hybrid market structure, a new framework is proposed to eliminate congestion when system stability is threatened. It is shown that by fully utilizing the available resources, system stability as well as the transactions in the bilateral and balancing markets can be secured after a stability contingency, while the total dynamic congestion management cost is minimized.