Competitive Power Market Research Articles

Dynamic bidding analysis in power market based on the supply function

This paper presents a dynamic bidding model of the power market based on the Nash equilibrium and a supply function. The new model is composed of different dynamic systems and semismooth equations by means of the nonlinear complementarity method. Comparing with those existing bidding models, the remarkable characteristic of the new model is twofold: First, it adopts a dynamic bid so that the bidding limit point is the Nash equilibrium point of the market; Second, it considers the system requirement and the market property such as involving the transmission constraints in the network, and using a supply function which is suitable for the oligopolistic competitive power market. All of these imply that the new model is very close to the practical power market. The computation of the dynamic model is discussed by using the semismooth theory. A numerical simulation is presented to test the model behaviors in the uncogestion and the congestion cases, respectively. The numerical tests include the computing behavior of the dynamic model to reach Nash equilibrium points, the influence of the adjusted parameters and the system parameters to the Nash equilibrium, the local stability of the model, and the comparison of simulation effect between the proposed model and the Cournot model. The simulations show that the new bidding model is valid.

Mathematical model of power system management in conditions of a competitive wholesale electric power (capacity) market in Russia

A mathematical model for optimized operation of the Russian unified power system is constructed by selecting loading modes of power-generating equipment on the basis of price bids by generating companies. The optimization is supposed to be conducted according to the criterion of the least cost subject to network limitations and conditions of flow distribution in the electric power system. The main stages of management decision making and their interrelation are considered. A nodal model of electric power pricing in this scheme is presented. The influence of different stages on the uniform nodal prices is investigated. The operation of the wholesale electric power (capacity) market model in Russia is described for the period starting July 1, 2008.

Maintaining of electric supply reliability and equipment maintenance in competitive electric power market

IEEJ Transactions on Electrical and Electronic EngineeringVolume 4, Issue 1 p. 24-25 Special Issue Review Papers Maintaining of electric supply reliability and equipment maintenance in competitive electric power market Ryuichi Yokoyama, Ryuichi Yokoyama yokoyama-ryuichi@waseda.jp Search for more papers by this author Ryuichi Yokoyama, Ryuichi Yokoyama yokoyama-ryuichi@waseda.jp Search for more papers by this author First published: 30 January 2009 https://doi.org/10.1002/tee.20356AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume4, Issue1Special Issue: Special Issue on IEEJ 120th Anniversary Special EditionJanuary 2009Pages 24-25 RelatedInformation

Application of Multi-objective Memetic Algorithm with Solution Diversity to Probabilistic Distribution Network Expansion Planning

This paper proposes a new efficient Multi-objective Memetic Algorithm (MOMA) for probabilistic distribution network expansion planning (DNEP). Recently, the deregulated and competitive power market brings about uncertainty, i.e., random output of distributed generation (DG) such as wind and photovoltaic power, load growths, etc. DG plays a key role to smooth distribution network planning. However, system planners are faced with new uncertain environment. This paper makes use of Monte-Carlo simulation to consider these uncertainties efficiently. Furthermore, a new method is proposed for multi-objective DNEP problems with MOMA that combines Multi-objective meta-heuristics with local search to obtain better solution sets. This paper proposes SPEA2 with Random Multi-start Variable neighborhood LS (RMSVLS) to consider the diversity and accuracy of solution sets. The proposed method is successfully applied to a sample system.

Achieving Environmental Goals in a Competitive Electricity Market?: Post-Colonial Hong Kong, Public Choice and the Role of Government

This paper critically assesses the discourse of electricity market liberalization in Hong Kong, China and makes policy recommendations from an environmental perspective. For both economic and environmental reasons, local advocates urge for an immediate opening of the electricity market currently monopolized by two private companies. Their neglect of the fact that a competitive green power market is a function of adequate public support in the form of market demand may place the environmental agenda at a disadvantage. Given that environmental issues are yet a dominant concern in this growth-oriented economy, a new facilitative role of the government is of particular importance to prevent the market from being dominated by short-term, rational economic behaviours. Mild changes with an emphasis of a transformation in the roles of different players, which the government is hesitant to initiate, are suggested.

Growing RBFNN-based soft computing approach for congestion management

In the emerging restructured power system, the congestion management (CM) has become extremely important in order to ensure the security and reliability of the system. In addition to this, lack of CM can impose a hindrance in electricity trading. This paper presents a novel, growing radial basis function neural network (GRBFNN)-based approach for CM. For achieving CM, Nodal congestion price (NCP) forecasting is performed in real time competitive power market. NCP forecasting is an effective way of price-based preventive CM as it directly indicates the presence as well as the severity of the congestion in the system. In present paper, GRBFNN has been developed for NCP forecasting dividing the whole power system into various congestion zones. An unsupervised learning vector quantization (VQ) clustering algorithm is applied as feature selection technique for the developed GRBFNN and for partitioning the power system into different congestion zones. For each congestion zone a separate neural network has been developed to ensure faster training and accurate forecasting results. The proposed approach of CM is implemented on an RTS 24-bus system. The results obtained are compared with a different constructive algorithm-based RBF network called as general regression neural network (GRNN) and two back-propagation algorithms based ANNs. Comparison results show that proposed GRBFNN is more computationally efficient with better predictive ability.

Strategic bidding in electricity markets using particle swarm optimization

Profit maximization for power companies is highly related to the bidding strategies used. In order to sell electricity at high prices and maximize profit, power companies need suitable bidding models that consider power operating constraints and price uncertainty within the market. In this paper, we present two particle swarm optimization (PSO) algorithms to determine bid prices and quantities under the rules of a competitive power market. The first method uses a conventional PSO technique to find solutions. The second method uses a decomposition technique in conjunction with the PSO approach. This new decomposition-based PSO dramatically outperforms the conventional form of PSO. We show that for nonlinear cost functions PSO solutions provide higher expected profits than marginal cost-based bidding.

Challenges of Iran's energy conversion agreements in future competitive market

Extensive need for electricity and lack of enough governmental resources for the development of related infrastructures forced the Iranian Government to invite private investors and to sign Energy Conversion Agreement (ECA) in the form of build–operate–transfer (BOT) and build–operate–own (BOO) contracts with them. Accordingly, electricity purchase would be based on a guaranteed price. Changes in some laws in 2007 caused the management of the ECAs and electricity purchase based on guaranteed price to face challenges. Shortening the commercial operation period of the earlier ECAs and signing some new short-term ECAs were the steps taken by the authorities to resolve the problems. By shortening the ECAs’ commercial operation period, it is likely to cause serious problems concerning the payments of the project companies, because of shortages in the government's financial resources. The findings of the present viewpoint suggest signing of new long-term contracts (20 years long) in the form of a combinational agreement for buying the produced electricity with a guaranteed price (in the first 5 years) and supplying it in the competitive power market (for the following years) would be a better way to reduce the problems.

Wind power in electricity markets: key issues and challenges

Due to the large penetration and continuous improvement in the wind power technology, wind farms are asked to operate similar to the conventional power plants. In emerging electricity market, wind power generators are now seen differently and should sustain with/without limited government support. Although cost reductions due to technological improvements bring wind power in competitions to conventional fossil-fuel generation, but until now various incentives are required to overcome wind power's cost disadvantage and these incentives may play a significant role in improving the competitiveness of wind power. However, the use and trade of wind power in the market is complicated because of the lack of guarantees of generation, mainly in the wind farms. This paper discusses several key issues and challenges which can be faced by the wind power in the competitive power market. With suitable market mechanism, wind power can be helpful in mitigating the market abuse up to some extent.

Stochastic programming to evaluate renewable power generation for small-scale desalination

Due in part to an increasing population and climatic change, fresh water demand is rapidly outpacing fresh water supply. In Australia desalination plants are already used to obtain fresh water from brackish water and seawater, but they have high energy requirements. Solar collectors could provide power, but solar irradiance is variable and desalination plants work most efficiently with constant power. We model a system of photovoltaic arrays and storage batteries. Daily solar intensity and water demand are stochastic. A stochastic linear program finds the optimal blend of water from available sources---groundwater, desalination and stormwater---to meet daily demand. The optimal use of a given size of solar irradiance collection system is found by stochastic dynamic programming. Long term net benefits are obtained as a function of the system size. References C. Koroneos, A. Dompros, and G. Roumbas. Renewable energy driven desalination systems modelling. Journal of Cleaner Production , 15 (5):449--464, 2007. doi:10.1016/j.jclepro.2005.07.017 . B. S. Richards and A. I. Schafer. Photovoltaic-powered desalination system for remote {A}ustralian communities. Renewable Energy , 28 (13):2013--2022, 2003. doi:10.1016/S0960-1481(03)00081-8 . H. E. S. Fath, F. M. El-Shall, G. Vogt, and U. Seibert. A stand alone complex for the production of water, food, electrical power and salts for the sustainable development of small communities in remote areas. Desalination , 183:13--22, 2005. doi:10.1016 . R. A. Howard. Dynamic programming and Markov processes . The MIT Press, Cambridge, Mass., 1960. M. Kaleta, W. Ogryczak, E. Toczylowski, and I. Zoltowska. On multiple criteria decision support for suppliers on the competitive electric power market. Annals of Operations Research , 121 (1--4):79--104, 2003. doi:10.1023/A:1023351118725 . P. A. Koklas and S. A. Papathanassiou. Component sizing for an autonomous wind-driven desalination plant. Renewable Energy , 31 (13):2122--2139, 2006. doi:10.1016 .

Optimal Spinning Reserve Capacity Determination Using a Chance-constrained Programming Approach

It is crucial to procure adequate amount of spinning reserve capacity for maintaining secure and stable operation of a power system. In the competitive power market environment, under the premise of well balancing the system reliability requirement and spinning reserve procuring cost, the determinination of the optimal spinning reserve capacity to account for the uncertainties of forced outage of generators as well as load forecasting errors is an important issue to be dealt with. In a chance-constrained programming framework, a mathematical model with the minimization of spinning reserve procurement cost as the objective and the system security requirement as a chance constraint is formulated. The problem is solved by a Monte-Carlo simulation-based genetic algorithm (GA). A test system with 18 generating units is employed to demonstrate the validity of the proposed model and the solution methodology.

Reconstructing clusters for preconditioned short‐term load forecasting

AbstractThis paper presents a new preconditioned method for short‐term load forecasting that focuses on more accurate predicted value. In recent years, the deregulated and competitive power market increases the degree of uncertainty. As a result, more sophisticated short‐term load forecasting techniques are required to deal with more complicated load behavior. To alleviate the complexity of load behavior, this paper presents a new preconditioned model. In this paper, clustering results are reconstructed to equalize the number of learning data after clustering with the Kohonen‐based neural network. That enhances a short‐term load forecasting model at each reconstructed cluster. The proposed method is successfully applied to real data of one‐step ahead daily maximum load forecasting. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 161(1): 26–33, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20300

Evolving cooperative bidding strategies in a power market

This paper presents an evolutionary algorithm to generate cooperative strategies for individual buyers in a competitive power market. The paper explores how buyers can lower their costs by using an evolutionary algorithm that evolves their group sizes and memberships. The evolutionary process uncovers interesting agent behaviors and strategies for collaboration. The developed agent-based model uses PowerWorld simulator to incorporate the traditional physical system characteristics and constraints while evaluating individual agent's behavior, actions and reactions on market dynamics. Simulation results on IEEE 14-bus system show that the evolutionary approach evolves mutually beneficial strategies that enhance the buyer's profitability. The buyers learn to achieve substantial cost savings by forming groups and adjusting their demand curves, without sacrificing much in desired power consumption.

Allocation of Transmission Loss for Determination of Locational Spot pricing

The deregulation problem has recently attracted attentions in a competitive electric power market, where the cost must be earmarked fairly and precisely for the customers and the Independent Power Producers (IPPs) as well. Transmission loss is an one of several important factors that determines power transmission cost. Because the cost caused by transmission losses is about <TEX>$3{\sim}5%$</TEX>, it is important to allocate transmission losses into each bus in a power system. This paper presents the new algorithm to allocate transmission losses based on an integration method using the loss sensitivity. It provides the buswise incremental transmission losses through the calculation of load ratios considering the transaction strategy of an overall system. The performance of the proposed algorithm is evaluated by the case studies carried out on the WSCC 9-bus and IEEE 14-bus systems.

An electric power purchase strategy for bilateral contracts focusing on reliability of customers

AbstractWith the introduction of the competitive electrical power market, large‐scale customers can select electric power suppliers. Customers need to consider not only the economical efficiency but also reliability, to decide the amount of electrical power to purchase. This paper develops an economic electric power purchase strategy for customers focusing on reliability. A bilateral contract model expressing electric power suppliers as generators with a forced outage rate is proposed and introduced to assess potential outage risks of the bilateral contract between an electric power supplier and a customer. The outage‐related cost, consisting of potential outage risks and the estimated outage cost, is also proposed and introduced as an index of the reliability on the customer side, and an optimal reliability level of a customer is obtained by using the index. Several numerical examples demonstrate the availability of the proposed electric power purchase decision method and reliability improvement strategies for customers are discussed. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 156(4): 9–21, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20345

06/01675 Congestion management in competitive power market: a bibliographical survey: Kumar, A. et al. Electric Power Systems Research, 2005, 76, (1–3), 153–164

06/01675 Congestion management in competitive power market: a bibliographical survey: Kumar, A. et al. Electric Power Systems Research, 2005, 76, (1–3), 153–164

Flexible transmission and network reinforcements planning considering congestion alleviation

A framework for obtaining an optimal network reinforcement scheme to jointly meet both long-term transmission capacity requirements as well as the alleviation of short- or medium-term congestion in a competitive power market is presented. A value-based network expansion strategy is formulated in which long-term network investment cost is justified by the long-term congestion cost saving. Network expansions and flexible AC transmission system (FACTS) device installations are investigated as possible alternatives for network reinforcement. These alternatives are considered separately as well as in different combinations. Two types of FACTS devices namely the phase shifter and the series compensator are explored for the purpose. The solution methodology is implemented using the generalised Benders decomposition technique in which the master problem is the network investment cost minimisation and the operational subproblem is the social cost minimisation in network constrained power dispatch. Case studies have been carried out on the modified IEEE 24-bus system to illustrate the features of the proposed technique and to demonstrate the ability of the technique to produce good network reinforcement schemes under various conditions.

A Gaussian Processes Technique for Short-term Load Forecasting with Considerations of Uncertainty

In this paper, an efficient method is proposed to deal with short-term load forecasting with the Gaussian Processes. Short-term load forecasting plays a key role to smooth power system operation such as economic load dispatching, unit commitment, etc. Recently, the deregulated and competitive power market increases the degree of uncertainty. As a result, it is more important to obtain better prediction results to save the cost. One of the most important aspects is that power system operator needs the upper and lower bounds of the predicted load to deal with the uncertainty while they require more accurate predicted values. The proposed method is based on the Bayes model in which output is expressed in a distribution rather than a point. To realize the model efficiently, this paper proposes the Gaussian Processes that consists of the Bayes linear model and kernel machine to obtain the distribution of the predicted value. The proposed method is successively applied to real data of daily maximum load forecasting.

Congestion management in competitive power market: A bibliographical survey

Congestion management is one of the major tasks performed by system operators (SOs) to ensure the operation of transmission system within operating limits. In the emerging electric power market, the congestion management becomes extremely important and it can impose a barrier to the electricity trading. This paper presents papers/literature on congestion management issues in the deregulated electricity markets. There are 211 citations referenced in this bibliography. The general electronic web sites and the web sites dealing with the issue of congestion management are also listed.

Development of a Competitive Electricity Market in Indonesia

As a consequence of the Asian financial crisis of 1997, the Government of Indonesia decided to make greater use of market mechanisms in the power sector and embarked upon a sector reform program. The ultimate goal is the establishment of a fully competitive power market. It is, though, recognized that introduction of market mechanisms will have to be confined to the power system of Java and Bali, and that a gradual transition will be required. The reform process has already made substantial progress to date, but much needs to be done before the final stage of a fully competitive market system can be accomplished.