Valuation of reactive power support services based on sensitivity and risk analysis

In deregulated power market environment system operators need to procure adequate reactive power support services to facilitate real power transactions and improve system security. However, reactive power produced by suppliers at different locations may have different effects on the economics and operating security of the system. Thus, the assessment of the value of these reactive power support services is an important issue to be dealt with. With this objective an evaluation methodology based on voltage sensitivity and risk analysis is proposed from the perspective of voltage regulation. The IEEE 14-node test system is used to illustrate and examine the features of the proposed methodology. Some interesting results are observed and discussed.     

Energy delivery capacity and generation scheduling in thederegulated electric power market

The generation schedules obtained in traditional hydrothermal scheduling or unit commitment programs are in hourly generation levels. In the new deregulated power market, the power transactions are processed in terms of hourly energy delivery. Failing to fulfil scheduled energy delivery may result in a penalty to the power producers. This paper shows that although ramp-rate constraints are satisfied in hydrothermal scheduling, taking a generation level schedule as an energy delivery schedule may not be realizable. Based on the maximum principle in optimal control theory, the energy delivery capacity across the scheduling horizon is established as a set of recursive equations with given ramp-rate constraints. A sufficient and necessary condition is obtained to check if an energy delivery schedule is realizable. Based on this condition, two cases, where ramp-rate constraints are both satisfied, are analyzed and an unrealizable energy delivery schedule is observed     

Effect of deregulation on hydrothermal systems with transmission constraints

In this paper, a procedure is presented to compute power transactions among hydrothermal utilities in deregulated power pools. The paper examines the problem of price definition in utilities with hydro and thermal resources. The approach is tested on an example and the results are discussed.     

Design of a robust PID-PSS for an uncertain power system with simplified stability conditions

In a deregulated power system uncertainty exists and lack of sufficient damping can lead to Low Frequency Oscillations (LFO). The problem can be addressed using robust Power System Stabilizers (PSS). In this paper, an optimal procedure to design a robust PID-PSS using interval arithmetic for the Single Machine Infinite Bus (SMIB) power system is proposed. The interval modelling captures the wide variations of operating conditions in bounds of system coefficients. In the proposed design procedure, simple and new closed loop stability conditions for an SMIB interval system are developed and are used to design an optimum PID-PSS for improving the performance of an SMIB system. The optimum PID-PSS is attained by tuning the parameters using the FMINCON tool provided in MATLAB. The robustness of the proposed PID-PSS design is validated and compared to other notable methods in the literature when the system is subjected to different uncertainties. The simulation results and performance error values show the effectiveness of the proposed robust PID-PSS controller.     

AGC of a multi-area multi-source hydrothermal power system interconnected via AC/DC parallel links under deregulated environment

This paper proposes the automatic generation control (AGC) of an interconnected multi-area multi-source hydrothermal power system under deregulated environment. The two equal control areas with hydro and thermal generating power sources are interconnected via AC/DC parallel links. The optimal proportional integral (PI) regulators are designed for the proposed power system to simulate all power market transactions which are possible in a restructured power system. The concept of DISCO participation matrix (DPM) is harnessed to simulate the transactions. Eigenvalue study is conducted to assess the effect of AC/DC parallel links on system performance. The study is also conducted, considering appropriate generation rate constraints (GRCs) for thermal and hydro generating sources. Further, the dynamic responses of the proposed multi-source hydrothermal power system are compared with single-source thermal–thermal power system and it has been ascertained that the responses of proposed power system are sluggish with large overshoots and settling times. Finally, the study is extended to frame and implement optimal PI regulators for the first time for the AGC of a conventional two-area non-reheat thermal power system with governor dead-band nonlinearity. The superiority of the optimal PI regulators has been established by comparing the results with recently published best claimed craziness based particle swarm optimization (CRAZYPSO) and hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) algorithms based PI controller tuned for the same interconnected power system.     

Load following in a deregulated power system with Thyristor Controlled Series Compensator

Load following is considered to be an ancillary service in a deregulated power system. This paper investigates the effect of a Thyristor Controlled Series Compensator (TCSC) for load following in a deregulated two area interconnected thermal system with two GENCOs and two DISCOs in either areas. Optimal gain settings of the integral controllers in the control areas are obtained using Genetic Algorithm by minimizing a quadratic performance index. Simulation studies carried out in MATLAB validates that a Thyristor Controlled Series Compensator in series with tie-line can effectively improve the load following performance of the power system in a deregulated environment.     

Transmission loss allocation using combined game theory and artificial neural network

An artificial neural network based method is suggested for allocation of transmission loss in a deregulated power system involving bilateral contract based power transactions between power suppliers and the distribution companies. The proposed method allocates transmission losses to operating transactions on the basis of Shapley value game theoretic approach. For this, bilateral transactions for power system are simulated and corresponding losses are obtained through load flow analysis. Shapley value approach is used to calculate the loss allocations. A large number of such simulation cases are generated and a large data pool stores these possible bilateral transactions and the corresponding loss allocations following Shapley value approach. For allocating losses to the transactions of an operating system environment the required neural network is developed online. A simple filtering technique is used to extract the suitable training data that are close enough to the actual operating condition from the generated data pool and neural network is trained online. The performance of such a network when tested on standard power networks has been found to be very encouraging.     

Impact of energy storage system on load frequency control for diverse sources of interconnected power system in deregulated power environment

This paper focuses Load Frequency Control (LFC) mechanism for multi-generating two areas interconnected power systems with energy storage system in a deregulated power environment. The two areas, demarcated as Area-I and Area-II, consist of thermal, hydro and gas power units. This paper also incorporates the economic load dispatch mechanism into the LFC for economical division of load during load deviation. Small signal stability analysis through participating factor has also been done to determine the oscillation state of the system, i.e., frequency deviation in both areas. Therefore, proper controller is required to reduce the oscillation of the system. The optimum value of the integral gain of the integral controller has to be selected to achieve the goal. Hence, Opposition-based Harmonic Search (OHS) technique is used for the optimization purpose. During major disturbance in the areas, primary and secondary controllers are not sufficient to reduce the frequency and tie-line power oscillation due to slow response of the governor mechanism. Therefore, energy storage system, i.e., Redox Flow Battery (RFB), is used for improvement of the dynamic response of the system which has very small time constant and quick response. The proposed control mechanism has been analyzed in a deregulated power environment with the help of different simulation case studies to find out improved dynamic performance over integral control strategies.     

An estimation method of available transfer capabilities from viewpoint of power system transient stability under deregulated environment

To conduct electric power transactions effectively and to operate a power system efficiently while maintaining reliability under the deregulated environment, it is required that ATC (Available Transfer Capability) be calculated at high speed and with reasonable precision. In order to address this issue, in this paper, an Artificial Neural Network‐based estimation method for evaluating Maximum Transmission Capability (MTC), which is a key step but also a highly time consuming process in ATC, is proposed. It is confirmed through simulation studies that the proposed method is capable of estimating MTC (ATC) with high speed and sufficient precision. Furthermore, the authors examined the reduction of calculation time at learning by using the transient stability index. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 167(1): 66–73, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20781     

Benefit optimization of centralized and decentralized power systemsin a multi-utility environment

The objective is to examine the theoretical bases for benefit optimization in centralized and decentralized electric power systems. In a centralized (single control) power system, cost, revenue, and profit are used as system performance criteria for benefit optimization, and the conditions are compared. The results show that conventional cost minimization is not necessarily equivalent to benefit optimization based on other criteria. In a decentralized (multi-utility) power system, benefit optimization and its allocation are investigated at the unconstrained economic optimum, economic optimum with constraints, and conditions under which the solution is an economic suboptimum. The data from the EPRI Regional Systems are used to show practical examples of the benefit allocation with wheeling transactions. Although some rules are better for society than others, there still is not one right answer to determine the rules for benefit optimization     

Day-ahead price forecasting in restructured power systems using artificial neural networks

Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg–Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania–New Jersey–Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate.     

ATC enhancement in electricity markets with GUPFC and IPFC – A comparison

Available transfer capability (ATC) needs to be declared well in advance by the system operator to reserve transactions and avoid any congestion in the network. In this paper, an optimal power flow based approach has been utilized for bilateral/multi-transactions deregulated environment to obtain the ATC. The ATC has been obtained with generalized unified power flow controller (GUPFC) and interline power flow controller (IPFC) for intact and line contingency cases. The impact of ZIP load model has been evaluated on the ATC with both the devices. The main contribution of the paper is the comparison of the ATC obtained with. GUPFC and IPFC for intact and contingency cases with constant P,Q load model and ZIP load model. The results have been determined for intact and line contingency cases taking simultaneous as well as single transaction cases. The results obtained are also compared with DC/AC and PTDFs. The proposed method have been applied for IEEE 24 bus RTS.     

联营交易模式下的阻塞成本分摊

在开放的电力市场环境下,输电阻塞不仅关系到系统的安全运行,还影响市场的有效运转.如何快速消除阻塞并实现其成本的合理分配是市场运行的基本内容之一.文章提出了一种联营交易模式下的阻塞成本分摊方法,该方法将对交易的分摊转化为全网→线路→交易的分摊,利用Shapley值实现阻塞成本在线路间的分配.多组测试数据表明,文中的方法能够实现收支平衡,分摊结果合理,且可避免针对交易进行成本分配而产生的计算量过大的问题.     

A unified management of congestions due to voltage instability and thermal overload

This paper proposes two approaches for a unified management of congestions due to voltage instability and thermal overload in a deregulated environment. Both techniques aim to remove, in some optimal manner, voltage and thermal congestions stemming from base case or post-contingency states, by a simultaneous handling of operating and security constraints with respect to several contingencies. The objective of the first approach is to adjust the market-based power injections (generator output and possibly load consumption) at the least cost while the second one aims at curtailing power transactions in a transparent and non-discriminatory way. These techniques rely on sensitivities which pinpoint the best remedial actions against congestions owing to voltage instability and thermal overload. Numerical results with both approaches are provided on a realistic 80-bus system model.     

Load frequency control of power system under deregulated environment using optimal firefly algorithm

In this paper, a novel Firefly Algorithm (FA) optimized hybrid fuzzy PID controller with derivative Filter (PIDF) is proposed for Load Frequency Control (LFC) of multi area multi source system under deregulated environment by considering the physical constraints such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB) nonlinearity. As the effectiveness of FA depends on algorithm control parameters such as randomization, attractiveness, absorption coefficient and number of fireflies are systematically investigated, the control parameters of FA are tuned by carrying out multiple runs of algorithm for each control parameter variation then the best FA control parameters are suggested. Additionally, the superiority of the FA is demonstrated by comparing the results with tuned Genetic Algorithm (GA). To investigate the effectiveness of the proposed approach, time domain simulations are carried out considering different contracted scenarios and the comparative results are presented. Further, sensitivity analysis is performed by varying the system parameters and operating load conditions. It is observed from the simulation results that the designed controllers are robust and the optimum gains of proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Finally, the effectiveness of the proposed control scheme is evaluated under random step load disturbance.     

Design and analysis of multi-source multi-area deregulated power system for automatic generation control using quasi-oppositional harmony search algorithm

The present article focuses on the study of automatic generation control (AGC) of a realistic power system having a distinct combination of multi-area multi-source generating units in each control area under deregulated framework. An attempt is made in this paper to integrate reheat thermal, hydro and gas generating unit in a single control area and, then, extended this combination to five control areas. In this work, six reheat thermal, six hydro and three gas generating units are taken into account for the modeling of five-area power system. Some important physical constraints like time delay, governor dead band and generation rate constraint are imposed in the power system dynamics to get an accurate perception of the deregulated AGC subject. The highlighting features of the present work are to model, simulate, optimize and co-relate their inter-related dynamic performances for the purpose of AGC study. For such a complex AGC model, the vital role of the proposed quasi-oppositional harmony search (QOHS) algorithm, as an optimizing tool, is signified while solving the AGC problem in deregulated regime. The simplicity of the structure and acceptability of the responses of the well-known proportional–integral–derivative controller, inherently, enforces to employ in this work. The three classes of extensive deregulated cases (in the presence of load following and physical constraints) are demonstrated by examining the closed loop performance of the studied model. The simulation results show that the designed power system model may be a feasible one and the proposed QOHS algorithm may be a promising optimization technique under these circumstances.     

Allocation of the reactive power support requirements inmultitransaction networks

This paper presents a new physical-flow-based mechanism for allocating the reactive power support requirements provided by the generators in multitransaction networks. The allocatable reactive support requirements are defined with respect to the support required for the network with no transactions in place. The requirements in the presence of the proposed transactions are formulated as the sum of two specific components-the voltage magnitude variation component and the voltage angle variation component. The formulation utilizes the multitransaction framework used for the allocation of losses. The formulation leads to a natural allocation as a function of the amount of each transaction. The physical interpretation of each allocation as a sensitivity of the reactive output of a generator is discussed. The extensive testing indicates that the allocation scheme approximates with good fidelity the actual net VAr outflow from each generator and is able to deal effectively with the nonlinearities due to the generator reactive power limits. The numerical results also indicate that the proposed scheme behaves in a physically reasonable and intuitive way     

Project valuation and power portfolio management in a competitivemarket

Deregulation in the US electric utility industry has lead to the entry of many new market participants, particularly power marketers, which in turn has lead to a much more active and fluid electricity market. With the advent of new market instruments that can be used to hedge one's exposure to market risks, power portfolio management takes on new meaning and new tools and techniques can be used to manage risk. This paper concentrates on long-term project valuation and power portfolio management issues in a competitive environment. It describes a method of valuing risky projects based on options pricing theory and decision analysis and its implementation to long-term power portfolio management or integrated resource planning for an electric utility in Texas. The author believes that the methodology described in this paper has wide application in the deregulated competitive electricity market from integrated resource planning for utilities facing competition and valuing a power plant investment by an independent power producer to pricing electricity options     

Analysis of interruptible electric power in deregulated power systems

Assuring appropriate levels of generation capacity adequacy at the minimum possible cost using market-based approaches is a contemporary issue attracting much attention in deregulated power systems. Introduction of interruptible electricity contracts could provide a possible solution to such a problem. However, theoretical frameworks for examining the impacts of these contractual arrangements on system operations have not been maturely developed. This paper presents a probabilistic production simulation based framework for modeling and analyzing interruptible electricity contracts. The interdependence between the pricing models of interruptible electric power and the operations of the whole system is addressed and solved by an iterative coordinating algorithm. The probabilistic production simulation framework allows the uncertainties inherent in both the forced outages of generators and system load demand at the contract delivery time to be taken into account. The proposed approach is capable of achieving comprehensive analysis and systematic pricing for interruptible electric power. A numerical case study is presented to illustrate the effectiveness and validity of the methodology.     

An efficient stochastic self-scheduling technique for power producers in the deregulated power market

Generation scheduling and dispatch are determined by individual power producers’ bids in a deregulated power market. The benefits obtained by a power producer will depend largely on how effectively it can incorporate the variation of the market price in its generation scheduling. A stochastic scheduling technique is presented in this paper for maximizing a producer’s benefit considering the stochastic nature of power price. Formulation of the problem has been presented in detail. Two approaches to the solution, namely two-stage and multi-stage stochastic methods, are presented which accommodate the features of the day-ahead and hour-ahead power markets, respectively.Implementation of the technique is illustrated by applying the technique to scheduling by a power producer with 11 generators in two different seasons. The price data from a real system has been used for the price forecasting and the formation of price scenario tree. It is shown through illustrative case studies that the proposed stochastic methods can enhance benefits if proper values are assigned for the probabilities of the price scenarios and if the selection of the probabilities of price scenarios are directly related to the variation of the actual power prices. The proposed technique is particularly effective when the uncertainty in the price is high and the price forecast is not very accurate.