Deregulated Power Market Research Articles

USING EXTREME VALUE THEORY TO EVALUATE CONDITIONAL VAR FOR RISK MANAGEMENT IN ELECTRICITY MARKETS

In a deregulated power market, generation companies attempt to maximize their profits and minimize their risks. This paper proposes a risk model for bidding strategy of generation companies based on EVT-CVaR method. Extreme Value Theory can overcome shortcomings of traditional methods in computing financial risk based on value-at-risk and conditional value-at-risk method. Also, generalized Pareto distribution is suggested to model tail of an unknown distribution and parameters of the GPD are estimated by likelihood moment method. Numerical results for risk assessment using the proposed approach are presented for IEEE 30-bus test system. According to the findings, this method can be used as a robust technique to calculate the risk for bidding strategy of generation companies.

A novel power market clearing model based on the equilibrium principle in microeconomics

Clean energy dominant and electricity centric is the inevitable trend of the future energy development. Electric power industry has entered into an increasingly competitive era in recent years due to the deregulation of power systems. However, power market should not be fully competitive and need to be regulated strictly to satisfy the safe and reliable operation of power systems. Base on the market equilibrium principle in microeconomics, a novel power market clearing model with limited competition was put forward, in which the limited competition power satisfies the power flow adjustment and the network losses distribution. Further, the free transaction ratio which can be regarded as the measurement of the degree of power market deregulation was analyzed according to the formulated market clearing model. Finally, three case studies were made on the IEEE-30 test system to verify the practicality and efficiency of the method. The results show that different transaction ratio has significant influence on the operation of power systems and the optimal transaction ratio can be determined.

Optimal sizing and sitting of DG with load models using soft computing techniques in practical distribution system

In the deregulated power market environment, distributed generation (DG) is an effective approach to manage performance, operation and control of the distribution system. Methods available in the literature for DG planning are often not able to simultaneously provide technical and economical benefits. Therefore an effective methodology is developed to improve the technical as well as economical benefits as compared with the existing approaches. This study reports the optimal installation of multi-DG in the standard 33-bus, 69-bus radial distribution systems and 54-bus practical radial distribution system. Several performance evaluation indices such as active and reactive power loss indices, voltage deviation index, reliability index and shift factor indices are used to develop a novel multi-objective function (MOF). A new set of equations is developed for representing different practical load models. A novel MOF has been solved to find optimal sizing and placement of DGs using genetic algorithm and particle swarm optimisation technique. The comparative result analysis is also discussed for both techniques. The result analysis reveals that system losses, energy not supplied, system MVA intakes are reduced, whereas available transfer capability, voltage profile, reliability and cost benefits are improved for the case with-DGs in the distribution system.

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Optimal Setting of FACTS Devices using Particle Swarm Optimization for ATC Enhancement in Deregulated Power System

Abstract In electricity supply industries, the word deregulation is disassembly of original structure and reassembly into another form for better efficiency, performance and to draw private sector investment. The major concerns in deregulated power market are transmission pricing, congestion management and Available Transfer Capability (ATC) that need to be dogged. Among these intriguing challenges, one that needs to be interrogated to provide assured open access transmission service is ATC. The way of improving ATC is either adding transmission facilities or using various FACTS devices. In this paper, our concern is incorporation of FACTS, a semiconductor based control devices to enhance the ATC of transmission system. Various FACTS devices like STATCOM, SSSC and UPFC are modeled by power flow equations and optimally set the FACTS parameters by particle swarm optimization (PSO) technique in such a way so as to enhance ATC in the deregulated electricity environment. This replaces the audacious job of erecting new transmission system or expanding the existing one. The non iterative, simple and fast Power Transfer Distribution Factors (PTDF) based sensitivity method is adopted to determine and enhance ATC on sample six bus system to prove the feasibility of ATC enhancement for bilateral and multilateral transactions.

A new graph theory based loss allocation framework for bilateral power market using diakoptics

Abstract Deregulation and privatization of power market worldwide has forced to identify the different ancillary services and the service providers so that all the services can be priced properly. Active power loss is a component that can never be avoided and constitutes a considerable part of economy. Hence, identification of the sources of active power loss is very important. In deregulated power market bilateral power trading plays a very important role where the amount of power and the flow path between the transacting generators and loads are fixed beforehand. But the power flow rarely follows contracted path, it flows according to physical property of the network. Graph theory based loss allocation methods are based on power flow paths and hence are more reliable. But proper transaction based power flow is not available and it makes graph theory based loss allocation for bilateral market a complex task. Present paper applies diakoptics algorithm to find out the loop flows that is the deviation from contracted path for each bilateral transaction which in turn allows finding out the loss allocated to any transaction. Diakoptics is a general concept in which larger problems are solved by dividing the larger problem into smaller problems and then finding out the overall solution by considering the solutions of each smaller problem together. The proposed method is applied to different test systems and results are discussed in detail.

Automatic generation control of multi‐unit multi‐area deregulated power system using a novel quasi‐oppositional harmony search algorithm

This initiative work deals with the automatic generation control (AGC) of multi-unit multi-area power system in the prospective of deregulated power environment owing to load following issues. In present outlook, a deregulated four-area power system having eight generation companies and eight distribution companies is treated as a plant model. The concerned objective is to intensify the bilateral behaviour and investigate the system AGC performance. In the state of deregulation, the influence of a novel quasi-oppositional harmony search (QOHS) algorithm based Sugeno fuzzy-logic (SFL) technique is proposed as an AGC optimisation task by showing its consequences on bilateral policy/dynamics. In the entire work, the proposed QOHS based SFL technique is incorporated for the tuning of on-line, off-nominal controller gains. The salient advantage of this control strategy is to provide good dynamic responses over a wide range of system parametric variations. The observations are based on the three deregulated cases which, generally, takes place in deregulated power market. The presented simulation results show that the proposed QOHS based SFL is a promising optimisation technique in the field of deregulated AGC study.

Modelling and optimization of CHP based district heating system with renewable energy production and energy storage

Renewable energy source (RES) is playing an increasingly important role to reduce fossil fuels in district heating (DH) and to alleviate the accompanying environmental impact. In this paper, a combined heat and power (CHP) based DH system with RES and energy storage system (ESS) is studied. A modelling and optimization method is developed for planning and operating such CHP-DH systems. The objective of the optimization is to minimize the overall costs of the net acquisition for heat and power in deregulated power market. A planning model consisting of energy balances and constraints for system control and operation is built and an efficient algorithm is developed. We demonstrate the method in a CHP-DH system with a solar thermal plant and a thermal energy storage (TES). Results indicate that the developed method is efficient and flexible for planning and operating CHP-DH systems. To simulate the future situation, we also optimize the same CHP-DH system with a higher share of RES and a bigger TES. Results show that TES is used more intensively in the future with more fluctuating CHP load and a higher share of RES.

Performance comparison of enhanced PSO and DE variants for dynamic energy/reserve scheduling in multi-zone electricity market

During the last decade, energy regulatory policies all over the globe have been influenced by the introduction of competition. In a multi-area deregulated power market, competitive bidding and allocation of energy and reserve is crucial for maintaining performance and reliability. The increased penetration of intermittent renewable generation requires for sufficient allocation of reserve services to maintain security and reliability. As a result the market operators and generating companies are opting for market models for joint energy and reserve dispatch with a cost minimization/profit maximization goal. The joint dispatch (JD) problem is more complex than the traditional economic dispatch (ED) due to the additional constraints like the reserve limits, transmission limits, area power balance, energy-reserve coupling constraints and separate sectional price offer curves for both, energy and reserve.The present work proposes a model for the joint static/dynamic dispatch of energy and reserve in deregulated market for multi-area operation using enhanced versions of particle swarm optimization (PSO) and differential evolution (DE). A parameter automation strategy is employed in the classical PSO and DE algorithms (i) to enhance their search capability; (ii) to avoid premature convergence; and (iii) to maintain a balance between global and local search. The performance of enhanced PSO and DE variants is compared for single/multi-area power systems for static/dynamic operation, taking both linear and non-smooth cost functions. The proposed approach is validated on two test systems for different demands, reserve requirements, tie-line capacities and generator outages.

AGC of a multi-area power system under deregulated environment using redox flow batteries and interline power flow controller

In this paper, Proportional Integral Derivative with Filter (PIDF) is proposed for Automatic Generation Control (AGC) of a multi-area power system in deregulated environment. Initially, a two area four units thermal system without any physical constraints is considered and the gains of the PIDF controller are optimized employing Differential Evolution (DE) algorithm using ITAE criterion. The superiority of proposed DE optimized PIDF controller over Fuzzy Logic controller is demonstrated. Then, to further improve the system performance, an Interline Power Flow Controller (IPFC) is placed in the tie-line and Redox Flow Batteries (RFB) is considered in the first area and the controller parameters are tuned. Additionally, to get an accurate insight of the AGC problem, important physical constraints such as Time Delay (TD) and Generation Rate Constraints (GRC) are considered and the controller parameters are retuned. The performance of proposed controller is evaluated under different operating conditions that take place in a deregulated power market. Further, the proposed approach is extended to a two area six units hydro thermal system. Finally, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values.

Dispatch Scheduling for a Wind Farm With Hybrid Energy Storage Based on Wind and LMP Forecasting

In a deregulated power market, the real-time wholesale market price of electricity varies dramatically within a single day due to the availability of the resources. Moreover, the price of electricity can be different from one location to the other at the same time period due to the location of the available resources and transmission constraints. This is the so-called locational marginal price (LMP). Since wind power is noncontrollable and partially unpredictable, it is difficult to schedule its output to exploit LMP variations. While energy storage system (ESS) may accommodate wind farm output, it requires significant initial financial commitment. Accurately forecasted wind power and LMP information can reduce the required capacity and make it financially feasible for the ESS to perform desired functions. In this paper, artificial neural network (ANN) technique is employed to forecast the day-ahead wind power and LMP, and a hybrid ESS consisting of two storage facilities is developed. The primary ESS is utilized for the optimizing wind-storage system production schedule with day-ahead forecasting data, while the secondary ESS is applied to address the forecasting errors during real-time operation. With this hybrid ESS design, financial benefits are achieved for the wind farm.

A hybrid DE–PS algorithm for load frequency control under deregulated power system with UPFC and RFB

Abstract In this paper, a Modified Integral Derivative (MID) controller is proposed for Load Frequency Control (LFC) of multi-area multi-source power system in deregulated environment. The multi-source power system is having different sources of power generation such as thermal, hydro, wind and diesel generating units considering boiler dynamics for thermal plants, Generation Rate Constraint (GRC) and Governor Dead Band (GDB) non-linearity. The superiority of proposed hybrid Differential Evolution and Pattern Search (hDE-PS) optimized MID controller over GA and DE techniques is demonstrated. Further, the effectiveness of proposed hDE-PS optimized MID controller over Integral (I) and Integral Derivative (ID) controller is verified. Then, to further improve the system performance, Unified Power Flow Controller (UPFC) is placed in the tie-line and Redox Flow Batteries (RFBs) are considered in the first area. The performance of proposed approach is evaluated at all possible power transactions that take place in a deregulated power market.

IJIREEICE - Electrical, Electronics, Instrumentation and Control

In this country development of electrical power system particular for transmission line expansion and interconnection of neighbouring power system try to improve system security and economic operation. Due to this province in large scale power system, operation , control and expansion planning for particular system become challenging task to reduce transmission loss as minimum as possible for optimal operation. Now days, One of the major problems in opened power markets is loss division. In this paper, a different method for allocating and identify real power transmission losses to pool market participants is proposed. The proposed method is fundamentally based on decomposition of loss function and different in generators voltage concept. The method has been implemented and tested on several networks and IEEE 5-bus test system sample summarized in the paper. The results show that the method is inclusive and fair to splitting the energy losses of a deregulated power market to its participants.

DE optimized fuzzy PID controller with derivative filter for LFC of multi source power system in deregulated environment

Abstract In this paper, Differential Evolution (DE) optimized fuzzy PID controller with derivative Filter (PIDF) is proposed for Load Frequency Control (LFC) of a deregulated power system with multi-source power generation and interconnected via parallel AC/DC transmission links. To get an accurate insight of the LFC problem, important physical constraints such as time delay and GRC are considered. The performance of proposed controller is evaluated at all possible power transactions that take place in a deregulated power market. The improvement in dynamic performance of the power system with DC link in parallel with AC tie-line is also assessed. Further, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. It is observed from the simulation results that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters.

Bidding strategy for wind generation considering conventional generation and transmission constraints

Under the environmental crisis of global warming, more efforts are put in application of low carbon energy, especially low-carbon electricity. Development of wind generation is one potential solution to provide low-carbon electricity source. This paper researches operation of wind generation in a de-regulated power market. It develops bidding models under two schemes for variable wind generation to analyze the competition among generation companies (GENCOs) considering transmission constraints. The proposed method employs the supply function equilibrium (SFE) for modeling the bidding strategy of GENCOs. The bidding process is solved as a bi-level optimization problem. In the upper level, the profit of an individual GENCO is maximized; while in the lower level, the market clearing process of the independent system operator (ISO) is modeled to minimize the production cost. An intelligent search based on genetic algorithm and Monte Carlo simulation (MCS) is applied to obtain the solution. The PJM five-bus system and the IEEE 118-bus system are used for numerical studies. The results show when wind GENCOs play as strategic bidders to set the price, they can make significant profit uplifts as opposed to playing as a price taker, because the profit gain will outweigh the cost to cover wind uncertainty and reliability issues. However, this may result in an increase in total production cost and the profit of other units, which means consumers need to pay more. Thus, it is necessary to update the existing market architecture and structure considering these pros and cons in order to maintain a healthy competitive market.

Bidding Strategy in Deregulated Power Market Using Differential Evolution Algorithm

The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in the bidding process in order to maximize the profit of suppliers and benefits of the consumers. Each supplier bids strategically by choosing the bidding coefficients to counter the competitors bidding strategy. Electricity or electric power is traded through bidding in the power exchange. GENCOs sell energy to power exchange and in turn ancillary services to Independent System Operator (ISO). In this paper, Differential Evolution algorithm is proposed for solving bidding strategy problem in operation of power system under deregulated environment. An IEEE 30 bus system with six generators and two large consumers is employed to demonstrate the proposed technique. The results show the adaptability of the proposed method compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Monte Carlo simulation in terms of Market Clearing Price (MCP).

Energy Storage in Flywheels: An Overview

In a deregulated power market with increasing penetration of distributed generators and renewable sources, energy storage becomes a necessity. Renewable energy sources are characterized by a fluctuating and intermittent nature, which simply means that energy may be available when it is not needed, and vice versa. Energy storage devices can help rectify the mismatch between generation and demand at any loading condition. Such devices can also provide some ancillary services, such as frequency regulation, voltage support, power quality improvement, transmission congestion relief, and system upgrade deferral. This paper presents an overview of the flywheel as a promising energy storage element. Electrical machines used with flywheels are surveyed along with their control techniques. Loss minimization and bearing system development are introduced. In addition, power system applications of flywheels are summarized. Finally, some outstanding technical difficulties are recommended for future research work.

Short-term LMP forecasting using an artificial neural network incorporating empirical mode decomposition

Summary A deregulated power market utilizes wholesale and retail transactions as two of its major business models. Locational marginal prices (LMPs) originating from power markets and system operations imply electricity values in a location or at a bus. Market participants can develop bidding strategies according to vital information of LMPs. The security coordinator can conduct market redispatch for congestion management based on LMP serving as a crucial indicator. This work presents a novel method using the empirical mode decomposition (EMD) incorporating correlation coefficients and neural networks using the back-propagation (BP) algorithm for undertaking short-term LMP forecasting. The correlation coefficient identifies coherent LMPs that act as inputs for the BP-based neural network to forecast the next LMPs. Additionally, validity of the proposed method is verified using historical LMPs in the PJM market in USA. Simulation results demonstrate that the proposed method can forecast short-term LMP values more efficiently than the traditional autoregressive integrated moving average, BP-based network without EMD, BP-based artificial neural networks with multi-resolution analysis of the wavelet transform and recurrent neural network. Copyright © 2014 John Wiley & Sons, Ltd.

Electricity price classification using extreme learning machines

Forecasting electricity prices has been a widely investigated research issue in the deregulated power market scenario. High price volatilities, price spikes caused by a number of factors such as weather uncertainty, fluctuating fuel prices, transmission bottlenecks, etc., make the task of accurate price forecasting a formidable challenge for the market participants. A number of models have been proposed by researchers; however, achieving high accuracy is always not possible. In some specific applications such as self-scheduling by demand side participants, certain price thresholds are more useful than accurate price forecasts. In this paper, we have investigated the application of a novel neural network-based technique called extreme learning machine for the problem of classification of future electricity prices with respect to certain price thresholds. Different models corresponding to different lead times are developed and tested with data corresponding to Ontario and PJM markets. It is observed that classification with ELM is fast, less sensitive to user defined parameters and easily implementable.

A Tri-Level Model of Centralized Transmission and Decentralized Generation Expansion Planning for an Electricity Market—Part I

We develop a tri-level model of transmission and generation expansion planning in a deregulated power market environment. Due to long planning/construction lead times and concerns for network reliability, transmission expansion is considered in the top level as a centralized decision. In the second level, multiple decentralized GENCOs make their own capacity expansion decisions while anticipating a wholesale electricity market equilibrium in the third level. The collection of bi-level games in the lower two levels forms an equilibrium problem with equilibrium constraints (EPEC) that can be approached by either the diagonalization method (DM) or a complementarity problem (CP) reformulation. We propose a hybrid iterative solution algorithm that combines a CP reformulation of the tri-level problem and DM solutions of the EPEC sub-problem.