Competitive Electricity Market Environment Research Articles

Regional Power Market Impact in Competitive Power Market: A Case Study in Zhejiang

Along with the progress of the electricity market reform, the formulation of electricity prices and the bidding in the electricity market have become hot issues. Given that electricity price contains a variety of information in the electricity market and reflects market changes, insufficient understanding of electricity prices or improper response will not only affect the revenue of power supply companies, but also have adverse effects on consumers in the downstream of the electricity market. Taking Zhejiang Province as an example, this paper uses a Multi-objective optimization based on genetic algorithm to calculate the optimal power structure of Zhejiang Province under a competitive electricity market environment and evaluate the impact of the market. The results show that as the market reduces the transaction cost of purchased power, the purchased power to Zhejiang Province has a significant positive role in power cost, stability and cleanliness. The electricity cost could be cut down by 18.3 billion yuan, which is 0.04 yuan/kWh. In terms of cleanliness and stability of electricity consumption, the competitive electricity market will also bring about benefits by 2.43% and 1.64% respectively.

Multi area AGC scheme using imperialist competition algorithm in restructured power system

This paper is focused on optimization based design methodology and application of PID controller in restructured, competitive electricity market environment, for AGC problem. The paper compares two search algorithms for designing of PID controller used for AGC in multiarea power system. The optimal parameters of PID controller have been determined with the use of Imperialist Competitive Algorithm (ICA). A deregulated scenario has been considered to develop the model of the multiarea AGC scheme. This paper presents that the ICA tuned PID (ICA-PID) controller can optimally regulate the generators output and can provide the best dynamic response of frequency and tie-line power on a load perturbation. The performance of proposed controller has been checked on 2-area thermal power system and 3-area thermal-hydro power system with the consideration of generation rate constraint (GRC). The results obtained by ICA-PID controller and genetic algorithm tuned (GA-PID) controller have been compared on the basis of performance parameters (settling time and oscillations). It is seen that ICA-PID controller shows the better performance as compared to GA-PID controller.

The Economic Benefits of Generation Revenue Assessment in Pool-Based Market Model for Restructured Electricity Supply Industry

The electricity supply industry had undergo deregulation and restructuring toward becoming a more transparent and competitive electricity market environment. The pool market model is amongst the most preferred electricity market model. Even though it is a safe option to be more competitive and transparent electricity supply industry, there are issues on the welfare of the generators involved. This paper addresses the pricing issue in the pool market by extending the capacity payment mechanism in the single auction power pool. In the proposed model, the approach of minimum capacity payment involving the efficiency of the generators is introduced. A case study is conducted to illustrate the proposed model. An economic analysis is performed to highlight the merits of the proposed model with the pure pool in term of generation revenue. © 2016 The Authors, published by EDP Sciences.

Effects of Interruptible Load on Decision Making of a Distribution Company in Competitive Environments

The main goal of this paper is to present a new day-ahead energy acquisition model for a distribution company (Disco) in a competitive electricity market environment with Interruptible Load (IL). The work formulates the Disco energy acquisition model as a bi-level optimization problem with some of real issues, and then studies and designs a Genetic Algorithm (GA) of this optimization problem too. To achieve this goal, a novel two-step procedure is proposed. At the first step, a realistic model for an industrial interruptible load is introduced, and it is shown that Interruptible load model may affect the problem modeling and solving. At the second step, Disco energy acquisition program is formulated and solved with this realistic model. As a result, this paper shows energy acquisition programming model with ILs, by considering real assumptions. The introduced method shows a good performance of problem modeling and solving algorithm both in terms of solution quality and computational results. In addition, a case study is carried out considering a test system with some assumptions. Subsequently results show the general applicability of the proposed model with potential cost saving for the Disco.

A HYBRID APPROACH FOR IPFC LOCATION AND PARAMETERS OPTIMIZATION FOR CONGESTION RELIEF IN COMPETITIVE ELECTRICITY MARKET ENVIRONMENT

The deregulated power system operation with competitive electricity market environment has been created many challenging tasks to the system operator. The competition with strategic bidding has been resulted for randomness in generation schedule, load withdrawal and power flows across the network. The economic efficiency of electricity market is mainly dependent on network support. In the event of congestion, it is required to alter the base case market settlement and hence the economic inefficiency in terms of congestion cost can occur. In order to anticipate congestion and its consequences in operation, this paper has been considered Interline Power Flow Controller (IPFC).This article proposed a tactical approach for optimal location and then its parameters in Decoupled Power Injection Modeling (DPIM) are optimized using Gravitational Search Algorithm (GSA). The case studies are performed on IEEE 30-bus test system and the results obtained are validating the proposed approach for practical implementations.

Multifractal based return interval approach for short‐term electricity price volatility risk estimation

With the ever-increasing penetration level of renewable energy generation in a power system, more uncertainties are introduced and hence risk management in the electricity market associated is becoming a more difficult issue for a market participant in the context of optimising his/her portfolio. Among a lot of risk factors in the competitive electricity market environment, the highly volatile electricity price contributes most to the financial risk of the power portfolio, especially in a short-term risk management scenario such as the spot market and real-time balancing market. Some research work has shown that the fluctuations of electricity prices exhibit multifractal characteristics, but less work has been done on the price volatility risk evaluation based on the multifractal theory. This study hence examines the feasibility of applying the multifractal theory to analyse the electricity price fluctuation, and applies the multifractal theory for evaluating the financial risk caused by electricity price volatility. A modified return interval approach considering the parameters of multifractal characteristics is employed to estimate the value-at-risk (VaR) of the electricity price. The fluctuant electricity price data series in the Pennsylvania-New Jersey-Maryland energy market are employed to demonstrate the effectiveness of the proposed VaR estimation method for short-term electricity price volatility risk evaluation.

Optimal Location of Thyristor-controlled-series-capacitor using Min Cut Algorithm

Existence of many different Operators in the new electricity has brought many challenges in the system operation and control to obtain minimum generation cost and security. With the growing demand of electricity in the competitive electricity market environment, one or more transmission lines could be overloaded, therefore causing congestion. The congestion can be eliminated /alleviated by improving transfer capability of the network. Thyristor controlled series compensators (TCSC), with its ability to directly control the power flow can be very effective to improve the operation of transmission network. This paper describes an approach for determining the most suitable locations for installing TCSC devices in order to eliminate line overloads and minimize generation costs. The proposed approach is based on the minimum cut methodology that reduces the search space and using benefit index to decide on the best locations for the TCSC. The 5-bus, IEEE 14-bus and 30-bus test systems are used to demonstrate the proposed approach. Results show that the proposed method is capable of finding the best location for TCSC installation to minimize total costs. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4423

Impact of UPFC on Competitive Electricity Market Settlement in Deregulated Power System

The operation and security management of deregulated power system with competitive electricity market environment is a typical task to the system operator due to its complexity under uncertainties. In order to enhance security level, unified power flow controller (UPFC) is proposed in this work. Under stressed conditions, the increment in market economic inefficiency is optimized with UPFC device support. The UPFC location and its parameters are controlled to minimize the total generation cost. The case studies are performed on IEEE 14-bus test system and the results validating the proposed approach for social welfare maximization in real time.

A novel multi-zone reactive power market settlement model: A pareto-optimization approach

This paper presents a Pareto-optimization based zonal day-ahead reactive power market settlement model named as multi-zone DA-RPMS model. Three competing objective functions such as Total Payment Function (TPF) for reactive power support services from generators/synchronous condensers, Total Real Transmission Loss (TRTL) and Voltage Stability Enhancement Index (VSEI) are optimized simultaneously by satisfying various power system operating constraints while settling the day-ahead reactive power market. The proposed multi-zone DA-RPMS model is tested and compared with single-zone DA-RPMS model on standard IEEE 24 bus reliability test system. A Hybrid Fuzzy Multi-Objective Evolutionary Algorithm (HFMOEA) approach is applied and compared with NSGA-II for solving these DA-RPMS models in competitive electricity market environment. Further, both the single-zone and multi-zone DA-RPMS models are also analyzed on the basis of market power owned by any generator/any generating company. The simulation results obtained confirm the superiority of HFMOEA in finding the better Pareto-optimal fronts in order to take better day-ahead reactive power market settlement decisions.

Multi-objective reactive power market clearing in competitive electricity market using HFMOEA

This paper presents an application of a hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) for solving a highly constraint, mixed integer type, complex multi-objective reactive power market clearing (RPMC) problem for the competitive electricity market environment. In HFMOEA based multi-objective optimization approach, based on the output of a fuzzy logic controller crossover and mutation probabilities are varied dynamically. It enhances stochastic search capabilities of HFMOEA. In multi-objective RPMC optimization framework, two objective functions namely the total payment function (TPF) for reactive power support from generators and synchronous condensers and the total real transmission loss (TRTL) are minimized simultaneously for clearing the reactive power market. The proposed HFMOEA based multi-objective RPMC scheme is tested on a standard IEEE 24 bus reliability test system and its performance is compared with five other multi-objective evolutionary techniques such as MOPBIL, NSGA-II, UPS-EMOA and SPEA-2 and a new extended form of NSGA (ENSGA-II). Applying all these six evolutionary techniques, a detailed statistical analysis using T-test and boxplots is carried out on three performance metrics (spacing, spread and hypervolume) data for RPMC problem. The obtained simulation results confirm the overall superiority of HFMOEA to generate better Pareto-optimal solutions with higher convergence rate as compared to above mentioned algorithms. Further, TPF and TRTL values corresponding to the best compromise solutions are obtained using said multi-objective evolutionary techniques. These values are compared with one another to take better market clearing decisions in competitive electricity environment.

Integrated Isolation Niche and Immune Genetic Algorithm for solving Bid-Based Dynamic Economic Dispatch

This paper presents a novel algorithm-Isolation Niche Immune Genetic Algorithm for solving power system Bid-Based Dynamic Economic Dispatch (INIGA–BDED). Economic Dispatch determines the electrical power to be generated by the committed generating units in a power system so that the generation cost can minimized, while simultaneously satisfying various load demands. The Bid-Based Dynamic Economic Dispatch model is proposed in order to maximize the social profit under a competitive electricity market environment. This model synthetically considers various constraints on ramp rates, transmission line capacity and emission constraints. The Isolation Niche Immune Genetic Algorithm was induced as a new solution for this model. With the introduction of niche technology, the immune genetic algorithm capability in dealing with multi-peak model function optimization was enhanced. This paper proposes the Niche based on the Isolation mechanism which is based on biological possesses. The proposed method effectively ensures diverse group solutions and also has a strong ability to guide evolution. Using the immune genetic algorithm itself is a very good and innovative method for multi-peak model function solutions. A new improved method for this algorithm is also presented in this paper. This research integrated these two methods to enhance the evolutionary capability in seeking a more optimal solution for solving nonlinear programming. The test results from this integrated method were very good.

A review on reactive power management in electricity markets

PurposeThe main purpose behind this work is to explore the methods already proposed in various literatures to overcome the issues associated with VAr management in a competitive environment. Managing reactive power support service in competitive electricity market environment has become an important constituent of ancillary services. The characteristics of VAr generation/absorption do not allow its transmission over a long distance. The issues associated with the localized nature of reactive power must be considered during the valuation, planning, pricing and allocation of VAr producing/absorbing resources. In this review work, the key issues associated with the reactive support and the techniques used to tackle these issues in various utilities across the globe are been discussed in brief. In the literature, numerous renowned authors propose various methods to manage reactive power with various types of structural and operational scenarios. These methods are also discussed briefly in this paper. The experience with VAr management in some matured electricity market is also discussed in this paper.Design/methodology/approachDiscussion of various issues associated with reactive power management and methods/techniques to overcome these, has been carried out in this paper. The methods were proposed in various literatures related to reactive power management by some of the renowned authors and adopted by various electric utilities.FindingsThe review work may be useful for utilities to develop a quick insight on reactive support services to control the voltage profile and also, it may be a useful asset for the researchers working in this area.Originality/valueThe paper is organized with different sections to elaborate the issues and associated methods. This paper is a single piece of work, which addresses reactive power planning, pricing for VAr support, market issues and valuation of VAr utilization.

POWER PLANT INVESTMENT PLANNING BY STOCHASTIC PROGRAMMING / STOCHASTINIO PROGRAMAVIMO NAUDOJIMAS PLANUOJANT ELEKTROS ENERGIJOS INFRASTRUKTŪRĄ IR GAMYBĄ

Although the problem of rational power generation has been extensively studied, traditional approaches for power optimization do not offer good solutions to this purpose, especially in a competitive electricity market environment where many factors are uncertain. In this paper, within the framework of two‐stage linear stochastic programming, the method for power planning has been developed, with uncertain factors taken into account, through a continuously distributed set of scenarios. The objective is to find the structure of the power plants capacity in the region which minimizes the sum of the investment and the expected operating costs over the long‐term planning horizon, taking into account the environmental impact. The structure of the considered task corresponds to a power investment planning problem that often arises in the developing regions. The method is developed for solving the stochastic optimization problem by the sequence of Monte‐Carlo sampling estimators. The procedures developed make it possible to solve stochastic problems with an admissible accuracy by means of an acceptable amount of computations. As follows from numerical experiments the approach presented enables us to decrease the total expected costs of power planning versus deterministic planning solution. Santrauka Nors elektros energijos infrastruktūros ir gamybos uždavinys sprendžiamas intensyviai, tradiciniai optimizavimo metodai nepateikia tinkamų sprendinių, ypač kai elektros energijos rinkoje daugelis veiksnių yra neapibrėžti. Šiame straipsnyje pateikiamas elektros energijos gamybos planavimo metodas, sukurtas remiantis dviejų etapų stochastiniu programavimu, kai neapibrežtumas aprašomas tolydžiaisiais pasiskirstymo scenarijais. Uždavinio tikslas – rasti tinkama regiono elektros jėgainiu struktūra, kuri minimizuotų investavimo ir ilgalaikės energijos gamybos sąnaudas. Sprendžiant uždavinį atsižvelgiama į gamtosaugos problemas. Taikant optimizavimo metodą naudojami baigtinių Monte Karlo sekų įverčiai. Siūloma procedūra leidžia išspręsti stochastinius uždavinius gana tiksliai, naudojant priimtinus skaičiavimo išteklius. Skaitiniai eksperimentai rodo, kad siūlomas metodas padeda sumažinti bendrasias elektros energijos gamybos sąnaudas, palyginti su deterministiniu uždavinio sprendiniu.

A Generic Operations Framework for Discos in Retail Electricity Markets

A generic operations framework for a distribution company (disco) operating in a competitive electricity market environment is presented in this paper. The operations framework is a two-stage hierarchical model in which the first deals with disco's activities in the day-ahead stage, the day ahead operations model (DAOM). The second deals with disco's activities in real-time and is termed real-time operations model (RTOM). The DAOM determines the disco's operational decisions on grid purchase, scheduling of distributed generation (DG) units owned by it, and contracting for interruptible load. These decisions are imposed as boundary constraints in the RTOM and the disco seeks to minimize its short-term costs keeping in mind its day-ahead decisions. A case-study is presented considering the well-known 33-bus distribution system and three different scenarios are constructed to analyze the disco's actions and decision-making in this context.

An incentive-based mechanism for transmission asset investment

This paper presents an incentive scheme to encourage investment in the improvement and expansion of the transmission in the competitive electricity market environment. To create these incentives, a decentralized transmission asset investment model is proposed, where the new assets are built by the investors. The incentives are based on the value added to the social welfare through each asset investment. By viewing each potential investor as a player in a cooperative game the Shapley value is used to reward investors according to the added value that they create. The proposed methodology is applied to the Garver 6-bus system and the IEEE 24-bus Reliability Test System to illustrate the capability and flexibility of the decision support system presented.

A Revenue and Profit Evaluation Method Based on the Equivalent Load Duration Curve for Electricity Market Simulations

This article presents an efficient algorithm for evaluating the profit and revenue of generating units in a competitive electricity market based on a variant probabilistic production costing technique considering the forced outages of generators. The accurate evaluation of the profit and revenue of generating units for long-term perspectives is one of the most important issues in a competitive electricity market environment. For efficient and exact calculation of the profit and revenue of generating units under the equivalent load duration curve, a new approach to figure out the marginal plants and the corresponding market clearing prices during a time period in a probabilistic manner is developed. The mathematical formulation and an illustrative application of the suggested method is presented. Also, the results obtained from the suggested method for the IEEE Reliability Test System are compared with those of the conventional chronological simulation technique enumerating all possible states and a Monte Carlo simulation.

An investigation of reactive power planning based on chance constrained programming

Deregulation in the electricity supply industry has brought many new challenges to the problem of reactive power planning. Although the problem has been extensively studied, available standard optimization models and methods do not offer good solutions to this problem, especially in a competitive electricity market environment where many factors are uncertain. Given this background, a novel method for reactive power planning based on chance constrained programming is presented in this paper, with uncertain factors taken into account. A stochastic optimization model is first formulated under the presumption that the generator outputs and load demands can be modeled as specified probability distributions. A method is then presented for solving the optimization problem using the Monte Carlo simulation method and genetic algorithm. Finally, a case study is used to illustrate the validity and essential features of the proposed model and methodology.

Short-term ANN load forecasting from limited data using generalization learning strategies

The emergence of the new competitive electricity market environment has made short-term load forecasting a more complex task, owing to the effect of marketers’ behavior on the load pattern and the reduction of available information due to commercial reasons. In recent years, many ANN-based forecasters are proposed for learning the highly nonlinear load pattern, yet their effectiveness are limited by the reduction of training data, which causes these ANN models to be susceptible to “over-fitting”. “Over-fitting” is a common ANN problem that describes the situation that the model memorizes the training data but fails to generalize well to new data. This paper discusses the problem of “over-fitting” and some common generalization learning techniques in the ANN literature, as well as introducing a new Genetic Algorithm-based regularization method called “GARNET” for short-term load forecasting. As an illustration, four generalization learning techniques, including Early-Stopping, Bayesian Regularization, Adaptive-Regularization and GARNET are applied to train Multi-Layer Perceptrons networks (MLP) for day-ahead load forecasting on limited amount of hourly data from a US utility. Results show that forecasters trained by these four methods consistently produce lower prediction error than those trained by the standard error minimization method.

A chance constrained programming approach to transmission system expansion planning

The transmission system plays a critical role in providing access to all participants in a competitive electricity market for supply and delivery of electric power. Deregulation of the power industry brings many new challenges to the transmission system optimal planning problem, such as how to handle uncertain factors concerning the locations and capacities of new power plants as well as the demand growth for the transmission system planning period studied. Although the transmission system optimal planning problem has been extensively studied, available standard optimization models and methods cannot well solve this problem for the competitive electricity market environment with many uncertain factors involved. Given this background, a new method for the optimal transmission system expansion planning based on chance constrained programming is presented in this paper with several uncertain factors such as the locations and capacities of new power plants as well as demand growth well taken into account. A stochastic optimization model is first formulated under the presumption that the locations and capacities of new power plants and future load demands could be modeled as specified probability distributions. A method is then presented for solving the optimization problem using the well-known Monte Carlo simulation method and genetic algorithm. Finally, a numerical example is served for illustrating the essential features of the developed model and method.

A LQG Based Load Frequency Controller in a Competitive Electricity Environment

This paper presents the design of a Linear Quadratic Gaussian (LQG) regulator for the frequency control of a multi-area power system in a restructured competitive electricity market environment. A general model of the LQG regulator has been developed for multi-area system (with hydro and thermal generators) having Poolco and bilateral transactions. To account for the modeling uncertainties and non-measurable states, a Kalman filter has been designed to estimate the state variables. The controller uses these estimates, optimizes a given performance index, and reschedules the generators’ outputs according to their bids for the frequency regulation. The functioning of the proposed LQG regulator has been demonstrated on a four area test system and the results have been compared with those obtained by using an optimal PID controller.