Deregulated Power Market Research Articles

A Graph-Theoretic Approach to Capacity Benefit Margin Calculation for Large Multiarea Power Systems

In a deregulated power market, independent system operators exchange information related to the available transfer capability (ATC) of their respective areas for efficient power system operation. Accurate ATC calculation is important for efficient market operations. Among the parameters required to calculate ATC, the capacity benefit margin (CBM) is an important one. CBM represents the interarea tie-lines capacity reserved in order to have access to external generation in an event of power supply shortage. This paper proposes a new technique for CBM evaluation using graph theory concepts. The proposed technique can be used as an effective tool to solve the CBM problem for large multiarea power systems which are not fully connected and may have multiple deficient areas, unlike previous approaches which considered only fully connected areas with only one deficient area. The IEEE 24-bus reliability test system is employed to implement the proposed method. A performance analysis of the proposed technique shows that it can find a feasible solution efficiently.

Performance comparison using firefly and PSO algorithms on congestion management of deregulated power market involving renewable energy sources

The power industry across the globe is subjected to a sweeping change in its business as well as in an operating model where the monopoly utilities are being liberalized and opened up for competition with private players. As an outcome of this, the transmission corridors evacuating the power of inexpensive generators would be burdened if all such transactions are admitted. One of the most proficient techniques for congestion management is rescheduling the generators. This research paper suggests a framework to regulate the power flows of the transmission lines within the stipulated limit in a deregulated electricity market environment through rescheduling with and without renewable energy sources (RES). The problem of rescheduling is framed with the intention of lessening the congestion cost. Unlike the traditional method, the best location for the placement of RES is established utilizing a novel weighted locational marginal price (LMP)-based method. The firefly algorithm (FA) and particle swarm optimization (PSO) algorithm are employed in order to get optimized results. The realistic cases are considered, and the results obtained with and without RES using FA and PSO are compared to prove the research study. The efficacy of the method is explored with IEEE 30-bus system.

A two step approach for improvement of economic profit and emission with congestion management in hybrid competitive power market

Integration of renewable energy with conventional energy sources is very challenging due to the uncertain nature of renewable energy, which creates imbalance and congestion in the system. Moreover, every market participant/player in deregulated power market wants to achieve the economic benefits within critical system constraints like congestion. Therefore, this paper presents a simple, efficient and reliable two-step optimization strategy to solve the congestion problem along with maximization of system profit, minimization of system generation cost, congestion cost and emission by optimal placement & numbers of Thyristor Controlled Series Compensator (TCSC) along with wind generators in a deregulated power market. Two new indices i.e. ‘Congestion Detection Index (CDI)’ and ‘Index of Total Congestion Cost (ITCC)’ have been proposed towards faster identification and reduction of system congestion effectively. The pool & bilateral deregulated market model has been considered for this study. The maximum system profit has been considerably achieved after optimal placement of wind generator and TCSC(s) in the system. The effectiveness of the proposed approach has been analyzed and tested in modified IEEE 14 bus and modified IEEE 118 bus test system. The proposed approach has been compared with the existing related approach for congestion mitigation, and better results have been achieved from the proposed approach.

Influence of thyristor-controlled series capacitor on wheeling cost incorporating the impact of real and reactive power losses

Electric power transmission and transmission pricing are the key issues in the deregulated electric power industry. Factors like fast power demand growth, competition, service outage, and scarce natural resources make transmission systems operate close to their thermal limits. However, new transmission systems cannot be built in due to economic, environmental, and political reasons. For better utilization of existing power system capacities, the power electronic technology-based power system equipment called flexible alternating current transmission system (FACTS) devices like thyristor-controlled series compensators (TCSCs) can be effectively used for operating the transmission grid economically, rapidly, dynamically, and efficiently with increased flexibility and efficiency under different loading conditions. TCSCs, being costly devices, may adversely affect some deregulated power market participants. This paper proposes a unit commitment algorithm for the minimization of power losses and determination of optimal location for TCSC placement. The impact of the TCSC in conjunction with the application of the proposed algorithm on the generation cost and wheeling cost is analyzed using a power flow-based line-by-line rolled-in transmission pricing scheme. The comparison between the annual generation costs and annual wheeling costs with and without the TCSC is carried out under different load conditions in the IEEE 30-bus system. Results and simulations validate the economy of the suitable TCSC's optimal presence over its absence.

Financial analysis of reactive power procurement in pool-based deregulated power market integrated with DFIG-based wind farms

Financial analysis of reactive power procurement in pool-based deregulated power market integrated with DFIG-based wind farms

On the Analogy Between the Electric Grid and Our Banking System: Investigating "Consumer Power" in Deregulated Power Markets

This article investigates the Product Electricity, the product being sold at our power markets and distributed via our electric grids. The paper aims to enhance the understanding of the physics behind the Product Electricity. The traditional (physical) perspective of the purchase of power might have been compromised already in the early 1900’s. This investigation aims to clarify what electric power really is, how it is traded, and if the different offers of so called ‘Green Power’ are valid. A deregulated power market is ruled by the laws of the free-market, i.e. Supply and Demand. It is not ruled by the laws of Ohm and Kirchhoff. To purchase the Product Electricity is to place an order of consumption beforehand, not specified in volume, space or time. The economical transaction; purchasing electric power, is strictly non-physical. Today, an active choice of not buying fossil power is advertised on the power markets. Customers who do not want to get electricity generated from fossil fuels are offered to stop buying it. This article tries to answer the question if that offer is valid.

AGC of multi area power system based PSO under deregulated conditions

In This research PIDF (Proportional Integral Derivative with Filter) is suggested to control the ACE (area control error) signal of automatic generation control circuit (AGC) for two-area multi units system under deregulated conditions, each area consist of two thermal reheat units with physical GRC (generating rate constrain). The parameters of the PIDF controller are tuned using PSO (particle swarm optimization) technique. To improve the system performance, Redox Flow Batteries (RFB) is presented in one area and one of FACTS components IPFC (Inter Line Power Flow Controller) is installed in tie line. The performance of the proposed controller is assessed under different working conditions of deregulated power market. Finally, a comparison will be made on the system response when testing with varying the load conditions and system parameter through MATLAB environment 2015Rb.

Advances in clean and low-carbon power generation planning

Increasing global energy consumption and consequent greenhouse gas emissions pose great challenges to the sustainable development of international human society. Electricity constitutes the largest part of energy carriers, and the power sector is identified as the key sector with great carbon dioxide mitigation potential. Therefore, power generation expansion planning (GEP) problem has drawn great attention due to its important role in global energy supply, renewable energy utilization and carbon dioxide mitigation. Several important issues, including renewable energy sources integration, operating reserve, deregulated power market, demand response and carbon pricing mechanism should be incorporated in a GEP problem. Energy system engineering provides a methodological framework to address the complex energy, economic and environmental problems by adopting an integrated systematic approach, featuring superstructure-based modeling, mixed-integer programming, multi-objective optimization, and optimization under uncertainty. Recent advances of these approaches in GEP problems related to the five issues mentioned above are reviewed and discussed in this article.

Application of Artificial Neural Networks for Short term Price Forecasting in Deregulated Power Market

Application of Artificial Neural Networks for Short term Price Forecasting in Deregulated Power Market

Optimal day‐ahead demand response contract for congestion management in the deregulated power market considering wind power

In the liberalised electricity market, congestion management (CM) with a high penetration of wind energy is a challenging task for Independent System Operators (ISOs). Even though demand response (DR) provides an opportunity to alleviate transmission congestion, strategic selection of aggregated loads to contract with for DR in the day-ahead economic dispatch is still under-investigated. To solve this problem, this study proposes a bi-level optimisation model to determine the optimal DR buses for CM in the day-ahead market considering the uncertainty of wind power. The upper model serves to compute the available transfer capability (ATC), while the lower model is to calculate the stochastic dynamic optimal power flow. Through converting the stochastic ATC values to a summation of load supply capability of each load node, the loads which will deteriorate transmission congestion if the corresponding demand grows are determined. The corresponding loads with DR are selected as the optimal candidates. Furthermore, this study constructs a two-stage optimisation model for optimal load dispatch by incorporating both price-based DR and incentive-based DR. The result can be used to assist system operators in decision-making of electricity biddings from DRs for load curtailment and shift in the market clearing. As a result, the difference of peak and valley loads is reduced; ISOs as well as DR participators can both get economic benefits. Simulation results from the PJM 5-bus training system and the modified IEEE 30-bus system demonstrate the effectiveness of the proposed algorithm for DR contracting in CM.

Business models for demand response aggregators under regulated power markets

With the boost of smart meter installations, the technical difficulties for demand response (DR) have been overcome a lot. For years, numerous DR programmes have been in operation to enhance the system reliability. Meanwhile, some DR programmes run on the profit of participating power market, like arbitraging between the wholesale market and the retail market in regions with a deregulated power market. In this study, the business model for DR aggregators under deregulated markets is revisited for comparison. The business models under regulated markets are then proposed targeting three different power dispatch modes: economic dispatch, energy-saving dispatch and open and impartial dispatch. Numerical cases are calculated to test if the utility can make profit through the proposed models. Furthermore, the influence of implementing such models on the system is analysed.

Population variant differential evolution-based multiobjective economic emission load dispatch

Summary This paper presents a novel heuristic optimization approach using population variant differential evolution algorithm for solving the multiobjective economic emission load dispatch (EELD) problem. The EELD problem simultaneously takes into consideration the effect of gaseous pollutants like NOx, SOx, etc, including the cost of the fossil fuel used in the thermal power plants. A population refreshment mechanism, based on the concept of interquartile range, has been applied to the classical differential evolution method to compute the EELD problem successfully. The algorithm has been tested on IEEE-30 bus 6-generator test system and a standard 40-generator test system, taking valve point loading effects into consideration for effective solutions. The results have been compared with the standard existing techniques in the literature such as linear programming, multiobjective stochastic search technique, nondominated sorting genetic algorithm, fuzzy clustering particle swarm optimization, and modified bacterial foraging algorithm, which authenticate the capability of the proposed algorithm. A novel concept of applying the proposed methodology to a deregulated power market has also been presented in this paper, where successful results have been obtained. This method appears as an efficient and robust optimization algorithm in terms of minimizing the total cost, emission, and computational time.

Hybrid Particle Swarm Optimization Algorithm to solve Profit based Unit Commitment Problem with Emission Limitations in Deregulated Power Market

Hybrid Particle Swarm Optimization Algorithm to solve Profit based Unit Commitment Problem with Emission Limitations in Deregulated Power Market

Transmission Pricing Practices: A Case Study

This paper presents a case study on transmission pricing practices. Now a days, restructuring of the power system market through deregulation is gaining attention as technical and economical benefits at generator and user. In India, after the Electricity Act 2003, restructuring has been introduced in the Indian power system market. Researchers are continuously working towards improvement of deregulation based on restructuring to improve transmission pricing practices and calculations in a better way. Therefore, this paper presents an overview of MWMile and postage stamp methods to estimate the transmission cost. Further, the North Indian practical power system of 37 bus test system has been analyzed by reverse, absolute and dominant Mw-Mile methods. The results obtained to be expected for deregulated power market.

An Approach on Non-Discriminatory Losses Charge Allocation for Deregulated Power Market Using Meta-Heuristic-Optimization-Based-Electricity-Tracing (MOET)

This paper presents an alternative technique for losses charge allocation using electricity tracing theory. The meta-heuristic optimization technique was incorporated to perform the tracing process of powers and losses contributed by individual generator and load. In producing a better optimization engine, hybridization between the well known Genetic Algorithm (GA) and continuous domain Ant Colony Optimization (ACOR) was performed for fast optimization process with optimal solution. The proposed method gives reliable perspective when allocating the losses charge on consumers as it is free from assumption and also applicable in any conditions of power system. Comparative studies and analysis have justified the capability of the proposed method for non-discriminatory losses charge allocation in deregulated power market. Furthermore, comparison between various meta-heuristic algorithms and analysis on their performance has reduced the doubt for real system application.

Congestion Management in Deregulated Electricity Power Market using STATCOM and TCSC

In last few years the electrical industry is deregulating all over the places. Deregulation has brought many changes in organization and operation of power industry. This deregulation increases competition among power utility and allows the customer to choose their supplier of electricity. Number of participant will increase in the competitive market of power industry and all the participant will try to get supply at low cost sources. After deregulation of power market, congestion in electric grid is quite often. So, it is very important to relieve problem of congestion. Problem of congestion, frequently occur in transmission system and distribution system. This paper proposes FACTS devices to relieve from congestion. FACTS devices will efficiently control the power flow. Modelling, simulation and analysis of 5 bus system using MATLAB is explained in this paper. Comparison with and without STATCOM and TCSC is done to control the power flow and result is obtained. Keywords:congestion;deregulation; power market; STATCOM; TCSC

A Chaotic Model Approach for Electricity Price Forecasting in Indian Scenario

In a deregulated power market, the electricity prices exhibit extreme volatility due to its non-storable nature, supply constraints at peak hours, transmission line congestion at peak hours and seasonal and diurnal variations. The objective of this paper is to model the day-ahead market price of Indian energy exchange (IEX) based on time series modelling approach. The detailed analysis of market clearing price (MCP) collected from IEX clearly indicates the chaotic nature of data. It is because of this chaotic behaviour, the phase space of the time series data is reconstructed using Taken's theorem. With the concept of the add-weighted one-rank multi-step model, the MCP of IEX is modelled as a chaotic model from this reconstructed phase space. Furthermore, the developed chaotic model is verified for forecasting MCP and the simulation results demonstrate that the chaotic model developed outperforms the autoregressive integrated moving average (ARIMA) model and generalised autoregressive conditional heteroscedasticity (GARCH) model in terms of in sample forecasting performances.

A chaotic model approach for electricity price forecasting in Indian scenario

In a deregulated power market, the electricity prices exhibit extreme volatility due to its non-storable nature, supply constraints at peak hours, transmission line congestion at peak hours and seasonal and diurnal variations. The objective of this paper is to model the day-ahead market price of Indian energy exchange (IEX) based on time series modelling approach. The detailed analysis of market clearing price (MCP) collected from IEX clearly indicates the chaotic nature of data. It is because of this chaotic behaviour, the phase space of the time series data is reconstructed using Taken's theorem. With the concept of the add-weighted one-rank multi-step model, the MCP of IEX is modelled as a chaotic model from this reconstructed phase space. Furthermore, the developed chaotic model is verified for forecasting MCP and the simulation results demonstrate that the chaotic model developed outperforms the autoregressive integrated moving average (ARIMA) model and generalised autoregressive conditional heteroscedasticity (GARCH) model in terms of in sample forecasting performances.

A Transmission Congestion Cost Allocation Method in Bilateral Trading Electricity Market

Bilateral trading in electricity is one of the typical deregulated power market trading methods and it has its own way to calculate and allocate congestion cost. In this paper, using as a practical bilateral structure example, the British electricity market is stated in details especially the operation mechanism and the methodology of imbalance settlement. A corresponding congestion cost allocation method for bilateral market is introduced briefly by equations and is simulated in a modified IEEE-14 bus model to investigate its pros and cons.

Market Clearing Price Calculation for a Deregulated Power Market

Background/Objectives: Most of the electricity markets are controlled by particular small group of firms rather than perfect competition. The electricity price determination is the long term process which depends upon cost of production, load demand, availability of generation, unit commitment and the transmission constraints. The main objective of this work is to maximize the social welfare function for the society. In the present scenario, the market clearing is based on stepped bids received from generators and consumers. Methods/Statistical Analysis: This paper focuses on implementing power systems optimization for forecasting Market Prices in deregulated electricity markets. In the recent energy trading scenario, determining the market clearing price place a vital role. Findings: The necessary solution is computing by collecting the clearing price from the generating station and load center and bids are offered in the closed loop form. Application/Improvements: To have a strategic bidding modelling the PDC in the oligopoly market is essential to have a accurate price in calculating the incomes from a new investment, so that it is not undervalued.