Competitive Power Market Research Articles

LFformer: An improved Transformer model for wind power prediction.

Wind power forecasting has complex nonlinear features and behavioral patterns across time scales, which is a severe test for traditional forecasting techniques. To address the multi-scale problem in wind power forecasting, this paper innovatively proposes an ultra-short-term forecasting model LFformer based on Legendre-Fourier, which firstly focuses on the important information in the input sequences by using the encoder-decoder architecture, and then scales the range of the original data with the Devlin normalization method, and then utilizes the Legendre polynomials to The data sequence is projected into a bounded dimensional space, the historical data is compressed using feature representation, then feature selection is performed using the low-rank approximation method of Fourier Transform, the prediction is inputted into the multilayer perceptron through the multi-scale mixing mechanism, and finally the results are outputted after back-normalization. The experimental results show that compared with the existing prediction methods, the model realizes the improvement of prediction accuracy and stability, especially in the ultra-short-term prediction scenario, with obvious advantages. The research results are not only valuable for improving the overall operational efficiency of the wind power system, but also help to enhance the stable operation of the power grid, which provides strong technical support and guarantee for wind power enterprises to improve the competitiveness of bidding for Internet access in the power market competition.

Metaphor-less Rao-3 and artificial neural network with parallel computing-based wheeling pricing in competitive power market

Metaphor-less Rao-3 and artificial neural network with parallel computing-based wheeling pricing in competitive power market

Cascaded deep NN‐based customer participation by considering renewable energy sources for congestion management in deregulated power markets

AbstractContinuously varying loading conditions and the cost‐based operation of a competitive power market lead to the problem of congestion as one of the most crucial issues. In day‐ahead power market operation (PMO), customer participation (CP) and generation rescheduling (GR) are the most effective techniques preferred by the system operator to eliminate congestion. In this paper, a cascaded Deep Neural Network (DNN) module has been presented for estimating customer participation and power generated by Wind Energy Source (WES) as on‐site generation (OSG) to manage congestion. The proposed module is a cascade combination of Artificial Neural Network (ANN) as a filtering module (FM) and DNN as a congestion management (CM) module. The CM module estimates the customer participation for all receptive costumers, power supplied by wind energy sources under uncertain conditions and generation rescheduling of all generators with minimum cost for all unseen congested power system loading patterns. The proposed CM approach provides an instant and efficient solution to manage congestion with minimum cost. The developed module has been examined on IEEE 30‐bus power system. The maximum error found in the testing phase is 1.1865% which is very less and within the acceptable limit.

POWER SYSTEM UNDER FREE MARKET CONDITIONS

During the past 20 years, we have seen an expanding role for the free market in the electricity sector as well as other sectors. A perfectly competitive power market is efficient but difficult to achieve. The special features of electricity should be taken into account. In this paper, it is shown how traditional Optimal Power Flow (OPF) can be extended to include the demand side, thereby reflecting the supply-demand balance of an open market. The OPF technique can be used for the calculation of a set of nodal prices representing the optimal price at each node in the grid, from which we can derive a set of optimal transmission prices. Depending on the market design, generation planning can be performed by central optimization or in a decentralized manner by each generation company. For an individual generating company, the planning criterion will change from cost minimization to profit maximization. Practical experiences are now available from various parts of the world. It is clearly demonstrated that a power market can be operated under free market conditions. Various solutions concerning ownership and market design have been implemented. However, experiences have been mixed. In the future, it will be important to draw upon the lessons that have been learned.

Effect of Chinese Currency Appreciation on Investments in Renewable Energy Projects in Countries along the Belt and Road

Foreign investment in renewable energy generation projects is a critical part of the Belt and Road Initiative. Under the background of the market economy, the electric energy will participate in power market competition among the countries along the line, and the sales revenue will be settled in the local currency. The exchange rate of the countries along the Belt and Road fluctuates frequently and widely, thereby posing significant risks to the investment income of the projects. To address this problem, this paper proposes the concept of Ek as the effective exchange rate expressed by the on-grid price, investment cost per kilowatt electricity generation equipment, and annual operating cost rate of unit power generation capacity. Moreover, this paper presents a model of power generation cost, income, and earning expressed by the real exchange rate. The flexibility formula of the fluctuation of power generation cost, income, profit, and internal rate of return relative(IRR) to Ek is derived, and the effect of exchange rate level and fluctuation on projects is analyzed. With the wind power projects invested by China in Pakistan taken as an example, the trend during the entire life cycle is calculated. The changes in net profit rate, IRR, and levelized cost of energy (LCOE) are calculated under Chinese currency appreciation of 10%, 20%, and 35% and 5% and 10% reduction of investment cost per unit. As the Chinese currency appreciates and the project IRR declines significantly, LCOE decreases slightly, but this decrease is not sufficient to compensate for the losses caused by the decline in IRR. The following effective measures are proposed to deal with the exchange rate fluctuation of foreign renewable energy generation projects: building energy Internet, reducing project cost, and using Chinese currency as the settlement currency. In this paper, a solution is provided for investments in renewable energy projects in regions where exchange rates fluctuate greatly.

Simulation of the Bidding Behavior of Electricity Retailers Based on the Q -Learning Algorithm

At present, the reform of electricity marketization has become a new trend in the development of the world’s electric power industry and electricity retailers have a huge role in further activating power market competition. Simulation of the bidding behavior of electricity sales companies is helpful to analyze the rationality of the transaction behaviors of electricity sales companies and predict the potential risks of market operation. Considering the complex and diverse behaviors of electricity retailers and the immaturity of existing simulation technologies in the power sales market, this paper proposes a Q -learning algorithm-based simulation method for the bidding behavior of electricity retailers. Firstly, we analyze the factors influencing the bidding behavior of electricity retailers, then, we construct a simulation model of electricity retailers’ bidding behavior based on the Q -learning algorithm, and finally, we simulate the bidding process of medium- and long-term trading in provincial power markets by using simultaneous learning from both sides of the generation and consumption. The results show that the behavior of different types of electricity retailers meets the expected level of the model and the simulation method has generalization and extension significance. On this basis, this paper also proposes a bid simulation system architecture based on the bid line simulation model of electricity retailers, which provides a certain reference value for market operation agencies to carry out the corresponding deduction work.

Hierarchical Energy Management System With a Local Competitive Power Market for Inter-Connected Multi-Smart Buildings

The energy management in new distribution paradigms are amongst one of core research dimension, particularly in smart grids. This paper proposes a hierarchical energy management system for inter-connected multi-smart buildings with an inclusion of local Power Market. As home appliances have huge contribution in load of buildings, the appliances are scheduled in order to minimize operational cost while taking into account the user comfort and other system constraints. The objectives of this paper aim to minimize operational cost, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions, grid dependency while maximize user comfort and revenue. The proposed technique enables a prosumer with two options, either they can sell excess energy to the utility or can bid and sell in market with high price compare to utility. Besides increase in revenue, the consumer is enabled to buy electricity from utility or from local market with low prices compare to utility grid aiming at reducing operational cost. The proposed framework is evaluated across three algorithms namely, JAYA, teacher learning based optimization (TLBO) and Rao1, respectively. As per comparative analysis, the JAYA algorithm outperforms the others in achieving the aimed objectives in-terms of favorable achieved numerical values. Different cases are created in order to test the effectiveness of proposed system. The overall simulation results validate the proposed approach with highest operational cost reduction of 151.48%, peak load reduction 76.76%, grid dependency reduction 95.61%, and minimum emission of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> is 3.70 Kg/Day as compare to base case.

Blockchain and Federated Learning Based Bidding Applications in Power Markets

Currently, power generators are facing an imperfectly competitive power market with relatively limited information, and they can increase their own economic returns by raising their quotations through game theory, which leads to an inflated price in the wholesale power market. In this paper, we propose a bidding application for the wholesale power market called Federated Learning Power Quotation System (FedLPQS), which combines federated learning and blockchain to consider the characteristics of the future power trading market of ”Liberalizing both ends”. FedLPQS using federated learning aggregates cost data of each power plant to achieve a certain degree of transparency and records the data in a blockchain platform based on the consortium chain to reduce the price of power traded in the wholesale power market.

Social Welfare Maximization of Competitive Congested Power Market Considering Wind Farm and Pumped Hydroelectric Storage System

The utilization of wind energy sources with energy storage systems has been increased in the power sector to satisfy the consumer’s energy demand with minimum price. This paper presents the impact of a wind farm (WF) and pumped hydroelectric storage (PHS) system in the competitive electricity market under a congested transmission system. The PHS system is used to compensate for the deviation of WF generation in the real-time electricity market. To investigate the impact of the proposed method, initially, the market-clearing power problem is solved without consideration of WF and PHS systems, and again it is solved with the WF and PHS systems. The optimal location of the WF and PHS systems is decided by the bus sensitivity factor (BSF) of these systems. The analysis is carried out by using generator sensitivity factor (GSF) with the help of the moth flame optimization (MFO) algorithm and thereby calculating market clearing price (MCP) and market clearing volume (MCV). The MFO algorithm is used here for the first time for solving the congested market-clearing power problem with the integration of WF and PHS systems under deregulated environment. The presented approach shows the improvement of social welfare after the placement of WF and PHS in the congested deregulated system. Modified IEEE 30 bus system is used to solve the market-clearing power problem and results obtained from the MFO algorithm are compared with the firefly algorithm (FA). Three different real-time wind speed data have been considered here to verify the proposed approach with uncertainty and the continuously changing nature of wind flow. It is discovered that social welfare is improved with the quantity addition of wind power, regardless of optimization techniques.

Cross-grid demand response (DR) coordinating framework in energy Internet – A case of power market participation of gas DR resources

Energy integration and energy internet enable cooperation of various energy network. Thus demand response (DR) can be extended from a pure power system issue to a cross-grid format. This paper initiates a coordinating market and operation framework for gas demand response to support power system supply–demand balancing. In the proposed framework, gas turbines help to transfer gas DR into power generation. The gas aggregators collect the flexible gas resources and make offers to power grid. The cross-grid demand response model of gas flexible loads is established to obtain the equivalent ‘Cost-Generation-Capacity’ for power market competition. The awarded generation capacity of gas DR aggregators will also be optimally distributed to all gas DR suppliers. A numerical study is implemented to demonstrate the feasibility of the proposed framework and model.

Optimal Dispatch of Renewable and Virtual Power Plants in Smart Grid Environment through Bilateral Transactions

In a competitive power market, assessment of optimal bilateral transactions is very important for achieving more financial benefits without any stability, security and reliability issues. Due to the open access transmission network and the wide deployment of renewable energy sources (RES) and virtual power plants (VPP), dispatch of VPP and RES units through bilateral transactions become complex. This paper presents an efficient approach for the determination of optimal and feasible bilateral transactions among the power traders in a smart grid based on proposed factors, i.e., active participation factor (APF), maximum loading point (MLP) and load distribution factor (LDF). The objective of the proposed tri-level dispatch model is to minimize the cost of generation and transmission cost of transactions by using a mixed integer nonlinear programming (MINLP) technique. The proposed market dispatch model efficiently gets rid of the congestion in the transmission network and enhances the power transfer capability of the network, i.e., transaction dispatch capability of the network. The performance of the proposed tri-level model has been validated on the modified IEEE-14 bus system and modified IEEE-30 bus system.

An economic risk based optimal bidding strategy for various market players considering optimal wind placements in day-ahead and real-time competitive power market

An economic risk based optimal bidding strategy for various market players considering optimal wind placements in day-ahead and real-time competitive power market

Bidding strategy of wind-solar hybrid generation system in the spot market considering bidding deviation penalty

In recent years, as State Grid Shandong Electric Power Company is one of the key pilot construction units of the new strategy of “realizing the coordination and interaction of source network, load and storage”, renewable energy power generation systems have gradually changed from a subsidy object to an independent bidding subject when participating in the spot power market of Shandong Province. The uncertainty of renewable energy output and spot market price leads to the loss caused by bidding deviation when participating in the power market competition, and the income drops significantly. The good complementarity of wind and solar energy can reduce the fluctuation of renewable energy output and promote renewable energy consumption. First, the joint output probability distribution of a wind-solar hybrid generation system is simulated based on Copula function. Then, aimed at maximizing the bidding profit of the wind-solar hybrid generation system in spot market, a two-stage mixed integer stochastic optimization bidding model considering bidding deviation penalty is established. The conditional value at risk theory is used to evaluate the risk of renewable energy and electricity price forecast deviation, and the optimal bidding strategy is obtained.

Interval prediction algorithm and optimal scenario making model for wind power producers bidding strategy

Nowadays, renewable energies are important sources for supplying electric power demand and a key entity of future energy markets. Therefore, wind power producers (WPPs) in most of the power systems in the world have a key role. On the other hand, the wind speed uncertainty makes WPPs deferent power generators, which in turn causes adequate bidding strategies, that leads to market rules, and the functional abilities of the turbines to penetrate the market. In this paper, a new bidding strategy has been proposed based on optimal scenario making for WPPs in a competitive power market. As known, the WPP generation is uncertain, and different scenarios must be created for wind power production. Therefore, a prediction intervals method has been improved in making scenarios and increase the accuracy of the presence of WPPs in the balancing market. Besides, a new optimization algorithm has been proposed called the grasshopper optimization algorithm to simulate the optimal bidding problem of WPPs. A set of numerical examples, as well as a case-study based on real-world data, allows illustrating and discussing the properties of the proposed method.

Analyzing Information Disclosure in the Chinese Electricity Market

Electricity generation relies heavily on fossil fuels in China, and this has posed great challenges for sustainable development. In 2015, China launched a new series of power reforms, aimed primarily at sustainable development and building a competitive power market system, where an information disclosure system plays an important role. This paper analyzes the effects of sensitive information disclosure, constructs different information disclosure scenarios, and compares the market clearing results under different scenarios. The results show that information transparency is conducive to the promotion of market efficiency. However, some problems, especially collusion, arise and inevitably bring negative impacts to the power market. Therefore, more attention should be paid to the content and quality of data transparency to avoid manipulations in the market. The data of power systems are complex and various, and thus big data applications may be conducive to effective information disclosure and better market regulation. Moreover, disclosure delay will help the electricity market become more transparent by reducing the risk of collusion. Besides, the information’s scope and contents, timing should also be taken into consideration. These findings may provide some references for the sustainable development of the electricity industry and have certain policy implications for policymakers.

Identification of Typical Behaviors of Market Subjects in Competitive Power Market Environment

In the power market, some illegal behaviors of market subjects violate the market rules. There also are some potential harmful behaviors, such as profiteering pricing, speculation, alliance, which do not violate market rules directly. These harmful market behaviors impair the competitiveness and effectiveness of the market, cause adverse effect for the optimal allocation of limited resources. Consequently, it will impair the efficiency of the electricity market. Market illegal behaviors can be judged, punished and controlled according to market rules; However, harmful market behaviors are difficult to be identified and controlled according to market rules, so an effective method must be established to identify, monitor and control them. According to the different characteristics of potentially hazardous behaviors, four methods are proposed in this paper, namely, index supervision method, quotation behavior supervision method, trading behavior analysis method based on expert system and market simulation test method to identify the illegal behaviors in the power market. Then, this paper takes the compliance analysis of market player behavior and the compliance analysis of electricity purchase price as an example to carry out simulation test.

Impact of renewable energy sources on modelling of bidding strategy in a competitive electricity market using improved whale optimization algorithm

In a competitive power market, generating utilities can be enhanced to achieve maximum profit by implementing a process of bidding strategy. Now-a-days renewable sources like solar and wind have become better alternatives significantly than other sources prior to power generation. These sources have extensive utilisation day-by-day in power sector and their impact in developing precise bidding strategies is getting more challenging aspect in the market. Since these renewable sources possess intermittent nature and undergo many uncertainties, the generating utilities encounter an unavoidable problem. Taking these aspects into consideration, attempts have been made using improved whale optimization algorithm to make the bidding strategy model for maximizing the power supplier's profit. Weibull and Beta distribution functions are used for modelling the stochastic characteristics of wind-speed profile and solar-irradiation, respectively. The proposed technique is tested and clarified with an IEEE-30, IEEE-57 and practical 75-bus Indian system. The outcomes of this method were taken into comparison with other optimization techniques and found that it has an advantage upon other methods in solving bidding problems. Further, it is observed that the impact of renewable sources on bidding strategy reduces the market clearing price and the generation of thermal power while increases the total bidding power.

A study of approach for best bidding price in competitive power market

Considering importance of determining the appropriate price in power market in this paper presented a pricing strategy for the optimal producer company in the perfectly competitive power market with uniform settlement law. Based on this strategy if power optimal producer company, Consider its bidding price about actual cost of production, the company will reach to all objectives. To prove this strategy, we simulate a power system by Q-Learning algorithm that results proved this strategy. The strategy also has been proved by logical mathematical principles.

Influence of guaranteed power supply service on competitive power market and compensation mechanism design

In the modern power market system, it is necessary to establish a mature power supply guarantee service mechanism to protect the rights and interests of all users, realize the reasonable allocation of power resources and achieve the “Pareto improvement” of social welfare. Based on a broader definition of guaranteed power supply service, this paper clarifies the scope and mode of guaranteed power supply service, calculates the power supply cost and market clearing electricity of guaranteed power supply service, and designs a corresponding compensation mechanism system to promote the sustainable development of guaranteed power supply service. In the long run, guaranteed power supply service should return to the market mechanism, pricing based on “power supply cost + reasonable profit”, and become an essential part of the competitive power market.

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.