Electric Power Market Research Articles

A Joint Optimization Algorithm for Renewable Energy System

Energy sustainability is a hot topic in both scientific and political circles. To date, two alternative approaches to this issue are being taken. Some people believe that increasing power consumption is necessary for countries’ economic and social progress, while others are more concerned with maintaining carbon consumption under set limitations. To establish a secure, sustainable, and economical energy system while mitigating the consequences of climate change, most governments are currently pushing renewable growth policies. Energy markets are meant to provide consumers with dependable electricity at the lowest possible cost. A profit-maximization optimal decision model is created in the electric power market with the combined wind, solar units, loads, and energy storage systems, based on the bidding mechanism in the electricity market and operational principles. This model utterly considers the technological limits of new energy units and storages, as well as the involvement of new energy and electric vehicles in market bidding through power generation strategy and the output arrangement of the virtual power plant’s coordinated operation. The accuracy and validity of the optimal decision-making model of combined wind, solar units, loads, and energy storage systems are validated using numerical examples. Under multi-operating scenarios, the effects of renewable energy output changes on joint system bidding techniques are compared.

An optimal power flow solution to deregulated electricity power market using meta-heuristic algorithms considering load congestion environment

In this article, the Improved Mayfly Algorithm (IMA) is used as an upgraded form of the Mayfly Algorithm (MA), featuring simulated binary crossover and polynomial mutation operators replacing the arithmetic crossover and standard distribution mutation operators of the MA. With MA, IMA's achievements and significance are acknowledged. The algorithms achieve a final best solution for the investigated objective functions of the optimal power flow problem in a deregulated electrical power market under different load conditions. The overall load of the power system varies between half of the base load (-50%) and twice the base load (+100%). The investigated objective functions are associated with the financial worth of generators, dissipation of active power in transmission lines, variation of voltage magnitudes at the system bus, and voltage stability index at the load bus of the power system networks. The result achieved by GA, PSO, MA and IMA are attained using the IEEE-30 bus test system in a deregulated power system. Investigations are conducted on the best solutions for each objective function; offers of generators and bids of loads; generator sales and load purchases; and system revenues associated with different load scenarios. The simulated outcomes have confirmed that IMA would triumph over GA, PSO and MA.

Electric power marketing system network security protection technical scheme

Electric power marketing system to electric power group provides the convenience, makes the power organization economic gains and also solve the many problems, but with the deepening of the application system, electric power group met with a lot of network attacks, shows that the system is under a state of risk, if attacked, the key information of power marketing may be leaked, resulting in economic and other losses. Therefore, in order to better protect the electric power marketing system, this paper has carried on the research. This paper introduces the current situation of network security protection of electric power marketing system, and then puts forward the protection technology scheme. The research shows that the technical scheme in this paper has better protection intensity and can protect the safety of electric power marketing system more effectively.

Consistency of trends in the economic and energy development of Ukraine: Assessment and analysis

Reforming Ukraine’s energy sector is targeted at the implementation of European legislation into national practice. However, the lack of a vision of the country’s energy model, coordinated with trends and priorities of its economic development, is creating a number of problems with its energy supply. The aim of the presented study is to assess the consistency of trends in the economic and energy development of Ukraine. To achieve the goal, a methodological approach to assessing the consistency of trends and structural changes in the economy and energy sector was developed. It was proposed to carry out the assessment based on the analysis of the resource / commodity and production / consumption chains of the electric power industry, and the conditions of the electric power market. It was proved that the trends and structural changes in the economy of Ukraine were not accompanied by corresponding changes in the energy sector of the country. Such inconsistency conditioned: a low level of utilization of installed power generation capacities and, consequently, a high level of unused power generation reserves; a high level of power generation reserves that determined a significant level of non-productive costs and, consequently, a high total cost of power production.

Further Results on Stability of Sampled-Data Systems Considering Time-Delay and Its Application to Electric Power Markets

Further Results on Stability of Sampled-Data Systems Considering Time-Delay and Its Application to Electric Power Markets

Design of Automatic Management System of Electric Power Marketing Charges and Accounts Based on Robot Process Automation

In the automation construction of marketing charge accounts in power supply enterprises, the key is the automation system of electric energy measurement. It covers all levels of measurement points and collection terminals in the whole network, and integrates measurement management, analysis, monitoring and information collection. The traditional electricity charge management of electric power enterprises is the process of manual meter reading, accounting and charging. Meter readers go to each customer every month to read the meter according to the meter reading time and meter reading card, record the number of electric energy meters on the meter reading card, and then copy the data of the meter reading card on the accounting card. Manual meter reading can't meet the current demand. Therefore, this paper will study the automatic management of electric power marketing charges based on RPA (Robot process automation), and build an automatic management system of electric power marketing charges. Through the comparative analysis of experimental data, the experimental results of the response accuracy of different systems show that this system is still more advantageous, and the accuracy of this system is the highest, with an average accuracy of 81.59%. However, the accuracy of reference method and reference method are 62.72% and 46.95% respectively. A perfect charging system based on RPA can not only improve the service level and corporate image of power supply companies, but also improve the management level of power supply companies, thus improving the economic benefits of enterprises.

Electricity supply chain management considering environmental evaluation: A multi-period optimization stochastic programming model

This paper aims to derive optimal combination of electric powers for electric power market network considering environmental evaluation. Currently, attention is being paid to increasing the ratio of renewable energy generation in the electric power market which called Green Energy Coefficient (GEC). Many studies have analyzed different purposes for power market, such as configuration problem of electric power price, customer's electric power purchase distribution problem, power consumption problem, the development problem of the power generating system and so on. In this research, we propose the optimization stochastic programming models for electricity supply chain under renewable integration. We explicitly consider intermittency of renewable energy by developing a scenario decision tree, and further formulate and solve a multistage stochastic supply balance model to meet the aggregate demand in each period. The case study application is used to illustrate the model and how it supports the electricity supply chain management strategy.

Meet the Editors

Meet the Editors

A review of market power‐mitigation mechanisms in electricity markets

AbstractParticipants in the supply and demand sides of electricity markets can potentially exercise their market power for their benefits. Hence, market power mitigation is an essential component of market management that physically and economically benefits operators by ensuring fairness in deregulated electricity markets. However, the mechanisms of mitigating market power in electricity markets pose several challenges in terms of market structure monitoring, market power evaluation, and market power mitigation. Therefore, this study provides a comprehensive analysis of market power mitigation mechanisms in electricity markets. First, a review of market structure monitoring and market power evaluation methods is provided to assess participants’ potential and degree of use/abuse of market power. Then, a detailed review of optimal bidding strategies in the supply and demand sides of electricity markets is provided. Accordingly, a systematic classification of market power mitigation is described. Finally, market power mitigation challenges and potential future research directions are discussed.

Analysis of Electricity Price and Control Strategy of Electricity Charge Risk in the Process of Electric Power Marketing

Analysis of Electricity Price and Control Strategy of Electricity Charge Risk in the Process of Electric Power Marketing

Analysis of Enterprise Marketing Data Management Integration and Innovation Path Based on BD

In recent years, the marketing data sources of enterprises are not sufficient, and the quality of data cannot be guaranteed. In addition, although the data has the characteristics of “large amount,” there is a lot of data garbage in it, and the amount of valuable data is not large. Taking electric power enterprises as an example, electric power marketing is the key of power supply enterprises, and its development determines the survival and development of enterprises to some extent, which is of great significance to power supply enterprises. Big data (BD) not only brings many positive effects to the marketing of electric power enterprises but also brings some negative effects to electric power enterprises. Based on the background of BD, this paper studies the integration and innovation path of marketing data management in electric power enterprises. The research shows that the accuracy rate of this method is the highest, followed by machine learning, and finally AI. With the increase of the number of samples, the accuracy of this method becomes higher and higher. When the number of samples reaches 13, the accuracy of this method can reach 85%, that of machine learning is 56%, and that of AI is 43%. It can be seen that this method is more suitable for data management integration of electric power marketing. According to the characteristics of the era of BD, electric power enterprises should innovate marketing data management system, construct efficient and perfect data processing flow, fully tap the potential value of data, and make contributions to the development of electric power enterprises.

Optimal integration of renewable distributed generators and electric vehicles in a radial distributed network

With the rising interest in sustainable transportation efforts, the optimal integration of Electric Vehicles (EVs) in the transportation business has come into existence very drastically. However, because of the increased power consumption, their impact on the electric power market might result in more enormous losses in the active power, a reduced voltage profile, as well as reduced voltage stability limits. In Radial Distribution System (RDS), getting the reduced effect of EVs load is crucial. It is significantly important to build charging stations for EVs and Distributed Generators (DGs) at the best locations. This article presents a combined approach to solve the optimal integration of DGs and EVs problem. First, the desired locations of DGs and EVs are found using the Voltage Stability Index (VSI) method.Further, the resource ratings are obtained by the Firefly algorithm (FA) method. EVs charging dump and smart charging strategies are used, and comparison is done with both systems. Furthermore, the main objective of this work is to reduce the power loss and maintain a good voltage profile at each bus in the RDS. Finally, the developed approach is tested on IEEE 69 bus system.

Game Theory and its Impact on Economic Profit in the Electric Power Market

Due to the creation of electricity markets, which are characterized by the competitiveness among participants, particularly in the generation of electric energy, this led to a considerable increase in the number of energy producers. However, in order to maximize their profit, some of them submit their bids beyond competitive prices, incurring in some irregularities in the market. Therefore, this paper analyzes the participants strategy using game theory, because the electricity market is a conflict scenario, in which participants interests are at play. For this purpose, a bilevel optimization model was employed, incorporating a cooperative game theory methodology known as incomplete information gaming, which maximizes the profit of the producers through an optimal strategy and minimizes the systems operating costs. The results obtained in this paper show that, using the game methodology, it is possible to achieve a positive impact on participants profit. However, it is necessary for the participants to be rational and logical when they make their bids. Moreover, the results indicate that this methodology can be used to identify irregularities in the market, such as market manipulation and market power indicators in the participants.

Marginal Cost-Based Reactive Power Reinforcement Using Dynamic and Static Compensators

The main philosophy of this article is to develop customer-persuaded electric power tariff scheme, based on the composition of additionally required services. Ancillary services may be categorized into desirable and essential electric power market services and so independent system operator (ISO) looks into the rights of all the utilities involved in the electric power market for getting the most acceptable rate of electrical energy in favor of all. Reactive power compensation, one of the 12 ancillary services underlined by North American Electric Reliability Council (NREC) along with Electric Power Research Institute (EPRI), is necessarily important to attend the voltage profile at load terminals. STATCOM, a dynamic compensator, has more advanced technologies over a fixed capacitor (FC), which is a static compensator; however, its involvement results in more financial burden to utilities. This article reinforces an adequate reactive power compensation scheme, for attending static and dynamic load disturbances, using ISE criterion-based proposed current-controlled STATCOM with FC in a grid-tied system. The composition of reactive power supplied by static and dynamic compensation is optimized using marginal cost, based on base and peak reactive power demand concept, keeping in mind the system frequency regulation. The results in terms of compensation scheme are compared for step changes in three load conditions and with and without static reactive power compensators.

Energy market optimization based on dynamic Pricing

With various social capital gradually into the electric power market, different virtual power plants will belong to different interest subjects in the future. The main development direction of China's energy market is to build a regional comprehensive energy market with multi-agent participation. Because the traditional power grid electricity price adopts monopoly transaction mode, the market enthusiasm is not high. In view of the situation, this paper proposes a two-level optimization model of master-slave game based on dynamic pricing. In order to encourage electric energy transactions between distribution network operators and multiple virtual power plants, distribution network operator solves the internal optimal electricity price according to the price response of virtual power plants in the optimization process. Simulation results show that the proposed strategy can promote energy sharing among integrated energy systems.

The Impact of Motivating Tariffs and Competition on Electric Power Market in OECD Country-Members

The positive impact of motivating tariffs on the development of renewable energy sources (RES) was confirmed by numerous empiric research. Similar impact of competition on electric power market has not been adequately studied. Current research shows the influence of competition on electric power market not only on the development of generation based on RES, but on the development of technologies of RES-generation and efficiency of relative mechanisms of state support. At the same time it should be mentioned that findings of research are not dubious. By analyzing literature the author formulated a hypothesis about growing impact of motivating tariffs on RES development with higher competition on electric power market. To verify the hypothesis the author used information about an increase in capacity of wind and solar power stations (WPS and SPS) per capita of the population in OECD country-members in 2000–2018 as key drivers of RES development in the region. Findings of the research show that in OECD country-members with higher competition on electric power market the impact of motivating tariffs on the growth in WPS and SPS capacity was much higher.

Features of the formation of the unified electric power market of the EAEU countries

Features of the formation of the unified electric power market of the EAEU countries

Прогнозирование электропотребления на горнопромышленных предприятиях с использованием статистических методов

Forecasting of electric power consumption with due account of assessed impact of various factors helps to make efficient technical and managerial decisions to optimize the electric power consumption processes, including preparation of bids for the wholesale electric power and capacity market. The article uses multivariate methods of statistical analysis and econometric methods based on time series analysis for model designing. The paper presents the results of developing the following models: a multifactor model of electrical power consumption using the regression analysis, the Principal Component Method with the assessment of the impact of production factors on electrical power consumption using elasticity coefficients, as well as the energy saving factor based on a variable structure model; trend additive and multiplicative forecast models of electrical consumption that take into account the seasonality factor, models with a change in trends, a linear dynamic model of electrical power consumption that takes into account the production output; a forecast adaptive polynomial model of electrical power consumption as well as the Winters model. The developed forecast models have a sufficiently high accuracy (accuracy of the MAPE was below 7%). The choice of the model type to forecast the electrical power consumption depends on the quantitative and qualitative characteristics of the time series, the structural relation between the series, the purpose and objectives of the modeling. In order to enhance the accuracy of the forecast it is required to regularly refine the model and adjust it to the actual situation with the due account of new factors and production trends while building different versions of scenarios and combined forecast models of electrical power consumption

Multi-energy microgrids: An optimal despatch model for water-energy nexus

This paper proposes an integrated scheduling model for optimal despatch of cooling, heating, power, gas and water sources in an energy-water microgrid, where the microgrid operator participates in the power, heat, and gas markets and utilizes energy conversion facilities to meet various demands. Further, the role of water and energy storage systems (WESSs) and demand response program (DRP) is investigated on optimal scheduling of the combined cooling, heating, power, gas, and water-based microgrid. In addition, a multi-objective two-stage stochastic optimization model is adopted to minimize the total cost, including operating and emission costs and the amount of potable water extracted from water wells due to the uncertainties of electrical demand, wind power, and electricity market price. Moreover, the epsilon-constraint method and fuzzy satisfying approach are applied to obtain the optimal solution in the multi-objective problem. Ultimately, the simulation results confirm the advantages of simultaneous consideration of WESSs and DRP on the total cost of the proposed energy-water microgrid.

Design and Analysis of Microservice Architecture of Electric Power Marketing System

Design and Analysis of Microservice Architecture of Electric Power Marketing System