Electricity Market Management Research Articles

Metaheuristic optimization algorithms comparison adopted for the profit maximization of electricity market participants

The electricity market faces numerous challenges due to the growing demand for energy, increasing penetration of renewable energy sources, and the need for grid reliability and efficiency. To address these challenges, optimization algorithms have emerged as essential tools for optimizing various aspects of the electricity market, including generation, transmission, distribution, and demand-side management. The review can be done by providing an overview of the key components and challenges of the electricity market, including generation dispatch, unit commitment, economic dispatch, transmission network optimization, and demand response management. It then systematically examines a wide range of optimization techniques employed in addressing these challenges, including linear programming, mixed-integer linear programming, nonlinear programming, dynamic programming, genetic algorithms, particle swarm optimization, simulated annealing, and machine learning-based approaches. This paper presents a comparison of optimization algorithms, RCEDUMDA (Ring-Cellular Encode-Decode Univariate Marginal Distribution Algorithm) and CL_HC2RCEDUMDA (Hill Climbing to Ring Cellular Encode-Decode Univariate Marginal Distribution Algorithm) for the profit maximization of Electricity Market consumers & prosumers.

Metaheuristic Optimization Algorithms Comparison Adopted for the Profit Maximization of Electricity Market Participants

The electricity market faces numerous challenges due to the growing demand for energy, increasing penetration of renewable energy sources, and the need for grid reliability and efficiency. To address these challenges, optimization algorithms have emerged as essential tools for optimizing various aspects of the electricity market, including generation, transmission, distribution, and demand-side management. The review can be done by providing an overview of the key components and challenges of the electricity market, including generation dispatch, unit commitment, economic dispatch, transmission network optimization, and demand response management. It then systematically examines a wide range of optimization techniques employed in addressing these challenges, including linear programming, mixed-integer linear programming, nonlinear programming, dynamic programming, genetic algorithms, particle swarm optimization, simulated annealing, and machine learning-based approaches. This paper presents a comparison of optimization algorithms, RCEDUMDA (Ring-Cellular Encode-Decode Univariate Marginal Distribution Algorithm) and CL_HC2RCEDUMDA (Hill Climbing to Ring Cellular Encode-Decode Univariate Marginal Distribution Algorithm) for the profit maximization of Electricity Market consumers & prosumers.

Decarbonizing the industry sector and its effect on electricity transmission grid operation—Implications from a model based analysis for Germany

Integrating large amounts of electrolyzer capacities poses particular challenges for grid operators along the entire hydrogen value chain. This research examines how hydrogen production capacities that support the decarbonization of German industrial sectors impact the electricity transmission grid. The operation of electrolyzer capacities and the production of green hydrogen result in increased electricity demand that stresses the power grids beyond conventional electricity load levels. The question arises to what extent electrolyzer capacities cause additional grid congestion and how flexible operation of electrolyzers can contribute to efficient management of future power grids. A scenario framework is created, differing in the decarbonizing strategy of industry sectors, operation mode of electrolyzers, and penetration levels of electrolyzer installations for a market projection of the future European electricity system. Model-based research is performed by applying a fundamental electricity market and congestion management optimization model of the European electricity systems for the set of scenarios. Results of the model-based investigation highlight the importance of integrating electrolyzer capacities into congestion management practices, primarily if corresponding decarbonized industries feature a more distributed allocation throughout Germany, such as the chemical, paper and printing industries. The findings of this work provide policymakers, system operators, and regulators with meaningful insights for designing future congestion management frameworks.

Forecasting different dimensions of liquidity in the intraday electricity markets: A review

<abstract><p>Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models.</p></abstract>

Cutting-edge public policy proposal to maximize the long-term benefits of distributed energy resources

In the context of increasing integration and relevance of distributed energy resources, this paper proposes an advanced public policy to maximize their long-term benefits through optimal regulated electricity market management. More specifically, the focus is on renewable distributed generation and energy storage systems. The proposed policy arises from three models or techniques, i.e., the optimized tariff model (regulated electricity market model), Bass diffusion model (long-term forecasting of the integration of distributed energy resources), and random walk (risk assessment technique), along with a stochastic optimization problem. The policy leads to socioeconomic welfare maximization while ensuring fairness for all the market players and enhanced control by the regulatory agency on the integration of distributed energy resources. By applying the policy to a concession area located in northeastern Brazil, where photovoltaic distributed generation systems dominate the market share, results demonstrate that it is highly valuable as a 156 (MR$) mean socioeconomic welfare gain was achieved when optimizing the design variables (compared to a non-optimal solution).

Efficient energy management of wireless charging roads with energy storage for coupled transportation–power systems

Wireless charging roads equipped with energy storage systems are promising electric vehicle charging solutions by virtue of their strong advantages in time saving and reduced pressure on the existing power infrastructure. Integration of wireless charging roads into the existing electricity market and efficient management of the corresponding energy storage system are crucial for successful implementation of the wireless charging road systems. In this work, we develop a coupled transportation–power system framework for incorporation of a wireless charging road system into the real-time electricity market. In addition, we propose a Lyapunov optimization-based control strategy to manage the energy storage system in a cost-efficient manner. Our simulation study demonstrates that efficient control of the energy storage system not only reduces the energy costs of the entire wireless charging road system but also alleviates the pressure produced by the wireless charging load on the existing power grid. In our two numerical examples, the energy costs are reduced by 2.61% and 15.34%, respectively. Two indicators of power grid pressure: time average of maximum and time average of standard deviation of locational marginal prices, are reduced by 10.65% and 69.33% for the first numerical example and 5.11% and 34.73% for the second numerical example.

Optimal Operation of Battery Energy Storage Under Uncertainty Using Data-Driven Distributionally Robust Optimization

This paper proposes a conditional value-at-risk (CVaR)-based approach to deal with uncertainties in optimizing the participation of a battery storage in both the electricity market and demand-side management (DSM). A data-driven distributionally robust optimization (DDRO) methodology is used to solve the proposed mean-risk portfolio optimization model. The participation of a battery in DSM along with day-ahead and real-time markets (e.g., energy, spinning reserve, regulation up, and regulation down) faces uncertainties in market-clearing prices, energy demand, and available power capacity. Since in this application, the distribution of the uncertain parameters is only observable through a finite training dataset, the proposed DDRO methodology uses the Wasserstein metric to construct a ball in the space of probability distributions, which is centered at the uniform distribution on the training samples. Then, based on the worst-case distribution within this Wasserstein ball, it seeks decisions that perform best. The proposed DDRO problem over Wasserstein balls is reformulated as a finite convex problem, tested, and verified using real market data and supply/demand profiles. The results are compared with the ones obtained from other optimization methods, including deterministic and classical stochastic optimization, which validates the high performance of the proposed DDRO over finite historical sample data.

Predicting the Creation of Smart Grids considering the Role of Big Data in North America's Electricity Market

Using dual-bandwidth digital technology, the smart grid delivers energy from manufacturers to customers, saving consumers energy by controlling their home appliances, reducing costs and enhancing reliability and transparency. The smart grid on the one hand and the shaping of the electricity market, on the other hand, have created a huge amount of data every day through the daily interactions of electricity and to decide which market players to include manufacturers, transmitters, distributors and in the near future on smart grids for electricity consumers, storage and then be analyzed. This huge amount of data, despite the many benefits it can bring, can cause problems in data storage, display and analysis. For example, one of the most important tasks of North America's electricity market management is to forecast electricity prices for future periods based on stored historical data and because of the type of data produced in this area of the big data typ. Data has problems with different fields, especially in data analysis. In this paper, the role of modern data mining methods for big data analysis is compared by comparing two traditional and new data mining methods and performing statistical experiments. The results of this paper show that a slight improvement in electricity price forecasts due to high volume of electricity exchanges can bring incredible savings to the players in this field. Therefore, the use of modern data mining methods in this field is very important and practical.

Credit evaluation method of generating companies considering the market behavior in China electricity market

AbstractSince the reformation of the electricity market of China is increasing and the modernization of the national governance system is being promoted, the electricity market management system needs to be gradually improved. Credit evaluation is an essential part of the electricity market management system. Therefore, a complete credit evaluation system plays a fundamental role in ensuring that the electricity market can operate safely, stably, efficiently, and reliably. The traditional credit evaluation methods focus on the financial situation and credit records of the generating companies. With the gradual development of the electricity market transactions, the market behavior needs to be included in the credit evaluation indicator system to standardize the behavior of the generating companies and reduce the risks of the market transactions. This study establishes a complete credit evaluation system from three perspectives: market behavior, financial situation, and social responsibility. In addition, to determine the weight of indicators in the credit evaluation, the analytic hierarchy process (AHP) algorithm, fuzzy theory, and the non‐dominated sorted genetic algorithm II (NSGA‐II) algorithm are combined. The variance of the evaluation results and the deviation degree of the indicator weight are considered the objective functions of the NSGA‐II algorithm to optimize the indicator weight. Finally, comparing as an example the results of the credit evaluation before and after the optimization of an electricity market shows that the credit evaluation method can effectively distinguish the generating companies that adopt different market behaviors and has a high practical significance.

Retracted: The impact of COVID‐19 on the electricity sector in Spain: An econometric approach based on prices

SummaryAccording to the World Health Organization (WHO), the COVID‐19 pandemic has been the greatest challenge to humanity since World War II. Spain is one of the countries experiencing severe consequences from the COVID‐19 pandemic outbreak. In such a situation, when the population is quarantined, hospitals are crowded, and industries are shutting down; energy supply becomes a critical and “hard to predict” service. In this way, the study of the impact of COVID‐19 on the selling price of electricity in Spain is highlighted more comprehensively. Given the high level of uncertainty in global energy markets, there is a research gap that needs to be filled, that is, the creation of new decision support mechanisms that can reduce risks and ensure more significant forecasting for investments and energy management to deal with seemingly unprecedented effects experienced through the current pandemic. This study examines and develops econometric models to determine the impact of COVID‐19 on the Spanish electricity market. This article's main result is a framework that predicts the dynamic electricity market's behavior during shifting periods, that is, the COVID‐19 pandemic period. Noteworthy that the result of this article is useful in electricity market management and strategic developments.

Towards the Integration of Modern Power Systems into a Cyber–Physical Framework

The cyber–physical system (CPS) architecture provides a novel framework for analyzing and expanding research and innovation results that are essential in managing, controlling and operating complex, large scale, industrial systems under a holistic insight. Power systems constitute such characteristically large industrial structures. The main challenge in deploying a power system as a CPS lies on how to combine and incorporate multi-disciplinary, core, and advanced technologies into the specific for this case, social, environmental, economic and engineering aspects. In order to substantially contribute towards this target, in this paper, a specific CPS scheme that clearly describes how a dedicated cyber layer is deployed to manage and interact with comprehensive multiple physical layers, like those found in a large-scale modern power system architecture, is proposed. In particular, the measurement, communication, computation, control mechanisms, and tools installed at different hierarchical frames that are required to consider and modulate the social/environmental necessities, as well as the electricity market management, the regulation of the electric grid, and the power injection/absorption of the controlled main devices and distributed energy resources, are all incorporated in a common CPS framework. Furthermore, a methodology for investigating and analyzing the dynamics of different levels of the CPS architecture (including physical devices, electricity and communication networks to market, and environmental and social mechanisms) is provided together with the necessary modelling tools and assumptions made in order to close the loop between the physical and the cyber layers. An example of a real-world industrial micro-grid that describes the main aspects of the proposed CPS-based design for modern electricity grids is also presented at the end of the paper to further explain and visualize the proposed framework.

A novel system for multi-step electricity price forecasting for electricity market management

Electricity price forecasting is an important and challenging issue for all participants in the power market because of the wide application of electricity in our society and its inherent features. In this context, some current forecasting systems use data preprocessing and optimization for theoretical and practical achievements. However, some limitations to these systems exist which need to be urgently solved. First, future information is overdrawn in the data preprocessing stage of these forecasting systems, which is actually unknown in practical applications. The crucial question, therefore, is how to develop a forecasting system without using any future information. Second, the complex features of original nonlinear and nonstationary electricity price have a negative influence on the generalization ability of these previously developed models. To decrease the negative effects on management, a method to develop a forecasting system to improve the model’s generalization ability is required. Therefore, in this study, we developed an adaptive deterministic and probabilistic interval forecasting system for multi-step electricity price forecasting, which can present more valuable information to power market decision makers. Two cases and one comparative study are provided and analyzed to validate the performance of the developed system in multi-step electricity price forecasting. Furthermore, further discussions are presented to illustrate the significance of this study, thus proving that the results of the present study fill the present knowledge gap and provide some new future directions for related studies.

Outlier-robust hybrid electricity price forecasting model for electricity market management

Electricity market management is of great importance for cleaner production in the development of society. However, despite this significance, electricity price forecasting remains a challenging task. Hybrid models are widely employed for forecasting electricity price, which has the characteristics of being non-stationarity, random, and non-linear. Despite their success, current hybrid models require improvement. In particular, data preprocessing, artificial intelligence optimization, feature selection, and basic forecasting engine selection should be considered. In this study, in addition to these issues, we consider the negative influence of outliers on the modeling of electricity price. In particular, a novel outlier-robust hybrid model is developed for forecasting electricity price, which combines a basic forecasting engine called outlier-robust extreme learning machine model and three new algorithms. Specifically, a new optimizer called chaotic sine cosine algorithm is developed to obtain the ideal parameters for phase space reconstruction, and then a novel feature selection method is developed to construct the optimal features in the modeling of electricity price. Moreover, an effective data preprocessing method is proposed for effective forecasting by capturing electricity price features. Subsequently, experiments based on electricity price data from the electricity markets of Australia and Singapore demonstrate that the proposed model is superior to other benchmark models. Further, the model can be a reliable forecasting method not only in electricity market management, but also in modeling time series with complex nonlinear characteristics and outliers.

The role of the electricity market management committee in the electricity market

With the continuous advancement of China’s electricity market construction, the market has increasingly become the main means of resource optimization and allocation. In the construction of the electricity market, government agencies, power grid utilities and other market entities usually have different demands, and there are different opinions in the process of electricity market framework design and rulemaking. The electricity market management committee can serve as an important deliberative entity in the construction of the electricity market. This paper sorts out the current situation of the construction of China’s electricity market management committee, and summarizes and analyses the functions of the electricity market management committee in the major electricity markets all over the world, which can serve as a reference for the construction of the China’s electricity market management committee.

LESSONS OF THE ENERGY REFORM:ILLUSIONS, MISCALCULATIONS, PROSPECTS

The article looks at the problems of the power sector reform based on the development of market relations in the industry. Factors that influence the effectiveness of the electricity market, the quality of an organizational model and the economic environment are considered within the framework of a methodological approach developed by the authors. The article studies discrepancies between ideal concepts that are inherent in the existing market model, and real economic relations that are directly linked to the technological features of the industry. It suggests ideas for alternative models of the electricity market and industry-specific management mechanisms and presents the authors’ concept and specific solutions for its implementation.

Neuro-Wavelet Approach to Time-Series Signals Prediction: An Example of Electricity Load and Pool-Price Data

Accurate electricity load and pool-price forecasting can provide a set of vital predicted information that helps generation, transmission and retailer participating companies to bid strategically into a deregulated electricity market in order to maximize their profits and increase returns to their stakeholders. Although a number of forecasting methods have been proposed to solve the short-term and long-term electricity load forecast, pool-price forecasting is a relatively new research area. In this article, we propose an autoregressive approach, based on a wavelet multiscale decomposition, for the prediction of one-hour ahead load and pool price based respectively on historical electricity load, and pool-price data. This approach is based on a multiple resolution decomposition of the signal using the redundant Haar à trous wavelet transform whose advantage is taking into account the asymmetric nature of the time-varying data. There is an additional computational advantage in that there is no need to re-compute the wavelet transform (wavelet coefficients) of the full signal if the electricity and pool price data (time series) is regularly updated. We assess results produced by this multiscale autoregressive method, in both linear and nonlinear variants, with single resolution autoregressive, multilayer perceptron, Elman recurrent neural network and the general regression neural network models. The input data consists of historical load and pool price data, which is collected over a period of 3 years (1999-2001), used for training, and 1 year (2002) used for testing. Experimental results are based on the New South Wales (Australia) electricity load and pool price data that is provided by the National Electricity Market Management Company.

Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting

We propose a wavelet multiscale decomposition-based autoregressive approach for the prediction of 1-h ahead load based on historical electricity load data. This approach is based on a multiple resolution decomposition of the signal using the non-decimated or redundant Haar à trous wavelet transform whose advantage is taking into account the asymmetric nature of the time-varying data. There is an additional computational advantage in that there is no need to recompute the wavelet transform (wavelet coefficients) of the full signal if the electricity data (time series) is regularly updated. We assess results produced by this multiscale autoregressive (MAR) method, in both linear and non-linear variants, with single resolution autoregression (AR), multilayer perceptron (MLP), Elman recurrent neural network (ERN) and the general regression neural network (GRNN) models. Results are based on the New South Wales (Australia) electricity load data that is provided by the National Electricity Market Management Company (NEMMCO).

Data management for the electricity market

Electricity markets provide economic signals that encourage network utilization to be maximized. This environment is encouraging network owners and operators to ensure that limits are consistent with actual system conditions. The National Electricity Market Management Company Limited (NEMMCO) is a government agent placed between market participants (generators and retailers) with the roles to manage the electricity market, maintain power system security, and coordinate power system planning. Currently, the eastern Australian states of Victoria, New South Wales, Queensland, and South Australia are involved in the market. Power system security and coordination of power system planning for electricity markets involve various power system analyses being done by NEMMCO and market participants. Efficiency of the data preparation for analysis and consistency and transparency of the data are important issues. A software system called the Operations and Planning Data Management System (OPDMS) is being developed to support planning and operations functions in the electricity market environment. One of its key features is the automated preparation of input data files for load flow and transient stability studies using the PSSE analysis package. These data files are generated from real-time power system data and, therefore, allow accurate analysis of actual system conditions. OPDMS improves NEMMCO's ability to develop limits that are consistent with actual system conditions.