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This study examined the price risk of the Belize–Mexico interconnection using ARMA-ARCH models to assess electricity pricing volatility and autoregression to determine the influence of conditional volatilities and import consumption. The findings revealed that the volatility of both markets, especially spot price, showed rare high-impact shocks and prolonged periods of volatile clusters. Volatile pricing tendencies, and forward pricing to a lesser extent, had positive effects on premiums. Premiums were largely dependent on pricing properties and system conditions of the supplier's (Mexico) side, with negligible influence from the buyer's (Belize) side. However, significant effects were found during certain hours with the buyer's loading and transmission loss patterns. Practical implications revealed the possibility of occasional losses caused mostly by information delays and misalignment of risk factors and premiums, which could affect Belize's power cost affordability and scheduling of local generation. Further research can broaden this current scope to enhance planning on local power supply by integrating current findings on the importation market behavior with the buyer's local power expansion plan. [ABSTRACT FROM AUTHOR]

For several stakeholders, including market players, customers, grid operators, policy-makers, investors, and energy efficiency initiatives, having a precise estimate of power pricing is crucial. It is easier for traders to plan, purchase, and sell power transactions with access to accurate electricity price forecasting (EPF). Although energy production and consumption topics are widely discussed in the literature, EPF and renewable energy trading studies receive less attention, especially for intraday market modeling and forecasting. Considering the rapid development of renewable energy sources, the article highlights the significance of integrating the deep learning model, long short-term memory (LSTM), with the proper trading strategy for short-term hourly renewable energy trading by utilizing two different spot markets. Day-ahead and intraday markets are taken into account for the West Denmark grid region (DK1). The time series analysis indicates that LSTM yields superior results compared to other benchmark machine learning algorithms. Using the predictions obtained by LSTM and the recommended trading strategy, promising profit values are achieved for the DK1 wind and solar energy use case, which ensures future motivation to develop a general and flexible model for global data. [ABSTRACT FROM AUTHOR]

Edge data centers (EDCs) have been widely developed recently to supply delay-sensitive computing services, which impose prohibitively increasing electricity costs for EDC operators. This paper presents a new spatiotemporal reallocation (STR) method for energy management in EDCs. This method uses spare resources, including servers and energy storage systems (ESSs) within EDCs to reduce energy costs based on both spatial and temporal features of spare resources. This solution: 1) reallocates flexible workload between EDCs within one cluster; and 2) coordinates the electricity load of data processing, ESSs and distributed energy resources (DERs) within one EDC cluster to gain benefits from flexible electricity tariffs. In addition, this paper for the first time develops a Bit-Watt transformation to simplify the STR method and represent the relationship between data workload and electricity consumption of EDCs. Case studies justifying the developed STR method delivers satisfying cost reductions with robustness. The STR method fully utilized both spatial and temporal features of spare resources in EDCs to gain benefits from 1) varying electricity tariffs, and 2) maximumly consuming DER generation.

Improving energy efficiency is crucial for China's power industry to meet global energy conservation and emission reduction goals. The rapid development of photovoltaic (PV) and hydropower has greatly assisted in the construction of China's novel power system. The stochastic characteristics of PV power generation pose significant challenges to the reliable and economical scheduling of power systems. In fact, the cascade hydropower station can effectively address the issue. To fully utilize the advantages of hydropower, this paper proposes a bi-layer scheduling optimization model for the cascade hydro-PV complementary system considering power market. The upper-layer model simultaneously maximizes the benefit and minimizes the output volatility of the complementary system. The lower-layer model carries out market clearing with the objective of social cost. Besides, PV uncertainty and market price volatility are considered in the decision-making process for power market transactions. To solve the bi-layer model, a novel meta-heuristic algorithm (geometric mean optimizer) is applied, demonstrating excellent performance compared to similar methods. For the complementary system, the results show that its total power output can be improved, and its output volatility can be effectively alleviated. [ABSTRACT FROM AUTHOR]

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The key to the reform of the power system is to design a fair bidding and trading system. Analyzing the transaction process of electricity price competition, suppressing market power and other unfavorable factors, and finding a perfect bidding system are the research goals of this paper. In order to study the competition in the power spot market and power contract market, this paper employs the game model of complete information and the game theory as a tool. The power spot market adopts the Market Clearing Price (MCP) settlement method, in which the power grid determines the maximal real-time price of the generator node as the MCP. The price is based on the three bidding strategy curves of the power plant. As a result, a Nash equilibrium of power plant revenue is formed. According to the Cournot model and Stackelberg model that analyze the power contract market, the long-term equilibrium price of Stackelberg model in the power contract market is higher than that of the perfectly competitive market and less than or equal to the output of perfect monopoly market. The long-term equilibrium price and output in the power contract market are both certain and stable. This paper has analyzed the static game of complete information in the power market and carried out practical application. The results show that the bidding strategies of power plants have a Nash equilibrium and they have an incentive to collude. The MCP mechanism cannot solve the problem of market power influence. The conclusion of the research provides a basis for the design of the power hybrid auction system. [ABSTRACT FROM AUTHOR]

In the United States, many states are implementing goals to achieve 100% carbon free power generation by 2050 or sooner. California has one of the more aggressive goals to achieve 100% carbon free generation of its retail power sales by 2045. California has excellent solar resources, but to accomplish this goal, California’s power utilities will need new zero carbon resources that can replace the natural gas units that they currently rely on for suppling power at night. Appropriately configured concentrating solar power plants with thermal energy storage are an option to serve this nighttime load. These plants would be designed to take advantage of CSP’s low-cost thermal energy storage and collect and store energy during the day and then be dispatched to produce power at night. The U.S. Department of Energy has funded a study to identify the design of a CSP plant that optimally meets the evolving CA grid needs. This will be done by taking a fresh look at how CSP technologies can best be designed to meet the emerging nighttime market for carbon free power generation or zero-carbon firm resources. The paper provides an overview of the study and present preliminary findings on the California power market requirements, CSP configurations, technoeconomic analysis, siting opportunities, and key issues for CSP deployment in this market.

The application projects of traditional virtual power plants generally have some significant problems, such as single energy structure, insufficient participation in the market, sparse energy coupling relationship and lack of new loads, which lead to poor operation stability, low economic benefits and difficult risk management of traditional virtual power plants. With the development of distributed energy resources such as electric vehicles, air conditioners, and electric heating loads and the advancement of information and communication technologies, virtual power plants have gradually evolved from aggregating a single type of energy resource to having the ability to meet the aggregation and management of multiple types of energy resources. Virtual power plant technology provides an effective means to aggregate distributed energy and user-side resources to participate in energy market transactions. A virtual power plant with multiple energy sources such as electricity, heat and gas coupling can effectively avoid the lack of system stability and security caused by a single resource structure, and effectively meet the needs of users for multiple types of energy. This paper focuses on multi-energy coupled virtual power plants, summarizes the current status of research on multi-energy coupled virtual power plants participating in power market operation from the aspects of multi-energy coupling mechanism and operation optimization in power market, and finally provides an outlook on the research direction of multi-energy coupled virtual power plants.

This study examined the price risk of the Belize–Mexico interconnection using ARMA-ARCH models to assess electricity pricing volatility and autoregression to determine the influence of conditional volatilities and import consumption. The findings revealed that the volatility of both markets, especially spot price, showed rare high-impact shocks and prolonged periods of volatile clusters. Volatile pricing tendencies, and forward pricing to a lesser extent, had positive effects on premiums. Premiums were largely dependent on pricing properties and system conditions of the supplier’s (Mexico) side, with negligible influence from the buyer’s (Belize) side. However, significant effects were found during certain hours with the buyer’s loading and transmission loss patterns. Practical implications revealed the possibility of occasional losses caused mostly by information delays and misalignment of risk factors and premiums, which could affect Belize’s power cost affordability and scheduling of local generation. Further research can broaden this current scope to enhance planning on local power supply by integrating current findings on the importation market behavior with the buyer’s local power expansion plan.

For several stakeholders, including market players, customers, grid operators, policy-makers, investors, and energy efficiency initiatives, having a precise estimate of power pricing is crucial. It is easier for traders to plan, purchase, and sell power transactions with access to accurate electricity price forecasting (EPF). Although energy production and consumption topics are widely discussed in the literature, EPF and renewable energy trading studies receive less attention, especially for intraday market modeling and forecasting. Considering the rapid development of renewable energy sources, the article highlights the significance of integrating the deep learning model, long short-term memory (LSTM), with the proper trading strategy for short-term hourly renewable energy trading by utilizing two different spot markets. Day-ahead and intraday markets are taken into account for the West Denmark grid region (DK1). The time series analysis indicates that LSTM yields superior results compared to other benchmark machine learning algorithms. Using the predictions obtained by LSTM and the recommended trading strategy, promising profit values are achieved for the DK1 wind and solar energy use case, which ensures future motivation to develop a general and flexible model for global data.

Improving energy efficiency is crucial for China’s power industry to meet global energy conservation and emission reduction goals. The rapid development of photovoltaic (PV) and hydropower has greatly assisted in the construction of China’s novel power system. The stochastic characteristics of PV power generation pose significant challenges to the reliable and economical scheduling of power systems. In fact, the cascade hydropower station can effectively address the issue. To fully utilize the advantages of hydropower, this paper proposes a bi-layer scheduling optimization model for the cascade hydro-PV complementary system considering power market. The upper-layer model simultaneously maximizes the benefit and minimizes the output volatility of the complementary system. The lower-layer model carries out market clearing with the objective of social cost. Besides, PV uncertainty and market price volatility are considered in the decision-making process for power market transactions. To solve the bi-layer model, a novel meta-heuristic algorithm (geometric mean optimizer) is applied, demonstrating excellent performance compared to similar methods. For the complementary system, the results show that its total power output can be improved, and its output volatility can be effectively alleviated.

One of the most critical tasks of China's power sector reformation is re-designing transmission and distribution (T&D) prices, which are essential in establishing fair and ordered power markets and encouraging the efficient allocation of resources. In view of the problems in China's power market, such as the lack of a market mechanism for the reasonable allocation of congestion revenue, unreasonable transmission cost composition, and the failure of the transmission pricing mechanism to reflect the utilization degree of transmission resources by users in different geographical locations, this study proposes a transmission price design under the application scenarios of regional power grids and provincial power grids dominated by meshed AC networks in China that considers the marginal benefit of the transmission and spatiotemporal information of electricity demand. The proposed method effectively tackles the above-mentioned problem, with the transmission cost being divided into two parts: the expansion cost reflecting the marginal benefits of transmission, and the residual cost reflecting the rest. The expansion cost is objective and based on power system economics; it is calculated as the congestion revenue under the location marginal price-based wholesale electricity markets, resulting in a more reasonable division of transmission costs and allocation of congestion revenue. Furthermore, allocating the residual cost with the power flow distribution factor and the postage stamp method also reflects the utilization degree of transmission resources by users in different geographical locations. The effectiveness of this transmission price design is confirmed by testing it against a 3-bus and an IEEE 30-bus system. [ABSTRACT FROM AUTHOR]

The global economy faces the challenges of climate change that generates the climate risk, which essentially affects the operational activity of power companies. The power sector is both, one of the most important greenhouse gases emitters, and an industry especially exposed to widely perceived climate risk. Changes in the natural environment cause negative impacts on the energy infrastructure (physical risk), while regulatory changes and society sentiments (transition risk) reshape the conditions of operational profitability of power companies. These phenomena, currently accelerating changes in the global economy, persuaded the authors to undertake a study on necessary changes in the core activities of power companies, which should secure the resilience of their profits in an uncertain future. The authors performed a comprehensive study of literature concerning climate risk exposure in the power sector by researching operational adjustments made by leading power companies in the green transformation process. To meet the abovementioned objectives of the article, the authors present the results of their research in the part of the article that discusses the recommendations for the necessary changes of the core activities of power companies. They intend to identify universal solutions that can be applied by power companies operating in the globalized economy. This requires a long-term approach to the process of re-designing the operational activities of power companies, which would secure physical assets responsible for the continuity of their operations, and hedge future profits against transition risk.

Accurate and seamless auxiliary services in the power market can guarantee smooth and continuous power system operation. China’s new round of power system reform has entered a critical period, and reform implementation requires comprehensive improvements in the maturity of the supporting auxiliary service market. This study reviews the development status and evolution path of the European unified power market and the US regional power market, provides experience for the development of China’s regional power market, then identifies the key influencing factors of auxiliary service trading mechanism design in regional power markets. To analyze the rationality of the auxiliary service trading evaluation index, this paper established an evaluation model for assessing regional power markets. Using combined weight optimization, the gray correlation TOPSIS method was applied to comprehensively evaluate auxiliary service trading in the regional power market. Finally, the application of the proposed evaluation method was briefly analyzed to examine four regional power markets in China and evaluate the effectiveness of current market construction in different regions and provide suggestions for future market construction.

Cascade hydropower producers face two stages of risk when participating in medium and long-term market transactions: transaction risk during the bidding stage; and the operational risk during the scheduling and operation stage due to the uncertainty of runoff and market-clearing prices. Therefore, how to measure the above risks and make corresponding decisions has become an urgent problem for producers.This paper combines the real market structure and rules of a certain hydropower dominated market in Southwest China, and establishes a mid-term operation and trading decision-making method based on the Joint Information Gap Decision Theory (IGDT) and Prospect Theory. To address the main uncertainty variables that producers face in participating in transactions, this paper obtains the maximum fluctuation range of variables that satisfy the expected revenue in a robust model based on IGDT. Then, using Prospect Theory, a bidding strategy model that takes into account the psychological factors of producers is constructed within this range.To solve the nonlinear programming problem and address the accuracy issues caused by curve fitting during the solution process, a nonlinear programming combined with an improved stepwise optimization hybrid algorithm is employed.Using actual data from a hydropower grid in southwest China participating in the market as an example. The results indicate that the method provides the fluctuation range of runoff and market prices under different expected return targets, and can formulate reasonable bidding decisions and operation plans based on producers different risk preferences within this range.

China's electricity demand has grown rapidly over only two decades and is currently the largest in the world. This was largely owing to a framework of regulation in which governments regulated prices and quantities, and there was ample incentive for investment. As the growth in electricity demand has slowed and the use of renewable energy has been impeded by integration-related challenges, China has embarked on a new round of power sector reform, with a focus on market construction. Considering four years of market experiments at the provincial level, we review the backgrounds and goals of the reforms, as well as the current progress toward meeting the initial targets. Then, we discuss the key challenges to be overcome to achieve complete implementation of the reforms, with special emphasis on the system of dispatch. We recommend that China's reforms, in order to achieve the goal of market-based competition, must embrace short-term markets, a greater role for independent regulation, compatibility with renewable energy, and inter-provincial coordination.

The flexibility of the power grid can be significantly promoted by the participation of power customers in dispatch. However, as the uncertainty of customer behavior,the development of demand response services is limited. To solve this problem,the framework of incentive-based demand response is constructed in this paper. The way that load aggregators integrate demand-side resources to participate in the power market is elaborated. And the behavior of power customers responding to incentive policies is transformed into demand elasticity. Then,a data-driven demand elasticity prediction method based on the integrated long short-term memory (LSTM) is proposed. Meanwhile,to improve the performance of the prediction model,the original data is smoothed and scaled,and the weight coefficients of the loss function are added. The simulation results show that,compared with the traditional LSTM algorithm and the k-proximity prediction method,the average forecasting error with the proposed model for the demand elasticity is reduced by 5.33% and 28.8%,and mean absolute percentage error (MAPE) for the total load prediction is reduced by 2.06% and 3.09%. Additionally,based on integrated LSTM,the influence of smoothing and scaling data preprocessing on prediction accuracy is analyzed. The results show that the prediction accuracy can be significantly promoted by data preprocessing.

There may exist system operational risks and the risks of losing the revenue because of the generating uncertainty of the stochastic units during the operational process of the virtual power plant (VPP),which contains multiple large scale stochastic units. To solve these problems existing in VPP,a two-stage bi-level decentralized planning (BLDP) model incorporating the conditional value-at-risk (CVaR) based on the characteristics of hierarchical control in the multi-agent system is built. Multi objective programming (MOP) model is selected and compared with the BLDP model constructed in this paper to verify the effectiveness of the BLDP model. It indicates that when VPP participates in the market independently and trades with other VPPs under the BLDP scenario,the system will achieve higher profits and be affected less by the risk levels. Meanwhile,the system can get higher yield rate under BLDP scenario than that under MOP scenario under different trading mechanisms,when VPP participates the power market independently without mutual dealing,results obtained under the BLDP scenario are 103% higher than that under the MOP scenario,while when it takes part in both the market and mutual dealing,the results under the BLDP scenario are 147% higher than that under the MOP scenario. The results have demonstrated the availability of the proposed model under different risk levels.

Led by the carbon peak and neutrality targets, there is a consensus for renewable energy to compete in the electricity market alongside conventional energy sources. Based on co-evolutionary algorithms, this paper proposes an equilibrium model of electricity market with the participation of virtual power plants composed of wind farms and distributed energy storage. The model constructs a secondary clearing mechanism with the aim of promoting hydropower consumption and simulates the game behavior of various players in a market environment from the perspective of power producers. A co-evolutionary algorithm is used to solve the market equilibrium point for the game model, and the optimal fitness function is used to judge whether the generator’s strategy combination is optimal. The simulation results validate the effectiveness of the market model and the co-evolutionary algorithm. The results show that the secondary clearance mechanism is effective in increasing the rate of wind power consumption and improving the overall welfare of society.

With the establishment of the smart grids platform, demand response (DR) programs have improved the operation and reliability of the power grid by increasing the participation of customers. This article investigates the issue of bidding strategy for an electricity retailer, which interacts with a set of residential customers. In this environment, the price-responsive customers react to the announced prices optionally. Thus, the retailer requires to learn the behavior of its customers. The proposed model enlisting the analytic hierarchy process (AHP) and a strength deep learning (DL) algorithm represents a pioneer study of applying a data-driven method into the bidding strategy problem. Herein, the energy usage patterns of customers in response to the dynamic pricing are firstly extracted, and a profit maximization problem of the retailer for the DR management is then developed with the consideration of the market constraints. The numerical results show the good performance of the proposed approach for improving the profitability of the retailer.

The optimization of power purchasing is the crucial part to enhance the core competitiveness of electricity retailers, which can help electricity retailers achieve economic efficiency improvement and risk reduction. Therefore, a power purchasing optimization model of electricity retailers based on IGDT is proposed in this paper. Firstly, considering the multiple uncertainties faced by electricity retailers when participating in the multi-time scale markets, the profits and risks of power purchasing strategies are quantified. Then, a power purchasing optimization model based on IGDT is constructed, which can assess the robustness of its purchasing strategies against load fluctuation under expected utility. Finally, the case studies using the data of a provincial power market in China illustrate that the proposed optimization model can effectively provide power purchasing strategies with robustness to balance the profit and risk of the electricity retailer, and as the risk attitude tends to be conservative, the proportion of power purchasing through bilateral contracts increases while the load fluctuation range that can be resisted decreases.

In view of the increasing trend of the proportion of new energy power generation, combined with the basic matching of the total potential supply and demand in the power market, this paper puts forward the bidding mode and the corresponding fluctuation suppression mechanism, and analyzes the feasibility of reducing the output fluctuation and improving the stability level of the system by installing energy storage systems. At the same time, through qualitative social utility analysis and quantitative energy storage capacity demand measurement, this strategy fully takes into consideration multiple key factors affecting the amount of energy storage configuration and gives a quantitative calculation formula, which provides new energy suppliers with an optimal cost-effective algorithm to calculate the best economical energy storage configuration quantity. At the meantime, the supporting use of the fluctuation suppression mechanism and the configuration strategy realizes the goal of improving the operation stability of the power system through market-based means. Mathematical proof and the result of numerical example simulation show that the energy storage configuration strategy proposed in this paper is effective, also the bidding mode and fluctuation suppression mechanism are feasible.

The rapid development of distributed photovoltaic power generation exercises an important influence on the current power dispatching operation and power market transactions. In the context of power market reform， this paper analyzes the organizational process for the participation of distributed photovoltaic power generation in power balance and the status quo of its participation in market trade； it summarizes the problems therein. Then based on the demands for market construction in China， the paper explores the process design of distributed photovoltaic power generation for the participation in power balance and related market-oriented mechanisms. Finally， the paper prospects for the key technologies that are urgently needed in the future to improve the monitoring， dispatching and control level of the power system for distributed photovoltaic power generation and establish a organization process and market-oriented mechanism of dispatching and production suitable for the development of distributed photovoltaic power generation.

Reliability Options (ROs) are one type of Capacity Remuneration Mechanisms (CRMS) to meet generation adequacy requirement, and it has been applied in some power markets. When a power producer sells a RO, he will receive an option fee but have to sell power at a price below the strike price which is set by the buying side. This mechanism may have some impacts on the investment decisions of power producers. In this paper, the investment timing of power producers is studied. A stochastic differential electricity price model and an investment decision model are built. Some characteristics, such as mean-reversion, multi-periodic and gradual growth are considered in the price model. Real option pricing model is used to make generation investment decision and it is equivalent to a linear complementarity problem. The numerical solution is obtained by the Successive Over Relaxation (SOR) method. The example results show that the different settings of strike price and option fee will lead to early or late investment.

The participation of incentive demand response (IDR) can improve power grid flexibility, and reduce peak shaving pressure. However, the further development of incentive demand response services will be limited by the uncertainty of user response behavior. To tackle this problem, this paper proposes a data-driven load forecasting method for IDR, which considers consumer behavior. Firstly, we describe the power market auxiliary service operation mechanism for load aggregators, through which the ability of demand-side resources to respond to auxiliary services is improved. Then, add an attention layer to improve the traditional long and short memory (LSTM) model, and propose an IDR load forecasting method based on this model, which considers the model’s learning of historical data of user behavior. Finally, establish a simulation model to verify the effectiveness of the improved LSTM model forecasting. The proposed method is simulated and compared with the traditional LSTM method and k-nearest neighbor prediction method. The results show that compared with the other two methods, the mean error, root means square error, and mean absolute percentage error in the improved LSTM method are reduced by 7.31 MW, 8.32 MW, 2.06% and 10.62 MW, 13.46 MW and 3.09%, respectively, which effectively improves the accuracy of forecasting and increase the participation of demand-side resources in the power auxiliary service market.

By aggregating the scattered dispatchable resources in power grids through dispatchable resource aggregation technology， coordination and control technology， and power market trading technology， virtual power plants （VPPs） have brought about mutually interactive operation in a friendly manner. Firstly， the paper summarizes the responsiveness assessment technology for dispatchable resource and the differentiated resource aggregation methods for participation in power markets in terms of VPP- aggregated modeling technology. Secondly， it analyzes different modes of VPP resource organization and market participation aimed at demand-side response and power spot market. Lastly， the paper analyzes key issues of VPPs for new power systems and has a full view of risk management， external environment， market promotion， as well as security potentially incurred by multiparty participation.

To ensure the stability of the electricity spot market and the safety of the provincial and regional power systems, a safety check method is proposed to maximize the effective reserve resources in the power system by optimizing the power of each tie-line. This safety check method accurately models the tie-line equipment and the effective reserve resources and is coupled with each constraint of the electricity spot market clearing model to form a safety check algorithm to optimize the power of tie-line power. The model involved in this paper is a linear model, which has a clear implementation method in practical dispatching applications. Through this method, the power configuration scheme of each tie-line to meet the electricity spot market constraints can be obtained, and the safety check results have the executability of the power market. The rationality and feasibility of the safety check algorithm results are verified by simulating the provincial-scale electricity spot market. According to the simulation results, this method can release effective reserve resources and provide more guarantees for the safe operation of the power grid. In addition, this method can save up to 4.9% of the total operation cost of the power system and improve the dispatching economy of the power system. This method is of great significance to ensure the safe operation of the power system and the day-ahead market and real-time market scheduling in the actual power spot system. In addition, this method also has great guiding significance for the analysis of the actual reserve situation of the power market after the event.

Although many countries prefer deregulated power markets as a means of containing power costs, a monopoly may still exist. In this study, an agent-based bidding simulation framework is proposed to detect whether there will be a monopoly in the power market. A security-constrained unit commitment (SCUC) is conducted to clear the power market. Using the characteristics that the agent can fully explore in a certain environment and the Q-learning algorithm, each power producer in the power market is modeled as an agent, and the agent selects a quotation strategy that can improve profits based on historical bidding information. The numerical results show that in a power market with monopoly potential among the power producers, the profits of the power producers will not converge, and the locational marginal price will eventually become unacceptable. Whereas, in a power market without monopoly potential, power producers will maintain competition and the market remains active and healthy. [ABSTRACT FROM AUTHOR]

With the continuous opening of China’s electricity market, power generation enterprises have shifted from planned operation mode to market competition mode. Guided by the idea of index, the power market trading index system of power generation enterprises is established by comprehensively considering the four aspects of electricity trading price, market activity, settlement timeliness and market benefit. This paper selects A company as an example to demonstrate the scientificity and practicability of the index system. Finally, according to the result of the case analysis, some suggestions are given to improve the market competitiveness of power generation enterprises.

India's power sector is transforming rapidly, marked by rising demand and a growing share of renewables. The short-term power market provides option for power distribution utilities to meet demand exigency. The paper presents trends and perspectives of short-term electricity trading of select states of India. Market transactions reflect seasonal demand variations, higher during summer-monsoon months, dipping in winters. Analysis of data highlights substantial share of short-term power in the total procurement. Quantum of power traded correlates inversely with operational performance of thermal plants as well as grid share of renewable energy. Suggested policy measures include honing of demand forecasting skills using advanced metering infrastructure, deploying energy storage technologies, including decentralized solar, ensuring sustained supply of coal and preventive maintenance of thermal power plants, initiating seasonal procurement contracts with merchant power plants, encouraging power banking arrangements and including storage component in solar and wind tenders. Power markets, if employed appropriately, can help minimize the demand-supply gap, facilitate energy access, and accommodate higher quantum of green power, thus fulfilling SDG-7 targets. • Strong role of climate in influencing power demand. • Distinct patterns of short-term power trading across Indian states. • Market inversely correlates with thermal PLF and RE penetration. • Need to enhanced forecasting skills. • Tenders clauses can include seasonal procurement and storage. [ABSTRACT FROM AUTHOR]

One of the most critical tasks of China’s power sector reformation is re-designing transmission and distribution (T&D) prices, which are essential in establishing fair and ordered power markets and encouraging the efficient allocation of resources. In view of the problems in China’s power market, such as the lack of a market mechanism for the reasonable allocation of congestion revenue, unreasonable transmission cost composition, and the failure of the transmission pricing mechanism to reflect the utilization degree of transmission resources by users in different geographical locations, this study proposes a transmission price design under the application scenarios of regional power grids and provincial power grids dominated by meshed AC networks in China that considers the marginal benefit of the transmission and spatiotemporal information of electricity demand. The proposed method effectively tackles the above-mentioned problem, with the transmission cost being divided into two parts: the expansion cost reflecting the marginal benefits of transmission, and the residual cost reflecting the rest. The expansion cost is objective and based on power system economics; it is calculated as the congestion revenue under the location marginal price-based wholesale electricity markets, resulting in a more reasonable division of transmission costs and allocation of congestion revenue. Furthermore, allocating the residual cost with the power flow distribution factor and the postage stamp method also reflects the utilization degree of transmission resources by users in different geographical locations. The effectiveness of this transmission price design is confirmed by testing it against a 3-bus and an IEEE 30-bus system.

The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors. Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy. However, this assumption may not be true in reality, particularly when a power market is newly launched. To help power suppliers bid with the limited information, a modified continuous action reinforcement learning automata algorithm is proposed. This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game. Simulation results verify the effectiveness of the proposed learning algorithm.

Virtual Power Plants (VPPs) allow the aggregated management of diverse power technologies in systems of different size, complexity and connectivity. The main objectives of a VPP are to ensure the correct integration of these technologies into power systems, to optimize the power production and load demand, and to meet the technical and commercial requirements, considering the stakeholders’ needs. This paper provides a literature review of the most influential factors as regards the management of VPPs. This literature review includes aspects like the evolutionary frequency of the concepts/definitions most used, the VPP operation, planning and interaction with the systems, programming methods, math techniques and software tools used to solve the issues proposed and the international VPP projects as reference for the systems development. Therefore, this work aims to provide the reader the information and concepts that can support for a better understanding of the role of VPPs in power systems, the approach to sustainability and the interaction with the power market as the good way to bring new economic opportunities to small producers, consumers and prosumers of renewable energy.

Load aggregators (LA) face the uncertainty of load and price when they participate in load curtailment bidding (LCB) in power market. A day ahead optimization model with the objective of minimizing the agency cost of LA is proposed. In this paper, the power purchase, LCB and the control of adjustable load are described as a mixed integer linear programming model. A bi-level programming model is used to deal with the uncertain load, so as to the historical scenario method to uncertain price combined with scenario reduction. Based on the data of Pennsylvania-New Jersey-Maryland (PJM) power market, the proposed strategy is analysed. The results show that the proposed strategy and scenario reduction method can reduce the aggregators' agency cost by more than 4% and risks by more than 10%, improve the performance rate by up to 17.8%, and provide bidding technical support for the LA under uncertain problems.

Electricity price forecasting (EPF) is essential to the bidding strategy formulation and market operation. Since EPF is important in the electricity market, lots of forecasting approaches are proposed. However, the new scene caused by the volatility of renewable power generation and other volatility factors has made previous methods inaccurate and inapplicable. To address this problem, we propose an online self-adaptive forecasting method based on random forest, which is different from the traditional batch learning. Our approach takes possible fluctuations of the market into consideration, and adapts to them by maintaining training sets of different sizes. A case study using actual electricity market data has shown that our proposed approach obtains higher accuracy than ordinary approaches, as well as sheds light on possible concept drift in the market. [ABSTRACT FROM AUTHOR]

It is of prime importance to realize the integration of variable renewable energy sources (VRE) for sustainable energy society. The frequency regulation becomes an important issue in the power system with more VRE integration due to uncertain output fluctuation of VRE generators. Another issue caused by VRE integration is congestion management in transmission network, and locational marginal price (LMP) has been applied in some power networks to avoid the congestion issue based on economic mechanism. The congestion issue has to be considered in the balancing control to cope with frequency fluctuation because more balancing power could be needed due to the VRE integration. Hence, in this paper, the application of LMP to the balancing power market in terms of capacity procurement was proposed. A part of transmission capacity was assigned to the balancing control and the location of the control reserves was given with LMP. The effectiveness of the proposed method was verified through numerical simulations based on a combined model for both the frequency regulation and the power flow calculation. It was shown that the congestion issue was properly mitigated with keeping the frequency fluctuation by using the proposed method. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Green hydrogen has become the key to social low-carbon transformation and is fully linked to zero carbon emissions. The carbon emissions trading market is a policy tool used to control carbon emissions using a market-oriented mechanism. Building a modular carbon trading center for the hydrogen energy industry would greatly promote the meeting of climate targets. Based on this, a "green hydrogen market—national carbon trading market–electricity market" coupling mechanism is designed. Then, the "green hydrogen market—national carbon trading market–electricity market" mechanism is modeled and simulated using system dynamics. The results are as follows: First, coupling between the green hydrogen market, carbon trading market and electricity market can be realized through green hydrogen certification and carbon quota trading. It is found that the coupling model is feasible through simulation. Second, simulation of the basic scenario finds that multiple-market coupling can stimulate an increase in carbon price, the control of thermal power generation and an increase in green hydrogen production. Finally, the proportion of the green hydrogen certification, the elimination mechanism of outdated units and the quota auction mechanism will help to form a carbon pricing mechanism. This study enriches the green hydrogen trading model and establishes a multiple-market linkage mechanism. [ABSTRACT FROM AUTHOR]