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Introduction: This paper constructs a revenue model for an independent electrochemical energy storage (EES) power station with the aim of analyzing its full life-cycle economic benefits under the electricity spot market. Methods: The model integrates the marginal degradation cost (MDC), energy arbitrage, ancillary services, and annual operation and maintenance (O&M) costs to calculate the net profits of the EES power station. Using an iterative optimization approach, we determine the optimal MDC and analyze the economic end of life (EOL) for different types of EES power stations. Results: By examining real-world examples from the California energy market, we find that the full life-cycle benefits of an EES power station peak when its MDC is optimal, at $45/MWh-throughput. Under these conditions, the economic and physical EOL of commercial/industrial EES power station is 9 years, while the economic EOL of residential-grade EES power station is 8 years, which is shorter than their physical EOL of 9 years. Discussion: The study further indicates that the economic life of an EES power station is influenced by multiple factors, and operators need to determine the optimal economic EOL to maximize revenue based on battery degradation characteristics, market conditions and operational strategy. [ABSTRACT FROM AUTHOR]

The "dual carbon" goal has made it a mainstream trend for new energy stations (NESs) and energy storage stations (ESSs) to jointly participate in market regulation. This paper proposes a multiple time scale distributed optimization method for NESs and ESSs based on the alternate direction multiplier method (ADMM). By first considering the uncertainty of new energy output and the volatility of electricity market prices, a multi time scale revenue model is constructed for day-ahead, intraday, and real-time markets. Then, the objective function is built by maximizing the comprehensive market revenues and is simplified using the synergistic effect of NESs and ESSs. Next, the simplified objective function is solved by the ADMM, and the revenues are maximized while each energy meets the relevant constraints. Lastly, the 33-node network topology is used to illustrate the feasibility of the proposed method. The simulation results show that after optimization, the output of NESs and ESSs can coordinate work in day-ahead, intraday, and real-time markets, while the abandonment power of wind and light is significantly improved. [ABSTRACT FROM AUTHOR]

Day-ahead electricity price forecasting (DAEPF) is vital for participants in energy markets, particularly in regions with high integration of renewable energy sources (RESs), where price volatility poses significant challenges. The accurate forecasting of high and low electricity prices is particularly essential, as market participants seek to optimize their strategies by selling electricity when prices are high and purchasing when prices are low to maximize profits and minimize costs. In Japan, the increasing integration of RES has caused day-ahead electricity prices to frequently fall to almost zero JPY/kWh during periods of high RES output, creating significant profitability challenges for electricity retailers. This paper introduces novel custom loss functions and metrics specifically designed to improve the forecasting accuracy of extreme prices (high and low prices) in DAEPF, with a focus on the Japanese wholesale electricity market, addressing the unique challenges posed by the volatility of RES. To implement this, we integrate these custom loss functions into a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, augmented by an ensemble learning approach and multimodal features. The proposed custom loss functions and metrics were rigorously validated, demonstrating their effectiveness in accurately predicting high and low electricity prices, thereby indicating their practical application in enhancing the economic strategies of market participants. [ABSTRACT FROM AUTHOR]

Energy prices are usually determined by the marginal costs obtained by solving economic dispatch problems without considering commitment costs. Hence, generating units are compensated through uplift payments. However, uplift payments may undermine market transparency as they are not publicly disclosed. Alternatively, energy prices can be obtained from the unit commitment problem which considers commitment costs. But, due to non-convexity, prices may not monotonically increase with demand. To resolve this issue, convex hull pricing has been introduced. It is defined as the slope of the convex envelope of the total cost function over the convex hull of a unit commitment (UC) problem. Although several approaches have been developed, a relevant survey has not been found to aid the understanding of convex hull pricing from the current limited literature. This paper provides a systematic survey of convex hull pricing. It reviews, compares, and links various existing approaches, focusing on the modeling and computation of convex hull prices. Furthermore, this paper explores potential areas of improvement and future challenges due to the ongoing efforts for power system decarbonization. [ABSTRACT FROM AUTHOR]

In the context of the electricity sector's liberalization and deregulation, the accurate forecasting of electricity prices has emerged as a crucial strategy for market participants and operators to minimize costs and maximize profits. However, their effectiveness is hampered by the variable temporal characteristics of real-time electricity prices and a wide array of influencing factors. These challenges hinder a single model's ability to discern the regularity, thereby compromising forecast precision. This study introduces a novel hybrid system to enhance forecast accuracy. Firstly, by employing an advanced decomposition technique, this methodology identifies different variation features within the electricity price series, thus bolstering feature extraction efficiency. Secondly, the incorporation of a novel multi-objective intelligent optimization algorithm, which utilizes two objective functions to constrain estimation errors, facilitates the optimal integration of multiple deep learning models. The case study uses electricity market data from Australia and Singapore to validate the effectiveness of the algorithm. The forecast results indicate that the hybrid short-term electricity price forecasting system proposed in this paper exhibits higher prediction accuracy compared to traditional single-model predictions, with MAE values of 7.3363 and 4.2784, respectively. [ABSTRACT FROM AUTHOR]

Due to the increasing demand for electricity, competitive electricity markets, and economic concerns, power systems are operating near their stability margins. As a result, power systems become more vulnerable following disturbances, particularly from a dynamic point of view. To maintain the stability of power systems, operators need to continuously monitor and analyze the grid's state. Since modern power systems are large-scale, non-linear, complex, and interconnected, it is quite challenging and computationally demanding to monitor, control, and analyze them in real time. State Estimation (SE) is one of the most effective tools available to assist operators in monitoring power systems. To enhance measurement redundancy in power systems, employing multiple measurement sources is essential for optimal monitoring. In this regard, this paper, following a brief explanation of the SE concept and its different categories, highlights the significance of Hybrid State Estimation (HSE) techniques, which combine the most used data resources in power systems, traditional Supervisory Control and Data Acquisition (SCADA) system measurements and Phasor Measurement Units (PMUs) measurements. Additionally, recommendations for future research are provided. [ABSTRACT FROM AUTHOR]

Electric power systems are currently undergoing a transformation towards a decentralized paradigm by actively involving prosumers, through the utilization of distributed multi‐energy sources. This research introduces a fully decentralized multi‐community peer‐to‐peer electricity trading mechanism, which integrates iterative auction and pricing methods within local electricity markets. The mechanism classifies peers in all communities on an hourly basis depending on their electricity surplus or deficit, facilitating electricity exchange between sellers and buyers. Moreover, communities engage in energy exchange not only within and between themselves but also with the grid. The proposed mechanism adopts a fully decentralized approach known as the alternating direction method of multipliers. The key advantage of this approach is that it eliminates the need for a supervisory node or the disclosure of private information of the involved parties. Furthermore, this study incorporates the flexibility provided by residential heating systems and energy storage systems into the energy scheduling of some prosumers. Case studies illustrate that the proposed multi‐community peer‐to‐peer electricity trading mechanism effectively enhances local energy balance. Specifically, the proposed mechanism reduces average daily electricity costs for individual prosumers by 63% compared to scenarios where peer‐to‐peer electricity trading is not employed. [ABSTRACT FROM AUTHOR]

Currently, the proportion of enterprise self-owned power plants (SPPs) is increasing, with a significant share occupied by small coal-fired units, severely affecting the absorption of new energy and causing substantial pollution. To address this issue, developing generation rights trading between SPPs and new energy enterprises is an effective solution. At present, research on generation rights trading is mainly based on early water and thermal generation rights replacement trading. This approach, to some extent, overlooks changes in electricity market policies. Based on this, a new generation rights trading bidding strategy incorporating price differences and time-of-use pricing settlement is proposed. Firstly, the relationship between price difference settlement and generation rights trading is studied and the win–win model of generation rights trading is improved. Secondly, in the time-of-use pricing settlement mode, the single bidding strategy is optimized with the objective of maximizing the total social benefits in the win–win model. Finally, an example analysis compares different bidding strategies under time-of-use pricing settlement. Even in the most extreme cases, the time-of-use bidding strategy can improve social benefits by 5.61% and reduce wind and solar curtailment by 7.25% compared to the single bid strategy. The results show that the optimized time-of-use bidding strategy significantly improves the efficiency of generation rights trading, greatly helping to promote the absorption of new energy and alleviate wind and solar power curtailment. [ABSTRACT FROM AUTHOR]

As more renewable energy sources are integrated into the United Kingdom's power grid, flexibility services are becoming integral to ensuring energy security. This has encouraged the proliferation of Lithium-ion battery storage systems, with 85 GW in development. However, battery degradation impacts both system lifespan and the economic viability of large-scale projects. With rising commodity costs and supply chain issues, maximising the value of energy storage is critical. Traditional methods of mitigating battery ageing rely on static limits based on inflexible warranties, which do not fully account for the complexity of battery degradation. This study examined an alternative, degradation-aware current derating strategy to improve system performance. Using an optimisation model simulating UK energy trading, combined with an electro-thermal and semi-empirical battery model, we assessed the impact of this approach. Interviews with industry leaders validated the modelled parameters and the relevance of the alternative strategy. Results show the degradation-aware strategy can extend battery lifetime by 5–8 years and improve net present value and internal rate of return over a 15-year period compared with traditional methods. These findings highlight the economic benefits of flexible, degradation-aware operational strategies and suggest that more adaptive warranties could accelerate renewable energy integration and lower costs for storage operators. [ABSTRACT FROM AUTHOR]

The emergence of variable renewable energy (VRE) technologies has created a range of different energy contracting techniques. Within Australia's National Electricity Market (NEM), Run‐of‐Plant (RoP) Power Purchase Agreements (PPAs) became the most common form of contract with purchasers of wind and solar energy agreeing to pay a fixed price for energy irrespective of when it is produced and, therefore, its actual value to the market. In November 2023, the Commonwealth Government adopted a 32 GW RoP PPA Contract‐for‐Difference (CfD) underwriting policy that aims to effectively shield the generator from market price risk. This article discusses different contract structures and their impact on participant behaviour during periods of material oversupply and negative prices. We find that embedded solar PV exports into the distribution network, which are not required to dynamically participate in the wholesale market, have increased wholesale energy supply, enabling profit maximising vertically integrated renewable firms to drive prices lower in a manner that partially strands the output of RoP PPA CfD generators with a $0/MWh price floor. A key conclusion from our analysis is that requiring embedded solar PV to effectively participate in the wholesale market appears to be a pre‐condition for the efficacy of government‐initiated RoP PPA CfDs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Society of Plant Engineers Japan is the property of Society of Plant Engineers Japan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article discusses research on time-of-use compensation pricing strategies for load aggregators based on user demand response. The study aims to address the issue of mismatch between purchased electricity and market bid volume, which results in punitive costs for companies. The researchers propose a tiered pricing strategy to adjust power distribution and establish a Stackelberg leader-follower game model to promote a mutually beneficial relationship between load aggregators and controllable load users. Through case studies, the paper examines the operational profits of load aggregators and the power adjustment behaviors of controllable load users under tiered and fixed compensation pricing schemes. The results indicate that tiered compensation pricing significantly reduces punitive costs and enhances user participation in demand response. The article also discusses a dynamic compensation pricing model for controllable load power adjustment in power systems, including interactions between load aggregators and controllable load users. A case study is presented to validate the model's effectiveness, and future work will explore the integration of artificial intelligence and machine learning technologies to further optimize demand response strategies. [Extracted from the article]

Insufficient flexibility is a major barrier to the development of new power systems. Leveraging the resource allocation function of the electricity market is a promising way to enhance the flexibility of power systems and promote the consumption of renewables. The reasonable allocation of ancillary service costs plays a pivotal role in this function. Towards the target of "who causes, who shares," various research related to cost allocation has been conducted. However, there is a lack of quantitative analysis of the impact of different cost allocation mechanisms on the market participants' revenues. Whether various cost allocation mechanisms can alleviate the insufficient flexibility problem of power systems needs to be validated. With this in mind, taking operating reserve ancillary services as an example, a long-term market operation simulation model with energy-reserve joint clearing is established in this paper based on the time series production simulation. According to this, the revenues of market participants under different reserve cost allocation mechanisms are quantified. Besides, a self-dispatch model for the energy storage (ES) equipped by renewables is established, based on which the impact of ES on the revenues of renewables under different cost allocation mechanisms is analyzed. Case studies based on practical data from a provincial power grid in China demonstrate that with the well-designed reserve cost allocation mechanism, the revenues of flexible resources can be ensured. Meanwhile, renewables are incentivized to reduce their fluctuations and uncertainties by equipping the ES. Hence, the insufficient flexibility problem of power systems can be alleviated from both supply and requirements perspectives. [ABSTRACT FROM AUTHOR]

The electricity sector plays a key role in achieving zero emissions targets. The required transition will lead to substantial changes in the supply, demand, and distribution of electricity, as well as in stakeholder roles. Future market designs may change substantially to accommodate these changes, address challenges, and take advantage of new opportunities. This paper reviews the characteristics of future carbon-neutral electricity systems and electricity market design options. To provide a guiding framework for the literature review, we transfer the complexity of electricity systems into a three-layer structure: Firstly, we analyze papers that rely on techno-economic modeling of the physical electricity system. As a case study, we analyze various studies focusing on a decarbonized European electricity system in 2050. Secondly, we review papers that investigate the economic behavior and effects of self-interest-seeking stakeholders such as producers, network operators, and consumers. Finally, we review papers focusing on policy and market design questions that guide policymakers in achieving a target physical asset combination while considering the behavior of stakeholders. We highlight common trends and disagreements in the literature, review the main drivers of future markets, and finally provide a mapping between those drivers, challenges, and opportunities. The review concludes that the most promising next step toward a fully comprehensive assessment approach is to combine existing approaches across topical and disciplinary boundaries. [ABSTRACT FROM AUTHOR]

Regulatory boards are promoting closed distribution systems (CDSs), which are different from traditional public-access networks, that can be owned and managed by energy communities (ECs). The inclusion of local renewable energy potential and an adequate schedule of storage devices in a CDS allow cooperation among the EC's members in order to reduce operational expenditure (OPEX), providing internally competitive electricity prices with respect to those provided by publicly regulated networks and electricity markets. The CDS operators can assume a new role as the centralized energy dispatchers of generation and storage assets in order to maximize the profits of the members of the EC. This paper proposes an innovative optimal active and reactive power dispatch model for maximum community welfare conditions. A key difference between this proposal and existing social-welfare-based dispatches on public-access networks is the exclusion of the profit of the external wholesale electricity market. The focus of the proposed method is to maximize the welfare of all community members. A remuneration framework based on a collective EC with a single frontier is adopted, considering agreements between members based on locational marginal pricing (CDS-LMP). Results from an illustrative case study show a reduction of 50% in the EC's OPEX with a payback time of 6 years for investments in CDSs, renewable sources, and storage. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method for scheduling a multi-energy VPP (Virtual Power Plant) supply–demand balance in the power market environment of Jiangxi Province. The primary objective of this method is to improve the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method takes into account the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Furthermore, it integrates real-time market price feedback to achieve the accurate allocation of power supply and demand. Through a case study of a multi-energy VPP in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply–demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%. [ABSTRACT FROM AUTHOR]

Energy communities are a transformative force in the electricity markets and ancillary services, reshaping the energy landscape through collective action. This paper explores the successful strategies adopted by these communities, highlighting real-world cases where they have participated directly in the market, or through aggregators, or sold their energy to retailers, which is of paramount importance because it serves as a foundation for those countries that wish to implement these entities as part of their decarbonization plan. It also serves as a model for the development of future citizen initiatives that aim to turn citizens into active users of the electricity system. The paper examines collaborative dynamics within the energy sector, highlighting how these communities optimize resource sharing and contribute to a more resilient and sustainable energy system. The study emphasizes the potential of energy communities in driving innovation and fostering a participatory approach to energy management. The results show that some pilot projects are being developed and several electricity cooperatives, one of the most common forms of energy communities, are participating in energy trading with their members and other entities. More efforts are also needed for energy communities to participate more directly in the market and/or through aggregators. [ABSTRACT FROM AUTHOR]

Accurate load forecasting, especially in the short term, is crucial for the safe and stable operation of power systems and their market participants. However, as modern power systems become increasingly complex, the challenges of short-term load forecasting are also intensifying. To address this challenge, data-driven deep learning techniques and load aggregation technologies have gradually been introduced into the field of load forecasting. However, data quality issues persist due to various factors such as sensor failures, unstable communication, and susceptibility to network attacks, leading to data gaps. Furthermore, in the domain of aggregated load forecasting, considering the potential interactions among aggregated loads can help market participants engage in cross-market transactions. However, aggregated loads often lack clear geographical locations, making it difficult to predefine graph structures. To address the issue of data quality, this study proposes a model named adversarial graph convolutional imputation network (AGCIN), combined with local and global correlations for imputation. To tackle the problem of the difficulty in predefining graph structures for aggregated loads, this study proposes a learnable adjacency matrix, which generates an adaptive adjacency matrix based on the relationships between different sequences without the need for geographical information. The experimental results demonstrate that the proposed imputation method outperforms other imputation methods in scenarios with random and continuous missing data. Additionally, the prediction accuracy of the proposed method exceeds that of several baseline methods, affirming the effectiveness of our approach in imputation and prediction, ultimately enhancing the accuracy of aggregated load forecasting. [ABSTRACT FROM AUTHOR]

• A tripartite evolutionary game model of participation in recycling is constructed. • The government does not provide enough incentives for consumers. • In this recycling system, the government will gradually reduce its regulation. • The regulation of power battery recycling by the Chinese government is imperfect. • Improving compensation can improve consumers' recycling enthusiasm. While electric vehicles (EVs) are developing at a high speed in China, the power battery market is facing a decommissioning peak. The problem is that the recycling situation of domestic power batteries is not ideal, partly due to neglect by consumers. By considering the recycling system, mode, and policy of China's EV power batteries, we construct a tripartite evolutionary game model of the government, consumers and EV manufacturers; analyse the stable strategy adjustment mechanisms of tripartite participation in this recycling cooperation game; and simulate the tripartite evolutionary game. The results show that when the initial willingness of the government, consumers and EV manufacturers to recycle power batteries is not strong, the government takes the lead, driving EV manufacturers and consumers to participate in power battery recycling. When the government, consumers and EV manufacturers have medium or high levels of initial willingness, the government evolves and chooses a nonregulation strategy. In addition, by simulating the impact of changes in consumer-related influencing factors on this tripartite evolutionary game, we find that subsidies for recycling power batteries are a key factor affecting consumers' strategy choices and that boosting recycling compensation for consumers can improve their enthusiasm to participate in such recycling. Therefore, to improve the recycling of power batteries for EVs, in terms of both efficiency and percentage of deployment, the Chinese government should strengthen public education on power battery recycling, further integrate informal recycling channels, and balance the distribution of profits among consumers for recycling compensation. [ABSTRACT FROM AUTHOR]

Electricity markets are intricate systems that facilitate efficient energy exchange within interconnected grids. With the rise of low-carbon transportation driven by environmental policies and tech advancements, energy trading has become crucial. This trend towards Electric Vehicles (EVs) is bolstered by the pivotal role played by EV charging operators in providing essential charging infrastructure and services for widespread EV adoption. This paper introduces a blockchain-assisted secure electricity trading framework between EV charging operators and the electricity market with renewable energy sources. We propose a single-leader, multi-follower Stackelberg game between the electricity market and EV charging operators. In the two-stage Stackelberg game, the electricity market acts as the leader, deciding the price of electric energy. The EV charging aggregator leverages blockchain technology to record and verify energy trading transactions securely. The EV charging operators, acting as followers, then decide their demand for electric energy based on the set price. To find the Stackelberg equilibrium, we employ a Deep Reinforcement Learning (DRL) algorithm that tackles non-stationary challenges through policy, action space, and reward function formulation. To optimize efficiency, we propose the integration of pruning techniques into DRL, referred to as Tiny DRL. Numerical results demonstrate that our proposed schemes outperform traditional approaches. [ABSTRACT FROM AUTHOR]

The market coupling of adjacent market zones is a key element in terms of coping with rising levels of uncertainty in the European energy system due to the adoption of more volatile renewable energy sources. An adequate analysis of this issue yet requires an appropriate consideration of uncertainty along with a detailed modelling of market coupling. Thus, we present a novel approach combining a comprehensive unit commitment and dispatch model with a sophisticated methodology to reflect the uncertainty of wind power generation by considering forecast updates for the German market region in the market clearing process. Subsequently, we investigate the impact of uncertainty on cross‐border electricity flows in a European case study. Our results show that all adjacent market zones and all relevant technologies significantly contribute to balancing forecast updates in Germany, underlining the benefits of intraday market coupling. Further, we observe that a joint and unrestricted intraday market is the most cost‐efficient market solution. Finally, our analyses show that more extensive market coupling is indispensable for the further integration of renewable energy sources in the future. [ABSTRACT FROM AUTHOR]

Green hydrogen or renewable hydrogen is promising cause it has no carbon emissions in production process, which is water electrolysis with electricity from renewables. Although grids can ensure stable and enough electricity supply, it is difficult to determine whether the grid electricity for hydrogen production is from renewable sources. This article uses EU rules to identify green hydrogen produced by grid electricity and analyse the production. European grids are classified according to the calculated indexes regarding renewables penetration and electricity carbon emission intensity. The negative correlation between green hydrogen production and electricity prices in different time scales and geographical distribution is concluded. Using imported electricity can improve the potential and diversity of green hydrogen production. Nordic countries and Albania have the most potential of green hydrogen production using grid electricity. The results can be a reference for the electricity market on flexibility supply and the future green hydrogen market development. • Nordic countries and Albania have the most green hydrogen production by grid power. • Other bidding zones with lower electricity prices have more green hydrogen production. • Green hydrogen production is negative correlated with hourly electricity prices. • Using imported electricity can improve the production potential and diversity. • Green hydrogen production by grid power can provide flexibility to electricity market. [ABSTRACT FROM AUTHOR]

Zooming in on the European Union's international identity, this paper argues that maturation, and the achievement of a solid international identity, may result from a process in which different identity traits are integrated, even strategically incorporated. It frames this hybridisation argument in terms of Dragon Power Europe and proceeds in two steps. In a first step, the paper develops an integrated analytical framework for studying the European Union's international identity by comparing the Normative Power Europe and Market Power Europe conceptualisations along three dimensions, namely drivers, actions and impact. In a second step, the paper demonstrates the framework's empirical value through an exploration of the European Union's functioning in relation to health information standardisation, investigating the logics behind the inclusion of a new chapter on diagnoses and syndromes associated with Traditional (Chinese) Medicine in the eleventh revision of the World Health Organization's International Classification of Diseases in 2019. The involvement of the European Union and its member states in this specific case of health (information) standardisation shows initial but compelling evidence of NPE-MPE dynamics shaping the EU's international functioning, in particular its drivers, actions and impact. [ABSTRACT FROM AUTHOR]

As new-energy electricity increasingly enters the post-subsidy era, traditional fixed feed-in tariffs and guaranteed purchase policies are not conducive to the optimal allocation of large-scale, high-proportion new-energy power due to the high pressure of subsidy funds and the fairness issues of power-generation grid connection. Encouraging new energy to participate in electricity market transactions is considered an effective solution. However, existing studies have presupposed the adverse effects of new energy in proposing market mechanism optimization designs for new-energy participation without quantitative results to support this, which is not conducive to a true assessment of the comprehensive impact of individual instances of new-energy participation in the market. To this end, this study, based on the actual experience and data cases of China's electricity spot market pilot provinces, considers both unit commitment and economic dispatch in the electricity distribution process, and constructs a two-stage optimization model for electricity spot market clearing. According to the differences in grid connection time and the construction costs of new-energy power, differentiated proportions of new-energy participation in the market and bidding strategies are set. By analyzing the quantitative results of new energy participating in spot market transactions under multiple scenarios, using both typical daily data for normal loads and peak loads, the study provides theoretical support and a data basis for the optimized design of market mechanisms. The research results show that there is a non-linear relationship between the scale of new energy entering the market and its bidding strategies and market-clearing electricity prices. In the transition phase of the low-carbon transformation of the power sector, the impacts of thermal power technology with a certain generation capacity and changes in the relationship between power supply and demand on electricity prices are significant. From the perspective of the individual interests of new-energy providers, the analysis of their bidding strategies in the market is important. [ABSTRACT FROM AUTHOR]

Forecasting the electricity market, even in the short term, is a difficult task, due to the nature of this commodity, the lack of storage capacity, and the multiplicity and volatility of factors that influence its price. The sensitivity of the market results in the appearance of anomalies in the market, during which forecasting models often break down. The aim of this paper is to present the possibility of using hybrid machine learning models to forecast the price of electricity, especially when such events occur. It includes the automatic detection of anomalies using three different switch types and two independent forecasting models, one for use during periods of stable markets and the other during periods of anomalies. The results of empirical tests conducted on data from the Polish energy market showed that the proposed solution improves the overall quality of prediction compared to using each model separately and significantly improves the quality of prediction during anomaly periods. [ABSTRACT FROM AUTHOR]

In order to achieve low carbon emissions in the power grid, the impact of new energy grid connections on the power grid should be reduced, as well as the peak-to-valley load difference caused by large-scale electric vehicle grid connections. This paper proposes a two-tier, low-carbon optimal dispatch master–slave game model involving virtual power plant operators as well as electric vehicle operators. Firstly, the carbon flow is tracked based on the proportional sharing principle, and the carbon emission factor during the charging and discharging process of electric vehicles is calculated. Secondly, the node carbon potential and time-sharing tariff are used to guide and change the charging behaviour of electric vehicles and to construct a master–slave game model for low-carbon optimal scheduling with the participation of multiple subjects, with economic scheduling at the upper level of the model and demand response scheduling at the lower level. Finally, the IEEE30 node system is used as an example to verify that the method adopted in this paper can effectively reduce the peak-to-valley difference of loads, reduce the carbon emissions of the grid, and reduce the cost of each participating entity. [ABSTRACT FROM AUTHOR]

We propose and develop a robust score-driven multiplicative dynamic factor model for the conditional volatility of electricity prices. This model allows us to identify both common and idiosyncratic conditional volatility factors in the spot price of electricity across key Nord Pool European markets. Both factors are extracted using a single observation-driven filter that is simple and parsimonious. The common factor is robust by construction as it averages information over the cross-section of the high-dimensional vector of electricity price changes. The idiosyncratic factors are designed to capture the erratic shocks in prices and therefore rely on a robust updating equation which allows for outliers and fat-tailed innovations. Our model is suitable for high-dimensional applications and does not necessarily suffer from the curse of dimensionality. The parsimonious nature of the observation-driven filter leads to simple computations for the estimation of parameters and the signal extraction of factors. We derive the stochastic properties of the filter and its underlying factor model, including bounded moments, stationarity, ergodicity, and filter invertibility. We further establish the consistency and asymptotic normality of the maximum likelihood estimator. The finite sample properties of the estimator and the filter are explored in a Monte Carlo study. Finally, we apply our model and filter to the Nord Pool spot price data and find evidence that net-import and net-export electricity markets exhibit substantially different common and idiosyncratic volatilities. In particular, we confirm that net-import markets are considerably more exposed to cross-market volatility spillovers, and we quantify the relevant degree of exposition. [ABSTRACT FROM AUTHOR]

This article aims to examine the national mechanism applied in Bulgaria to tackle the high electricity prices in the period 2021-2023 and its impact on state-owned enterprises, which are the main source of the compensation funds. The proposed thesis is that with the collection of the revenues of state-owned companies, they are posted in conditions of restriction in terms of development opportunities, which is why the imposed emergency measures should be reviewed in the future. Although the measures themselves are widely discussed, the mechanisms for providing financial support, the financial magnitude of this support, as well as its effect, are yet to be clarified, and the data collected in the material may be useful for further analyses and evaluations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The dynamic structure of electricity markets, where uncertainties abound due to, e.g., demand variations and renewable energy intermittency, poses challenges for market participants. We propose generative adversarial networks (GANs) to generate synthetic electricity price data. This approach aims to provide comprehensive data that accurately reflect the complexities of the actual electricity market by capturing its distribution. Consequently, we would like to equip market participants with a versatile tool for successfully dealing with strategy testing, risk model validation, and decision-making enhancement. Access to high-quality synthetic electricity price data is instrumental in cultivating a resilient and adaptive marketplace, ultimately contributing to a more knowledgeable and prepared electricity market community. In order to assess the performance of various types of GANs, we performed a numerical study on Turkey's intraday electricity market weighted average price (IDM-WAP). As a key finding, we show that GANs can effectively generate realistic synthetic electricity prices. Furthermore, we reveal that the use of complex variants of GAN algorithms does not lead to a significant improvement in synthetic data quality. However, it requires a notable increase in computational costs. [ABSTRACT FROM AUTHOR]

As the Chinese government proposes ambitious plans to promote low-carbon transition, energy storage will play a pivotal role in China's future power system. However, due to the lack of a mature electricity market environment and corresponding mechanisms, current energy storage in China faces problems such as unclear operational models, insufficient cost recovery mechanisms, and a single investment entity, making it difficult to support the rapid development of the energy storage industry. In contrast, European and American countries have already embarked on certain practices in energy storage operation models. Through exploration of key issues such as investment entities, market participation forms, and cost recovery channels in both front and back markets, a wealth of mature experiences has been accumulated. Therefore, this paper first summarizes the existing practices of energy storage operation models in North America, Europe, and Australia's electricity markets separately from front and back markets, finding that perfect market mechanisms and reasonable subsidy policies are among the main drivers for promoting the rapid development of energy storage markets. Subsequently, combined with the actual development of China's electricity market, it explores three key issues affecting the construction of cost-sharing mechanisms for energy storage under market conditions: Market participation forms, investment and operation modes, and cost recovery mechanisms. Finally, in line with the development expectations of China's future electricity market, suggestions are proposed from four aspects: Market environment construction, electricity price formation mechanism, cost sharing path, and policy subsidy mechanism, to promote the healthy and rapid development of China's energy storage industry. [ABSTRACT FROM AUTHOR]

The accurate short-term point and probabilistic load forecasts are critically important for efficient operation of power systems and electricity bargain in the market. Fuzzy systems achieved limited success in electric load forecasting. On the other hand, support vector regression models have seldom been part of a winning solution of the electric load forecasting competitions during the last decade. In this paper, we propose a methodology to integrate the fuzzy memberships with support vector regression (SVR) and support vector quantile regression (SVQR) models for short-term point and probabilistic load forecasting, respectively. One fuzzy membership function is proposed to efficiently calculate the relative importance of the observations in the load history. Three SVR and one SVQR models, including L1-norm based SVR, L2-norm based SVR, kernel-free quadratic surface SVR and SVQR models, are utilized to demonstrate the effectiveness of the proposed methodology. For point load forecasting, we compare the proposed fuzzy SVR models with a multiple linear regression, a feed-forward neural network, a fuzzy interaction regression, and four SVR models. For probabilistic load forecasting, the proposed fuzzy SVQR model is compared with a quantile regression model, a quantile regression neural network, and a SVQR model. The results on the data of global energy forecasting competition 2012, demonstrate that the proposed fuzzy component can improve the underlying SVR and SVQR models to outperform their counterparts and commonly-used models for point and probabilistic load forecasting, respectively. [ABSTRACT FROM AUTHOR]

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How do multinational firms trapped in host markets suffering from political violence find security? Terrorism and conflict repel foreign investment and recent research has sought to understand heterogeneity across firms in their response to violence. When multinational firms are unable to move their investments in the face of violence, we argue that their home governments use diplomatic capacity and security and economic resources to secure protection from host governments. Multinational corporations from home markets with significant leverage over host markets are therefore less likely to curtail their operations or investments in the wake of violence. Examining dyadic data on foreign direct investment (FDI), we show that home market diplomatic missions, aid, and alliances substantially abate the negative effects of violence on FDI. Consistent with our argument, this effect is observed after violence begins and is not an artifact of host markets garnering security aid from great powers other than the home market. A strong diplomatic presence matched to both hard and soft power resources positively contributes to the defense of core foreign policy interests. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article provides a description of the microgrid function optimization model for solving the problem of reducing the cost of electricity purchases by the owner of such a microgrid on the retail electricity market. The objective function of minimizing the cost of electricity purchase has been formalized. Approaches to determining the coefficients of penalty functions designed to cut off technically unacceptable modes of the microgrid have been determined. A mathematical model of an energy storage system (ESS) is proposed, in which the functions of calculating the technological costs of electricity are formalized. A system of constraints for the problem of minimization of the cost of electricity purchase is given. Approaches to solving the problem of evaluating the effect of SPP and ESS operation in microgrids using optimization results are determined. Examples of microgrid operation optimization for an enterprise with its own SPP and ESS are presented. The need for additional control of the economic expediency of involving ESS in the regulation of electricity balances at the enterprise was noted. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESS) are becoming increasingly important and their number of applications in energy systems is constantly increasing. Although batteries cannot solve the problem of electricity storage in the long term, BESS systems have proven to be suitable for short-term provision of flexibility within the day. Their real usability is shaped by the provision of balancing services to ensure a stable supply of electricity in distribution networks. BESS systems were able to respond to the dynamic development of the electricity market in recent years and achieve excellent results from the point of view of economic return. The aim of the contribution is to present the results achieved in selected cases of BESS use and to determine their economic return. The size of the battery (installed power and capacity) is designed to be able to work in individual modes and operating cases. A critical element for the design of the battery size is the technical conditions for the provision of balancing services. On the basis of market research, we determine the costs of acquiring and operating the battery, including the need for a software superstructure for controlling the battery itself and ensuring the necessary business functions. According to the defined criteria, we evaluate the technical operation of the battery located at the point of consumption (behind the meter) using the battery model in several operating cases. Subsequently, we analyse its sales based on the development of market prices and the conditions in which the battery is placed. [ABSTRACT FROM AUTHOR]

Optimal Reactive Power Dispatch (ORPD) is a power system optimization tool that modifies system control variables such as bus voltage and transformer tap settings, and it compensates devices' Volt Ampere Reactive (VAR) output. It is used to decrease real power loss, enhance the voltage profile, and promote stability. Furthermore, several issues have been faced in electricity markets, such as price volatility, transmission line congestion, and an increase in the cost of electricity during peak hours. Programs such as demand response (DR) provide system operators with more control over how small customers participate in lowering peak-hour energy prices and demand. This paper presents an extensive study on ORPD methodologies and DR programs for lowering voltage deviation, limiting cost, and minimizing power losses to create effective and economical operations systems. The main objectives of this work are to minimize costs and losses in the system and reduce voltage variation. The Grasshopper Optimization Algorithm (GOA) and Dragonfly Algorithm (DA) have been implemented successfully to solve this problem. The proposed technique has been evaluated by using the IEEE-30 bus system. The results obtained by the implementation of demand response systems show a considerable reduction in costs and load demands that benefit consumers through DR considerations. The results obtained from the GOA and DA are compared with those generated by other researchers and published in the literature to ascertain the algorithm's efficiency. [ABSTRACT FROM AUTHOR]

Featured Application: This article mainly utilizes the advantages and characteristics of evolutionary game theory and, based on the ideas and methods of evolutionary game theory, describes the relationship between the renewable energy generation enterprise group and the power grid enterprise group as a "learning" progressive evolution system, focusing on the evolution process of the relationship between various stakeholders and the influencing factors of evolutionary stability. It provides a reasonable explanation for the spontaneous formation of interest equilibrium between power generation enterprises and power grid companies and provides theoretical reference and policy recommendations for government regulation of the electricity bidding market. The insights derived from the simulations offer a framework that can inform practical applications, particularly in improving grid stability and promoting renewable energy adoption through effective peak shaving mechanisms and electricity pricing strategies. This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid. [ABSTRACT FROM AUTHOR]

Electricity price forecasting (EPF) is an emergent research domain that focuses on forecasting the future electricity market price both deterministically and probabilistically. EPF has attracted enormous interest from both practitioners and scholars since the deregulation of the power market and wide applications of renewable energy sources, such as wind and solar energy. However, forecasting the electricity price accurately and efficiently is an extremely challenging task because of its high volatility, randomness, and fluctuation. Although quantile regression averaging (QRA) has been demonstrated to be efficacious in probabilistic EPF since the global energy forecasting competition in 2014 (GEFCom2014), it is sensitive to nuisance variables especially when the number of variables is large. The forecasting accuracy will be negatively affected by these nuisance variables. To address these challenges, this study investigates a nonconvex regularized QRA in probabilistic forecasting. Two types of nonconvex regularized QRA select the important inputs obtained from point forecasting to obtain more accurate forecasting outcomes. To demonstrate the effectiveness of the proposed EPF model, two real datasets from the European power market are considered. [ABSTRACT FROM AUTHOR]

Disruptive technology (DT) has the power to reshape markets and societal structures, necessitating both protection and regulation. This paper aims to examine DT protection and regulation strategies at both micro and macro levels through policy texts. We construct an analysis framework integrating Responsible Research and Innovation (RRI) principles, innovation theory, and DT growth process. Key measures are extracted by content analysis, and dynamic topic model is used to discover evolving policy focuses. The framework is applied to analyze DT policy files of major powers. The results indicate that supply-side policy tools are employed to promote sustainable development in DT innovation, while environmental tools are utilised for early governance to preempt potential risks and innovation damage. The focuses of DT policies exhibit three evolution trends: attenuation, reinforcement, and fluctuation. Protective policies show concentration and coherence, while regulatory policies demonstrate integration and correlation. This study broadens existing perspectives and analytical frameworks, providing valuable insights for DT development and governance. [ABSTRACT FROM AUTHOR]

Effective energy management within microgrids is crucial, especially given system uncertainties. This study presents a novel Energy Management System (EMS) designed for microgrids with diverse energy sources, notably hydrogen and fuel cells. The EMS integrates artificial intelligence algorithms to predict and adapt to rapid changes, enhancing energy resource efficiency and minimizing wastage. Responsive in nature, the EMS enhances efficiency, reliability, and cost reduction in microgrid energy, dynamically responding to varying conditions for improved stability and environmental protection. Employing a random pattern approach, the EMS focuses on operational efficiency and reliability in supplying electrical and thermal loads. Addressing uncertainties in the electricity market and renewable production, a stochastic framework with scenario generation enables operation in grid-connected and island modes. Enhanced optimization through an improved particle swarm algorithm ensures cost-effectiveness and system reliability in dynamic environmental conditions. This research pioneers innovative microgrid energy management, emphasizing hydrogen and fuel cell technologies to elevate efficiency and performance reliability. • Renewable integration: Emphasis on renewables and storage for sustainable energy. • Micro-CHP importance: Dual function for reliable energy supply. • Advanced EMS algorithms: Cutting-edge tech for enhanced microgrid operations. • Holistic energy management: Comprehensive strategy for reliability and sustainability. • Practical implementation insights: Theory-practice bridge for adaptive strategies. [ABSTRACT FROM AUTHOR]

In the current model, the unclear and unreasonable method of revenue sharing among wind-solar-storage hybrid energy plants may a lso hinder the effective measurement of energy storage power station costs. This lack of clarity discourages energy storage from effectively collaborating with renewable energy stations for green power trading and spot trading. Therefore, this study proposes an optimal revenue sharing model of wind-solar-storage hybrid energy plant under medium and long-term green power trading market to facilitate the coordinated operation and equitable revenue allocation. Firstly, a method for decomposing transaction volume of green power is introduced by considering the uncertainty of spot market prices and physical delivery characteristics of green power trading. Then, a coordinated scheduling strategy of hybrid renewable energy plant is proposed to maximize revenues generated from both the green power and spot markets. Consequently, a cost-benefit contribution index system is developed to quantify the contribution of energy storage in the wind-solar-storage hybrid power plant. The revenue sharing model based on the minimum cost-remaining savings (MCRS) method can significantly increase overall revenue for renewable energy plants by reducing deviation penalties. It also enhances the operating revenue of energy storage power stations by considering the contributions of both energy storage and renewable energy plant in the green power market. The superiority of the proposed cooperation revenue sharing model for profitability enhancement of energy storage is validated through comparative case studies. [ABSTRACT FROM AUTHOR]

Medium–long-term photovoltaic (PV) output forecasting is of great significance to power grid planning, power market transactions, power dispatching operations, equipment maintenance and overhaul. However, PV output fluctuates greatly due to weather changes. Furthermore, it is frequently challenging to ensure the accuracy of forecasts for medium–long-term forecasting involving a long time span. In response to the above problems, this paper proposes a medium–long-term forecasting method for PV output based on amplitude-aware permutation entropy component reconstruction and the graph attention network. Firstly, the PV output sequence data are decomposed by ensemble empirical mode decomposition (EEMD), and the decomposed intrinsic mode function (IMF) subsequences are combined and reconstructed according to the amplitude-aware permutation entropy. Secondly, the graph node feature sequence is constructed from the reconstructed subsequences, and the mutual information of the node feature sequence is calculated to obtain the graph node adjacency matrix which is applied to generate a graph sequence. Thirdly, the graph attention network is utilized to forecast the graph sequence and separate the PV output forecasting results. Finally, an actual measurement system is used to experimentally verify the proposed method, and the outcomes indicate that the proposed method, which has certain promotion value, can improve the accuracy of medium–long-term forecasting of PV output. [ABSTRACT FROM AUTHOR]

Electricity retailers in an electricity market use over-the-counter (OTC) contracts, or bilateral, and spot market purchases to meet the energy demands of their users. In some markets, OTC contracts face issues with price discrimination and accessibility. This study reveals some inefficiencies of OTC contracts in Colombia that expose regulated users—approximately 70% of the national demand—to market risk. This risk is aggravated by the current tariff design. To mitigate these inefficiencies, this article proposes the incorporation of a market maker that will improve the liquidity of existing energy futures in the country. These futures are mechanisms that the retailers could implement to hedge their demand and reduce the adverse effects of market risk. The characteristics of the market maker and a quantitative analysis of its impact are developed in this paper. While the characterization of the problem with its solution is developed with Colombian data, the conceptual framework could be extended to other countries that are concerned about how energy users are being affected by increases in tariffs due to high exposure to spot market price volatility. [ABSTRACT FROM AUTHOR]

This study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting. [ABSTRACT FROM AUTHOR]

In this study, we examine how the strategies of the players over multiple time scales impact the decision making, resulting payoffs and the costs in non-cooperative strategic games. We propose a dynamic generalized Nash equilibrium problem for non-cooperative strategic games which evolve in multidimensions. We also define an equivalent dynamic quasi-variational inequality problem. The existence of equilibria is established, and a spot electricity market problem is reformulated in terms of the proposed dynamic generalized Nash equilibrium problem. Employing the theory of projected dynamical systems, we illustrate our approach by applying it to a 39-bus network case, which is based on the New England system. Moreover, we illustrate a comparative study between multiple time scales and a single time scale by a simple numerical experiment. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESSs) operate as independent market participants and collaborate with photovoltaic (PV) generation units to enhance the flexible power supply capabilities of PV units. However, the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood. This study introduces a dual-timescale dynamics model that integrates a spot market clearing (SMC) model into a system dynamics (SD) model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems (IESSs) with that of an ESS integrated within a PV (PV-ESS). Furthermore, this study aims to ascertain the optimal allocation of the PV-ESS. First, SD and SMC models were set up. Second, the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem, a dual-timescale model was constructed. Finally, a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation. Additionally, negative electricity prices were considered during clearing processes. The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS, helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs. [ABSTRACT FROM AUTHOR]

The power grid, as the hub connecting the power supply and consumption sides, plays an important role in achieving carbon neutrality in China. In emerging carbon markets, assessing the investment benefits of power-grid enterprises is essential. Thus, studying the impact of the carbon market on the investment and operation of powergrid enterprises is key to ensuring their efficient operation. Notably, few studies have examined the interaction between the carbon and electricity markets using system dynamics models, highlighting a research gap in this area. This study investigates the impact of the carbon market on the investment of power-grid enterprises using a novel evaluation system based on a system dynamics model that considers carbon-emissions from an established carbon-emission accounting model. First, an index system for benefit evaluation was constructed from six aspects: financing ability, economic benefit, reliability, social responsibility, user satisfaction, and carbon-emissions. A system dynamics model was then developed to reflect the causal feedback relationship between the impact of the carbon market on the investment and operation of power-grid enterprises. The simulation results of a provincial power-grid enterprise analyze comprehensive investment evaluation benefits over a 10-year period and the impact of carbon emissions on the investment and operation of power-grid enterprises. This study provides guidelines for the benign development of power-grid enterprises within the context of the carbon market. [ABSTRACT FROM AUTHOR]