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In Indonesia, the power generation sector is the primary source of carbon emissions, largely due to the heavy reliance on coal-fired power plants, which account for 60% of electricity production. Reducing these emissions is essential to achieve national clean energy transition goals. However, achieving this initiative requires careful consideration, especially regarding the complex interactions among multiple stakeholders in the Indonesian electricity market. The electricity market in Indonesia is characterized by its non-competitive and heavily regulated structure. This market condition often requires the PLN, as the system operator, to address multi-objective and multi-constraint problems, necessitating optimization in the generation dispatch scheduling scheme to ensure a secure, economical, and low-carbon power system operation. This research introduces a multiparadigm approach for GS optimization in a regulated electricity market to support the transition to clean energy. The multiparadigm integrates multi-agent system and system dynamic paradigms to model, simulate, and quantitatively analyze the complex interactions among multiple stakeholders in the Indonesian regulated electricity market. The research was implemented on the Java–Madura–Bali power system using AnyLogic 8 University Researcher Software. The simulation results demonstrate that the carbon policy scheme reduces the system's carbon emissions while increasing the system's cost of electricity. A linear regression for sensitivity analysis was conducted to determine the relationship between carbon policies and the system's cost of electricity. This research offers valuable insights for policymakers to develop an optimal, acceptable, and reasonable power system operation scheme for all stakeholders in the Indonesian electricity market. [ABSTRACT FROM AUTHOR]

This study combines the Nash–Cournot competition model and the stochastic optimization model to examine the impact of electric vehicle (EV) quantity fluctuations on microgrid operations, aiming to optimize energy usage in a competitive electricity market. Integrating distributed energy resources and bidirectional charging, microgrids offer a novel approach for energy optimization, aiding in renewable energy generation, peak demand management, and emission reduction. Empirical evidence highlights benefits in Taiwan's electricity market and net-zero emissions target by 2050, with a case study demonstrating enhanced local renewable energy generation due to EVs and microgrid integration. As the number of EVs increases, electricity sales from microgrids decline, but electricity purchases remain stable. The degree of electricity liberalization also influences the supply and demand dynamics of the electricity market. Microgrids selling electricity only to the main grid increases total power consumption by 65.55 million MWh, reducing the market share of the state-owned utility (Taipower). Conversely, allowing retailers to purchase from microgrids increases total consumption by 30.87 million MWh with a slight market share decrease for Taipower. This study contributes to providing an adaptable and flexible general model for future studies to modify and expand based on different scenarios and variables to shape energy and environmental policies. [ABSTRACT FROM AUTHOR]

This research leverages bibliometric methodologies, enhanced by the visual analytics capabilities of CiteSpace, to meticulously examine the evolution and current trends in energy trading within power markets, analyzing 642 scholarly articles from the Web of Science Core Collection spanning from 1996 to 2023. The study aims to illuminate the prevailing research landscape, growth patterns, and future directions in energy trading dynamics. Key findings include: (1) A noticeable escalation in the volume of publications, especially from 2021 to 2023, indicating a burgeoning interest and rapid evolution in this research area; (2) The author and institutional collaboration networks are in a nascent stage, with a predominantly China-centric international collaboration pattern, including significant partnerships with the United States, Australia, and the United Kingdom; (3) The focal points of research are centered around themes such as "energy management", "demand-side innovation", "decentralized energy trading", and "strategic optimization", covering areas such as intelligent grid technologies, energy market dynamics, and sustainable energy solutions. The study recommends enhancing collaborative networks, fusing technological and strategic dimensions in research, increasing focus and funding for emerging technologies, and promoting wider international and cross-disciplinary collaborations to enrich the understanding of energy trading dynamics in the context of electricity markets. [ABSTRACT FROM AUTHOR]

Rapid progresses concerning wearable electronics call for stretchable batteries. Owing to the merits of high energy density and wide operating voltage range, organic electrolyte‐based lithium‐ion batteries (LIBs) take a large proportion in the powering markets. Due to the high sensitivity to moisture and oxygen, it is difficult to obtain long‐term reliable stretchable organic electrolyte‐based LIBs when they are directly exposed to the air. Benefiting from the outstanding gas impermeability of liquid metal (LM) and relatively good barrier properties for reactive acid gas molecules by polyisobutylene (PIB), an LM‐PIB seal is demonstrated for organic electrolyte‐based LIBs. In the seal, LM can prevent the inward permeation of oxygen and water vapor, and PIB can block the outward permeation of hydrolysis acid generated from the side reactions during the operation of the organic electrolyte‐based LIBs, which helps prevent the detrimental interaction between the hydrolysis acid and LM. A specific capacity of over 120 mAh g−1 is achieved and the battery shows consistent electrochemical performances under multiple deformations. This study offers a different design approach for long‐term operation of stretchable organic electrolyte‐based LIBs. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & 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.)

Interconnectors play a crucial role in electric power systems. They contribute to balancing demand and supply in real-time, guaranteeing efficient dispatch in wide geographic regions, and increasing competition by creating large markets. However, interconnector capacity is a scarce resource because vertically integrated utilities were required to have generating capacity enough to supply most customers within their operating region under a regulated monopoly. Hence, identifying the efficient allocation method is essential, particularly after recent electricity market restructuring. This study evaluates the competitive effect of the implicit auction on the interconnector transmission capacity. The implicit auction allocates all the interconnector capacity simultaneously with electric energy in the day-ahead market. This method prevents market participants from strategically withholding the physical interconnector capacity ex ante to exercise market power, as allowed under the first come, first served rule. This study empirically shows how the capacity was withheld from the day-ahead market under the first come, first served rule using detailed reservation data. Next, I show that the implicit auction increases interconnector capacity available at the day-ahead market and trade volume. I use machine-learning methods, such as random forest and deep neural networks, to predict the counterfactual market outcomes without implicit auction. I find that the gain from trade under the implicit auction is more than US$55 million per year in Japan, which is more than 100 times the implementation cost of the implicit auction. [ABSTRACT FROM AUTHOR]

Currently, both regulated and deregulated power trading exist in China’s power system, which has caused imbalanced funds in the electricity market. In this paper, a simulation analysis of the electricity market with wind energy resources is conducted, and the calculation methods of unbalanced funds are investigated systematically. In detail, the calculation formulas of unbalanced funds are illustrated based on their definition, and a two-track electricity market clearing model is established. Firstly, the concept of the dual-track system is explained, and the specific calculation formulas of various types of unbalanced funds are provided. Next, considering the renewable energy consumption, the market clearing model based on DC power flow is constructed and solved; by combining fitting methods of mid- and long-term curves, the unbalanced funds are calculated based on clearing results and formulas. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature 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.)

In today's electricity markets, accurate electricity price forecasting provides valuable insights for decision-making among participants, ensuring reliable operation of the power system. However, the complex characteristics of electricity price time series hinder accessibility to accurate price forecasting. This study addressed this challenge by introducing a novel approach to predicting prices in the Peruvian electricity market. This approach involved preprocessing the monthly electricity price time series by addressing missing values, stabilizing variance, normalizing data, achieving stationarity, and addressing seasonality issues. After this, six standard base models were employed to model the time series, followed by applying three ensemble models to forecast the filtered electricity price time series. Comparisons were conducted between the predicted and observed electricity prices using mean error accuracy measures, graphical evaluation, and an equal forecasting accuracy statistical test. The results showed that the proposed novel ensemble forecasting approach was an efficient and accurate tool for forecasting monthly electricity prices in the Peruvian electricity market. Moreover, the ensemble models outperformed the results of earlier studies. Finally, while numerous global studies have been conducted from various perspectives, no analysis has been undertaken using an ensemble learning approach to forecast electricity prices for the Peruvian electricity market. [ABSTRACT FROM AUTHOR]

Abstact: In the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high‐frequent electricity price for market decision‐making. However, the uncertainties associated with electricity prices, such as non‐stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better‐informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi‐step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real‐data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real‐world applications. [ABSTRACT FROM AUTHOR]

The creation of features makes the difference in improving the photovoltaic forecast (PVF) for on‐grid, hybrid and off‐grid PV systems. The importance of the PVF is tremendous, and it can be essential in optimizing the home appliances to maximize the Renewable Energy Sources (RES) usage or to create performant bids for the electricity market. Several use cases are considered from the connectivity point of view. Therefore, in this paper, we propose a Weather Prediction Error (WPE)‐based method that uses a Stacking Regressor (SR) for various PV systems that coexist in the emerging Energy Communities (EC) landscape. The novelty of the research we conduct consists in proposing several features and determining the coefficients to adjust the PVF based on WPE. The forecast results of four types of PV systems from size and connectivity point of view are investigated. Compared with individual Machine Learning (ML) models, R2 increases with more than 3% with the SR and with more than 6% after applying the adjustment coefficients. Nevertheless, the major improvement is recorded for the off‐grid inverter and for the large industrial PV power plant, demonstrating that the proposed model is suitable for these types of systems. The other metrics improved as well, especially Mean Average Error (MAE) that decreases between 10% and 23%. A significant decrease is in the case of the industrial on‐grid PV, from 130 kW to 123 kW using the SR and to 107 kW after adjustments. This represents around 18% from the initial MAE. The ratio of daily deviations is also improved using the SR. For all the PV systems, the ratio stabilizes in a shorter interval, from daily values between 0.78 and 1.33 obtained with the ML models to values between 0.83 and 1.24 obtained with the SR. After the final adjustments, the interval becomes shorter, having daily values between 0.90 and 1.10. [ABSTRACT FROM AUTHOR]

The wind power market is expanding quickly and the cost of wind power equipment is decreasing, making wind power technology a key player in the world's energy transition. Assessing wind potential and selecting the right wind turbine site are crucial parameters for developing a wind farm. Vietnam focuses on onshore and nearshore wind power projects due to its promising wind power potential and supportive policies. However, Vietnam has diverse climate characteristics and wind patterns. Therefore, initial basic research is necessary to evaluate the feasibility of investing in wind power projects. This study examines the technical feasibility of a typical wind power project in the Southeast region of Vietnam using Wind Atlas Analysis and Application Program (WAsP) software. The results indicate that the wind turbine's type and installation location significantly affect wind power plants' efficiency. The total power output of the wind power project after factoring with losses at a rate of 17%, is 304,149 MWh. [ABSTRACT FROM AUTHOR]

Aimed at the coupling problem of the combined wind-storage system participating in different call time scale scenarios in electricity markets, an optimal dispatching method of the combined wind-storage system oriented to the application of multi-time scale scenarios in electricity markets is proposed to guide the combined wind-storage system to suppress short-term wind power fluctuation, and participate in the electric energy market and the reserve ancillary service market, so as to realize the collaborative optimization among different call time scale scenarios application and maximize the economic benefits of the combined wind-storage system. First, considering the profit mechanism of different scenarios, the objective function is established with the objective of maximizing the economic benefits of multiple scenarios of the combined wind-storage system. Then, the constraints of the combined wind-storage system participating in various application scenarios and multi call time scale coupling constraints are established. Finally, the numerical simulation verifies that the proposed method can improve the comprehensive operation profit of the combined wind-storage system in the day-ahead electric energy market and the reserve ancillary service market while ensuring that the wind power fluctuation does not exceed the limit.

The topic of pricing non-convexities in power markets has been explored vividly in the literature and among practitioners for the past twenty years. The debate has been focused on indivisibilities in short-term auctions, the computational tractability of some pricing proposals, and the economic analysis of their behavior. In this paper, we analyse a source of non-convexities that is not discussed as broadly: the indivisibilities in investment decisions. The absence of equilibrium that we are primarily concerned about is the long-term equilibrium. We derive a capacity expansion model with indivisibilities and we highlight the issues arising from it. We discuss its relevance and address one particular argument for neglecting indivisibilities in investment, namely market size. We investigate to what extent a capacity market that clears discrete offers can mitigate the lumpiness problem. We particularly introduce the novel concept of convex hull pricing for capacity auctions. We illustrate the main findings with a numerical experiment conducted on the capacity expansion model used by ENTSO-E to assess the adequacy of the entire European system. [ABSTRACT FROM AUTHOR]

Vehicle to grid (V2G) is one the most important ways to effectively integrate electric vehicles (EVs) with electric power systems. The important benefits can be made by V2G such as reducing/increasing the cost/revenue of EV owners and technically sup-porting electric power systems. Concerning technical and regulatory constraints, EV owners must participate in electricity mar-kets via aggregators. This paper proposes a robust optimal coordinated charging (OCC) model including bidding ancillary ser-vices for regulation and spinning reserve markets. The presented work handles the uncertain behavior of the electricity market that are ancillary service prices and their deployment signals by the robust optimization approach. The aim of optimization is the maximization of the aggregator's profits from V2G by joining the ancillary services markets. The recommended robust OCC model which is a robust linear problem (RLP) model is simulated by the CPLEX solver in GAMS software. An assumed set of 10000 EVs in the electric reliability council of Texas (ERCOT) electricity markets is considered for doing simulations. Employing the presented model in this test system shows the efficacy of the proposed model in comparison to other deterministic and stochastic models. [ABSTRACT FROM AUTHOR]

In this paper, virtual power plant (VPP) planning is done using distributed generation sources to create a safe platform for electricity exchange and to increase the profitability and sustainability of electricity. In the proposed model, the effect of micro-grid interaction with the electricity market in the presence of distributed generation resources and storage is investigated. To solve this problem, an improved artificial bee colony algorithm using the accept-reject method (AR-ABC) is used. The AR method is employed to limit the initial search space as well as for the scenario reduction process. Also, uncertainties related to loads and renewable sources are formulated in a sample micro-grid including micro-turbine (MT), fuel cell (FC), wind turbine (WT), photovoltaic cells (PV) and batteries for storage; the results are compared with those of other methods, which shows this method works better than others. The software simulations of this research are done in the MATLAB software environment. [ABSTRACT FROM AUTHOR]

This study aims to carry out a techno-economic analysis of different hydrogen supply chain designs coupled with the Swedish electricity system to study the inter-dependencies between them. Both the hydrogen supply chain designs and the electricity system were parameterized with data for 2030. The supply chain designs comprehend centralised production, decentralised production, a combination of both, and with/without seasonal variation in hydrogen demand. The supply chain design is modelled to minimize the overall cost while meeting the hydrogen demands. The outputs of the supply chain model include the hydrogen refuelling stations' locations, the electrolyser's locations and their respective sizes as well as the operational schedule. The electricity system model shows that the average electricity prices in Sweden for zones SE1, SE2, SE3 and SE4 will be 4.28, 1.88, 8.21, and 8.19 €/MWh respectively. The electricity is mainly generated from wind and hydropower (around 42% each), followed by nuclear (14%), solar (2%) and then bio-energy (0.3%). In addition, the hydrogen supply chain design that leads to a lower overall cost is the decentralised design, with a cost of 1.48 and 1.68 €/kgH 2 in scenarios without and with seasonal variation respectively. The seasonal variation in hydrogen demand increases the cost of hydrogen, regardless of the supply chain design. • Techno-economic study of Hydrogen supply chains coupled with electricity system. • Centralized, decentralised, and hybrid hydrogen production models are designed. • Optimal location, sizing and operational schedules are determined. • Decentralised design offers cost efficiency in both demand scenarios. [ABSTRACT FROM AUTHOR]

This paper examines the electrification of transportation as a response to environmental challenges caused by fossil fuels, exploring the potential of battery electric vehicles and hydrogen fuel cell vehicles as alternative solutions. However, a significant barrier to their widespread adoption is the limited availability of charging infrastructure. Therefore, this study proposes the development of comprehensive charging stations capable of accommodating both battery and hydrogen vehicles to address this challenge. The energy is purchased from the day-ahead and intraday auction-based electricity markets, where the electricity price is subject to uncertainty. Therefore, a two-stage stochastic programming model is formulated while the price scenarios are generated utilizing a k-means clustering algorithm. Given the complexity of the proposed model, an efficient solution approach is developed through the hybridization of the Benders decomposition algorithm and stochastic dual dynamic programming. In the Benders master problem, day-ahead bidding variables are determined, whereas the Benders sub-problem addresses intraday bidding and charging station scheduling variables, employing stochastic dual dynamic programming to tackle its intractability. Additionally, we transform the mixed integer linear program model of the second stage problem into a linear program, confirming its validity through KKT conditions. Our model provides practical insights for making informed decisions in electricity markets based on sequential auctions. While the bidding curves submitted to the day-ahead market remain unaffected by scenarios, those submitted to the intra-day market show dependence on fluctuations in day-ahead market prices. • Designing a decision framework for a hybrid electricity/hydrogen station. • Participating in day-ahead and intraday electricity auction markets effectively. • Modeling price uncertainties using scenarios and k-means clustering. • Applying Benders decomposition for day-ahead and intraday bidding decisions. • Using stochastic dual dynamic programming for the Benders sub-problem. [ABSTRACT FROM AUTHOR]

We consider a stylized model for investment into renewable power plants under long-term uncertainty. We model risk-averse agents facing heterogeneous weather conditions and a common noise including uncertainty on demand trends, future fuel prices and the average national weather conditions. The objective of each agent is to maximize multistage profit by controlling investment in discrete time steps. We analyze this model in a noncooperative game setting with N players, where the interaction among agents occurs through the spot price mechanism. Our model extends to a mean field game with common noise when the number of agents is infinite. We prove that the N-player game admits a Nash equilibrium. Moreover, we prove that under appropriate assumptions, any sequence of Nash equilibria to the N-player game converges to the unique solution of the MFG game. Our numerical experiments highlight the impact of the risk aversion parameter and the importance of correctly specifying the distribution of the heterogeneity among agents. Moreover, we demonstrate that the results obtained by our model cannot be replicated by a model based on a representative agent with a unique parameter that would represent homogenized weather conditions. This emphasizes the importance of including explicit modeling of heterogeneity in prospective models when a heterogeneous parameter is expected to have a significant influence on the outcomes. [ABSTRACT FROM AUTHOR]

Renewable energy resources possess unique characteristics—intermittency and uncertainty—that pose challenges to electricity grid operations. We study these characteristics and find that uncertainty, represented by wind forecast error, has larger grid impacts than intermittency, represented by hourly wind generation changes. Compared to wind generation that was correctly forecast, uncertainty yields roughly double the effect on the marginal cost of operating the grid and greater adjustment costs as conventional generators must start up to balance the grid. While this finding is important given the persistence of wind generation uncertainty over our study period, reducing wind forecast error to the level of demand forecast error would lower costs by a modest half a million dollars per year. [ABSTRACT FROM AUTHOR]

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]

This work considers the issue of modeling and forecasting electricity prices within the functional time series approach. As this is often performed by estimating and predicting the different components of the price dynamics, we study whether jointly modeling the components, able to account for their inter-relations, could improve prediction with respect to a separate instance of modeling. To investigate this issue, we consider and compare the predictive performance of four different predictors. The first two, namely Smoothing Splines-Seasonal Autoregressive (SS-SAR) and Smoothing Splines-Functional Autoregressive (SS-FAR) are based on separate modeling while the third one is derived from a single-step procedure that jointly estimates all the components by suitably including exogenous variables. It is called Functional Autoregressive with eXogenous variables (FARX) model. The fourth one is a combination of the SS-FAR and FARX predictors. The predictive performances of the models are tested using electricity price data from the northern zone of the Italian electricity market (IPEX), both in terms of forecasting error indicators (MAE, MAPE and RMSE) and by means of the Diebold and Mariano test. The results point out that jointly estimating the components leads to significantly more accurate predictions than using a separate instance of modeling. In particular, the MAE, MAPE, and RMSE values for the best predictor, based on the FARX( 3 , 0 , 4 ) model, are 4.25, 9.28, and 5.38, respectively. The percentage error reduction is about 20% with respect to SS-SAR (3 , 1) and about 10% with respect to SS-FAR(5). Finally, this study suggests that the forecasting errors are generally higher on Sunday and Monday, from hours 3 to 6 in the morning and 14 to 15 in the afternoon, and in June and December. On the other hand, prices are relatively lower on Wednesday, Thursday, and Friday, from hour 20 to 1 a.m., and in January and February. [ABSTRACT FROM AUTHOR]

In order to improve the operating benefits of the distribution network and reduce the energy consumption costs of small-micro-industrial parks, an electricity market clearing considering small-micro-industrial parks is proposed based on the Stackelberg game. First, an optimal operating model of multiple stakeholders is established for integrated energy suppliers, the electricity market, and small-micro-industrial parks. In this model, an optimal electricity supply model for integrated energy suppliers is established with the goal of maximizing the operating benefits. A market clearing optimization model is established for the electricity market with the goal of maximizing the social surplus profit. In addition, an energy utilization optimization model is established for the small-micro-industrial parks with the goal of minimizing the energy procurement costs. Second, with the electricity market as the leader, the integrated energy suppliers and the small-micro-industrial parks as the followers, a leader-follower game strategy is proposed based on the Stackelberg game theory to achieve the maximizing benefits for multiple stakeholders. Finally, the simulation indicates that the proposed strategy can find the best profit point during the game process and achieve a balance between supply and demand. [ABSTRACT FROM AUTHOR]

We use a fixed point gradient flow algorithm to compute the equilibria of first-price procurement auctions in the presence of losses and Bayesian priors. We use this efficient algorithm to compare optimal, first-price and VCG auctions. This allows us to numerically estimate the social cost of sub-optimality of the nodal pricing mechanism in wholesale electricity markets. We also derive a closed form expression of the optimal mechanism procurement cost when the types are uniformly distributed. [ABSTRACT FROM AUTHOR]

The cost reflectivity of electricity distribution network tariffs has been debated in several countries, and various ways to enhance it have been investigated in recent years. However, the recent academic literature regarding the approach based on cost causation has a clear gap because no case studies show how distribution network tariffs can be determined in practice for large customer groups. This article offers a calculation methodology to determine distribution network tariffs based on cost causation along with a case study where unit prices are determined for the tariff structures still widely used today using the data for two separate network areas being operated by two Finnish distribution system operators (DSOs) in an unbundled electricity market environment. The results of the case study show that the total differences between the target and the realized turnovers in both investigated networks are smaller than 1%, which means almost a full cost recovery. In addition to traditional tariff structures, the proposed calculation methodology can also be modified to design and determine other pricing schemes. The need for systematic calculation processes is growing to improve the cost reflectivity of present tariffs and adapt to the needs of the evolving operating environment, novel tariff structures, and new emerging customer groups. [ABSTRACT FROM AUTHOR]

Amidst the global energy crisis in 2022, the Spanish and Portuguese governments introduced a subsidy to natural gas ("the Iberian exception"), attempting to lower the wholesale electricity market prices, with the understanding that gas-fired-combined cycle gas turbines (CCGTs) are price-setting technologies most of the time, directly or indirectly. The subsidy succeeded in lowering the market price but induced several other effects, such as (1) the increase in cleared energy in the Spanish market (mostly produced with gas), (2) the bias in the import/export cross-border position between Spain and France (Spain became a net exporter to France immediately), or (3) the consequent increase in congestion rents, which serve to lightly finance the subsidy, among other effects. This paper provides a framework for clustering the different effects based on the market participation phases: the subsidy, the market bidding, the market results, and surplus and rents. Moreover, this paper builds on the theoretical market models, with and without subsidies, and with and without cross-border exchanges. Based on the real market bids, the subsidies, and the generators' data, we reconstruct the supply and demand curves and simulate the counterfactual market scenarios in order to illustrate and quantify the effects. We highlight the quantification of the theoretical effect of the transfer of rents, from non-fossil to fossil fuel producers, induced by the gas subsidy. [ABSTRACT FROM AUTHOR]

In the current context throughout Latin America, where power groups and the market are increasingly interested in the natural and cultural resources of indigenous territories, indigenous peoples are once again being denied, and their identities, preexistence in the territories, and rights are being questioned. As archaeologists, we must recognize that our work is not merely about investigating past landscapes but also about conducting archaeology in territories currently intersected by recurring conflicts. This article proposes and discusses the conceptual and theoretical-methodological foundations of a committed and activist archaeology, one that contributes to social justice and the emancipation of historically subordinated minorities, such as indigenous peoples. This archaeology must be based on two fundamental premises: the recognition of indigenous peoples as subjects of rights and the implementation of interculturality. Based on these principles, two lines of work are proposed, each involving specific methods: (1) the development of demand-driven research that produces knowledge serving the projects and struggles of indigenous peoples, and (2) the creation of multivocal products where indigenous voices appear in the first person, contributing to their internal processes and their articulation with various societal and state actors. This article advocates for a transformation of contemporary Latin American archaeology, both in its relationship with the social environment where research is conducted and in the way knowledge is produced, placing the discipline at the service of the projects, causes, and struggles of subjugated minorities. [ABSTRACT FROM AUTHOR]

With the continuous deepening of electricity marketization reform and the large-scale integration of clean energy into the system, the problem of transmission congestion in the power system is frequent, and research on congestion management methods has become a major focus. Therefore, this paper studies the optimization strategy of transmission congestion management in the power spot market. Firstly, the existing congestion management mechanisms were sorted out from two aspects: dynamic management mechanisms, and market steady-state mechanisms. At the same time, the advantages and disadvantages of various mechanisms were summarized. Then, optimization strategies for congestion management were proposed from three aspects: cost optimization, surplus diversion, and risk optimization. Firstly, based on the ATC reduction method and rescheduling method, a congestion cost optimization strategy is proposed. Secondly, a congestion cost pool and multidimensional allocation indicators, are established and a congestion surplus diversion strategy is proposed. Thirdly, a two-level market for transmission rights trading has been constructed to optimize congestion risk. Finally, a case study was conducted in a certain province in eastern and western China, and the results showed that: 1) Considering the rescheduling method and ATC reduction method to optimize congestion costs can reduce congestion costs by 8.22% and 18.83%, compared to only considering the scheduling method and using ATC reduction method. 2) Establishing a congestion cost pool and multi-dimensional allocation indicators to channel congestion surplus can form price guidance signals. 3) Constructing a two-level market for transmission rights trading can increase the efficiency of system operators by 62.75%. [ABSTRACT FROM AUTHOR]

With the gradual shift from dual control of energy consumption to dual control of carbon emissions, energy quota trading (EQT) and carbon emissions trading (CET) policies will be implemented in parallel in China. EQT policy will significantly affect the market performance and policy effects of CET, which consequently affects the dual control of carbon emissions. Therefore, constructing a system dynamics model coupling the national CET, EQT, and electricity markets, this paper researches the impact of EQT policy and its parameter design on the market performance and policy effects of CET through scenario analysis. The results show that: (1) Implementing EQT policy will reduce CET price and trading volume, negatively affecting CET market performance. This indicates that there is a certain substitution between EQT and CET. (2) EQT policy can enhance the emission reduction and decarbonization effects of CET policy. By curbing thermal power investment and generation and promoting green power, the EQT policy reduces carbon emissions by about 4.87% and optimizes the power supply structure. (3) To scientifically design the EQT policy parameters, lower energy saving targets and energy quota benchmark prices can alleviate the negative impact on CET market performance, but are not conducive to improving CET policy effects. [ABSTRACT FROM AUTHOR]

To reduce the financial burden brought by the feed-in tariff (FIT) policy, China is shifting its renewable energy support policy to the renewable portfolio standard with tradable green certificates (RPS/TGC). Moreover, the two policies are being implemented in parallel in the transitional period. In this context, this study investigates the game between renewable energy power plants (REPPs) regarding electricity outputs and tradable green certificate (TGC) prices under different renewable energy support policies practiced in China. We focus on analyzing the impact of policy changes on the competing REPPs' decisions and the renewable electricity market with TGCs. The results show that implementing the RPS/TGC policy inhibits the development of renewable electricity unless the subscription of TGCs is promoted. Government subsidy arrears significantly affect electricity outputs and TGC subscriptions. The low subscription rate of TGCs is caused not by the subsidy-based TGC pricing mechanism but by government subsidies. This suggests that the government should implement the RPS/TGC policy, and demand-side power consumption responsibility weights should also be strengthened to promote TGC subscriptions. [ABSTRACT FROM AUTHOR]

The paper examines the status of energy decentralization in three key Arab League countries, namely, Egypt, the United Arab Emirates (UAE), and Saudi Arabia. It specifically focuses on decentralization in their electricity markets. The paper analyzes the regulatory environment in these three countries and then looks at how things stand regarding new tools and technologies to facilitate decentralization. It explores the progress in deployment of these tools and technologies, and the specific regulatory barriers in the three nations. The paper also offers recommendations for how regulations can be more encouraging and discusses electricity interconnections as a vital step towards decentralization that will boost energy security and energy efficiency. [ABSTRACT FROM AUTHOR]

Residential energy storage systems offer significant potential for price arbitrage by capitalizing on fluctuations in electricity prices throughout the day. This study investigates the potential revenue from optimal battery scheduling for residential storage in different north-western European electricity price zones during 2023. Using Nord Pool day-ahead prices, we applied an optimization model to determine the revenue for two types of batteries: 5 kW/10 kWh and 10 kW/10 kWh. The analysis considered battery capacity, charging and discharging efficiency, and maximum charge/discharge rates. Our results show notable variations in potential revenue across different regions, with the Baltic states demonstrating the highest revenue potential. The findings indicate that while 10 kW batteries can generate higher total revenue, 5 kW batteries are more efficient in terms of revenue per cycle. These regional disparities underscore the need for targeted incentives and policies to enhance the economic viability of residential energy storage. The research results provide valuable insights into optimizing residential battery storage for price arbitrage, offering guidance for consumers, policymakers, and energy providers to maximize economic benefits in various electricity markets. [ABSTRACT FROM AUTHOR]

This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, focusing on enhancing profitability while factoring in battery degradation. This study incorporates the effects of battery degradation on the dynamics in the optimisation framework. Considering this cost in economic analysis and operational strategies is essential to optimise long-term performance and economic viability. Neglecting degradation costs can lead to suboptimal operation and dispatch strategies. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell's behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium's day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximising net revenues, maximising profits considering capacity degradation, and maximising profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery's dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. This paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues. [ABSTRACT FROM AUTHOR]

This paper is focused on researching the behaviour of heating companies in connection with current developments in the electricity market and flexibility in the context of market behaviour. The work assesses the increase in profitability through the creation of a technical–economic model using an objective function with profit maximization. The objective of the paper is to present the procedure and methodology for creating a model using the basic scheme of production processes integrated into the system platform. The result of the work is a comparative analysis of modelled cases of implemented operation deployment according to a defined period and modelling modes on selected time series. The description of individual outputs demonstrates the economic advantage of using combinations of modes of combined electricity and heat production, and non-combined electricity and heat production, including the use of heat-suppression mode because of overproduction of electricity. [ABSTRACT FROM AUTHOR]

The authors present a Web application they designed in the R programming language as an experiential learning tool for teaching production theory. The app simulates production decisions where a manager is tasked to find the optimal mixture of inputs through experimentation. Users of the application are instructed to use calculations and intuitions from production theory to improve profitability quickly. The underlying parameters and starting points are randomized to allow for additional practice. Variations of the exercise correspond to cost-minimization and profit-maximization problems with and without market power in the output market. Also, the app provides dynamic, interactive visualizations in 3D in order to assist in a better understanding of standard production theory graphs. The app is located at . [ABSTRACT FROM AUTHOR]

Renewable energy, such as wind power and photovoltaic power, has uncertain and intermittent characteristics and zero marginal cost characteristics. The traditional power market mechanism is difficult to adapt to the new power system with a high proportion of renewable energy, and the original market system needs to be reformed. This paper discusses the application of a VCG auction mechanism in the electricity market, proposes a two-stage VCG market-clearing model based on the VCG mechanism, including the day-ahead market and the real-time market, and discusses the nature of the VCG mechanism. In order to address the discrepancy between the actual output of stochastic generator sets in the real-time market and their pre-scheduled output in the day-ahead market due to prediction deviations, a method for calculating punitive costs is proposed. A reallocation method based on market entities' contributing factors to budget imbalance is proposed to address the issue of budget imbalance under the VCG mechanism, in order to achieve revenue and expenditure balance. Through an example, the incentive compatibility characteristics of the VCG mechanism are verified, the problems of the locational marginal pricing (LMP) mechanism in the stochastic electricity market with a high proportion of renewable energy are analyzed, the electricity prices of the LMP mechanism and the VCG mechanism under different renewable energy proportions are compared, and the redistribution of the budget imbalance of the VCG mechanism is analyzed. [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]

Flexibility provision for ancillary services and electricity markets has been widely seen as crucial for the future of highly interconnected energy systems with high shares of renewables. Yet, little research has so far addressed (1) how its procurement could be best operationalized and (2) how limited flexible resources can be used more efficiently given the growing system needs. This paper focuses on flexibility services for transmission operators, specifically balancing and redispatch, as well as the intraday market within the context of the European electricity market. To analyze possible services and/or market combinations, we compare three modes of flexibility procurement: (a) sequential, (b) parallel and (c) combined. We evaluate the different modes of procurement options based on eight criteria. We further investigate how the procurement of flexibility, including small-scale technical units, could be organized via a flexibility platform given the most promising implementation setup, and detail the process for "flexibility service provider <> flexibility platform <> market interaction", taking the multi-use-case logic into account. [ABSTRACT FROM AUTHOR]

Long-term transmission expansion planning has to face the energy transition in a restructured electricity market environment. Increased transmission capacity within and between Member States is likely to play an essential role in maintaining the secure and economic operation of the whole European power system and ensuring the integration of growing renewable generation. This paper proposes a novel iterative methodology aimed at assessing an optimal level of interconnection between relevant bidding zones, simultaneously investigating different potential alternatives. Starting from a reference grid, a multi-criteria analysis is adopted to select the additional transmission capacities to be tested in each iteration via network and market simulations in order to confirm that transmission expansion benefits outweigh the estimated realization costs. The proposed approach is applied to the Italian case in two contrasting energy scenarios for the mid-term 2030 and very-long-term 2040 horizons: different development strategies are derived, and the least regret criterion is applied to define the most cost-effective as the target development strategy for the Transmission System Operator (TSO). Furthermore, sensitivity analyses on relevant input data variation are performed to test the robustness of the results obtained. [ABSTRACT FROM AUTHOR]

In the context of the reform of transmission and distribution prices, how to improve the investment efficiency of power networks is a noteworthy issue. This article constructs a transmission and distribution price system under the electricity market price system from the perspective of efficiency, and evaluates the effectiveness of each indicator based on the upper limit, benchmark, and profit criteria of transmission and distribution prices. It establishes evaluation methods for investment efficiency of power grids in various regions and provides the optimal solution based on gross profit margin. Through example analysis, the relationship between transmission coefficient, asset return rate, and investment and energy utilization rate was obtained, which is an important basis for making safe and reliable investment decisions. The experiment shows that the accuracy of the three stage DEA method based on the dataset is higher than 89%, which is higher than other algorithms. [ABSTRACT FROM AUTHOR]

As the power grid undergoes transformation and the Internet's influence grows, the electricity market is evolving towards informatization. The expanding scale of the power grid and the increasing complexity of operating conditions have generated a substantial amount of data in the power market. The traditional power marketing model is no longer suitable for the modern power market's development trend. To tackle this challenge, this study employs random forest and RBF models for processing electricity market data. Additionally, it explores the synergy of graph convolutional network and spectral clustering algorithms to enhance the accuracy and efficiency of data mining, enabling a comprehensive analysis of data features. The experimental results successfully extracted various electricity consumption features. This approach contributes to the informatization efforts of power grid enterprises, enhances power data perception capabilities, and offers reliable support for decision makers. [ABSTRACT FROM AUTHOR]

In the context of the evolving landscape of reduction in carbon emissions and integration of renewable energy, this study uses system dynamics (SD) modeling to explore the interconnected dynamics of carbon trading (CT), tradable green certificate (TGC) trading, and electricity markets. Using differential equations with time delays, the study provides a comprehensive analysis of structural relationships and feedback mechanisms within and between these markets. Key findings reveal the intricate interplay between carbon prices, green certificate prices, and electricity prices under various coupling mechanisms. For example, under the three-market coupling mechanism, carbon trading prices stabilize around 150 Yuan/ton, while green certificate prices reach a peak of 0.45 Yuan/KWH, impacting electricity prices, which fluctuate between 0.33 and 1.09 Yuan / KWH during the simulation period. These quantitative results shed light on nuanced fluctuations in market prices and the dynamics of anticipated purchases and sales volumes within each market. The insights gleaned from this study offer valuable implications for policy makers and market stakeholders in navigating the complexities of carbon emission reduction strategies, the integration of renewable energy and market equilibrium. By understanding the dynamics of multi-market coupling, stakeholders can better formulate policies and strategies to achieve sustainable energy transitions and mitigate impacts of climate change. [ABSTRACT FROM AUTHOR]

To steadily promote the reform of sales electricity prices, China has innovatively proposed to carry out grid enterprises' agency power purchase business (GEAPPB) as a transitional scheme for industrial and commercial users to enter the power market in the future. However, because the GEAPPB covers a wide range of users and involves complex business processes, the risks throughout the process of the GEAPPB will affect the organization and operation of the business. This paper proposes a GEAPPB risk assessment technique. First, a risk evaluation index system containing 10 indicators is designed based on the characteristics and development process of GEAPPB. Second, a risk assessment model based on the fuzzy full consistency method (FUCOM-F) and gray relational analysis (GRA)-improved MARCOS is constructed. Finally, seven grid enterprises in China are taken as examples to verify the validity and scientificity of the model. The results show that improving the accuracy of power forecasting, optimizing the agency power purchase strategy, and strengthening demand response management are the key schemes to reduce the risk of GEAPPB. Compared with the other three evaluation models, the proposed model has good applicability and effectiveness for the risk evaluation of GEAPPB. [ABSTRACT FROM AUTHOR]

Several large scenario exercises in the last years present decarbonizing transitional energy pathways to 2050 and beyond. This changing energy landscape toward net zero is new territory to explore but is expected to be more intensive in mineral based materials than the current system. Mapping this territory and understanding the critical material needs to support the transition are essential for demanders and suppliers as well as policy makers seeking to orchestrate the transition. Our contribution is to provide such decision makers for electricity markets with a transparent tool that can be easily understood and modified as our transitional knowledge improves. In this tool, we take the International Energy Agency's conservative Beyond Two Degrees scenario, which projects renewable energy penetration for 15 electricity technologies, supplemented by Bloomberg's Electrical Vehicle Outlook. Coupling these electricity projections with estimates of material use per GW of new capacity, we estimate resulting needs for 33 materials through 2050. Assuming constant material intensities and recycle rates, our model finds dramatic increases in most included materials from 2021 to 2050. The total projected tonnage increases in materials used for the transition is 294% with a compounded average annual growth rate of 4.8%. However, there is wide heterogeneity across materials (from slightly negative for tungsten to nearly 1300% for lithium). Projected 2050 sales vary from less than 30 tonnes for hafnium and yttrium (with quantity demanded growth of − 4.8% from 2021 to 2050) to more than 17 million tonnes for steel (with growth of 291%) and aluminum (growth 419%). At 2021 prices, 2050 sales revenue varies from less than a million dollars for boron (growth of 164%) to more than $42 billion for aluminum (growth 419%), nickel (growth of 279%), and steel (growth of 291%). [ABSTRACT FROM AUTHOR]