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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]

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]

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]

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]

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.)

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]

Copyright of Acta Scientiarum Naturalium Universitatis Pekinensis is the property of Editorial Office of Acta Scientiarum Naturalium Universitatis Pekinensis 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 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.)

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]

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]

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.)

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 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]

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 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]

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]

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]

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]

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]

The so-called black swans, COVID-19 and the invasion in Ukraine, have led to an unprecedented increase in electricity prices. Since 2021, after lockdowns, the electricity price has started to increase due to economic recovery, rising prices of the tCO2 and other primary sources that become unavailable or at much higher prices. In this context, we noticed that the variation of electricity prices in one country can be explained by the price fluctuations of the previous day in the neighboring countries. For instance, the prices for the current day (d) in the Romanian Day-Ahead Market is strongly correlated with the prices of the previous day (d−1) on DAMs of its neighboring countries. It is worth mentioning that the target can be switched by the rest of the variables. Not only the price in Romania can be estimated using the proposed Electricity Price Forecast (EPF) method, but also the prices in other neighboring countries can be a target for prediction because the regional prices on similar markets contain most of society's distress. Another interesting aspect is that the proposed forecasting methodology is robust, as proved by testing it on a varied and longer time interval (from January 2019 to August 2022). Furthermore, the proposed price forecasting methodology includes the adjustment of training interval according to the price standard deviation and weighing the results of the five individual Machine Learning (ML) algorithms to further improve the prediction performance. The set consists of data collected between 1st of January 2019—one year before COVID-19 pandemic outburst and 31st of August—several months after the war has started in the Black Sea region. [ABSTRACT FROM AUTHOR]

The imbalance cost pass-through (ICPT) is a flexible component of the incentive-based regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Meanwhile, if an increment in the ICPT price signal can be provided to electricity providers and consumers, early preparation for operation budgeting can be realised, and energy management program development can be properly prepared. Due to this reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia that will benefit the stakeholders. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed using the mean absolute percentage error (MAPE). In light of our findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the moving average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July–December 2022 and January–June 2023 is MYR 0.21/kWh for the ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB's ICPT tariff. As for forecasting, the ICPT price is expected to drop in the next announcement. The findings of this study may have a positive impact on the sustainability of the energy sector in Malaysia. [ABSTRACT FROM AUTHOR]

The electricity market in Spain holds significant importance in the nation's economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country's ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI's ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins. [ABSTRACT FROM AUTHOR]

Accurate prediction of electricity day-ahead prices is essential in competitive electricity markets. Although stationary electricity-price forecasting techniques have received considerable attention, research on non-stationary methods is comparatively scarce, despite the common prevalence of non-stationary features in electricity markets. Specifically, existing non-stationary techniques will often aim to address individual non-stationary features in isolation, leaving aside the exploration of concurrent multiple non-stationary effects. Our overarching objective here is the formulation of a framework to systematically model and forecast non-stationary electricity-price time series, encompassing the broader scope of non-stationary behavior. For this purpose, we develop a data-driven model that combines an N-dimensional Langevin equation (LE) with a neural-ordinary differential equation (NODE). The LE captures fine-grained details of the electricity-price behavior in stationary regimes but is inadequate for non-stationary conditions. To overcome this inherent limitation, we adopt a NODE approach to learn, and at the same time predict, the difference between the actual electricity-price time series and the simulated price trajectories generated by the LE. By learning this difference, the NODE reconstructs the non-stationary components of the time series that the LE is not able to capture. We exemplify the effectiveness of our framework using the Spanish electricity day-ahead market as a prototypical case study. Our findings reveal that the NODE nicely complements the LE, providing a comprehensive strategy to tackle both stationary and non-stationary electricity-price behavior. The framework's dependability and robustness is demonstrated through different non-stationary scenarios by comparing it against a range of basic naïve methods. [ABSTRACT FROM AUTHOR]

The required load in a typical microgrid structure fluctuates from hour to hour. Based on the peaks and troughs of the load demand curve, the power system utilities preset different price of electricity at various periods of the same day which is referred to as time-of-use (TOU) pricing policy. An optimal economic strategy called demand side management (DSM) reduces load during peak hours by shifting the elastic loads and compensates the shifted load by increasing the demand during valley hours. This recreates the total demand pattern on the demand price elasticity relation. The novel benefaction of this research suggests a DSM approach based on a hybrid intelligence technique to attain a compromised solution between minimum generation costs and pollutants emitted by DERs of an LV grid-connected microgrid system. Several grid involvement strategies, power market pricing types, and demand response mechanisms are examined in five separate instances for the microgrid system. The outcomes achieved in each example show that the recommended DSM technique may be applied and is appropriate in terms of cost reduction. When 30–40% of customers participated in the DSM curriculum, the generation cost was reduced by 10–13%. Environment constrained economic dispatch provided a superior compromise amongst lowest generation cost and emission for different microgrid load models. Statistical analysis and comparison of the results from previously published literatures validates the superiority of the proposed approach incorporated with DSM policy. [Display omitted] • Restructuring of load demand to study impact of DSM. • Comparison amongst different CEED types with and without DSM. • Effects of different grid participation strategies for economic generation of microgrid. • Effects of different grid pricing strategies for economic generation of microgrid. • Superiority of proposed CSAJAYA algorithm compared to published results. [ABSTRACT FROM AUTHOR]

In recent years, as a result of emerging renewable energy markets, several developed regions have already launched Real‐Time Pricing (RTP) strategies for electricity markets. Establishing optimal pump operation for water companies in RTP electricity markets presents a challenging problem. In a RTP market, both positive and negative electricity prices are possible. These negative prices create economically attractive opportunities for Water Distribution System (WDS) to dispatch their energy consumption. On the other hand, excessively high prices may put WDS at risk of supply disruptions and reduced service levels. However, the continuous development of wind power and photovoltaics results in more volatile and unpredictable fluctuations in the price of renewable energy. The risk arising from uncertainty in electricity prices can lead to a significant increase in actual costs. To address this issue, this paper develops an a posteriori random forest (AP‐RF) approach to forecast the probability density function of electricity prices for the next day and provide a risk‐constrained pump scheduling method toward RTP electricity market. The experimental results demonstrate that the developed method effectively addresses the issue of increased costs caused by inaccurate electricity price forecasting. Plain Language Summary: With the emergence of renewable energy markets in recent years, several developed regions have introduced Real‐Time Pricing (RTP) strategies for their electricity markets. This has created a difficult challenge for water companies seeking to establish the optimal pump operation in RTP markets. This study investigates the use of a risk‐constrained optimization scheduling approach for water distribution networks to mitigate the risks associated with inaccurate real‐time electricity price forecasting. Our proposed method is designed to reduce the costs associated with inaccurate electricity price prediction. Key Points: A robust pump scheduling approach toward real‐time electricity price market is developedDeveloping a posteriori random forest algorithm to predict the probability density function of Real‐time electricity priceOptimal scheduling with risk constraints is an effective approach to mitigating the risks associated with inaccurate electricity forecasting [ABSTRACT FROM AUTHOR]

The integration of renewable generation sources into wholesale electricity markets is expected to reduce day-ahead marginal prices. This effect has been widely evidenced by previous literature and is commonly referred to as the merit order effect. However, the factors influencing the components of final prices, other than the day-ahead market price, have not been subjected to as much study. Nevertheless, they may prove crucial in understanding the dynamics between the interrelated trading segments in the wholesale electricity market. Furthermore, in the context of the energy transition process, the penetration of intermittent renewable energy sources (mainly wind and solar photovoltaic) and the non-storability of electricity at a large scale may result in increased market balancing needs and costs. The objective of this study was to identify the primary drivers of final wholesale electricity prices in the Iberian electricity market, apart from the day-ahead market price, using machine learning techniques. The results indicate that the share of renewable generation in the day-ahead market is a significant factor influencing both the cost of managing technical constraints, which aims to address network capacity issues, and the cost of managing balancing processes and resolving adjustment issues by the TSO. However, both of these costs can be readily accommodated by the market, as they represent a minimal percentage of the final price. These findings are of interest to both practitioners and regulators, as they provide a better understanding of the functioning of the market and have implications for the restructuring of the market towards a more sustainable and competitive electricity system. [ABSTRACT FROM AUTHOR]

The existence of chaos is particularly relevant, as the identification of a chaotic behavior in a time series could lead to reliable short-term forecasting. This paper evaluates the existence of nonlinearity and chaos in the underlying process of the spot prices of the Spanish electricity market. To this end, we used daily data spanning from 1 January 2013, to 31 March 2021 and we applied a comprehensive framework that encompassed a wide range of techniques. Nonlinearity was analyzed using the BDS method, while the existence of a chaotic structure was studied through Lyapunov exponents, recurrence plots, and quantitative recurrence analysis. While nonlinearity was detected in the underlying process, conclusive evidence supporting chaos was not found. In addition, the generalized autoregressive conditional heteroscedastic (GARCH) model accounts for part of the nonlinear structure that is unveiled in the electricity market. These findings hold substantial value for electricity market forecasters, traders, producers, and market regulators. [ABSTRACT FROM AUTHOR]

Residential electricity markets in European countries are still characterized by low consumer engagement, especially where regulated and liberalized markets coexist. Using an original dataset on 2015--2018 prices for the Italian electricity market, augmented with the number of residential consumers, we study the presence and magnitude of switching costs--i.e., time-based and cognitive-based costs on consumers changing providers--in the liberalized market. We find that switching from the incumbent involves high costs--almost as high as the yearly energy expenditure--while switching from competitors is less expensive. We also carry out two counterfactual analyses. In the first, we show that consumers would have incurred lower average switching costs over the years had the market been less concentrated. In the second, we simulate how switching costs could evolve once regulated prices are phased out, and the market is fully liberalized. [ABSTRACT FROM AUTHOR]

In recent years, machine and deep learning models have attracted significant attention for electricity price forecast in global wholesale electricity markets. Yet, a predominant focus on point forecast in most parts of literature limits the practical application of these models due to the absence of uncertainty quantification. In this study, we first perform an analysis of the electricity price trends in the Mexican wholesale electricity market to determine the influence of key variables. Using independent component analysis and wavelet coherence analysis, we were able to identify primary determinants influencing locational marginal electricity prices. Subsequently, we applied four different models covering the most important algorithms proposed in the literature for electricity price forecast. Our findings revealed that the most accurate forecasting results were achieved using a deep learning‐based method with a decision tree‐based model trailing closely. Finally, we incorporate conformal prediction for uncertainty quantification by calculating the prediction intervals with a target coverage level of 95%. The conformal prediction intervals provide a more comprehensive view of the possible future scenarios, enhancing economic efficiency, risk management, and decision‐making processes. This is particularly important because of the dynamic nature of electricity markets, where prices are strongly influenced by multiple factors. [ABSTRACT FROM AUTHOR]

This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task employs two tree-based machine learning classifiers and a deterministic point forecaster; a statistical regression model. Hyperparameters for the tree-based classifiers are optimized for statistical performance based on recall, precision, and F1-score. The deterministic forecaster is adapted from the literature on electricity price forecasting for the classification task. Additionally, one tree-based model prioritizes interpretability, generating decision rules that are subsequently utilized to produce price spike forecasts. For all models, we evaluate the final statistical and economic predictive performance. The interpretable model is analyzed for the trade-off between performance and interpretability. Numerical results highlight the significance of complementing statistical performance with economic assessment in electricity price spike forecasting. All experiments utilize data from Alberta's electricity market. [ABSTRACT FROM AUTHOR]

With the continuous promotion of the unified electricity spot market in the southern region, the formation mechanism of spot market price and its forecast will become one of the core elements for the healthy development of the market. Effective spot market price prediction, on one hand, can respond to the spot power market supply and demand relationship; on the other hand, market players can develop reasonable trading strategies based on the results of the power market price prediction. The methods adopted in this paper include: Analyzing the principle and mechanism of spot market price formation. Identifying relevant factors for electricity price prediction in the spot market. Utilizing a clustering model and Spearman's correlation to classify diverse information on electricity prices and extracting data that aligns with the demand for electricity price prediction. Leveraging complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to disassemble the electricity price curve, forming a multilevel electricity price sequence. Using an XGT model to match information across different levels of the electricity price sequence. Employing the ocean trapping algorithm-optimized Bidirectional Long Short-Term Memory (MPA-CNN-BiLSTM) to forecast spot market electricity prices. Through a comparative analysis of different models, this study validates the effectiveness of the proposed MPA-CNN-BiLSTM model. The model provides valuable insights for market players, aiding in the formulation of reasonable strategies based on the market's supply and demand dynamics. The findings underscore the importance of accurate spot market price prediction in navigating the complexities of the electricity market. This research contributes to the discourse on intelligent forecasting models in electricity markets, supporting the sustainable development of the unified spot market in the southern region. [ABSTRACT FROM AUTHOR]

With the development of the electricity spot market, pumped-storage power stations are faced with the problem of realizing flexible adjustment capabilities and limited profit margins under the current two-part electricity price system. At the same time, the penetration rate of new energy has increased. Its uncertainty has brought great pressure to the operation of the power system. The ramp market and its market mechanism have emerged as the times require. To this end, this article proposes a bidding strategy for pumped-storage power stations to participate in multi-level markets such as the ramp market. Considering the demand calculation of ramping services, a two-layer model of pumped storage's participation in multiple markets is constructed. The upper level makes trading decisions with the goal of maximizing pumped-storage revenue; the lower level aims to minimize the total social cost and jointly clears the primary and auxiliary markets. The income from pumped storage participating in the main energy and ramp-up auxiliary markets at the same time is significantly higher than the income from the two-part electricity price system. Its flexible adjustment ability can be quantified, reducing dependence on capacity electricity charges and providing a theoretical reference for cost recovery and profitability of future pumped-storage power stations. [ABSTRACT FROM AUTHOR]

This paper analyses the recent evolution of the electricity price of one of the East-European countries' Balancing Markets (BM) - Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants' trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players. [ABSTRACT FROM AUTHOR]

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In pursuit of Finland's carbon neutrality objective by 2035, integrating renewable energy sources into the power grid is essential. To address the stochastic nature of these resources, additional sources of flexibility are required to maintain grid stability. Meanwhile, district heating network (DHN) operators in Finland are decommissioning fossil fuel-based combined heat and power plants (CHPs) and electrifying heating systems with heat pumps (HPs) and electric boilers. A techno-economic assessment and the optimized operation of DHN-connected HPs and electric boilers in providing ancillary balancing services were explored in this study. The primary goal was to maximize the potential revenue for DHN operators through participation in the day-ahead electricity market and frequency containment reserve (FCR) balancing markets. Three interconnected DHNs in the Helsinki metropolitan area were optimized based on 2019 data and each operator's decarbonization strategies for 2025. HPs are expected to achieve the highest profit margins in the FCR-D up-regulation market, while electric boilers could generate substantial profits from the FCR-D down-regulation market. In contrast to other balancing markets studied, the FCR-N market exhibited limited profit potential. Sensitivity analysis indicated that spot electricity prices and CO2 emission allowance prices significantly influence the profitability derived from balancing markets. [ABSTRACT FROM AUTHOR]

Time-of-use pricing in retail electricity markets implies that wholesale market scarcity becomes easily communicated to end consumers. Yet, it is not well-understood if and how the price formation process in retail electricity markets will help to reward the demand for operational flexibility due to growth in intermittent generation. To contribute to this discussion, this paper develops a partial equilibrium model of the retail electricity market calibrated to Chinese data. The paper finds that tariffs in this market may not be significantly suppressed by growth in near-zero costs renewable sources when controlling for flexibility restrictions on thermal generation assets and when a significant curtailment of variable renewable resources exists in the market. In addition, it shows that the price formation process in retail electricity markets which controls for flexibility restrictions on thermal generation while allowing for consumers to respond slowly to price changes is a feasible strategy to reward the demand for operational flexibility. Finally, the paper reveals that while integrating intermittent generation beyond levels which the available storage capacities can accommodate may result in losses to producers, benefits to consumers may offset these losses, leading to overall welfare gains. [ABSTRACT FROM AUTHOR]

In the Spanish electricity market, after the daily market is held in which prices are set for the next day, the secondary and tertiary markets take place, which allow companies more accurate adjustment of the electricity they are able to offer. The objective of this paper is to predict the final price reached in these markets by predicting the supply curve in advance, which is the aggregate of what companies offer. First, we study a procedure to represent the supply curves, and then we consider different machine learning approaches to obtain the day-ahead supply curves for the secondary market. Finally, the predictions of the supply curves are crossed with the system requirements to obtain the expected price predictions. Histogram-Based Gradient Boosting is the best performing algorithm for predicting supply curves. The most relevant variables for the prediction are the lagged values, the daily market price, the price of gas and values of the wind recorded in the Spanish provinces. [ABSTRACT FROM AUTHOR]

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

With the deepening of the concept of low-carbon environmental protection, the use of renewable energy in social production has become more and more popular. More and more consumers in the distribution network have begun to change their identities to become consumers with the continuous reform of the electricity sales side. Electricity trading and settlement methods in the electricity market directly affect the relevant income of market entities, so a fair and reasonable settlement mechanism is an important factor for the normal operation of the electricity market. Electricity trading refers to the buying and selling of electricity commodities and services, including electricity trading, auxiliary service trading, and transmission rights trading. With the expansion of the electricity market, how to deal with the deviation of electricity is also a difficult problem that requires electricity settlement. This paper aimed to study the application of blockchain technology in the autonomous electricity transaction and settlement at the end of the distribution network. It was expected to use the blockchain technology to set up related transaction mechanisms, provide new solutions for electricity transaction and settlement, and improve the efficiency and fairness of electricity settlement. Blockchain technology does not rely on additional third-party management agencies or hardware facilities, and there is no central control. Except for the self-contained blockchain itself, through distributed accounting and storage, each node realizes information self-verification, transmission and management. On the basis of sorting out the principles of electricity balance and electricity price formation, this paper proposed a contract decomposition method with a higher degree of fit, and revealed the settlement principle of deviation electricity in the electricity market environment. Aiming at the application scenario of blockchain in the energy Internet, an energy trading platform based on blockchain was designed, and the test results showed the feasibility of blockchain in electricity settlement scenarios. The experimental results of this paper showed that in the traditional electricity purchase fee settlement, the average transaction price is stable at 0.6 yuan/KWh. In the blockchain settlement process, the electricity purchase is 25 yuan when the electricity purchase is 50 KWh. Therefore, blockchain settlement is used more frequently in the market, and the profits are higher. [ABSTRACT FROM AUTHOR]

The financial viability of Sub-Sahara Africa (SSA) electricity sectors has become a central energy policy issue in recent years. This follows persistent under-recovery of costs which are amongst some of the highest in the world. Discussions, however, focus on tariff and utility reforms and inadequately on costs efficiency and the role of broader sector reforms in persistently high costs. Through a synthesis of reform theories and case studies and using small electricity systems as a surrogate for liberalized electricity sectors without competitive markets, this paper examines the connection between sector reforms and costs. It brings an economic perspective to the discussion on utility financial performance in SSA electricity systems and the need for a holistic policy approach for sustainable cost-recovery. In this, we recommend the promotion of mobile power plants to facilitate contestability in generation and need for non-island small systems to participate in regional power markets to neutralize the scale limitations of their autarkic demand. Utilities and regulatory agencies should form platforms to share information on cost opportunities and possibilities to inform procurement designs and regulatory benchmarks. Regional markets could partner with national governments to develop subsidy schemes such as Contracts for Differences to remove rigidities imposed by national power purchasing contracts to promote deeper participation of small systems in regional power markets. Yardstick competition in the distribution segment remains viable in many small electricity systems and should be pursued at the regional level in the short run to medium term and at the national levels in the long term following deconcentration and the introduction of private sector participation. [ABSTRACT FROM AUTHOR]

To promote the use of renewable energy (RE), several types of RE policies have been implemented globally. Among these, feed-in tariff (FiT) is one of the most accepted mechanisms of pricing policy. However, choosing the best policy for a market with a high RE penetration is a challenge. A case of Bhutan is considered in this paper as the source of electrical energy is predominantly hydropower. Additionally, a generous subsidy is provided by the government to keep the electricity tariff to a minimum. Recently, there has been an increase in the advocacy and motivation for other forms of RE sources (RES) to supplement hydropower and increase energy security in Bhutan. Bhutan aims to achieve a total of 20 MW of non-hydro RES by 2025 as per the RE policy of Bhutan. However, Bhutan still does not have an RE pricing policy, and therefore, there is a need to institute a suitable pricing mechanism to accommodate the penetration of the planned non-hydro RES. This paper discusses the challenges in introducing FiT for non-hydro RES in an electricity market dominated by hydropower in Bhutan. Subsequently, recommendations are made in the wake of subsidised electricity tariff, which is the lowest in the region at 0.0171 USD/kWh for low voltage customers. FiTs for solar photovoltaic based on different categories of customers have been computed and proposed. [ABSTRACT FROM AUTHOR]