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Building the regional electricity spot market (RESM) in a representative area is an important move to promote the electricity market reform and new power system construction in China. In this paper, the RESM operation model and optimization method are established, which take into account the special power grid operation mechanism and market construction achievements in the provincial electricity spot market. Firstly, the influencing factors, core elements, market structure, and operation model of RESM construction in China are analyzed. Secondly, a bi-level optimization model of the RESM is established. The lower layer is the pre-clearing model of the provincial electricity spot market, which is used to optimize the unit combination strategy, considering unit operation constraints and power grid security constraints in the province. The upper layer is the optimization clearing model of the RESM, which is used to optimize the clearing price and adjust the unit operation strategy and inter-provincial electricity trading strategy, considering the security constraints of regional power grid tie lines. Finally, the RESM composed of power grids in the Yangtze River Delta region of China is simulated as an example. The analysis focuses on the operational state of the power grid after the operation of the RESM, considering its safety benefits, economic benefits, and environmental benefits. The optimization of the RESM can effectively solve the serious regional power grid congestion problem, which is achieved through the superposition and printing of pre-clearing results in various provinces, and the average daily cost of electricity purchasing in the region has been reduced by about CNY 11 million, while the annual cost has been reduced by about CNY 4 billion. In addition, the total carbon emissions have been reduced by 11,000 tons per day and 0.18 kg per kilowatt hour on average, and scenes without power abandonment account for more than 95% of the total scenes.

The cost of electricity and gas has a direct influence on the everyday routines of people who rely on these resources to keep their businesses running. However, the value of electricity is strongly related to spot market prices, and the arrival of winter and increased energy use owing to the demand for heating can lead to an increase in energy prices. Approaches to forecasting energy costs have been used in recent years; however, existing models are not yet robust enough due to competition, seasonal changes, and other variables. More effective modeling and forecasting approaches are required to assist investors in planning their bidding strategies and regulators in ensuring the security and stability of energy markets. In the literature, there is considerable interest in building better pricing modeling and forecasting frameworks to meet these difficulties. In this context, this work proposes combining seasonal and trend decomposition utilizing LOESS (locally estimated scatterplot smoothing) and Facebook Prophet methodologies to perform a more accurate and resilient time series analysis of Italian electricity spot prices. This can assist in enhancing projections and better understanding the variables driving the data, while also including additional information such as holidays and special events. The combination of approaches improves forecast accuracy while lowering the mean absolute percentage error (MAPE) performance metric by 18% compared to the baseline model.

The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper evaluates adaptive boosting (AdaBoost), bootstrap aggregation (bagging), gradient boosting, histogram-based gradient boosting, and random forest ensemble learning models for forecasting energy prices in Latin America, especially in a case study about Mexico. Seasonal decomposition of the time series is used to reduce unrepresentative variations. The Optuna using tree-structured Parzen estimator, optimizes the structure of the ensembles through a voter by combining several ensemble frameworks; thus an optimized hybrid ensemble learning method is proposed. The results show that the proposed method has a higher performance than the state-of-the-art ensemble learning methods, with a mean squared error of 3.37 × 10−9 in the testing phase.

With the continuous expansion of new energy installed capacity, the flexible regulation role of energy storage in the electricity spot market is becoming more and more prominent. However, traditional trading models often ignore the multiple trading preferences of energy storage. In this paper, we propose an electricity spot market trading model that considers the trading preferences of energy storage to incentivize energy storage to participate more actively in the market. First, the trading preferences of energy storage are modeled with a utility function in which the time preference coefficient and price elasticity are introduced. Then, the utility function is embedded into the spot market clearing model to establish a two-tier model of the spot market, which maximizes social welfare in the upper tier and maximizes energy storage benefits in the lower tier. Finally, the model is solved using KKT and large M methods, and its effectiveness is evaluated on the IEEE39 node system and on a real grid in a specific region.

We examine possible accuracy gains from forecast averaging in the context of interval forecasts of electricity spot prices. First, we test whether constructing empirical prediction intervals (PI) from combined electricity spot price forecasts leads to better forecasts than those obtained from individual methods. Next, we propose a new method for constructing PI—Quantile Regression Averaging (QRA)—which utilizes the concept of quantile regression and a pool of point forecasts of individual (i.e. not combined) models. While the empirical PI from combined forecasts do not provide significant gains, the QRA-based PI are found to be more accurate than those of the best individual model—the smoothed nonparametric autoregressive model.

Due to the development of China’s electricity spot market, the peak-shifting operation modes of energy storage devices (ESD) are not able to adapt to real-time fluctuating electricity prices. The settlement mode of the spot market aggravates the negative impact of deviation assessments on the cost of electricity retailers. This article introduces the settlement rules of China’s power spot market. According to the electricity cost settlement process and the assessment methods, this paper proposes a comprehensive electricity cost optimization algorithm that optimizes day-ahead market (DA) electricity cost, real-time market (RT) electricity cost and deviation assessment through ESD control. According to the trial electricity price data of the power trading center in Guangdong province (China), many typical load curves and different deviation assessment policies, the algorithm calculates DA electricity cost, RT electricity cost and deviation assessment cost by utilizing a comprehensive electricity cost optimization algorithm. Compared with the original electricity cost and optimization cost, this method is proven to effectively save overall electricity costs under the spot market settlement system. Based on three different initial investment prices of ESD, this paper analyzes the economics of the ESD system and proves that ESD investment can be recovered within 5 years. Considering the small amounts of operating data in China’s power spot market, the algorithm generates random data according to characteristics of these data. Then, this paper verifies that the comprehensive electricity cost optimization algorithm remains reliable under random circumstances.

The introduction of Renewable Energy Sources (RESs) into the electricity grid is changing the price dynamics of the electricity market and creating room for flexibility on the consumption side. This paper investigates different aspects of the interaction between the RES share, electricity spot prices, and electric vehicle (EV) charging strategies. Based on empirical data from Germany, France, and the Czech Republic for the period 2015–2025, four research hypotheses are tested using correlation and regression analysis, cost simulations, and classification algorithms. The results confirm a negative correlation between the RES share and electricity prices, as well as the effectiveness of smart charging in reducing costs. At the same time, it is shown that the occurrence of negative prices is significantly affected by a high RES share. The correlation analysis further suggests that higher production from RESs increases the potential for price optimisation through smart charging. The findings have implications for policymaking aimed at flexible consumption and efficient RES integration.

As the proportion of new energy sources, such as wind power, in the electricity system rapidly increases, their participation in spot market competition has become an inevitable trend. However, the uncertainty of clearing price and wind power output will lead to bidding deviation and bring revenue risks. In response to this, a bidding strategy is proposed for wind farms to participate in the spot market jointly with carbon capture power plants (CCPP) that have flexible regulation capabilities. First, a two-stage decision model is constructed in the day-ahead market and real-time balancing market. Under the joint bidding mode, CCPP can help alleviate wind power output deviations, thereby reducing real-time imbalanced power settlement. On this basis, a tiered carbon trading mechanism is introduced to optimize day-ahead bidding, aiming at maximizing revenue in both the electricity spot market and carbon trading market. Secondly, conditional value at risk (CVaR) is introduced to quantitatively assess the risks posed by uncertainties in the two-stage decision model, and the risk aversion coefficient is used to represent the decision-maker’s risk preference, providing corresponding strategies. The model is transformed into a mixed-integer linear programming model using piecewise linearization and McCormick enveloping. Finally, the effectiveness of the proposed model and methods is verified through numerical examples.

Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conducting an empirical study involving datasets from two major power markets (Nord Pool and PJM Interconnection), three expert models, two multi-parameter regression (called baseline) models and four variance stabilizing transformations combined with the seasonal component approach, we discuss the optimal way of implementing the LASSO. We show that using a complex baseline model with nearly 400 explanatory variables, a well chosen variance stabilizing transformation (asinh or N-PIT), and a procedure that recalibrates the LASSO regularization parameter once or twice a day indeed leads to significant accuracy gains compared to the typically considered EPF models. Moreover, by analyzing the structures of the best LASSO-estimated models, we identify the most important explanatory variables and thus provide guidelines to structuring better performing models.

Market power remains a persistent challenge in liberalized electricity spot markets, where generators can manipulate bids to distort prices and extract rents. Traditional monitoring approaches—such as structural indices or simulation-based models—offer partial insights but fail to capture the nonlinear, spatially correlated propagation of strategic behavior across transmission-constrained networks. This paper develops a diffusion neural learning framework for market power risk assessment that integrates welfare optimization, nodal pricing dynamics, and graph-based deep learning. Specifically, a Graph Diffusion Network (GDN) is trained on simulated spot market scenarios to learn how localized strategic deviations spread through the network, distort locational marginal prices, and alter system welfare. The modeling framework combines a system-wide welfare maximization objective with multi-constraint market clearing, while the GDN embeds network topology into predictive learning. Results from a case study on an IEEE 118-bus system demonstrate that the proposed method achieves an R2 of 0.91 in predicting market power indices, outperforming multilayer perceptrons, recurrent neural networks, and Transformer baselines. Welfare analysis reveals that distributionally robust optimization safeguards up to 3.3 million USD in adverse scenarios compared with baseline stochastic approaches. Further, congestion mapping highlights that strategic bidding concentrates distortions at specific nodes, amplifying rents by up to 40 percent. The proposed approach thus offers both predictive accuracy and interpretability, enabling regulators to detect emerging risks and design targeted mitigation strategies. Overall, this work establishes diffusion-based learning as a novel and effective paradigm for electricity market power assessment under high uncertainty and renewable penetration.