Application of artificial intelligence technology in real-time electricity price forecasting: window-based XGBoost

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments.

Based on the current electric power market, a real-time electricity price dynamic game linkage model is established. In this paper, three cases are considered for the simulation, and a real-time electricity price is formed through the dynamic game among various participants. The result of simulation shows that large-scale energy storage system set up on the demand side improve system character. Keywords: Electric power market, Battery storage system, Real-time electricity price, Dynamic game

How real time pricing modifies Chinese households’ electricity consumption

Electricity prices based on real-time pricing (RTP) strategy provides consumers the opportunity to respond to the price changes and hence, reduce their electricity bills. Automatic home load control systems also help consumers to simply manage their usage profile more efficiently. This paper proposes a stochastic formulation for automatic home load controllers to schedule the operation period of various home appliances such that the expected electricity payment is minimized. The level of risk imposed by price volatility during the following hours is also taken into consideration in the developed formulation. Difference between the expected payment and the payment in the worst scenario is used as the risk measure. The price uncertainty is modeled via normal probability density functions with increasing standard deviations. Finally, effectiveness of the developed formulation is demonstrated by applying to a typical home.

This paper provides a comprehensive analysis of how the correlation between wind power and electricity prices affects the offer strategies of wind power producers (WPPs). In a market that has a high penetration of wind energy, the correlation can be noticeable and thus should be considered by the WPPs when building offer strategies. First, this paper aims to build an advanced offer curve that considers the joint information of wind power and real-time (RT) prices. Then, we explore the sensitivity of the expected profit, value at risk (VaR), and conditional value at risk (CVaR) for the advanced offer curves to the probabilistic parameters of wind power and RT prices. We find that an offer curve which reflects this correlation generally results in a small improvement in expected profit, but a meaningful reduction in risks. The benefits of the advanced offer curves increase as the correlation becomes more negative or the variances of wind power and prices increase.

The impact of wind generation on wholesale electricity market prices in the midcontinent independent system operator energy market: An empirical investigation

Background:Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods:In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results:This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions:Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.

The merits of electricity grid in Shanghai and sodium sulfur (NaS) storage techniques situation are introduced. High-energy NaS battery energy storage system (BESS) is very suitable for peak shaving of electricity grid. A cost–benefit analysis model of NaS BESS is established to study the electricity price mechanism in load shift in the light of an example of NaS BESS in Meisei University. Capacity price, energy price and twofold electricity price mechanism are discussed under the fixed payback period. The results show that twofold electric power price mechanism is fitter for NaS BESS than onefold energy price mechanism while onefold capacity price mechanism is not suitable for NaS BESS. The discharge price of NaS BESS has an advantage over Shanghai's electricity price in industrial and commercial peak periods when its construction cost descends to 1000 yuan kWh−1. Copyright © 2015 John Wiley & Sons, Ltd.

A time-indexed integer programing approach is developed to optimize the manufacturing schedule of a factory for minimal energy cost under real-time pricing (RTP) of electricity. The approach is demonstrated using a flow shop operating during different time periods (i.e., day shift, swing shift, and night shift) in a microgrid, which also serves residential and commercial users. Results show that electricity cost can be reduced by 6.2%, 12.3%, and 21.5% for the three time periods considered, respectively. Additionally, a 6.3% cost reduction can be achieved by the residential and commercial buildings through adopting energy-conscious control strategies in this specific case study example.

Exploring demand-side flexibility through electricity pricing mechanisms is a core pathway to reducing costs in low-carbon power systems. While existing studies have confirmed differences in the effectiveness of various pricing mechanisms, they lack systematic quantitative analysis, making it difficult to support the optimization of pricing strategies. This paper constructs a bi-level simulation framework based on a comprehensive dataset from Anhui Province, China, to conduct empirical analysis of the system-level benefits of typical pricing mechanisms including Real-Time Pricing (RTP) and Time-of-Use Pricing (TOU) in exploring demand-side flexibility. The key findings reveal that from 2025 to 2050, as renewable energy penetration grows, RTP’s average daily cost savings compared to TOU increase from 70,000 Chinese yuan (CNY) to 393,000 CNY, whereas TOU’s efficiency index declines from 43.62% to 29.02%. Notably, the efficiency gap between RTP and TOU remains insignificant at present; yet as renewable energy penetration further increases, this gap will become far more pronounced. In practical terms, this implies TOU can serve as a short-term transitional mechanism, while the power market should gradually shift toward RTP in the long run to adapt to the rising penetration of renewable energy and fully tap into demand-side flexibility. This study provides empirical support for improving electricity market pricing mechanisms to unlock user-side flexibility resources.

This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources.

We analyze the effects of the adoption of real‐time pricing (RTP) of electricity when generating firms have market power. We find that an increase in consumers on RTP contracts decreases peak prices and increases off‐peak prices, increases consumer surplus (both for switching and non‐switching consumers) and welfare, while decreasing industry profits, with these effects being magnified by the extent of market power. We illustrate these results by calibrating our model to the New Zealand electricity market, and find that taking into account the market power of generating firms increases the efficiency gains from RTP adoption by 41%.

<p indent="0mm">To achieve carbon neutrality in the power system, renewable energies (RENs), such as wind and solar power, are being rapidly installed. However, due to their strong intermittency and high uncertainty, balancing power supply and demand is becoming increasingly difficult. Time-of-use and real-time pricing (RTP) are crucial methods to ensure effective consumption of RENs, encouraging flexible resources on the demand side to work with RENs through demand-side response. Air conditioning loads represent a significant portion of urban loads, with &gt;50% of the peak load in China. They can use the building’s thermal inertia to provide regulation services, a current area of focus for demand-response research. To adapt to the RTP mechanism in the future electricity market, this study explores demand-side optimization control technology for an ice storage system in a large-scale district cooling system (DCS). An optimal control method for an ice storage system is proposed, based on compound second-sampling reinforcement learning (RL), which can effectively manage uncertainties from users’ cooling demands and real-time market prices. First, a Markov decision process (MDP) is constructed to address the operational control issue of the DCS. Second, a model-free RL algorithm is used to solve the MDP. Third, the compound second-sampling mechanism is proposed to improve training efficiency and convergence performance by combining immediate return and temporal-difference error to overcome the issue of low learning efficiency caused by uniform random sampling in the traditional RL algorithm. Finally, the experimental results confirm the effectiveness of the proposed method.

Model-predictive control for dispatch planning of concentrating solar power plants under real-time spot electricity prices

Real-Time Pricing in the Nordic Power markets