Electricity load and price forecasting in Spain: A hybrid deep learning framework leveraging temporal and seasonal dynamics

Dynamic spatiotemporal electric vehicle charging station placement and pricing using mixed integer linear programming

The flexibility-efficiency nexus in industrial heat electrification: Optimal operation of heat pump-storage systems under variable electricity price

Techno-economic analysis of PV-assisted alkaline water electrolysis hydrogen production system based on dynamic matching of electricity prices and solar irradiance

Temporal feature mixed inverted transformer: An inverted transformer for effective real-time electricity price forecasting

Renewable sources and short-to-mid-term electricity price forecasting

Comparing time series and neural network models of long memory for electricity price forecasting

It is shown that an inertial fusion energy power plant, utilizing D-D fusion reactions and ignition of microexplosions by or with the use of microexplosions, can produce during 1 year at least about 6.86 kg of 3He per 1 GW of its fusion power. The minimum requirements of the energetic parameters of the driver, igniting microexplosions in such a power plant, are approximately the same as for indirect ignition or indirect compression, fast ignition of D-T microexplosions with a yield of about 1 GJ. The prices of 3He and electricity, produced by the power plant, can be comparable. The atomic fraction of 3He in the arising mixture of 3He and 4He will be at least about 0.167.

This study develops a system dynamics model to analyze Indonesia’s transition from fossil-fueled electricity generation to renewables and its effects on residential energy affordability. The model integrates energy supply, demand, and fiscal modules to capture the interplay among generation mix, pricing, subsidies, and household income and affordability index for different consumer electricity segments, categorized as 450 VA and 900 VA recipients’ group. Three policy scenarios—Business-as-Usual, Coal Phase Down, and Coal Phase Out—are simulated from 2020 to 2060. Results indicate that while an accelerated shift toward renewable energy supports national decarbonization targets, it also tends to increase electricity generation costs and prices. A rapid coal phase-out could impose higher tariff burdens and diminish affordability for vulnerable households if subsidy reforms are not carefully managed. These insights suggest that a balanced, gradual approach is needed—one that supports renewable capacity expansion while providing targeted measures to protect low-income consumers during the transition.

The optimal control of sustainable energy supply systems, including renewable energies and energy storage, takes a central role in the decarbonization of industrial systems. However, the use of fluctuating renewable energies leads to fluctuations in energy generation and requires a suitable control strategy for the complex systems in order to ensure energy supply. In this paper, we consider an electrified power-to-heat system which is designed to supply heat in the form of superheated steam for industrial processes. The system consists of a high-temperature heat pump for heat supply, a wind turbine for power generation, a sensible thermal energy storage for storing excess heat, and a steam generator for providing steam. If the system’s energy demand cannot be covered by electricity from the wind turbine, additional electricity must be purchased from the power grid. For this system, we investigate the cost-optimal operation, aiming to minimize the electricity cost from the grid by a suitable system control depending on the available wind power and the amount of stored thermal energy. This is a decision-making problem under uncertainty regarding the future prices for electricity from the grid and the future generation of wind power. The resulting stochastic optimal control problem is treated as finite-horizon Markov decision process for a multi-dimensional controlled state process. We first consider the classical backward recursion technique for solving the associated dynamic programming equation for the value function and compute the optimal decision rule. Since that approach suffers from the curse of dimensionality, we also apply reinforcement learning techniques, namely Q-learning, that are able to provide a good approximate solution to the optimization problem within reasonable time.

This paper presents robust energy-demand and renewable power forecasts for the microgrid using deep learning-based forecasting and a metaheuristic-based optimization model. A Long Short-Term Memory (LSTM) is used to model the temporal nonlinear dynamics of the energy datasets. A new Improved Dynamic Arithmetic Optimization Algorithm (IDAOA) is developed to fine-tune LSTM parameters, incorporating inertial weights, a mutation factor, and the triangle mutation operator to balance exploration and exploitation. The model's performance is verified on various datasets, including wind turbines (WT), photovoltaic (PV) systems, load demands, and day-ahead electricity pricing. This work shows that the IDAOA-LSTM model outperforms other strategies. Practically, the Root Mean Squared Error (RMSE) was 0.021 in the forecast of WT power and 0.031 in the case of PV power. The model performs well in predictions, with high coefficient of determination (R²) values (R² ≥ 0.98) throughout all tasks. These findings strengthen the applicability of the proposed method to enhance energy-saving measures while preserving the stable operation of those microgrid (MG) systems.

ABSTRACT Amid the global energy transition and crises, electricity price volatility has become a key determinant of financial stability. This study investigates the dynamic, time–frequency causal relationship between electricity price volatility and financial stress in five European countries–Germany, France, the UK, the Netherlands, and Belgium–over the period 2007–2024. Employing a continuous wavelet transform framework that integrates wavelet coherence and wavelet-based Granger causality, the analysis reveals pronounced time-varying, scale-dependent, and country-specific heterogeneity, as well as asymmetric multi-scale causal mechanisms linking electric and financial markets. The results indicate that during financial crises, financial stress leads electricity price volatility, and during energy crises, electricity price volatility drives financial stress through inflationary and liquidity channels. These findings provide practical insights for policymakers seeking to mitigate energy–finance spillovers, enhance market resilience, and design flexible, country-specific stabilization strategies.

Abstract The widespread integration of photovoltaic (PV) power, energy storage systems, and other demand‐side resources highlights the importance of optimal dispatching for the PV‐storage‐load virtual power plant (VPP). However, the fluctuation of the PV power generation and the uncertainty of the electricity prices exacerbate the economic operation risks of the VPP. To address these challenges, an optimal dispatching strategy for the PV‐storage‐load VPP is proposed, with due consideration given to the dual uncertainties of electricity prices and PV power output. Firstly, the conditional value‐at‐risk theory is employed to quantify the uncertainty risk of VPP revenue caused by electricity price fluctuations. Secondly, in view of the asymmetric fluctuation intervals of PV power output, a quantification method for PV uncertainty and dispatch robustness is developed using the confidence gap decision theory. Furthermore, by combining the regulation reserve model of multi‐type flexible resources, a robust optimization model for the PV‐storage‐load VPP is constructed with the objective of maximizing comprehensive operational revenue, which includes the provision of upward and downward reserve services. Finally, case studies based on a PV‐storage‐load VPP in a Chinese province are conducted to validate the effectiveness and superiority of the proposed model. The simulation results indicate that the proposed robust optimization strategy effectively reflects the relationship between the uncertainty of PV power output and the risk preference of decision‐maker, mitigates the fluctuation risks of electricity prices to ensure the stability of the power system, and enhances the economic efficiency and flexibility of the PV‐storage‐load VPP operation.

ABSTRACT To address current energy trends, integrated energy systems must achieve multi-energy complementarity, enabling oil, coal, natural gas, and electricity to interact synergistically within the system. Hydrogen, as a clean secondary energy, features high capacity, high energy density, and low pollution. When applied to IES, it plays a crucial role in enhancing the coupling between different energy forms. This is essential for improving system energy efficiency, operational flexibility, and reducing low-carbon emissions. This paper proposes a hydrogen-coupled integrated energy system (HIES) and designs a two-layer scheduling optimization framework based on model predictive control (MPC) and deep reinforcement learning (DRL). The upper layer employs a long short-term memory (LSTM) algorithm for load forecasting at a 15-min time scale for typical days. The lower layer utilizes deep deterministic policy gradient (DDPG), incorporating time-of-use electricity pricing and carbon pricing mechanisms, to perform low-carbon economic scheduling at 15-min intervals, forming a two-layer rolling optimization framework.

In deregulated electricity markets, Generation Companies (GENCOs) are exposed to substantial financial risk due to volatile and uncertain electricity prices. Traditional generation asset valuation approaches, which rely primarily on expected profit, fail to adequately capture downside risk under market uncertainty. This study proposes an integrated risk-aware framework for generation asset valuation by embedding Value-at-Risk (VaR) into a Price-Based Unit Commitment (PBUC) model. VaR is employed to quantify potential profit losses at different confidence levels, enabling GENCOs to explicitly assess downside exposure associated with electricity price fluctuations. Spot price uncertainty is modeled using the Delta-Normal approach based on historical PJM market data. The resulting nonlinear mixed-integer optimization problem is solved using an Improved Immune Algorithm (IIA) enhanced with the Taguchi Method to improve convergence stability and solution diversity. Case studies on the IEEE 15-unit system demonstrate that the proposed IIA consistently outperforms conventional evolutionary algorithms in terms of profitability, robustness, and convergence reliability. The VaR analysis further reveals pronounced left-tail risk in profit distributions, particularly during peak-load periods, highlighting the importance of risk-adjusted commitment strategies. The proposed framework provides a practical decision-support tool for GENCOs to balance profitability and downside risk in competitive electricity markets.

Abstract The European Union’s (EU) climate strategy, anchored in the European Green Deal, the Fit-for-55 package, and updated National Energy and Climate Plans (NECPs), requires a rapid transformation of the energy system to meet the legally binding target of net-zero greenhouse gas emissions by 2050 and a 55% reduction by 2030 relative to 1990 levels. Yet, how national plans align with EU-wide ambition, alongside the implications for investment, infrastructure, and power-system operation, remain insufficiently assessed. We address this gap by linking an EU-specific implementation of a prominent integrated assessment model with Member State-level disaggregation (GCAM-Europe) with a higher-resolution European electricity system model (EXPANSE). This modelling framework captures national heterogeneity, sectoral detail, and spatiotemporal variability in electricity demand, renewable supply, and storage, enabling the assessment of grid investments and infrastructure. We analyse four scenarios representing EU-wide (Fit-for-55) or national (NECP) targets, implemented through explicit policies (POLICY) or cost-optimal carbon caps (COST_OPTIMAL). Results show all scenarios achieve the −55% fossil CO2 reduction by 2030, with the electricity sector driving the largest chunk. Renewable energy nearly doubles, with POLICY scenarios accelerating electrification and heat pump deployment, while COST_OPTIMAL scenarios leaning more on biofuels. Efficiency targets are partially met, with POLICY scenarios distributing savings more evenly across Member States compared to concentrated reductions (COST_OPTIMAL). By 2035, power system transformation diverges strongly: COST_OPTIMAL scenarios expand about 1,250 GW of new capacity, concentrated in few resource-rich regions, while POLICY scenarios reach around 1,750GW with broader spatial distribution, requiring higher investments in renewables and grids. Average wholesale electricity prices are higher and more heterogeneous under POLICY scenarios, reflecting carbon costs, transmission bottlenecks, and reliance on fossil backup. Results highlight trade-offs between economic efficiency and equitable burden-sharing, underscoring the importance of coordinated EU governance, infrastructure planning, and complementary policies to balance cost-effectiveness with political feasibility and social acceptance.

Abstract Capacity sizing and calculating cost savings for residential households in a rapidly evolving energy market, influenced by fluctuating electricity prices and changing government incentives, is a highly complex problem. The key challenges stem from multiple interacting factors, including retail electricity prices, the desired payback period, household size, applicable electricity schemes, and the capacity factor of the photovoltaic (PV) system. The nominal power output of the solar energy system is constrained by both the specifications and the number of installed inverters and PV panels. As solar generation is intermittent and non-dispatchable, it is inherently weather-dependent and often unable to align with the dynamic fluctuations in household electricity consumption. From a financial modelling perspective, the length of the accounting period directly determines the time resolution of the model, influencing both the accuracy of cash flow estimation and investment decision-making. The proposed two-level investment planning model is based on the process network synthesis approach. At the upper level of the process model, solar generation technologies, including inverters and solar panels, are technically and economically assessed. At the lower level, which represents the load consumption side, the periodical energy balances for production, storage, demand, and purchase are considered. In order to accurately evaluate the solar energy system, the model is developed with both a monthly framework and a detailed hourly framework. The time resolution allows the model to account for grid intake, electricity sold, and storage inventory conditions over the defined periods, ultimately providing the optimal sizing for a solar system equipped with battery storage. Case studies are conducted to investigate the effects of household size, extended payback periods, varying retail electricity prices, and grid reliability. These scenarios demonstrate the key parameters that significantly influence the economic feasibility and optimal sizing of the solar energy system, which are discussed in detail in this paper.

To achieve “carbon peak and carbon neutrality” in manufacturing, this paper tackles high energy consumption in flexible job shop production by developing a low-carbon scheduling optimization model with time-of-use electricity pricing, incorporating a photovoltaic microgrid. The model minimizes makespan, carbon emissions, and costs, considering photovoltaic power uncertainty, energy storage dynamics, and time-of-use pricing. To address coupled scheduling and energy management challenges, a three-stage bilevel collaborative optimization framework is proposed, enhancing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to develop a Collaborative MOPSO (CMOPSO). The improved algorithm features a four-layer encoding mechanism with energy factors, chaotic mapping for better global search, and adaptive mutation for population diversity. Validation using the Brandimarte benchmark demonstrates the algorithm’s robustness. Specifically, comparative experiments reveal that the proposed strategy significantly outperforms the traditional scheduling mode. While maintaining a similar makespan, the proposed method reduces production costs by 44.3% and carbon emissions by 29%. Simulations confirm that the method effectively shifts tasks to low-price periods and leverages photovoltaic energy during peak hours, supporting the manufacturing industry’s green transition.

The acceleration of the green energy transition has reinforced the importance of reliable, cost-effective hydrogen production technologies. Alkaline water electrolyzers (AWEs) have become a critical option due to their lack of requirement of platinum group metals, as well as their scalability; however, the materials, geometry, and operating conditions used must be comprehensively evaluated alongside electricity costs. This study presents an approach that directly integrates a COMSOL-based electrochemical polarization model with a techno-economic module and validates the results against published U–J curves and 2024 public LCOH ranges. The scans across the 25 kW–10 MW range show that temperature and separator porosity are the most powerful factors affecting performance; narrow cell gaps significantly reduce ohmic losses, and the electrolyte concentration provides limited additional benefit beyond a certain threshold. KOH outperforms NaOH under most conditions, but the difference between the two electrolytes narrows as temperature increases. Economic analyses confirm that electricity price is the dominant determinant of LCOH; levels of 4–5 $·kg−1 are achievable at the MW scale, while high-cost scenarios reach 7–10 $·kg−1. In conclusion, the study provides a validated and scalable framework for the joint optimization of AWE design and operation.

The glass industry faces significant challenges in achieving carbon neutrality due to its reliance on fossil fuels and process-related CO2 emissions from raw material decomposition. While most defossilization efforts focus on CO2-neutral heating, batch-related emissions remain largely unaddressed. This study investigates a closed carbon cycle approach for glass manufacturing by integrating carbon capture and utilization (CCU) with power-to-gas technologies. The proposed process captures both combustion- and batch-related CO2 emissions and converts them into synthetic natural gas using renewable hydrogen. The techno-economic model, based on a typical oxy-fuel container glass furnace (300 t per day) and current (2022) German market conditions, covers all key process steps: flue gas cleaning, CO2 separation, hydrogen production via electrolysis, and methanation. Results show that more than 99 % of scope 1 emissions and about 62% of scope 1+2 emissions can be abated. However, the process is associated with high energy demand and costs, with energy supply alone amounting to €559 (2022) per metric ton glass at an electricity price of €60 per MWh. The cost of CO2 abatement is estimated at €1132 (2022) per metric ton. While all process steps are based on established industrial technologies, the overall economic viability remains highly sensitive to electricity prices and further technological improvements. The approach is especially relevant for high-quality glass production with low cullet content and in regions with abundant renewable electricity.