Search Results (3,639)

The Vehicle-to-Grid (V2G) technology is considered one of the best solutions for integrating renewable energy systems; however, most literature reports favorable economic results using synthetic data, without accounting for seasonal or market limitations. The current research presents the results of the MATLAB R2023b (Version 23.2, MathWorks, Natick, MA, USA) simulation of the 100-household energy community in Debrecen, Hungary, with 30 electric vehicles (EVs) using entirely simulation-based Lithium Iron Phosphate (LiFePO4) batteries, a simulation-based 150 kW solar photovoltaic (PV) system, and a simulation-based 200 kW wind power system, using real meteorological data for January 2024. The optimization of charging/discharging for electric vehicles was performed using a multi-objective genetic algorithm (GA) over 30 days at a 15 min time resolution, accounting for stochastic loads and temperature effects on battery degradation, with a sensitivity analysis of key parameters. The results of the optimized solution for the electric vehicle charging/discharging were unexpected: the total energy cost increased by 68.9% ($4337.65 to $7327.54), the peak demand increased by 266.2% (31.9 to 116.9 kW), the degradation cost was $479.63, the load factor was reduced from 0.847 to 0.722, and the SOC constraint was violated for 0.758% of measurements. The V2G is not economically viable under current Hungarian pricing and Central Europe winter conditions. Results are robust for varying parameters using sensitivity analysis and Pareto front tracing. The break-even point is achieved when ratios of peak-to-off-peak prices are above 3.5:1. Seasonal policies and market reforms are critical for V2G viability. Importantly, the influence of inherent design deficiencies in the optimization model on the reported results cannot be ruled out. Full article

Modern power systems increasingly integrate renewable energy sources (RESs), electric mobility, and dynamic market participation. Dynamic electricity pricing, reflecting real-time market conditions, is increasingly important for prosumers worldwide, enabling flexible and efficient energy management. The growing adoption of electric vehicles (EVs) and bidirectional charging technologies (V2G, V2H) allows EVs to act as mobile battery energy storage systems (mBESSs). This study presents a Python 3.11-based application for simulating and analyzing energy flows in residential systems with photovoltaic (PV) installations, EVs acting as mBESS, and optional stationary battery energy storage systems (BESSs), using real 2024 data on consumption, PV production, and market prices. The energy management system (EMS) employs a rule-based algorithm to optimize energy use and economic benefits, adjusting dispatch between PV systems, the grid, mBESSs, and BESSs based on price coefficients $\alpha$ and $\beta$. Simulation scenarios were developed based on two EV availability patterns: Profile 1, representing users unavailable during standard working hours, and Profile 2, representing users with intermittent availability for brief excursions. The results demonstrate substantial electricity cost reductions: For a Nissan Leaf e+ with Profile 1, annual costs decrease by approximately 20% compared to a system without EVs. With PV generation and Profile 2, costs drop by 57% relative to the baseline, while adding a stationary BESS further reduces costs by nearly 95%. It should be noted that the results were obtained assuming zero energy costs for propulsion. Therefore, the economic benefits reported here represent an upper-bound estimate and would be lower under real-world driving conditions. These findings highlight that coordinated EMS operation with EVs as mBESSs, supported by optional BESSs, can maximize economic performance and provide prosumers with a practical framework for flexible and efficient energy management. Full article

To address the coupled challenges of renewable power volatility, high operating cost, and electrolyzer degradation in grid-connected wind–PV hydrogen production systems, this paper proposes a hierarchical day-ahead scheduling strategy under time-of-use (TOU) electricity prices. The upper layer performs price-responsive economic dispatch to coordinate renewable utilization, battery operation, grid transactions, and aggregate hydrogen-production power with the objective of minimizing lifecycle operating cost. The lower layer introduces a health-aware non-uniform rotation mechanism to allocate the aggregate power command among electrolyzer units, thereby reducing fluctuation exposure and balancing lifetime consumption across the array. Practical constraints, including multi-state electrolyzer operation, unit-commitment logic, battery state-of-charge dynamics, hydrogen storage limits, and system power balance, are explicitly considered. A case study of a wind–PV hydrogen production project in Northern China shows that the proposed strategy shifts electricity purchases to valley-price periods and promotes electricity export during peak-price periods. Compared with the benchmark strategy, hydrogen production during low wind–PV generation periods increases from 342,000 to 381,000 Nm³, the share of fluctuating operating time decreases from 62.5% to 12.5%, and the average daily start–stop frequency declines from 8.0 to 4.8. Consequently, the degradation penalty is reduced by about 40%, and lifecycle operating cost decreases by 27.3%. Full article

This study presents a structured methodology for mid-term electricity demand forecasting in the Greek interconnected power system, incorporating climate-sensitive and socio-economic variables. A set of linear regression models was developed to produce forecasts at both monthly and daily resolutions, aiming to balance accuracy with transparency and computational efficiency. Monthly demand was modeled using macro-trend variables such as GDP, population, and energy prices, while daily demand was approached through a disaggregated modeling structure, assigning a distinct regression model to each day of the week. Temperature effects were introduced at both levels using cooling and heating degree days, estimated based on seasonal weather forecasts provided by 51 meteorological models. The modeling approach developed shows a high predictive value. The monthly electricity demand forecast over a six-month horizon exhibits a mean absolute percentage error and a maximum error of approximately 1.4% and 3.9%, respectively, when actual meteorological data are employed, and 3.7% and 8.5%, respectively, when seasonal meteorological forecasts are used for the entire year 2022, in which it has been tested. Adjusting the model for projecting, the monthly peak load in the same time horizon, presents less accurate yet satisfactory results, with a mean and maximum error of 2.9% and 9.6%, respectively, when actual meteorological data are used, and 5.3% and 12.9%, respectively, when seasonal meteorological forecasts are employed. Full article

Electricity price forecasting has become a critical tool for decision-making in energy markets, particularly as the increasing penetration of renewable energy has introduced greater volatility and uncertainty. Historically, research in this field has been dominated by point forecasting methods, which provide single-value predictions but fail to quantify uncertainty. However, as power markets evolve due to renewable integration, smart grids, and regulatory changes, the need for probabilistic forecasting has become more pronounced, offering a more comprehensive approach to risk assessment and market participation. This paper presents a review of probabilistic forecasting methods, tracing their evolution from Bayesian and distribution based approaches to quantile regression techniques to recent developments in conformal prediction. Particular emphasis is placed on advancements in probabilistic forecasting, including validity-focused methods that address key limitations in uncertainty estimation. Additionally, this review extends beyond the day-ahead market to include the intra-day and balancing markets, where forecasting challenges are intensified by higher temporal granularity and real-time operational constraints. We examine state-of-the-art methodologies, key evaluation metrics, and ongoing challenges, such as forecast validity, model selection, and the absence of standardised benchmarks, providing researchers and practitioners with a comprehensive and timely resource for navigating the complexities of modern electricity markets. Full article

The extreme volatility of European energy markets in 2022 has exposed the limitations of conventional forecasting models, necessitating more robust architectures capable of handling non-linear price shocks. This study proposes a novel multi-parallel hybrid forecasting framework that integrates seven heterogeneous neural networks to predict day-ahead electricity prices. The architecture employs a hierarchical approach where six parallel base models (NN1–NN6) feed into a meta-network (NN7) to generate baseline forecasts. To further enhance predictive fidelity, these results undergo a calibration stage using probabilistic error distribution analysis to produce final probability-adjusted forecasts. The model was validated using the Lithuanian electricity market during the highly volatile period of 2020–2022. Empirical results demonstrate a clear “stacking effect,” where the incremental integration of base networks consistently reduces forecasting residuals. The final probability-adjusted configuration achieved a notable nMAE of 1.57% and a sMAPE of 34.25%, significantly outperforming baseline ensemble outputs and state-of-the-art benchmarks reported in recent literature. Specifically, the probability-based refinement proved highly effective in mitigating systematic biases during nighttime and early morning hours, confirming the model’s capacity to maintain accuracy under extreme market stress. Full article

The increasing frequency and severity of extreme droughts caused by climate change has emerged as a key risk factor exerting complex effects on the overall national economy through a structure of interconnected industries. The Water Input–Output Linear Programming (WIOLP) model was applied to data from 2015 to 2018 to quantitatively assess the effects of drought-induced water use constraints on production and socioeconomic potential losses. By modeling scenarios in which water use decreased by 10% from 100%, changes in the gross output, the value added, the socioeconomic potential loss, and the shadow price by industry were evaluated. Results showed that socioeconomic potential losses increased nonlinearly, with maximum potential losses of 311,118 billion Korean Won (KRW) in 2015 and 355,260 billion KRW in 2018. The shadow price rose from 7311 to 73,186 KRW/m³ in 2015 and from 3291 to 89,586 KRW/m³ in 2018, confirming that the marginal productivity of water increased exponentially under stricter constraints. Industry-level analysis revealed the largest losses in high water use industries (e.g., agriculture, forestry, fisheries, chemicals, and non-metals), whereas electricity, electronics, and machinery sectors maintained relatively stable production. This study demonstrates that the WIOLP model can empirically analyze nonlinear economic ripple effects under resource constraints, overcoming limitations of conventional input–output and computable general equilibrium models. Full article

Accurate estimation of the mean-reversion speed $\alpha$ in the AR(1) process $X_{t+1}=(1-\alpha )X_t+{\epsilon }_t$ is central to energy-commodity modelling. Classical estimators such as GARCH, jump-diffusion, and regime-switching produce model-conditioned estimates by embedding $\alpha$ within distributional assumptions, so that different model choices yield different $\widehat{\alpha }$ values from the same series without a principled criterion to adjudicate. We propose a distribution-free neural estimator based on a Temporal Convolutional Network (TCN) trained on synthetic AR(1) series with Sinh-ArcSinh (SAS) innovations. Distribution-free here means that no parametric family is assumed for the innovation distribution at inference time: the estimator imposes no distributional hypothesis when processing a new series. The SAS family serves as a training vehicle—not a model for the real data—chosen for its ability to span a broad range of tail weights and asymmetry profiles. The theoretical foundation is spectral invariance: the Yule–Walker equations establish that the autocorrelation structure ${\rho }_k={(1-\alpha )}^k$ depends on $\alpha$ alone, provided innovations are uncorrelated across lags—a condition satisfied not only by i.i.d. innovations but also by conditionally heteroscedastic processes such as GARCH. The TCN therefore generalises to volatility-clustering environments without modification, learning to extract $\alpha$ from temporal dependence alone, independently of the marginal innovation distribution and of the temporal variance structure. On held-out test series the estimator outperforms all classical competitors, with the advantage growing monotonically with non-Gaussianity. A robustness analysis on three out-of-distribution innovation families and on AR(1)-GARCH(1,1) processes empirically validates the spectral invariance guarantee across both marginal and temporal variance structure, including near-integrated GARCH processes where innovation kurtosis far exceeds the training range. The distribution-free $\widehat{\alpha }$ enables a two-stage pipeline in which $\alpha$ and the innovation distribution are characterised independently—a decoupling structurally impossible in classical likelihood-based approaches. Once trained, the TCN acts as a universal mean-reversion estimator applicable to any price series without re-fitting. Applied to four energy markets—Italian natural gas (PSV price), Italian electricity (PUN price), US Henry Hub, and US PJM West Hub—spanning log-return kurtosis from near-Gaussian to strongly heavy-tailed, the TCN yields robust, distribution-free estimates of mean-reversion speed. Full article

As the electrification of transportation systems accelerates, incentivizing electric vehicle (EV) participation in vehicle-to-grid (V2G) operations is becoming increasingly crucial. This paper introduces a dynamic EV charging and discharging strategy that incorporates integrated energy station (IES) congestion and electricity purchase and sale scenarios. The proposed strategy seeks to facilitate orderly EV charging and discharging within a real-time simulation framework that integrates the transportation network (TN), IES, and the external grid (EG). First, we develop a real-time collaborative simulation framework that combines microscopic traffic flow (MTL) and IES–grid energy interaction models to account for mutual feedback among these components. Second, we propose an EV IES selection strategy aimed at maximizing discharge revenue, which takes into account various factors, including driving distance, time costs, battery degradation, discharge benefits, and government subsidies. Finally, we design a dynamic discharge pricing model based on real-time vehicle arrival patterns at the IES and the status of electricity purchases and sales. Simulation results show that the EV IES selection strategy, optimized for discharge revenue, reduces average user waiting time by 5.36%, decreases network time loss by 3.86%, and increases EV discharge revenue by 6.79%. Furthermore, the introduction of dynamic pricing leads to additional reductions in waiting time and network time loss by 3.46% and 4.80%, respectively. The proposed mechanism and pricing strategy effectively mitigate traffic congestion, enhance user discharge revenue, and provide flexible scheduling options for IES operations. Full article

To address the challenges posed by multiple uncertainties in modern power systems to the market bidding of Virtual Power Plants (VPPs), this paper proposes an adaptive bidding strategy based on Deep Reinforcement Learning (DRL). First, a heterogeneous VPP aggregation model integrating dedicated energy storage, Vehicle-to-Grid (V2G), and flexible loads is constructed, incorporating complex physical and operational constraints. Second, to overcome the “myopic” local optimality problem of traditional DRL in temporal arbitrage tasks, a potential-based reward shaping mechanism linked to future price trends is designed to guide the agent toward long-term optimal strategies. Finally, multi-dimensional comparative experiments and mechanism analyses are conducted in a simulated day-ahead electricity market. Simulation results demonstrate the following: (1) The proposed algorithm exhibits robust convergence stability and effectively handles stochastic noise in market prices and renewable generation. (2) Economically, the strategy significantly outperforms the rule-based strategy and remains highly competitive with the deterministic-optimization benchmark under perfect-information assumptions. (3) Mechanism analysis further reveals that the DRL agent breaks through the rigid logic of fixed thresholds, learning a non-linear dynamic game mechanism based on “Price-SOC” states, thereby achieving full-depth utilization of energy storage resources. This work provides an interpretable data-driven paradigm for intelligent VPP decision-making in uncertain environments. Full article

This paper describes the benchmark dataset for the electric vehicle routing problem with time windows. It is designed to facilitate the large-scale and reproducible evaluation of routing approaches under diverse charging scenarios. It is an extension of the Homberger 1000-customer vehicle-routing benchmark dataset through the incorporation of computationally derived charging-station data. For the 60 base instances included in the dataset, charging-station locations are randomly generated within the customer-coordinate bounds, and two variants are provided, resulting in 120 benchmark problems used in the validation and baseline analyses. A normalized local customer-density score is derived for each station. It is used to determine charging rates and log-normal parameters for prices and waiting times. Two variants are included in the dataset. Variant A maintains the original customer time-window constraints, while Variant B relaxes customer due dates based on the distance from the depot, subject to the depot closing time. The dataset is complemented by instance files, station attributes, parameters, and scripts. It also includes the results of feasibility tests, baseline solver tests, difficulty analyses, and sensitivity tests. These results show that the benchmark includes both easier and harder instance classes under different charging settings. Overall, the dataset is intended to support its use as a reproducible benchmark. Full article

Strategic bidding for wind–battery hybrid systems is increasingly critical as electricity spot markets transition toward market-oriented mechanisms, particularly in Chinese pilot regions. However, dual uncertainties—wind generation variability and volatile locational marginal prices (LMPs)—expose market participants to significant financial tail risk. This study develops a risk-constrained reinforcement learning framework for optimal bidding of wind–storage hybrid systems. We employ soft actor–critic (SAC) for continuous action control and integrate conditional value-at-risk (CVaR) into reward design to explicitly penalize low-probability, high-loss outcomes. The framework incorporates realistic operational constraints, including linearized battery degradation costs and a market-compatible single-bid abstraction for hourly settlement. Using one-year historical operational data from a 150 MW wind farm (with a 91-day test period), we find that storage integration increases annual profit by 108.4–114.2% relative to wind-only operation. Critically, the SAC–CVaR policy (η = 0.35) preserves 97.3% of risk-neutral profit ($7.71 M vs. $7.93 M) while substantially mitigating downside risk: CVaR@95% improves by 42.4% (−$549 vs. −$952) and VaR@95% improves by 30.1% (−$275 vs. −$393). The trained policy achieves sub-millisecond inference (0.262 ms per decision, ~3820 decisions/s), corresponding to a 3.8 × 10⁴–5.7 × 10⁴× speedup over optimization-based solvers (10–15 s per decision), enabling real-time deployment. Behavioral analysis reveals that the agent learns adaptive, forecast-normalized bidding strategies with more conservative reporting in high-price regimes and counter-cyclical battery dispatch patterns, demonstrating effective coordination between profitability and risk control under volatile market conditions. Full article

To address the issues that carbon trading fails to cover the full life cycle and that traditional demand response achieves poor emission reduction due to a lack of accurate carbon-intensity feedback in park integrated energy systems (PIESs) during low-carbon transition, this study proposes a two-layer optimal scheduling method synergizing life-cycle stepwise carbon trading and low-carbon demand response (LCDR) to balance low-carbon performance and economic efficiency. Firstly, based on life cycle theory, embodied carbon from new energy equipment manufacturing and transportation is incorporated into accounting, with a stepwise carbon trading mechanism designed. Secondly, corrected dynamic carbon emission factors for power and heating networks are constructed to quantify real-time carbon intensity. A dual-driven LCDR model (electricity price and carbon factor) is established to coordinate shiftable and sheddable electric-thermal loads and is combined with a two-layer scheduling model (pre-scheduling and re-scheduling) targeting the minimal total operation cost. Simulation results of a South China park show that life-cycle stepwise carbon trading reduces emissions by 16.7%, and LCDR further cuts 4.05%. Their synergy achieves significant carbon reduction with a slight cost increase, while supplementary sensitivity analyses further confirm the scalability and robustness of the proposed framework under varying load levels and demand response capabilities. Full article

One of the main goals of heat and electricity producers in Latvia is to reduce the use of fossil fuels and introduce alternative fuel types that could help in reducing carbon dioxide emissions. This work focuses on addressing the set issue for a medium-capacity automated gas boiler plant, which provides heat for a local residential district. The following solutions were selected for boiler plant optimization: an electric boiler, a heat storage system, and solar collectors. Operating mode simulations were conducted for the electric boiler and solar collectors using Excel and Polysun (Standard) software. Simulations were created based on energy resource demand data obtained from a residential district located in Latvia and local energy resource prices/heat energy tariffs for the year 2024. The results from the simulations were used for technical and economic calculations to determine the payback period of the project. The electric boiler, together with the thermal energy storage tank and solar collectors, can produce 5903.04 MWh/year (~70% of local district heat demand) of thermal energy. This reduces the CO₂ emissions of the boiler plant by at least 1186.51 tCO₂ per year, which, at an emission quota price of 63.80 EUR/tCO₂, allows for savings of 75,699.34 EUR per year (12.82 EUR/MWh heat energy). The project’s discounted payback period is 4.12 years, considering the reduction in the cost of the CO₂ emission quota. The results of this study show that the chosen technologies are straightforward solutions that can be used to optimize existing boiler plants with limited space and can provide financial benefits to heat energy producers. Full article

The rapid growth of renewable energy and the inherent volatility of wind power grid integration have imposed stringent requirements on power system security and economic operation. To address this challenge, energy storage systems (ESSs) are widely adopted as flexible regulation tools; however, their high capital costs make the shared energy storage model a more efficient and viable solution. This paper proposes an optimal configuration model for wind farms participating in shared energy storage (SES) based on cooperative game theory. First, integrating wind power output forecasting data and market electricity price information, a wind-storage combined optimization model accounting for wind power uncertainty is first established. Subsequently, a core pricing strategy integrating the core allocation rule with the Vickrey–Clarke–Groves (VCG) auction mechanism is proposed to realize the fair allocation of energy storage resources and effective revenue incentives. Finally, comparative experiments between the proposed core pricing mechanism and the fixed pricing mechanism verify its superiority in terms of social welfare, budget balance, and allocation fairness. The results demonstrate that the proposed mechanism not only enhances the overall social benefits of the wind-storage system but also effectively ensures the incentive compatibility of all participants and the stability of the alliance, providing feasible theoretical and methodological support for the economic dispatch of wind-farm-shared energy storage. Full article