Electricity load forecasting is of high importance for electricity management. Modern power systems are complex and diverse, resulting in increased randomness and nonlinear factors of electricity load data, which greatly increases the difficulty of forecasting. This paper proposes a hybrid-deep-learning-based load forecasting method, named DCFformer (DFT-CNN-FEDformer), for short-term load forecasting (STLF) tasks. The method first employs the discrete Fourier transform (DFT) to denoise time-sequence data on electricity load, so that fluctuations caused by incidents can be reduced. Secondly, it utilizes a convolutional neural network (CNN) that produces sequences of local features extracted from the denoised time sequences. Thirdly, a FEDformer network is applied to perform load forecasting by using extracted feature sequences. In the experiments, we utilize datasets from three regional power systems or apparatuses to compare the proposed DCFformer with other approaches, and the results show that, under the same conditions, DCFformer outperforms the competitors in forecasting precision, which proves the significance of its performance and practicality.
Amirhosein Hayati, Sina Samadi Gharehveran, Kimia Shirini
Abstract Accurate electricity price forecasting is essential for optimizing market operations, enhancing resource allocation, and ensuring sustainable energy management in volatile and complex markets. This research introduces a comprehensive ensemble meta-modeling framework that integrates machine learning techniques with SHAP (SHapley Additive exPlanations) for enhanced interpretability and PCA (Principal Component Analysis) for effective dimensionality reduction. The methodology capitalizes on the complementary strengths of predictive models such as XGBoost, LSTM, and CNN to address the non-linear and temporal intricacies of electricity price datasets. Two ensemble approaches were implemented: (1) Weighted Averaging, assigning weights inversely proportional to model RMSE, achieving an RMSE of 2.126761, and (2) Meta-Model Ensemble, employing Linear Regression, achieving superior accuracy with an RMSE of 1.939032. SHAP analysis provided actionable insights into model contributions, highlighting XGBoost and LSTM as key components. Furthermore, error trajectory analysis demonstrated the robustness of the ensembles in minimizing cumulative forecasting errors over time. This study contributes to the field by combining advanced machine learning models, ensemble strategies, and explainability frameworks to deliver an interpretable, high-performing electricity price forecasting system. The results inform policy-making and lay the foundation for scalable, data-driven energy market solutions.
To address the issues of carbon trading cost allocation and emission reduction incentives in integrated energy system, a low-carbon economic dispatch strategy considering multi-agent green certificate-carbon trading bidirectional interaction and cost allocation is proposed. First, by introducing green certificate trading and a tiered carbon emission trading mechanism, a bidirectional interaction model for green certificates and carbon quotas is constructed, and the low-carbon value of electricity-heat-hydrogen hybrid energy storage is quantified. Second, a multi-agent carbon trading cost allocation model is designed, and a leader-follower game model is established with energy marketer as the leader and energy supplier and load aggregator as followers. Dynamic time-of-use carbon pricing is used to guide the optimization of equipment output and energy consumption strategies. On this basis, a triple incentive strategy based on electricity price incentives, green certificate revenue, and carbon trading compensation is proposed to enhance the economic feasibility of electricity-heat-hydrogen hybrid energy storage. Additionally, a lifespan degradation model is established to more accurately evaluate its long-term operational costs.Research findings indicate that adopting a dual-interaction mechanism combining green certificates and tiered carbon trading reduced the comprehensive operating costs of IES by 1.72% and lowered carbon emissions by 0.53%. The triple incentive strategy increased the operational revenue of the electricity-heat-hydrogen hybrid energy storage system by 76.16%, while improving the utilization rates of wind and solar power generation by 1.26% and 2.6%, respectively. The multi-party carbon trading cost-sharing mechanism boosted the system's overall total revenue by 3.71%, reduced total costs by 11.97%, lowered carbon trading costs by 21.06%, and decreased total carbon emissions by 19%.
Saudi Arabia is currently implementing fuel price reforms. The reforms are being executed in phases, where the ultimate goal is to have fuel prices approach their market equivalent values. Using the KAPSARC Energy Model, this analysis shows that such reforms would be more costly for Saudi Arabia without the availability and cost reductions of renewable electricity technologies. Solar photovoltaic technologies, in particular, have made enacting fuel price reforms more tenable. As the use of oil products for power generation and industrial processes cease due to price reforms, renewable technologies mitigate the scarcity of natural gas. For instance, lower natural gas use by the electricity sector would result in a market-clearing (meaning, demand equals supply) natural gas price of 3–4 $/mmBtu in 2040 with the deployment of renewable technologies. The projected rise in Saudi natural gas supply and these prices are sufficient to accommodate nearly 50 GW of renewable electricity capacity. Comparatively, this natural gas price would be above 7 $/mmBtu without renewable electricity. The advent of inexpensive renewable electricity lowers fuel costs for all natural gas users in industry and utilities. Moreover, the marginal electricity generation cost with renewable electricity drops by 30 %, on average. While renewable electricity reduces energy system costs, the magnitude of these benefits is highly dependent on the exogenously-defined domestic gas availability and the global LNG price. These findings suggest aligning fuel price regulations with renewable electricity deployment to minimize cost shocks and reduce oil use in industrial and utility sectors.
To address the challenges of source-load uncertainty and insufficient scheduling flexibility in microgrids， an optimization method for microgrid operation based on deep reinforcement learning （DRL） is proposed. First， a microgrid model comprising photovoltaic （PV）， energy storage， and generation equipment is constructed， along with its constraint conditions. Second， a multi-objective optimization framework is established to minimize operating costs and imbalance of the system， considering uncertainties such as PV generation， load demand， and electricity prices. The twin delayed deep deterministic policy gradient （TD3） algorithm is employed to derive microgrid scheduling strategies in a data-driven manner. Third， a penalty term for high-proportion erroneous actions is incorporated into the reward function to constrain the output of each device within a reasonable range， mitigating the risk of insufficient safety guarantees inherent in reinforcement learning methods. Finally， simulation results demonstrate that， compared to the deep deterministic policy gradient （DDPG） algorithm， the proposed method achieves superior economic efficiency and stability， with economic costs closer to those of ideal deterministic optimization methods.
In this paper, a dual-band circularly polarized (CP) shared-aperture microstrip patch antenna with a high frequency ratio of 4.1 is presented for S-/X-band satellite communications. The unit cell of the designed antenna is composed of a single perforated S-band trimmed patch and four X-band trimmed patches configured in a 2 × 2 array. The S- and X-band patches are interlaced, thus sharing their apertures within the same layer, which enhances isolation between the two bands and prevents the degradation of CP characteristics. The antenna was designed and optimized for the S-band frequency range of 1.9–2.05 GHz and the X-band frequency range of 7.8–9 GHz. To verify feasibility, the performance of the proposed antenna was measured in a full anechoic chamber. The measured results showed good agreement with the simulated results. The −10 dB impedance bandwidths of the S- and X-band antennas were 7.8% and 13.5%, respectively, while their measured 3-dB axial ratio bandwidths were 2% and 3.9%, respectively. Furthermore, the unit cell antenna achieved left-handed circularly polarized peak gains of 5.7 dBic at 2.02 GHz and 11.6 dBic at 8.28 GHz.
Electrical engineering. Electronics. Nuclear engineering, Electricity and magnetism
Malte Schäfer, Caroline Herlev Gebara, Anders Bjørn
et al.
The current greenhouse gas accounting rules for reporting emissions associated with electricity consumption (known as ‘scope 2’ emissions) have been criticised for failing to differentiate between impactful and non-impactful actions, and for producing inaccurate value chain inventories. One proposed solution to these problems is to introduce an additionality test to demonstrate that there is a causal relationship between a reporting company and the emissions rate that it reports. However, few accounting proposals provide detailed information on how to test for additionality. This paper aims to contribute to discussions on this issue by identifying the range of existing additionality tests that could be used in this context and the results reveal nine relevant types of additionality test. For each type, we discuss the rationale behind it, list illustrative examples, and identify challenges related to applying the test. As two novel contributions, this paper (1) identifies cases where additionality tests have already been applied to electricity attribute procurement and reporting, and (2) examines a number of concepts and principles for selecting appropriate tests, e.g. balancing incidence of false positives and negatives, with specific reference to scope 2. Although this paper is focused on scope 2 market-based instruments, the identified tests and our discussions of them may also be useful for implementing additionality requirements for other emerging environmental attribute markets.
Efficient Radio-Frequency (RF) stealth is crucial for Dual-Function Radar-Communication (DFRC) systems that detect radar stealth and con vert communication transmission. However, traditional beamforming schemes based on phased arrays and Multiple-Input Multiple-Output (MIMO) systems lack the ability to control the radiation energy in the range dimension, resulting in the facile interception of integrated transmission signals by enemy-owned passive detection systems. To address this issue, a joint transmit-receive beamforming design for Frequency Diversity Array MIMO (FDA-MIMO) DFRC systems is designed herein to achieve RF stealth. First, an integrated transmission signal model based on orthogonal waveform generation, frequency diversity modulation, and weighted transmission beamforming is constructed. The two-dimensional expression of the distance angle between the radar equivalent transmission beam pattern and the communication transmission channel is obtained through matched filtering and reception beamforming. Second, with communication information embedding and communication reachable rate as constraints, a joint optimization model for FDA-MIMO radar communication integrated transmission and reception beams for RF stealth is established. The model aims to simultaneously minimize the equivalent transmission beam power at the radar target and maximize the output signal-to-noise ratio. Finally, a joint optimization algorithm based on Weighted Mean-Square Error Minimization (WMMSE) and the Consensus Alternating Direction Method of Multiplier (C-ADMM) is proposed. Closed form expressions for each variable are derived and combined with convex optimization algorithms to achieve low-complexity solutions. The simulation results show that radar detection and communication transmission using the proposed method form a “point-to-point” pattern on the two-dimensional plane of range and angle, exhibiting good RF stealth capability. Simultaneously, this method can provide high clutter and interference suppression performance as well as a low communication bit error rate.
This study analyzes the influence of adequate electricity supply on the industrial sector in developing nations, utilizing panel data from 2000 to 2022. Contrary to original beliefs, the study examines industry output as the dependent variable, with renewable energy as the main explanatory factor. The study incorporated control variables such as CO2 emissions, government expenditure, GDP per capita, labor force participation, and gross capital formation. The investigation included panel Autoregressive Distributed Lag (ARDL) models, unit root tests, and causality tests. In emerging countries, industrial growth is positively impacted by government spending, labor force involvement, CO2 emissions, and GDP per capita. Developed countries demonstrate favorable impacts on industrial growth through gross fixed capital formation, renewable energy, and other factors, as indicated by the long-term outcomes of the ARDL method. Policymakers in developing nations may contemplate raising government spending in pertinent sectors, encouraging worker engagement, and enacting laws to decrease CO2 emissions based on these findings. Developed countries' authorities should prioritize improving gross fixed capital creation, integrating more renewable energy sources, and sustaining factors boosting industry growth.
Energy industries. Energy policy. Fuel trade, Energy conservation
Amir F. N. Abdul-Manan, Victor Gordillo Zavaleta, Avinash Kumar Agarwal
et al.
India’s plans to electrify transport is complicated by its reliance on coal-power. Here the authors call for diverse policy and technology solutions, including a focus on cleaner grids, electric 2-wheelers, and hybrid 4-wheelers in the near-term.
In experimental studies of laser-plasma interactions, the laser radiation can exist inside plasma regions where the electron density is below the critical density (“underdense” plasma), as well as at the surface of the critical density. The surface of the critical density could exhibit a rich physics. Namely, the incident laser radiation can get converted in transverse electromagnetic waves of significantly higher amplitudes than the incident radiation, due to various nonlinear processes. We proposed a diagnostic method based on the laser-produced satellites of hydrogenic spectral lines in plasmas. The method allows measuring both the laser field (or more generally, the field of the resulting transverse electromagnetic wave) and the opacity from experimental spectrum of a hydrogenic line exhibiting satellites. This spectroscopic diagnostic should be useful for a better understanding of laser-plasma interactions, including relativistic laser-plasma interactions.
Piezoelectric cantilever beams, which have simple structures and excellent mechanical/electrical coupling characteristics, are widely applied in energy harvesting. When the piezoelectric cantilever beam is in a wind field, we should consider not only the influence of the wind field on piezoelectric beam but also the electromechanical coupling effect on it. In this paper, we design and test a wind-induced flag-swing piezoelectric energy harvester (PEH). The piezoelectric cantilever beam may vibrate in the wind field by affixing a flexible ribbon to the free end as the windward structure. To fulfill the goal of producing electricity, the flexible ribbon can swing the piezoelectric cantilever in a wind-induced unstable condition. The experimental findings demonstrate that the flag-swing PEH performs well in energy harvesting when the wind field is excited. When the wind speed is 15 m/s, the peak-to-peak output AC voltage may reach 13.88 V. In addition, the voltage at both ends of the closed-loop circuit’s external resistance is examined. The maximum electric power of the PEH may reach 43.4 μW with an external resistance of 650 kΩ. After passing through the AC-DC conversion circuit, the flag-swing PEH has a steady DC voltage output of 1.67 V. The proposed energy harvester transforms wind energy from a wind farm into electrical energy for supply to low-power electronic devices, allowing for the creation and use of green energy to efficiently address the issue of inadequate energy.
Abdurrahman Shuaibu Hassan, Yanxia Sun, Zenghui Wang
Numerous potential advantages to the requirements and effectiveness of the supplied electricity can be accomplished by the installation of distributed generation units. In order to take full advantage of these benefits, it is essential to position the Distributed Generation (DG) units in appropriate locations. Otherwise, their installation may have an adverse effect on the quality of energy and system operation. Several optimization techniques have been created over the years to optimize distributed generation integration. Optimization techniques are therefore constantly changing and have been the main attention of many fresh types of research lately. This article evaluates cutting-edge techniques of optimizing the issue of positioning and sizing distributed generation units from renewable energy sources based on recent papers that have already been applied to distribution system optimization. Furthermore, this article pointed out the environmental, economic, technological and regulatory drivers that lead to a rapid interest in the DG system based on renewable sources. A summary of popular meta-heuristic optimization tools discussed in table form with merits and demerits to increase fresh prospective paths to multi-approach that have not yet been studied.
Before the establishment of the spot electricity market in China, an energy imbalance settlement (EIS) mechanism has been proposed as a transitional solution to address the imbalances between actual consumption and contracted energy in the forward electricity market. It plays a crucial role in cultivating electricity retailers, maintaining the stability of the balancing account, and promoting a smooth transition to market-oriented reforms. Given this background, a novel EIS mechanism with a piecewise linear penalty pricing scheme is proposed, learning from the performance-based regulation (PBR) in distribution systems. For optimizing the parameters in the proposed EIS mechanism, a stochastic bilevel model is presented considering two kinds of stakeholders involved, namely the power exchange (PX) and retailers. In the upper-level model, an optimal parameter setting model for policymakers to minimize the variance of deposit in the balancing account of PX is presented. In the lower-level model, a decision-making tool for retailers under the renewable portfolio standard (RPS) is developed. As a self-balance measure of retailers, flexible demands are incorporated into the lower-level problem. Consumer psychology is applied to quantify customer consumption adjustments in response to the financial incentives given by retailers. The risk faced by the retailers is modeled using conditional value-at-risk (CVaR), taking into account the uncertainties associated with renewable energy production and customer demand. Simulation results of a provincial electricity market in China show that the proposed method can effectively motivate retailers to improve their imbalance management capability and assist policymakers in determining the parameters of the EIS mechanism. Besides, the proposed method provides insights into the impacts of parameter setting of the EIS mechanism on the behavior and performance of retailers.
On-line partial discharge (PD) monitoring is being increasingly adopted to improve the asset management and maintenance of medium-voltage (MV) motors. This study presents a novel method for autonomous analysis and classification of motor PD patterns in situations where a phase-reference voltage waveform is not available. The main contributions include a polar PD (PPD) pattern and a fractal theory-based autonomous PD recognition method. PPD pattern that is applied to convert the traditional phase-resolved PD pattern into a circular form addresses the lack of phase information in on-line PD monitoring system. The fractal theory is then presented in detail to address the task of discrimination of 6 kinds of single source and 15 kinds of multi-source PD patterns related to motors, as outlined in IEC 60034. The classification of known and unknown defects is calculated by a method known as centre score. Validation of the proposed method is demonstrated using data from laboratory experiments on three typical PD geometries. This study also discusses the application of the proposed techniques with 24 sets of on-site PD measurement data from 4 motors in 2 nuclear power stations. The results show that the proposed method performs effectively in recognising not only the single-source PD but also multi-source PDs.
Amidst increasingly constrained public budgets and inadequate service delivery, private sector participation through public private partnerships is increasingly being used as a means for delivering physical infrastructure. The government of Uganda, which is currently grappling with a crippling electricity power deficit, has over the years, pursued a number of strategies to encourage private sector participation in the electricity sector, but with limited success. This paper presents the findings of research into the relative importance as perceived by sector stakeholders, of factors that hamstring private sector participation in the development of hydropower generation facilities through public private partnerships in Uganda. The stakeholders considered in this paper are those representing the government and private sector entities in the development of the partnerships. A review of literature and project documents enabled the identification of relevant factors. Data was collected from the respondents by means of a self administered structured questionnaire and quantitative methods used for data analysis. Key findings from the research indicate that the respondents regarded the regulatory and legal frameworks as being attractive for private sector participation and this business environment is further enhanced by their confidence in the government’s commitment to honour its contractual obligations. In contrast, difficulties in structuring and obtaining finance together with issues over the cumbersome approval process and resistance from environmental groups were identified as the most significant constraints to the development and implementation of public private partnerships in the Ugandan electricity sector. Recognizing the importance of an adequate and reliable supply of power in Uganda, as in so many other sub-Saharan countries, it is anticipated that the identification of the relative importance of the constraints as perceived by stakeholders, will inform the process of developing measures and strategies to mitigate the constraints thus facilitating the speedy implementation and deal closure of public private partnership initiatives with the ensuing benefits.
The state of emergency in the energy sector generates technical problems such as: disruption in the operation of machinery and equipment, replacement of equipment or others needs. Besides the technical problems which appear, also intervene economic and financial issues that generate costs for the replacement of machines, equipment and installations and expenses of compensation to third parties who have suffered losses by disconnecting the power supply or lack of electricity over a longer or shorter period. Modern methods of risk management include also economic solutions. Some of these solutions will be treated in this paper.
Advantages and disadvantages of heat-dissipating method of electrical equipments for open-pit shearer were analyzed. According to actual condition of open-pit shearer, filter cartridge deduster was used to ventilate and dissipate heat for frequency-conversion speed-regulation device and step-down transforms of open-pit shearer, and the original dedusting ventilation equipment was used to ventilate and heat operating room. The application of filter cartridge deduster and ventilation equipment in the open-pit shearer, not only ensures reliable operation of open-pit shearer and prolongs service life of components of electrical equipment, but also make heat distributed by electrical equipment recycle and reuse and achieve effect of energy saving.