Search Results (195)

In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article

To fully accommodate renewable and derivative energy sources in mine energy systems under supply and demand uncertainties, this paper proposes an optimized electricity–heat scheduling method for mining areas that incorporates Power-to-Gas (P2G) technology and Conditional Value-at-Risk (CVaR). First, to address uncertainties on both the supply and demand sides, a P2G unit is introduced, and a Latin hypercube sampling technique based on Cholesky decomposition is employed to generate wind–solar-load sample matrices that capture source–load correlations, which are subsequently used to construct representative scenarios. Second, a stochastic optimization scheduling model is developed for the mine electricity–heat energy system, aiming to minimize the total scheduling cost comprising day-ahead scheduling cost, expected reserve adjustment cost, and CVaR. Finally, a case study on a typical mine electricity–heat energy system is conducted to validate the effectiveness of the proposed method in terms of operational cost reduction and system reliability. The results demonstrate a 1.4% reduction in the total operating cost, achieving a balance between economic efficiency and system security. Full article

Under the construction background of carbon peak and carbon neutrality, high-energy-consuming enterprises, represented by the electrolytic aluminum industry, have become important carriers for energy conservation and emission reduction. These enterprises are characterized by significant energy consumption and high carbon emissions, greatly impacting the economic and environmental benefits of regional power grids. Existing research often focuses on grid revenue, leaving high-energy-consuming enterprises in a passive regulatory position. To address this, this paper constructs an economic dispatch strategy for power grids that considers waste heat utilization in high-energy-consuming enterprises. A typical representative, electrolytic aluminum load and its waste heat utilization model, for the entire production process of high-energy-consuming loads, is established. Using a tiered carbon trading calculation formula, a low-carbon production scheme for high-energy-consuming enterprises is developed. On the grid side, considering local load levels, the uncertainty of wind power output, and the energy demands of aluminum production, a robust day-ahead economic dispatch model is established. Case analysis based on the modified IEEE-30 node system demonstrates that the proposed method balances economic efficiency and low-carbon performance while reducing the conservatism of traditional optimization approaches. Full article

With the support of the dual-carbon strategy and related policies, China’s offshore wind power has experienced rapid development. However, constrained by the inherent intermittency and volatility of wind power, large-scale expansion poses significant challenges to grid integration and exacerbates government fiscal burdens. To address these critical issues, this paper proposes a multi-market coupling trading model integrating energy storage-equipped offshore wind power into electricity–carbon–green certificate markets for large-scale grid networks. Firstly, a day-ahead electricity market optimization model that incorporates energy storage is established to maximize power revenue by coordinating offshore wind power generation, thermal power dispatch, and energy storage charging/discharging strategies. Subsequently, carbon market and green certificate market optimization models are developed to quantify Chinese Certified Emission Reduction (CCER) volume, carbon quotas, carbon emissions, market revenues, green certificate quantities, pricing mechanisms, and associated economic benefits. To validate the model’s effectiveness, a gradient ascent-optimized game-theoretic model and a double auction mechanism are introduced as benchmark comparisons. The simulation results demonstrate that the proposed model increases market revenues by 17.13% and 36.18%, respectively, compared to the two benchmark models. It not only improves wind power penetration and comprehensive profitability but also effectively alleviates government subsidy pressures through coordinated carbon–green certificate trading mechanisms. Full article

Forecasting the energy consumption of different consumers became an important procedure with the creation of the European Electricity Market. This study presents a methodology for 24-hour ahead prediction of the energy consumption, which is suitable for application in animal husbandry facilities, such as pig farms. To achieve this, 24 individual models are trained using artificial neural networks that forecast the energy production 1 to 24 h ahead. The selected features include power consumption over the last 72 h, time-based data, average, minimum, and maximum daily temperatures, relative humidities, and wind speeds. The models’ Normalized mean absolute error (NMAE), Normalized root mean square error (NRMSE), and Mean absolute percentage error (MAPE) vary between 16.59% and 19.00%, 22.19% and 24.73%, and 9.49% and 11.49%, respectively. Furthermore, the case studies showed that in most situations, the forecasting error does not exceed 10% with several cases up to 25%. The proposed methodology can be useful for energy managers of animal farm facilities, and help them provide a better prognosis of their energy consumption for the Energy Market. The proposed methodology could be improved by selecting additional features, such as the variation of the controlled meteorological parameters over the last couple of days and the schedule of technological processes. Full article

After primary frequency regulation in large-scale wind farms is completed, the power dip phenomenon occurs during the rotor speed recovery phase. This phenomenon may induce a secondary frequency drop in power systems, which poses challenges to system frequency security. To address this issue, this paper proposes a frequency security-oriented optimal dispatch model for multi-regional power systems, taking into account the risks of secondary frequency drop. In the first stage, risk-averse day-ahead scheduling is conducted. It co-optimizes operational costs and risks under wind power uncertainty through stochastic programming. In the second stage, frequency security verification is carried out. The proposed dispatch scheme is validated against multi-regional frequency dynamic constraints under extreme wind scenarios. These two stages work in tandem to comprehensively address the frequency security issues related to wind power integration. The model innovatively decomposes system reserve power into three distinct components: wind fluctuation reserve, power dip reserve, and contingency reserve. This decomposition enables coordinated optimization between absorbing power oscillations during wind turbine speed recovery and satisfies multi-regional grid frequency security constraints. The column and constraint generation algorithm is employed to solve this two-stage optimization problem. Case studies demonstrate that the proposed model effectively mitigates frequency security risks caused by wind turbines’ operational state transitions after primary frequency regulation, while maintaining economic efficiency. The methodology provides theoretical support for the secure integration of high-penetration renewable energy in modern multi-regional power systems. Full article

The rising share of wind generation in power systems, driven by the need to decarbonise the energy sector, is changing the relationship between wind speed and electricity prices. In the case of Poland, this relationship has not been thoroughly investigated, particularly in the aftermath of the restrictive legal changes introduced in 2016, which halted numerous onshore wind investments. Studying this relationship remains necessary to understand the broader market effects of wind speed on electricity prices, especially considering evolving policies and growing interest in renewable energy integration. In this context, this paper analyses wind speed, wind generation, and other relevant datasets in relation to electricity prices using multiple statistical methods, including correlation analysis, regression modelling, and artificial neural networks. The results show that wind speed is a significant factor in setting electricity prices (with a correlation coefficient reaching up to −0.7). The findings indicate that not only is it important to include wind speed as an electricity price indicator, but it is also worth investing in wind generation, since higher wind output can be translated into lower electricity prices. This study contributes to a better understanding of how natural variability in renewable resources translates into electricity market outcomes under policy-constrained conditions. Its innovative aspect lies in combining statistical and machine learning techniques to quantify the influence of wind speed on electricity prices, using updated data from a period of regulatory stagnation. Full article

Diversified application scenarios and business models are effective ways to improve the utilization and economic benefits of energy storage systems. In response to the current problems of single application scenarios, high idle rates, and imperfect price formation mechanisms faced by energy storage on the power generation side, a robust two-stage optimization operation strategy for shared energy storage is proposed, taking into account leasing demand and multiple uncertainties, from the perspective of the sharing concept. A multi-scenario application framework for shared energy storage is established to provide leasing services for wind farm clusters, as well as auxiliary services for participating in the electric energy markets and frequency regulation markets, and the participation sequence is streamlined. Based on the operating and opportunity costs of shared energy storage, a pricing mechanism for leasing services is designed to explore the driving forces of wind farm clusters participating in leasing services from the perspective of cost assessment. Considering the uncertainty of wind power output and market electric prices, as well as the market operational characteristics, an optimized operation model for shared energy storage in the day-ahead and real-time stages is constructed. In the day-ahead stage, a Stackelberg game model is introduced to depict the energy sharing between wind farm clusters and shared energy storage, forming leasing prices, leasing capacities, and energy storage pre-scheduling plans at different time periods. In the real-time stage, the real-time prediction results of wind power output and electric prices are integrated with scheduling decisions, and an improved robust optimization model is used to dynamically regulate the pre-scheduling plan for leasing capacity and shared energy storage. Based on actual data from the electricity market in Guangdong Province, effectiveness verification is conducted, and the results showed that diversified application scenarios improve the utilization rate of shared energy storage in the power generation side by 52.87%, increasing economic benefits by CNY 188,700. The proposed optimized operation strategy has high engineering application value. Full article

Under the “dual-carbon” strategic framework, the installed capacity of renewable energy sources has continuously increased, while that of conventional generation units has progressively decreased. This structural shift significantly diminishes the operational flexibility of power generation systems and intensifies grid imbalances caused by renewable energy volatility. To address these challenges, this study proposes a carbon-aware multi-timescale virtual power plant (VPP) scheduling framework with coordinated multi-energy integration, which operates through two sequential phases: day-ahead scheduling and intraday rolling optimization. In the day-ahead phase, demand response mechanisms are implemented to activate load-side regulation capabilities, coupled with information gap decision theory (IGDT) to quantify renewable energy uncertainties, thereby establishing optimal baseline schedules. During the intraday phase, rolling horizon optimization is executed based on updated short-term forecasts of renewable energy generation and load demand to determine final dispatch decisions. Numerical simulations demonstrate that the proposed framework achieves a 3.76% reduction in photovoltaic output fluctuations and 3.91% mitigation of wind power variability while maintaining economically viable scheduling costs. Specifically, the intraday optimization phase yields a 1.70% carbon emission reduction and a 7.72% decrease in power exchange costs, albeit with a 3.09% increase in operational costs attributable to power deviation penalties. Full article

The volatility and uncertainty of wind power output pose significant challenges to the safe and stable operation of power systems. To enhance the economic efficiency and reliability of day-ahead scheduling in wind farms, this paper proposes a day-ahead planning and scheduling method for wind/storage systems based on multi-scenario generation and Conditional Value-at-Risk (CVaR). First, based on the statistical characteristics of historical wind power forecasting errors, a kernel density estimation method is used to fit the error distribution. A Copula-based correlation model is then constructed to generate multi-scenario wind power output sequences that account for spatial correlation, from which representative scenarios are selected via K-means clustering. An objective function is subsequently formulated, incorporating electricity sales revenue, energy storage operation and maintenance cost, initial state-of-charge (SOC) cost, peak–valley arbitrage income, and penalties for schedule deviations. The initial SOC of the storage system is introduced as a decision variable to enable flexible and efficient coordinated scheduling of the wind/storage system. The storage system is implemented using a 1500 kWh/700 kW lithium iron phosphate (LiFePO₄) battery to enhance operational flexibility and reliability. To mitigate severe profit fluctuations under extreme scenarios, the model incorporates a CVaR-based risk constraint, thereby enhancing the reliability of the day-ahead plan. Finally, simulation experiments under various initial SOC levels and confidence levels are conducted to validate the effectiveness of the proposed method in improving economic performance and risk management capability. Full article

As the share of renewable energy in modern power systems continues to grow, its inherent uncertainty and variability pose severe challenges to grid stability and the accuracy of traditional thermal power dispatch. To address this issue, this study fully exploits the fast response and flexible operation of variable-speed pumped storage (VS-PS) by developing a day-ahead scheduling model for a wind–photovoltaic–thermal–VS-PS system. The optimization model aims to minimize system operating costs, carbon emissions, and thermal power output fluctuations, while maximizing the regulation flexibility of the VS-PS plant. It is assessed using the improved multi-objective jellyfish search (IMOJS) algorithm, and its effectiveness is demonstrated through comparison with a fixed-speed pumped storage (FS-PS) system. Simulation results show that the proposed model significantly outperforms the traditional FS-PS system: it increases renewable energy accommodation capacity by an average of 68.51%, reduces total operating costs by 14.13%, and lowers carbon emissions by 3.63%. Full article

Accurate recognition of both cold wave occurrence and wind power during cold wave events is critically significant to ensure the stable operation of the power system under extreme meteorological conditions. In view of the above problems, this paper proposes a cold wave event recognition method and a day-ahead wind farm cluster power forecasting model. In order to effectively recognize cold wave events, this paper proposes a cold wave discrimination criterion based on wind turbine operation characteristics and NWP data. The proposed recognition criterion employs seasonal meteorological features processed through a GAN-enhanced multi-modal U-Net architecture, effectively mitigating sample scarcity issues. To improve the forecasting accuracy of wind farm cluster power during cold wave events, a combined forecasting model based on the Ns-Transformer model is constructed by combining NWP data with cold wave recognition results. A wind farm cluster is taken as an example to verify the effectiveness of the proposed method. Compared to that of LSTM, Random Forest, BP, and Transformer models, the RMSE of the proposed method is reduced by 5.65%, 5.58%, 4.81%, and 0.44%, respectively, during cold wave occurrence seasons. The results show that the proposed method is superior to the conventional method in terms of cold wave recognition ability and wind power forecasting accuracy. Full article

Accurate interval forecasting of wind power is crucial for ensuring the safe, stable, and cost-effective operation of power grids. In this paper, we propose a hybrid deep learning model for day-ahead wind power interval forecasting. The model begins by utilizing a Gaussian mixture model (GMM) to cluster daily data with similar distribution patterns. To optimize input features, a feature selection (FS) method is applied to remove irrelevant data. The empirical wavelet transform (EWT) is then employed to decompose both numerical weather prediction (NWP) and wind power data into frequency components, effectively isolating the high-frequency components that capture the inherent randomness and volatility of the data. A convolutional neural network (CNN) is used to extract spatial correlations and meteorological features, while the bidirectional gated recurrent unit (BiGRU) model captures temporal dependencies within the data sequence. To further enhance forecasting accuracy, a multi-head self-attention mechanism (MHSAM) is incorporated to assign greater weight to the most influential elements. This leads to the development of a day-ahead wind power interval forecasting model based on GMM-FS-EWT-CNN-BiGRU-MHSAM. The proposed model is validated through comparison with a benchmark forecasting model and demonstrates superior performance. Furthermore, a comparison with the interval forecasts generated using the NPKDE method shows that the new model achieves higher accuracy. Full article

With an increasing global emphasis on reducing carbon emissions and enhancing energy efficiency, the rising popularity of electric vehicles (EVs) has played a pivotal role in facilitating the transition to electrification within transportation sectors. However, the variability in their charging behavior has posed challenges for grid loads. In this study, a day-ahead scheduling model is developed for an integrated energy system to assess the impact of various electric vehicle charging modes on energy economics during typical days in summer, winter, and transition seasons. Additionally, the influence of optimized charging strategies on increasing the utilization of renewable energy and enhancing the operational efficiency of the grid is explored. The findings reveal that the abandonment rates of wind and solar energy associated with the orderly charging mode are 0 during typical days in winter and summer but decrease by 64.83% during the transition seasons. Furthermore, the power purchased from the grid declines by 18.79%, 19.34%, and 53.31% across these seasonal conditions, in respective. Consequently, the total load cost associated with the ordered charging mode decreases by 29.69%, 25.96%, and 43.71%, respectively, for summer, winter, and transition seasons. Full article

With the increasing utilization of renewable energy sources, hydrogen production from complementary wind and solar (HPCWS) systems has become a part of the construction of the integrated energy system (IES). However, renewable energy generation faces uncertainty; in addition, the IES lacks model representation. To solve this problem, this study proposes a carbon day-ahead optimal dispatch model for an integrated energy system with HPCWS and establishes carbon equations for conventional power generation and natural gas. The demand-side response of the IES is considered in conjunction with the objective functions of low-carbon operation and hydrogen storage gain maximization; furthermore, constraints are established to keep the dispatch results of the equipment within reasonable limits. Secondly, the scheduling model requires a faster and more accurate solution algorithm, so an improved particle swarm algorithm is proposed to solve the minimum of the objective function, and the superior convergence speed and accuracy of the algorithm are verified. The comparison of the IES before and after the introduction of HPCWS yields the changes in carbon emission values and hydrogen production before and after the optimization for the respective seasons and scenarios. In addition, the article also discusses the effect of season on the optimization results. Full article