Search Results (333)

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

In the context of urban decarbonization, multi-energy microgrids (MEMGs) are gaining increasing relevance due to their ability to enhance synergies across multiple energy vectors. This study presents a block-based MILP framework developed to optimize the operations of a real MEMG, with a particular focus on accurately modeling the structure of electricity and natural gas bills. The objective is to assess the added economic value of integrating a battery energy storage system (BESS) under the assumption it is employed to provide implicit flexibility—namely, bill management, energy arbitrage, and peak shaving. Results show that under assumed market conditions, tariff schemes, and BESS costs, none of the analyzed BESS configurations achieve a positive net present value. However, a 2 MW/4 MWh BESS yields a 3.8% reduction in annual operating costs compared to the base case without storage, driven by increased self-consumption (+2.8%), reduced thermal energy waste (–6.4%), and a substantial decrease in power-based electricity charges (–77.9%). The performed sensitivity analyses indicate that even with a significantly higher day-ahead market price spread, the BESS is not sufficiently incentivized to perform pure energy arbitrage and that the effectiveness of a time-of-use power-based tariff depends not only on the level of price differentiation but also on the BESS size. Overall, this study provides insights into the role of BESS in MEMGs and highlights the need for electricity bill designs that better reward the provision of implicit flexibility by storage 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

Despite the growing application of storage for curtailment mitigation, its cost-effectiveness remains uncertain. This study evaluates the Levelized Cost of Storage, which also represents an implicit threshold revenue, for Lithium-ion Battery Energy Storage Systems deployed for photovoltaic curtailment mitigation. Specifically, the LCOS is assessed—using a mathematical simulation model—for various curtailment scenarios defined by maximum levels (10–40%), hourly profiles (upper limit and proportional), and growth rates (2, 5, and 10 years) at three storage system capacities (0.33, 0.50, 0.67 h) and two European locations (Cagliari and Berlin). The results indicate that the LCOS of batteries deployed for curtailment mitigation is, on average, comparable to that of systems used for bulk energy storage applications (155–320 EUR/MWh) in Cagliari (180–410 EUR/MWh). In contrast, in Berlin, the lower and more variable photovoltaic generation results in significantly higher LCOS values (200–750 EUR/MWh). For both locations, the lowest LCOS values (180 EUR/MWh for Cagliari and 200 EUR/MWh for Berlin), obtained for very high curtailment levels (40%), are significantly above average electricity prices (108 EUR/MWh for Cagliari and 78 EUR/MWh for Berlin), suggesting that BESSs for curtailment mitigation are competitive in the day-ahead market only if their electricity is sold at a significantly higher price. This is particularly true for lower curtailment levels. Indeed, for a curtailment level of 10% reached in 5 years, the LCOS for a 0.5 h BESS capacity is approximately 255 EUR/MWh in Cagliari and 460 EUR/MWh in Berlin. The study further highlights that the curtailment scenario significantly affects the Levelized Cost of Storage, with the upper limit hourly profile being more conservative. Full article

Battery energy storage systems (BESS) rely on accurate electricity price forecasts to maximize arbitrage profits in day-ahead markets. We examined whether specific forecasting models, ranging from statistical benchmarks to machine learning methods, consistently deliver superior financial outcomes for storage operators. Using real market data from the Italian day-ahead electricity market over 2020–2024, we compared univariate singular spectrum analysis (SSA), ARIMA, SARIMA, random forests, and a 30-day simple moving average under a unified trading framework. All models were evaluated based on their ability to generate arbitrage profits. Univariate SSA clearly outperformed all alternatives, achieving on average 98% of the theoretical maximum profit while maintaining the lowest forecast error. Among the other models, simpler approaches performed surprisingly well: they achieved comparable, if not superior, profit performance to more complex, hour-specific, or computationally intensive configurations. These results were robust to plausible variations in battery parameters and retraining schedules, suggesting that univariate SSA offers a uniquely effective forecasting solution for battery arbitrage and that simplicity can often be more effective than complexity in operational revenue terms. Full article

As an essential platform for aggregating and coordinating distributed energy resources (DERs), the virtual power plant (VPP) has attracted widespread attention in recent years. With the increasing scale of VPPs, energy interaction and sharing among VPP clusters (VPPCs) have become key approaches to improving energy utilization efficiency and reducing operational costs. Therefore, studying the coordinated operation mechanism of VPPCs is of great significance. This paper proposes a two-stage coordinated operation model for VPPCs based on energy interaction to enhance the overall economic performance and coordination of the cluster. In the day-ahead stage, a cooperative operation model based on Nash bargaining theory is constructed. The inherently non-convex and nonlinear problem is decomposed into a cluster-level benefit maximization subproblem and a benefit allocation subproblem. The Alternating Direction Method of Multipliers (ADMM) is employed to achieve distributed optimization, ensuring both the efficiency of coordination and the privacy and decision independence of each VPP. In the intra-day stage, to address the uncertainty in renewable generation and load demand, a real-time pricing mechanism based on the supply–demand ratio is designed. Each VPP performs short-term energy forecasting and submits real-time supply–demand information to the coordination center, which dynamically determines the price for the next trading interval according to the reported imbalance. This pricing mechanism facilitates real-time electricity sharing among VPPs. Finally, numerical case studies validate the effectiveness and practical value of the proposed model in improving both operational efficiency and fairness. Full article

Accurate electricity wholesale price (EWP) forecasting is crucial for advancing sustainability in the energy sector, as it supports more efficient utilization and integration of renewable energy by informing when and how it should be consumed, dispatched, curtailed, or stored. However, high fluctuations in EWP, often resulting from demand–supply imbalances typically caused by sudden surges in electricity usage and the intermittency of renewable energy generation, and unforeseen external events, pose a challenge for accurate forecasting. Incorporating local temporal information (LTI) in time series, such as hourly price changes, is essential for accurate EWP forecasting, as it helps detect rapid market shifts. However, existing methods remain limited in capturing LTI, either relying on point-wise input sequences or, for fixed-length, non-overlapping segmentation methods, failing to effectively model dependencies within and across segments. This paper proposes the Local-Temporal Convolutional Transformer (LT-Conformer) model for day-ahead EWP forecasting, which addresses the challenge of capturing fine-grained LTI using Local-Temporal 1D Convolution and incorporates two attention modules to capture global temporal dependencies (e.g., daily price trends) and cross-feature dependencies (e.g., solar output influencing price). An initial evaluation in the Australian market demonstrates that LT-Conformer outperforms existing state-of-the-art methods and exhibits adaptability in forecasting EWP under volatile market conditions. Full article

Electricity price forecasting plays a fundamental role in ensuring efficient market operation and informed decision making. With the growing integration of renewable energy, prices have become more volatile and difficult to predict, increasing the necessity of accurate forecasting in bidding, scheduling, and risk management. This paper provides a comprehensive review of point forecasting models for electricity markets, covering classical statistical approaches both with and without exogenous inputs, and modern machine learning and deep learning techniques, including ensemble methods and hybrid architectures. Unlike standard reviews focused solely on the day-ahead market, we assess model performance across day-ahead, intra-day, and balancing markets, with each posing unique challenges due to differences in time resolution, data availability, and market structure. Through this market-specific lens, the paper merges insights from a broad set of studies; identifies persistent challenges, such as data quality, model interpretability, and generalisability; and outlines promising directions for future research. Our findings highlight the strong performance of hybrid and ensemble models in the day-ahead market, the dominance of recurrent neural networks in the intra-day market, and the relative effectiveness of simpler statistical models such as LEAR in the balancing market, where volatility and data sparsity remain critical challenges. Full article

Driven by increased integration of renewable energy sources, the widespread decarbonization of power systems has led to energy price fluctuations that require greater adaptability and flexibility from grid users in order to maximize profits. Industrial loads equipped with flexible resources can optimize energy consumption rather than merely reacting to immediate events, thereby capitalizing on volatile energy prices. However, the absence of sufficient measured data in industrial processes limits the ability to fully harness this flexibility. To address this challenge, we presents a black-box optimization model for optimizing the energy consumption of cooling systems in the aquaculture industry using Extreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO). XGBoost is employed to establish a nonlinear relationship between cooling system power consumption and available measured data. Based on this model, Bayesian Optimization with the Lower Confidence Bound (LCB) acquisition function is used to determine the optimal discharge temperature of water into breeding pools, minimizing day-ahead electricity costs. The proposed approach is validated using real-world data from a case study at the Port of Hirtshals, Denmark based on measurements from 2023. Our findings illustrate that leveraging the inherent flexibility of industrial processes can yield financial benefits while providing valuable signals for grid operators to adjust consumption behaviors through appropriate price mechanisms. Furthermore, machine learning techniques prove effective in optimizing energy consumption for industries with limited measured data, delivering accurate and practical estimations. Full article

This study proposes a novel hybrid model for stock volatility forecasting by integrating directional and temporal dependencies among financial time series and market regime changes into a unified modeling framework. Specifically, we design a novel Hurst Exponent Effective Transfer Entropy Graph Neural Network (H-ETE-GNN) model that captures directional and asymmetric interactions based on Effective Transfer Entropy (ETE), and incorporates regime change detection using the Hurst exponent to reflect evolving global market conditions. To assess the effectiveness of the proposed approach, we compared the forecast performance of the hybrid GNN model with GNN models constructed using Transfer Entropy (TE), Granger causality, and Pearson correlation—each representing different measures of causality and correlation among time series. The empirical analysis was based on daily price data of 10 major country-level ETFs over a 19-year period (2006–2024), collected via Yahoo Finance. Additionally, we implemented recurrent neural network (RNN)-based models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) under the same experimental conditions to evaluate their performance relative to the GNN-based models. The effect of incorporating regime changes was further examined by comparing the model performance with and without Hurst-exponent-based detection. The experimental results demonstrated that the hybrid GNN-based approach effectively captured the structure of information flow between time series, leading to substantial improvements in the forecast performance for one-day-ahead realized volatility. Furthermore, incorporating regime change detection via the Hurst exponent enhanced the model’s adaptability to structural shifts in the market. This study highlights the potential of H-ETE-GNN in jointly modeling interactions between time series and market regimes, offering a promising direction for more accurate and robust volatility forecasting in complex financial environments. Full article

The increasing integration of renewable energy sources necessitates efficient energy storage solutions, with large-scale battery energy storage systems (BESS) playing a key role in grid stabilization and time-shifting of energy. This study presents a multi-level simulation framework for optimizing BESS operation across multiple markets while incorporating degradation-aware dispatch strategies. The framework integrates a day-ahead (DA) dispatch level, an intraday (ID) dispatch level, and a high-resolution simulation level to accurately model the impact of operational strategies on state of charge and battery degradation. A case study of BESS operation in the German electricity market is conducted, where frequency containment reserve provision is combined with DA and ID trading. The simulated revenue is validated by a battery revenue index. The study also compares full equivalent cycle (FEC)-based and state-of-health-based degradation models and discusses their application to cost estimation in dispatch optimization. The results emphasize the advantage of using FEC-based degradation costs for dispatch decision-making. Future research will include price forecasting and expanded market participation strategies to further improve and stabilize the profitability of BESS in multi-market environments. 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

With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the power system. This paper aims to design a demand bidding (DB) mechanism to collaborate between customers and suppliers on demand response (DR) to prevent the risks of energy shortage and realize energy conservation. The concurrent integration of the energy, transmission, and reserve capacity markets necessitates a new formulation for determining schedules and marginal prices, which is expected to enhance economic efficiency and reduce transaction costs. To dispatch energy and reserve markets concurrently, a hybrid approach of combining dynamic queuing dispatch (DQD) with direct search method (DSM) is developed to solve the extended economic dispatch (ED) problem. The effectiveness of the proposed approach is validated through three case studies of varying system scales. The impacts of tie-line congestion and area spinning reserve are fully reflected in the area marginal price, thereby facilitating the determination of optimal load reduction and spinning reserve allocation for demand-side management units. The results demonstrated that the multi-area bidding platform proposed in this paper can be used to address issues of congestion between areas, thus improving the economic efficiency and reliability of the day-ahead market system operation. Consequently, this research can serve as a valuable reference for the design of the demand bidding mechanism. Full article

In the context of the clean and low-carbon transformation of power systems, addressing the challenge of day-ahead electricity market price prediction issues triggered by the strong stochastic volatility of power supply output due to high-penetration renewable energy integration, as well as problems such as limited dataset scales and short market cycles in test sets associated with existing electricity price prediction methods, this paper introduced an innovative prediction approach based on a multi-modal feature fusion and BiGRUSA-ResSE-KAN deep learning model. In the data preprocessing stage, maximum–minimum normalization techniques are employed to process raw electricity price data and exogenous variable data; the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods are utilized for multi-modal decomposition of electricity price data to construct a multi-scale electricity price component matrix; and a sliding window mechanism is applied to segment time-series data, forming a three-dimensional input structure for the model. In the feature extraction and prediction stage, the BiGRUSA-ResSE-KAN multi-branch integrated network leverages the synergistic effects of gated recurrent units combined with residual structures and attention mechanisms to achieve deep feature fusion of multi-source heterogeneous data and model complex nonlinear relationships, while further exploring complex coupling patterns in electricity price fluctuations through the knowledge-adaptive network (KAN) module, ultimately outputting 24 h day-ahead electricity price predictions. Finally, verification experiments conducted using test sets spanning two years from five major electricity markets demonstrate that the introduced method effectively enhances the accuracy of day-ahead electricity price prediction, exhibits good applicability across different national electricity markets, and provides robust support for electricity market decision making. Full article

The large-scale integration of renewable energy into power grids introduces substantial stochasticity in generation profiles and operational complexities due to electricity’s non-storable nature. These factors cause significant fluctuations in day-ahead market prices. Accurate price forecasting is crucial for market participants to optimize bidding strategies, mitigate renewable curtailment, and enhance grid sustainability. However, conventional methods struggle to address the nonlinearity, high-frequency dynamics, and multivariate dependencies inherent in electricity prices. This study proposes a novel multi-objective optimization framework combining an improved non-dominated sorting genetic algorithm II (NSGA-II) with a radial basis function (RBF) neural network. The improved NSGA-II algorithm mitigates issues of population diversity loss, slow convergence, and parameter adaptability by incorporating dynamic crowding distance calculations, adaptive crossover and mutation probabilities, and a refined elite retention strategy. Simultaneously, the RBF neural network balances prediction accuracy and model complexity through structural optimization. It is verified by the data of Singapore power market and compared with other forecasting models and error calculation methods. These results highlight the ability of the model to track the peak price of electricity and adapt to seasonal changes, indicating that the improved NSGA-II and RBF (NSGA-II-RBF) model has superior performance and provides a reliable decision support tool for sustainable operation of the power market. Full article