Search Results (173)

To address the limited flexibility of pumped storage power stations (PSPSs) under hierarchical clearing of energy and ancillary service markets, this study proposes a joint clearing mechanism for multi-level electricity markets. A bi-level optimization model based on the Stackelberg game is developed to characterize the strategic interaction between PSPSs and the market operator. Simulation results on the IEEE 30-bus system demonstrate that the proposed mechanism captures the dynamics of nodal supply and demand, as well as time-varying network congestion. It guides PSPSs to operate more flexibly and economically. Additionally, the mechanism increases PSPS profitability, reduces system costs, and improves frequency regulation performance. This game-theoretic framework offers quantitative decision support for PSPS participation in multi-level spot markets and provides insights for optimal storage deployment and market mechanism improvement. Full article

The introduction of Renewable Energy Sources (RESs) into the electricity grid is changing the price dynamics of the electricity market and creating room for flexibility on the consumption side. This paper investigates different aspects of the interaction between the RES share, electricity spot prices, and electric vehicle (EV) charging strategies. Based on empirical data from Germany, France, and the Czech Republic for the period 2015–2025, four research hypotheses are tested using correlation and regression analysis, cost simulations, and classification algorithms. The results confirm a negative correlation between the RES share and electricity prices, as well as the effectiveness of smart charging in reducing costs. At the same time, it is shown that the occurrence of negative prices is significantly affected by a high RES share. The correlation analysis further suggests that higher production from RESs increases the potential for price optimisation through smart charging. The findings have implications for policymaking aimed at flexible consumption and efficient RES integration. Full article

The electricity market can optimize the resource allocation in power systems by calculating the market clearing problem. However, in the market clearing process, various market operation requirements must be considered. These requirements might cause the obtained power system dispatch instructions to deviate from the optimal solutions of original market clearing problems, thereby compromising the economic properties of locational marginal price (LMP). To mitigate the adverse effects of such optimality limitations, this paper proposes a pricing method for improving economic properties under the optimality limitation of power system dispatch instructions. Firstly, the underlying mechanism through which optimality limitations lead to economic property distortions in the electricity market is analyzed. Secondly, an analytical framework is developed to characterize economic properties under optimality limitations. Subsequently, an optimization-based electricity market pricing model is formulated, where price serves as the decision variable and economic properties, such as competitive equilibrium, are incorporated as optimization objectives. Case studies show that the proposed electricity market pricing method effectively mitigates the economic property distortions induced by optimality limitations and can be adapted to satisfy different economic properties based on market preferences. Full article

This paper examines arbitrage opportunities available in one of the largest wholesale electricity markets in the world, the Midcontinent Independent System Operator (MISO) electricity exchange. While prior research suggests that market efficiency on the exchange has increased over time, this study reveals that historical pricing information can still be used to generate positive returns. I find that a trading rule based on prior spot and forward prices generates statistically and economically significant risk-adjusted returns across the entire MISO footprint. These returns may in part be explained by the relatively small number of financial traders in the market and the ability of generation owners to exercise market power. Full article

The configuration of energy storage systems in offshore wind farms can effectively suppress fluctuations in wind power and enhance the stability of the power grid. However, the economic balance between the cost of energy storage systems and the fluctuations in wind power remains an urgent challenge to be addressed, especially against the backdrop of widespread spot trading in the electricity market. How to achieve effective wind power stabilization at the lowest cost has become a key issue. This paper proposes three different energy storage configuration strategies and adopts the non-dominated sorting genetic algorithm (NSGA-II) to conduct multi-objective optimization of the system. NSGA-II performed stably in dual-objective scenarios and effectively balanced the relationship between the investment cost of the energy storage system and power fluctuations through the explicit elite strategy. Furthermore, this study analyzed the correlation between the rated power and rated capacity of the energy storage system and the battery life, and corrected the battery life of the Pareto frontier solution obtained by NSGA-II. The research results show that when only considering the investment cost of the energy storage, the optimal configuration was a rated power of 4 MW and a rated capacity of 28 MWh, which could better balance the investment economy and power fluctuation. When further considering the participation of energy storage systems in the electricity spot market, the economic efficiency of the energy storage systems could be significantly improved through the fixed-period electricity price arbitrage method. At this point, the optimal configuration was a rated power of 8 MW and a rated capacity of 37 MWh. The corresponding project investment cost was CNY 242.77 million, and the annual fluctuation rate of the wind power output decreased to 17.84%. Full article

The huge penetration of renewable energy sources poses several challenges for the function of electricity markets, such as increased price volatility and massive curtailments. This paper investigates the current structure of the wholesale electricity market in Greece under the Target Model guidelines. Our analysis put under scrutiny the formation and function of both spot and balancing markets by highlighting key challenges and reforms. Empirical evidence reveals that the domestic market is currently in accordance with the European Target Model; however, the anticipated benefits in terms of more competitive prices are not evident yet. The oversupply of electricity accompanied by low demand that is apparent in the Greek market points to the rapid participation of storage units in the system. The paper provides a detailed description of the recent support mechanism to facilitate the integration of BESS into the system. Eventually, this is anticipated to reduce price volatility and smoothen the price curves. Full article

Short-term load forecasting is important for proxy electricity purchasing in the electricity spot trading market. In this paper, a model SFPFMformer for short-term power load forecasting is proposed to address the issue of balancing accuracy and timeliness. In SFPFMformer, the random forest algorithm is applied to select the most important attributes, which reduces redundant attributes and improves performance and efficiency; then, multiple timescale segmentation is used to extract load data features from multiple time dimensions to learn feature representations at different levels. In addition, fusion time location encoding is adopted in Transformer to ensure that the model can accurately capture time-position information. Finally, we utilize a depthwise separable convolution block to extract features from power load data, which efficiently captures the pattern of change in load. We conducted extensive experiment on real datasets, and the experimental results show that in 4 h prediction, the RMSE, MAE, and MAPE of our model are 1128.69, 803.91, and 2.63%, respectively. For 24 h forecast, the RMSE, MAE and MAPE of our model are 1190.51, 897.26, and 2.97%, respectively. Compared with existing methods, such as Informer, Autoformer, ETSformer, LSTM, and Seq2seq, our model has better precision and time performance for short-term power load forecasting for proxy spot trading. Full article

With the continuous expansion of new energy installed capacity, the flexible regulation role of energy storage in the electricity spot market is becoming more and more prominent. However, traditional trading models often ignore the multiple trading preferences of energy storage. In this paper, we propose an electricity spot market trading model that considers the trading preferences of energy storage to incentivize energy storage to participate more actively in the market. First, the trading preferences of energy storage are modeled with a utility function in which the time preference coefficient and price elasticity are introduced. Then, the utility function is embedded into the spot market clearing model to establish a two-tier model of the spot market, which maximizes social welfare in the upper tier and maximizes energy storage benefits in the lower tier. Finally, the model is solved using KKT and large M methods, and its effectiveness is evaluated on the IEEE39 node system and on a real grid in a specific region. Full article

As the second largest power source in the world, hydropower plays a crucial role in the operation of power systems. This paper focuses on the key issues of regulating hydropower stations participating in the spot market. It aims at the core challenges, such as the conflict of cascade hydro plants’ joint clearing, the lack of adaptability for different types of power supply bidding on the same platform, and the contradiction between long-term operation and the spot market. Through the construction of a water spillage management strategy and settlement compensation mechanism, the competitive abandoned water problem caused by mismatched quotations of cascade hydro plants can be solved. In order to achieve reasonable recovery of the power cost, a separate bidding mechanism and capacity cost recovery model are designed. Subsequently, the sufficient electricity supply constraint of the remaining period is integrated into the spot-clearing model, which can coordinate short-term hydropower dispatch with long-term energy storage demand. The operation of the Yunnan electricity spot market is being simulated to verify the effectiveness of the proposed method. Full article

Energy storage technology is a critical component in supporting the construction of new power systems and promoting the low-carbon transformation of the energy system. Currently, new energy storage in China is in a pivotal transition phase from research and demonstration to the initial stage of commercialization. However, it still faces numerous challenges, including incomplete business models, inadequate institutional policies, and unclear cost and revenue recovery mechanisms, particularly on the generation and grid sides. Therefore, this paper focuses on grid-side new energy storage technologies, selecting typical operational scenarios to analyze and compare their business models. Based on the lifecycle assessment method and techno-economic theories, the costs and benefits of various new energy storage technologies are compared and analyzed. This study aims to provide rational suggestions and incentive policies to enhance the technological maturity and economic feasibility of grid-side energy storage, improve cost recovery mechanisms, and promote the sustainable development of power grids. The results indicate that grid-side energy storage business models are becoming increasingly diversified, with typical models including shared leasing, spot market arbitrage, capacity price compensation, unilateral dispatch, and bilateral trading. From the perspectives of economic efficiency and technological maturity, lithium-ion batteries exhibit significant advantages in enhancing renewable energy consumption due to their low initial investment, high returns, and fast response. Compressed air and vanadium redox flow batteries excel in long-duration storage and cycle life. While molten salt and hydrogen storage face higher financial risks, they show prominent potential in cross-seasonal storage and low-carbon transformation. The sensitivity analysis indicates that the peak–valley electricity price differential and the unit investment cost of installed capacity are the key variables influencing the economic viability of grid-side energy storage. The charge–discharge efficiency and storage lifespan affect long-term returns, while technological advancements and market optimization are expected to further enhance the economic performance of energy storage systems, promoting their commercial application in electricity markets. Full article

As the global ecological environment faces serious challenges and extreme climate change threatens the survival of humankind, the promotion of green development has become the focus for all countries in the world. As one of the world’s major greenhouse gas emitters, China has put forward the “twin goals” of achieving carbon peaking and carbon neutrality and is committed to promoting the green and low-carbon transformation of its cities. As the core of economic and social development, cities are the main source of carbon emissions. In response to the dual challenges of carbon emission control and traffic growth, it is particularly important to promote the development of green transportation. With the acceleration of urbanization, urban traffic pollution is becoming more and more serious. As a zero-emission transportation mode, electric vehicles have become a key way to achieve the carbon peak and carbon neutrality targets. In order to deeply analyze the research status of electric vehicles in the field of the green and low-carbon transformation of urban transportation in China and to explore the research hot spots, evolution trends, and their roles and strategies in the construction of green transportation networks, this paper uses the CiteSpace, VOSviewer, and Tableau analysis tools to review and analyze the 2460 articles and reviews in the Web of Science Core Collection (WOS) and 2650 articles and reviews in the China National Knowledge Infrastructure (CNKI), including the “publication volume and publication trend”, “subject citation path”, “countries cooperation and geographical distribution”, “author cooperation and institution cooperation”, “keyword co-occurrence and keywords clusters”, and the “evolution trend of research hot spots in timeline”. The results show that: (1) Since 2010, the research focus on electric vehicles has gradually increased, and especially in the past three years, the number of such publications has increased significantly. (2) China holds the lead in research output regarding electric vehicles and related fields, but its international cooperation needs to be strengthened. (3) In recent years, the research has focused on “energy transformation”, “energy-saving technology”, “carbon emissions”, “battery recycling”, and other relevant topics. The promotion and development of electric vehicles will continue to usher in new opportunities concerning technological innovation, policy support, and market expansion. Finally, based on the research hot spots and evolution trends of electric vehicles in the field of urban green transportation and low-carbon transportation in China, this paper discusses the key paths and strategies for electric vehicles to promote the transformation of urban transportation in China to green and low-carbon types and looks forward to future research directions. The research in this paper can provide theoretical support and practical guidance for China to promote electric vehicles, build low-carbon cities, and realize green transportation. It is expected to act as a useful reference for relevant policy formulation and academic research. Full article

This study explores the application of Temporal Fusion Transformers (TFTs) to improve the predictability of hourly potential hydropower production for a small run–of–the–river hydropower plant in Portugal. Accurate hourly power forecasts are essential for optimizing participation in the spot electricity market, where deviations incur penalties. This research introduces the novel application of the TFT, a deep–learning model tailored for time series forecasting and uncovering complex patterns, to predict hydropower production based on meteorological data, historical production records, and plant capacity. Key challenges such as filtering observed hydropower outputs (to remove strong, and unpredictable human influence) and adapting the historical series to installed capacity increases are discussed. An analysis of meteorological information from several sources, including ground information, reanalysis, and forecasting models, was also undertaken. Regarding the latter, precipitation forecasts from the European Centre for Medium–Range Weather Forecasts (ECMWF) proved to be more accurate than those of the Global Forecast System (GFS). When combined with ECMWF data, the TFT model achieved significantly higher accuracy in potential hydropower production predictions. This work provides a framework for integrating advanced machine learning models into operational hydropower scheduling, aiming to reduce classical modeling efforts while maximizing energy production efficiency, reliability, and market performance. Full article

With the evolution of the power market and the increase in the new energy penetration rate, the power industry will present diversified characteristics. The continuous development of the electric energy market (EEM) and the peak regulation market (PRM) is also affecting the economic benefits of cascade hydropower stations, in which the EEM, as a market for electric energy trading in the power market, develops synergistically with the PRM and creates the conditions for the consumption of new energy sources; for this reason, this paper, while considering the benefits of cascade hydropower stations in the EEM in different time scales and the impact of the spot market, combines the compensation mechanism and apportionment principle of the PRM. This paper proposes an optimal economic scheduling strategy for cascade hydropower stations. Specifically, firstly, the strategy adopts multi-objective optimization. The objective function takes into account the generation capacity of the cascade hydropower stations, the benefits of the EEM, the influence of the spot market, the compensatory benefits of peaking, and the sharing expenses of peaking; secondly, the constraints at the level of the power grid, the level of the cascade hydropower stations, and the level of the market are taken into account comprehensively, and the Harris Hawk Algorithm is used to solve the model; lastly, by comparing different schemes, it is observed that under varying inflow conditions, the proposed dispatch strategy in this paper yields slightly lower revenue in the EEM than other schemes. However, due to its comprehensive consideration of the synergy between the PRM and the EEM, its overall economic benefits surpass those of other schemes. This fully validates the effectiveness and economic efficiency of the proposed dispatch strategy. Full article

This paper studies the volatility of electricity spot prices in the Nordic market (Sweden, Finland, Denmark, and Norway) under regime switching. Utilizing Markov-switching GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, we provide strong evidence of nonlinear regime shifts in the volatility dynamics of these prices. Using in-sample criteria, we find that regime-switching models have lower AIC (Akaike information criterion) than single-regime GARCH models. In addition, out-of-sample forecasts indicate that regime-switching GARCH models have superior Value-at-Risk (VaR) prediction ability relative to single-regime models, which is directly pertinent to risk management. These findings highlight the importance of incorporating regime shifts into volatility models for accurately assessing and mitigating risks associated with electricity price fluctuations in deregulated markets. Full article

Building the regional electricity spot market (RESM) in a representative area is an important move to promote the electricity market reform and new power system construction in China. In this paper, the RESM operation model and optimization method are established, which take into account the special power grid operation mechanism and market construction achievements in the provincial electricity spot market. Firstly, the influencing factors, core elements, market structure, and operation model of RESM construction in China are analyzed. Secondly, a bi-level optimization model of the RESM is established. The lower layer is the pre-clearing model of the provincial electricity spot market, which is used to optimize the unit combination strategy, considering unit operation constraints and power grid security constraints in the province. The upper layer is the optimization clearing model of the RESM, which is used to optimize the clearing price and adjust the unit operation strategy and inter-provincial electricity trading strategy, considering the security constraints of regional power grid tie lines. Finally, the RESM composed of power grids in the Yangtze River Delta region of China is simulated as an example. The analysis focuses on the operational state of the power grid after the operation of the RESM, considering its safety benefits, economic benefits, and environmental benefits. The optimization of the RESM can effectively solve the serious regional power grid congestion problem, which is achieved through the superposition and printing of pre-clearing results in various provinces, and the average daily cost of electricity purchasing in the region has been reduced by about CNY 11 million, while the annual cost has been reduced by about CNY 4 billion. In addition, the total carbon emissions have been reduced by 11,000 tons per day and 0.18 kg per kilowatt hour on average, and scenes without power abandonment account for more than 95% of the total scenes. Full article