Search Results (361)

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. 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

An efficient integrated energy system (IES) can enhance the potential of building energy conservation and carbon mitigation. However, imbalances between user-side demand and supply side output present formidable challenges to the operational dispatch of building energy systems. To mitigate heat rejection and improve dispatch optimization, an integrated building energy system incorporating waste heat recovery via an absorption heat pump based on the flow temperature model is adopted. A comprehensive analysis was conducted to investigate the correlation among heat pump operational strategies, thermal comfort, and the dynamic thermal storage capacity of piping network systems. The optimization calculations and comparative analyses were conducted across five cases on typical season days via the CPLEX solver with MATLAB R2018a. The simulation results indicate that the operational modes of absorption heat pump reduced the costs by 4.4–8.5%, while the absorption rate of waste heat increased from 37.02% to 51.46%. Additionally, the utilization ratio of battery and thermal storage units decreased by up to 69.82% at most after considering the pipeline thermal inertia and thermal comfort, thus increasing the system’s energy-saving ability and reducing the pressure of energy storage equipment, ultimately increasing the scheduling flexibility of the integrated building energy system. Full article

The increasing integration of wind and photovoltaic energy into power systems brings about large fluctuations and significant challenges for power absorption. Wind–solar–hydro–storage multi-energy complementary systems, especially joint dispatching strategies, have attracted wide attention due to their ability to coordinate the advantages of different resources and enhance both flexibility and economic efficiency. This paper develops a capacity optimization model for a wind–solar–hydro–storage multi-energy complementary system. The objectives are to improve net system income, reduce wind and solar curtailment, and mitigate intraday fluctuations. We adopt the quantum particle swarm algorithm (QPSO) for outer-layer global optimization, combined with an inner-layer stepwise simulation to maximize life cycle benefits under multi-dimensional constraints. The simulation is based on the output and load data of typical wind, solar, water, and storage in Yunnan Province, and verifies the effectiveness of the proposed model. The results show that after the wind–solar–hydro–storage multi-energy complementary system is optimized, the utilization rate of new energy and the system economy are significantly improved, which has a wide range of engineering promotion value. The research results of this paper have important reference significance for the construction of new power systems and the engineering design of multi-energy complementary projects. Full article

Driven by the goal of “double carbon”, we propose an investment proportion optimization method based on cooperative game theory to optimize the investment of multiple entities and evaluate the effectiveness of the new power system. The asymmetric Nash negotiation model is introduced to balance the interests of each investment entity. At the same time, a comprehensive investment benefit evaluation index system covering economic, environmental, and social benefits is constructed, and the overall investment benefit evaluation is obtained by using the Delphi method, analytic hierarchy process, and fuzzy comprehensive evaluation method. Through the case analysis of the multi-energy complementary energy system project investment, the validity of the multi-subject investment proportion optimization model and the investment benefit analysis model are verified, and the feasibility of the project investment is demonstrated to provide theoretical guidance and practical reference for the research in related fields. Full article

The large-scale integration of volatile and intermittent renewables necessitates greater flexibility in the power system. Improving this flexibility is key to achieving a high proportion of renewable energy consumption. In this context, the scientific selection of energy storage technology is of great significance for the construction of new power systems. From the perspective of life cycle cost analysis, this paper conducts an economic evaluation of four mainstream energy storage technologies: lithium iron phosphate battery, pumped storage, compressed air energy storage, and hydrogen energy storage, and quantifies and compares the life cycle cost of multiple energy storage technologies. On this basis, a three-dimensional multi-energy storage comprehensive evaluation indicator system covering economy, technology, and environment is constructed. The improved grade one method and entropy weight method are used to determine the comprehensive performance, and the fuzzy comprehensive evaluation method is used to carry out multi-attribute decision-making on the multi-energy storage technology in the source, network, and load scenarios. The results show that pumped storage and compressed air energy storage have significant economic advantages in long-term and large-scale application scenarios. With its fast response ability and excellent economic and technical characteristics, the lithium iron phosphate battery has the smallest score change rate (15.2%) in various scenarios, showing high adaptability. However, hydrogen energy storage technology still lacks economic and technological maturity, and breakthrough progress is still needed for its wide application in various application scenarios in the future. Full article

To address the challenges of high complexity in modeling the correlation of multi-dimensional stochastic variables and the difficulty of solving long-term scheduling models in continuous action spaces in multi-energy complementary systems, this paper proposes a long-term optimization scheduling method based on Deep Deterministic Policy Gradient (DDPG). First, an improved C-Vine Copula model is used to construct the multi-dimensional joint probability distribution of water, wind, and solar energy, and Latin Hypercube Sampling (LHS) is employed to generate a large number of water–wind–solar coupling scenarios, effectively reducing the model’s complexity. Then, a long-term optimization scheduling model is established with the goal of maximizing the absorption of clean energy, and it is converted into a Markov Decision Process (MDP). Next, the DDPG algorithm is employed with a noise dynamic adjustment mechanism to optimize the policy in continuous action spaces, yielding the optimal long-term scheduling strategy for the water–wind–solar multi-energy complementary system. Finally, using a water–wind–solar integrated energy base as a case study, comparative analysis demonstrates that the proposed method can improve the renewable energy absorption capacity and the system’s power generation efficiency by accurately quantifying the uncertainties of water, wind, and solar energy and precisely controlling the continuous action space during the scheduling process. Full article

The low-carbon transition of the global energy system is an urgent necessity to address climate change and meet growing energy demand. As a major source of energy consumption and emissions, buildings play a key role in this transition. This study systematically analyzes the flexible resources of building energy systems and vehicle-to-grid (V2G) interaction technologies, and mainly focuses on the regulation characteristics and coordination mechanisms of distributed energy supply (renewable energy and multi-energy cogeneration), energy storage (electric/thermal/cooling), and flexible loads (air conditioning and electric vehicles) within regional energy systems. The study reveals that distributed renewable energy and multi-energy cogeneration technologies form an integrated architecture through a complementary “output fluctuation mitigation–cascade energy supply” mechanism, enabling the coordinated optimization of building energy efficiency and grid regulation. Electricity and thermal energy storage serve as dual pillars of flexibility along the “fast response–economic storage” dimension. Air conditioning loads and electric vehicles (EVs) complement each other via thermodynamic regulation and Vehicle-to-Everything (V2X) technologies, constructing a dual-dimensional regulation mode in terms of both power and time. Ultimately, a dynamic balance system integrating sources, loads, and storage is established, driven by the spatiotemporal complementarity of multi-energy flows. This paper proposes an innovative framework that optimizes energy consumption and enhances grid stability by coordinating distributed renewable energy, energy storage, and flexible loads across multiple time scales. This approach offers a new perspective for achieving sustainable and flexible building energy systems. In addition, this paper explores the application of demand response policies in building energy systems, analyzing the role of policy incentives and market mechanisms in promoting building energy flexibility. Full article

This paper proposes a ladder carbon trading-based low-carbon economic dispatch model for integrated energy systems (IESs), incorporating flexible load optimization and hybrid energy storage systems consisting of battery and thermal energy storage. First, a ladder-type carbon trading mechanism is introduced, in which the carbon trading cost increases progressively with emission levels, thereby providing stronger incentives for emission reduction. Second, flexible loads are categorized and modeled as shiftable, transferable, and reducible types, each with distinct operational constraints and compensation mechanisms. Third, both battery and thermal energy storage systems are considered to improve system flexibility by storing excess energy and supplying it when needed. Finally, a unified optimization framework is developed to coordinate the dispatch of renewable generation, gas turbines, waste heat recovery units, and multi-energy storage devices while integrating flexible load flexibility. The objective is to minimize the total system cost, which includes energy procurement, carbon trading expenditures, and demand response compensation. Three comparative case studies are conducted to evaluate system performance under different operational configurations: the proposed comprehensive model, a carbon trading-only approach, and a conventional baseline scenario. Results demonstrate that the proposed framework effectively balances economic and environmental objectives through coordinated demand-side management, hybrid storage utilization, and the ladder-type carbon trading market mechanism. It reshapes the system load profile via peak shaving and valley filling, improves renewable energy integration, and enhances overall system efficiency. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

Significant uranium exploration breakthroughs have been achieved in the eolian deposits of the uranium reservoirs in the southwestern part of the Ordos Basin. The redox environment remains a crucial factor in controlling the migration and precipitation of uranium. This study, through rock mineralogical observations and hydrocarbon gas composition analysis, combined with the regional source rock and basin tectonic evolution history, reveals the characteristics of the reducing medium and the mineralization mechanisms involved in uranium ore formation. The Lower Cretaceous Luohe Formation uranium reservoirs in the study area exhibit a notable lack of common reducing media, such as carbonaceous debris and pyrite. However, the total hydrocarbon gases in the Luohe Formation range from 2967 to 20,602 μmol/kg, with an average of 8411 μmol/kg—significantly higher than those found in uranium reservoirs elsewhere in China, exceeding them by 10 to 100 times. Due to the absence of other macroscopically visible organic matter, hydrocarbon gases are identified as the most crucial reducing agent for uranium mineralization. These gases consist predominantly of methane and originate from the Triassic Yanchang Formation source rock. Faults formed during the Indosinian, Yanshanian, and Himalayan tectonic periods effectively connect the Cretaceous uranium reservoirs with the oil and gas reservoirs of the Triassic and Jurassic, providing pathways for the migration of deep hydrocarbon fluids into the Cretaceous uranium reservoirs. The multiphase tectonic evolution of the Ordos Basin since the Cenozoic has facilitated the development of faults, ensuring a sufficient supply of reducing media for uranium reservoirs in an arid sedimentary context. Additionally, the “Replenishment-Runoff-Drainage System” created by tectonic activity promotes a continuous supply of uranium- and oxygen-bearing fluids to the uranium reservoirs, resulting in a multi-energy coupling mineralization effect. Full article

To mitigate challenges arising from renewable energy volatility and multi-energy load uncertainty, this paper introduces a dynamic hydrogen blending (DHB) strategy for an integrated energy system. The strategy is categorized into Continuous Hydrogen Blending (CHB) and Time-phased Hydrogen Blending (THB), based on the temporal variations in the hydrogen blending ratio. To evaluate the regulatory effect of DHB on uncertainty, a data-driven distributionally robust optimization method is employed in the day-ahead stage to manage system uncertainties. Subsequently, a hierarchical model predictive control framework is designed for the intraday stage to track the day-ahead robust scheduling outcomes. Experimental results indicate that the optimized CHB ratio exhibits step characteristics, closely resembling the THB configuration. In terms of cost-effectiveness, CHB reduces the day-ahead scheduling cost by 0.87% compared to traditional fixed hydrogen blending schemes. THB effectively simplifies model complexity while maintaining a scheduling performance comparable to CHB. Regarding tracking performance, intraday dynamic hydrogen blending further reduces upper- and lower-layer tracking errors by 4.25% and 2.37%, respectively. Furthermore, THB demonstrates its advantage in short-term energy regulation, effectively reducing tracking errors propagated from the upper layer MPC to the lower layer, resulting in a 2.43% reduction in the lower-layer model’s tracking errors. Full article

Pumped storage power stations (PSPSs, hereafter) have garnered significant attention due to their critical roles in peak regulation and frequency modulation, contributing to the advancement of global new energy and power systems. Site selection of power stations is the key to successful operation. In this paper, a new site selection index system and evaluation model covering hydrogeology, construction, social economy, and energy grid are proposed to meet the multi-energy complementary needs of new energy sources. The index system was constructed by the literature review and Delphi method, the subjective and objective weights were calculated by the G1 method and Gini weighting method, and the combined weights were obtained by modifying the G1 method based on the Gini coefficient. The VIKOR method was used to evaluate the pre-selected sites, determine the best scheme, and verify the stability of the results. The results of the case study show that the Centian station site in Guangdong Province is the most promising. This study provides decision support for the construction of pumped storage power plants and has important significance for the development of clean energy and new power systems. Full article

Integrated energy systems (IESs) serve as pivotal platforms for realizing the reform of energy structures. The rational planning of their equipment can significantly enhance operational economic efficiency, environmental friendliness, and system stability. Moreover, the inherent randomness and intermittency of renewable energy generation, coupled with the peak and valley characteristics of load demand, lead to fluctuations in the output of multi-energy coupling devices within the IES, posing a serious threat to its operational stability. To address these challenges, this paper focuses on the economic and stable operation of the IES, aiming to minimize the configuration costs of hybrid energy storage systems, system voltage deviations, and net load fluctuations. A multi-objective optimization planning model for an electric–hydrogen hybrid energy storage system is established. This model, applied to the IEEE-33 standard test system, utilizes the Multi-Objective Artificial Hummingbird Algorithm (MOAHA) to optimize the capacity and location of the electric–hydrogen hybrid energy storage system. The Multi-Objective Artificial Hummingbird Algorithm (MOAHA) is adopted due to its faster convergence and superior ability to maintain solution diversity compared to classical algorithms such as NSGA-II and MOEA/D, making it well-suited for solving complex non-convex planning problems. The simulation results demonstrate that the proposed optimization planning method effectively improves the voltage distribution and net load level of the IES distribution network, while the complementary characteristics of the electric–hydrogen hybrid energy storage system enhance the operational flexibility of the IES. Full article

In the context of global climate governance and the low-carbon energy transition, virtual power plant (VPP), a key technology for integrating distributed energy resources, is urgently needed to solve the problem of decentralization and lack of synergy in electricity, carbon, and green certificate trading. Existing studies mostly focus on single energy or carbon trading scenarios and lack a multi-market coupling mechanism supported by blockchain technology, resulting in low transaction transparency and a high risk of information tampering. For this reason, this paper proposes a synergistic optimization strategy for electricity/carbon/green certificate virtual power plants based on blockchain cross-chain transactions. First, Latin Hypercubic Sampling (LHS) is used to generate new energy output and load scenarios, and the K-means clustering method with improved particle swarm optimization are combined to cut down the scenarios and improve the prediction accuracy; second, a relay chain cross-chain trading framework integrating quota system is constructed to realize organic synergy and credible data interaction among electricity, carbon, and green certificate markets; lastly, the multi-energy optimization model of the virtual power plant is designed to integrate carbon capture, Finally, a virtual power plant multi-energy optimization model is designed, integrating carbon capture, power-to-gas (P2G) and other technologies to balance the economy and low-carbon goals. The simulation results show that compared with the traditional model, the proposed strategy reduces the carbon emission intensity by 13.3% (1.43 tons/million CNY), increases the rate of new energy consumption to 98.75%, and partially offsets the cost through the carbon trading revenue, which verifies the Pareto improvement of environmental and economic benefits. This study provides theoretical support for the synergistic optimization of multi-energy markets and helps to build a low-carbon power system with a high proportion of renewable energy. Full article