Abstract:Driven by the "dual carbon" goal, an important role is played by the integration of multiple carbon-emission reduction value warrants in the low-carbon economic transformation of the integrated energy system. To this end, an optimal scheduling strategy for the combined heat and power integrated energy system, based on the joint interaction mechanism of green certificate-carbon trading-China certified emission reduction is proposed. Firstly, the integrated energy system is guided to constrain carbon emissions by considering the joint mechanism of green certificate trading, ladder carbon trading, and national certified voluntary emission reductions. Then, a new cogeneration model with an adjustable heat-to-electricity ratio is proposed so that the flexible resources in the integrated energy system can be fully utilized and the low-carbon and economic performance of the integrated energy system can be further improved. Finally, the rationality and effectiveness of the proposed strategy are verified by setting the comparison scenario and using MATLAB to solve the constructed low-carbon scheduling model of the integrated energy system. Through example analysis and demonstration, it is shown that all methods can promote the low-carbon economic operation of the integrated energy system.

Abstract:To address the challenges posed by the frequent occurrence of typhoons, which threaten the reliability and stability of distribution networks, a resilient-oriented expansion planning method for distribution networks considering typhoon impacts under uncertainties is proposed. First, considering the vulnerability of the distribution network during typhoon events, a fault scenario set for power lines is constructed. Second, for regular scenarios, a scenario clustering method is employed to generate typical scenarios that capture the fluctuations in power generation and load demand. Subsequently, with the objective of minimizing total planning costs and outage losses caused by typhoons during the planning period, constraints such as investment budget, power flow, load shedding at nodes, and operational variable limits are integrated into the model. Based on stochastic optimization, a distribution network expansion planning model is developed, incorporating resilience requirements under uncertainty. The model is formulated as a mixed-integer linear programming (MILP) problem, which can be effectively solved using commercial solvers. Simulation results demonstrate that the method significantly enhances the load supply capacity and disaster resistance of the distribution system during the planning period, providing technical support for planning resilient distribution networks.

Abstract:In view of the diversification and significant differentiation of load regulation potential of high-energy-consuming industrial users, in order to realize accurate bidding in the multivvariety demand response market, a quantitative evaluation method considering the flexibility of load regulation and a strategy of cross-time participation in demand response quotation are proposed. Firstly, a multi-time scale load control flexibility evaluation index system based on the order relation analysis method-entropy weight method-TOPSIS is constructed to quantify the difference in load control flexibility. Secondly, a dynamic matching mechanism between cross-time load resources and multiple types of demand response varieties considering the flexibility of load regulation is proposed, and a cross-time quotation model considering the maximization of total revenue of demand response is established. Finally, the PSO algorithm is used to solve the optimal power allocation plan and quotation combination of an industrial user when participating in the agreed demand response (ADR), fast peak avoidance response (FPAR) and real-time demand response (RDR), and the case study shows that the strategy can maximize the total benefit of the user's participation in various types of demand response.

Abstract:Under the drive of the “dual-carbon” goals, constructing an integrated wind-solar-storage base is a critical pathway for promoting the green and low-carbon transformation of the energy sector. A planning methodology that couples the spatio-temporal characteristics of wind and solar resources with a coordinated hybrid energy storage strategy is proposed. The capacity scales of photovoltaic, wind power, and energy storage are treated as decision variables. A complementary model is constructed with the optimization objective of matching the power delivery curve. Feasible configuration schemes are screened through annual chronological production simulation and subsequently compared and selected using an economic evaluation model. A study on capacity planning and generation configuration decision-making is conducted. The study derives an economically optimal planning scheme for the integrated base, which can effectively utilize the region’s wind power peak periods and midday photovoltaic output characteristics. Through hierarchical dispatch of energy storage, the scheme reduces the curtailment rate of renewable energy in the outbound transmission channels.

Abstract:With the increasing penetration of distributed renewable energy in smart microgrids, the management of demand-side resource scheduling has imposed higher requirements on the accuracy of wind power prediction. To address this challenge, a short-term wind power prediction model based on enhanced artificial hummingbird algorithm (EAHA)-optimized multi-task learning (MTL)-attention mechanism (ATT)-neural hierarchical interpolation for time series (NHITS) is proposed. Firstly, an NHITS prediction model under the MTL framework is constructed, which simultaneously considers two related tasks: wind speed prediction and wind power prediction. By sharing partial parameters, the generalization ability of the model is enhanced. The introduction of the ATT mechanism dynamically allocates the output weights of each stack, thereby more effectively capturing key features at different time scales. Secondly, to further optimize the hyperparameters of the prediction model, the traditional AHA is enhanced. Chaotic sequences are utilized to initialize the population, enriching its diversity, and a crossover learning strategy is introduced to optimize information exchange among individuals, thereby improving the global search capability and convergence accuracy of the algorithm. Finally, the effectiveness of the proposed method is validated through case analysis based on actual data from a wind farm in Shanxi Province.

Abstract:In response to the pressing issues of wasted resources in mining areas and the urgent demand for large-scale energy storage, a novel integrated energy system framework for mining areas with composite energy storage is proposed. The framework utilizes abandoned underground mine caverns as spatial resources and harnesses the storage capacity from the huge potential difference of mine water inflow along with its contained low-enthalpy geothermal energy. A multi-level energy recovery approach is adopted to minimize resource waste by coupling pumped hydro storage, compressed air energy storage, and water-source heat pump technologies. To overcome the limitations of traditional optimization modeling methods, the operational optimization problem of the integrated energy system is transformed into a markov decision process. An optimal scheduling strategy is then developed based on the deep Q-learning network reinforcement learning algorithm, aiming to maximize net operational profit, enhance wind power consumption, and reduce carbon emissions. Finally, case studies under different scenarios are conducted through simulation analysis. The results verify that the proposed DQN-based optimization strategy can effectively address system nonlinearities and uncertainties from wind power and load demand, while ensuring real-time response capability for scheduling. Moreover, the proposed system model is demonstrated to achieve significant energy savings, improved energy storage density, and considerable economic and environmental benefits.

Abstract:In multi-microgrid systems, rising renewable energy penetration causes severe operational volatility and economic challenges, as single-type independent energy storage fails to satisfy dual power and energy requirements. A hybrid shared energy storage architecture is proposed, and a bi-level coordinated optimization model is established based on master-slave nash hybrid game theory, integrating peer-to-peer power trading and shared energy storage agent mechanisms. The upper level is targeted at minimizing storage capacity configuration costs, while the lower level is aimed at optimizing multi-microgrid operational costs. A bi-level hybrid iterative method combining Gurobi and an improved differential evolution algorithm is adopted for solution. Simulation results verify that the proposed strategy significantly improves system economy and renewable energy consumption, effectively coordinates multi-peer-to-peer transactions and shared energy storage scheduling, and provides a novel approach for microgrid cluster optimization in multi-market environments.

Abstract:To address the challenge of inaccurate electricity load forecasting in start-up enterprises caused by insufficient historical data, highly fluctuating production plans, and unstable electricity consumption patterns, an electricity load forecasting method for start-up enterprises based on conditional generative adversarial networks (CGAN) and bidirectional gated recurrent units (BiGRU) is proposed. Firstly, on the basis of an analysis of key internal and external factors affecting enterprise electricity consumption, a structured influencing-factor system is established, and a multivariate feature set including power distribution data, equipment energy consumption, production planning information, and meteorological conditions is constructed. Subsequently, multivariate features are fused with electricity load time-series data to form original samples, and data augmentation is implemented using CGAN. Finally, the augmented samples are fed into a BiGRU network for training, enabling the capture of complex bidirectional temporal dependencies and the realization of electricity load forecasting. Experimental results based on a case study of an aircraft engine maintenance start-up enterprise demonstrate that high prediction accuracy can be maintained under data-scarce conditions, with prediction errors significantly reduced compared to baseline models, thereby effectively validating the superiority of the proposed data and feature enhancement strategy. Theoretical and practical support is provided for electricity load forecasting in data-limited scenarios.

Abstract:Accurate mid-term power load forecasting is crucial for power dispatch and resource optimization. Addressing the practical need for daily peak/valley load management in power scheduling, medium-term forecasting is studied with daily maximum/minimum load as the prediction granularity. To overcome the error accumulation caused by the decay of coupling relationships between historical loads and multi-dimensional external variables in traditional methods, a deep neural network time-series forecasting approach incorporating a cross multi-head attention (CMA) mechanism is proposed. The model features three innovative designs: first, a dual-branch convolutional neural network (CNN) and long short-term memory (LSTM) network are used to extract the local pattern features of the load sequence and the global temporal correlation of auxiliary variables; second, a cross-multi-head attention layer is designed to establish a dynamic weight mapping between historical load and external variables in future periods; finally, a gated recurrent unit (GRU) achieves adaptive fusion of multi-scale features. Experimental results demonstrate that the model achieves high accuracy and ro-bustness in power load forecasting tasks.

Abstract:Subway load forecasting can facilitate the stable and efficient operation of subway power systems. Most existing methods for forecasting subway power load utilize statistical or machine learning models, such as linear regression or support vector machines. However, due to the difficulty in effectively capturing the spatial-temporal characteristics of subway power systems, particularly time-varying nature and non-linear complexities of the load, these methods are limited in the precision of prediction. To further enhance the precision of subway load forecasting, a subway power load forecasting method based on spatial-temporal graph neural networks (STGNN) is proposed to predict the power traction load of each station during subway operations. STGNN extracts spatial-temporal relationships from multiple perspectives of subway stations by constructing multiple-perspective spatial-temporal graphs that integrate a geographical distance graph, a load similarity graph, and a dynamic learning graph. It comprehensively captures the spatial-temporal dynamic changes of the subway power system, where the dynamic learning graph mechanism adaptively adjusts the adjacency matrix, thereby improving the ability of the model to perceive non-linearity and the evolutionary temporal characteristics. Experiments are conducted on historical data of power load fromsubway stationsin some city. Results show that STGNN achieves a high prediction precision of 89.37%, which is 3.16%, 3.90%, 11.38% and 2.10% higher than those of XGBoost, LightGBM, LSTM and MTGNN models respectively, indicating that STGNN has broad application prospects in subway power load forecasting.

Abstract:In order to solve the problem that the selection of carbon emission factors on the power side is too rough, and further promote the low-carbon electricity consumption behavior of high-carbon emission enterprises, a low-carbon demand response method for flat glass manufacturers considering fine carbon emission factors is proposed. Firstly, taking flat glass as a typical manufacturer, the carbon emission accounting model of flat glass enterprises is established by analyzing the carbon emission of flat glass production. Then, considering the power flow loss allocation, based on the method of proportional power flow tracking, combined with the power consumption characteristics of flat glass enterprises, the carbon emission factors on the user side are refined. Finally, by combining carbon emission factor and carbon tax, a low-carbon demand response model based on carbon emission factor is proposed to encourage enterprises to reduce carbon electricity consumption. Through the example analysis, it is proved that the proposed method can realize the refinement of carbon emission factors in the spatial and temporal distribution level, and guide enterprises to adjust their own electricity consumption behavior and tap their carbon emission reduction potential.

Abstract:In view of the current problem of lack of key guiding signals for low-carbon energy consumption on the user side, a dynamic carbon emission factor prediction method taking into account the low-carbon demand response behavior on the user side is proposed. First, a user dynamic electricity carbon emission factor calculation model is constructed based on the carbon emission flow theory, and a carbon emission factor data pool is constructed in combination with system operation simulation. Second, a low-carbon energy consumption response behavior model for power users facing dynamic carbon emission factors is constructed, and a dynamic carbon emission factor prediction method taking into account the low-carbon demand response behavior on the user side is proposed. Carbon emission factor prediction is carried out based on LSTM neural network, and effective prediction of node-level dynamic carbon emission factors for a given system based on arbitrary source and load input is achieved. Finally, a case analysis is carried out based on a PJM-5 node power system and a 36-node power system with a high proportion of renewable energy, which verifies the effectiveness of the proposed method in predicting node-level electricity carbon emission factors taking into account the user's low-carbon demand response.

Abstract:Under the deep implementation of the “Dual-Carbon” strategy, integrated energy systems (IESs) in industrial parks are increasingly taking on the dual roles of energy management and resource scheduling. However, demand response and optimal dispatch are often investigated separately, and coordinated supply-demand low-carbon optimization remains insufficient. To address this gap, a bi-level low-carbon optimization model for industrial-park IESs considering electricity-carbon demand response is proposed. A unified framework that couples demand response and optimal dispatch from the operator perspective is constructed. A dynamic multi-energy carbon-emission factor calculation method is developed, and a comprehensive demand response model integrating both electricity and carbon perspectives is established. A bi-level optimization model is further formulated, where carbon-emission factors are iteratively updated based on load adjustments to achieve solution convergence. Case studies show that, compared with the electricity-only demand response case, the total cost for operators is reduced by 4.15%, and carbon emissions are reduced by 14.3%.

Abstract:Existing regulation methods for industrial parks seldom balance the multi-objective synergies among carbon emissions, economic benefits, and power grid balance. Therefore, an optimized scheduling method for industrial parks that considers carbon trading and demand response is proposed. Firstly, an improved stepped carbon trading mechanism is constructed based on the carbon trading price mechanism in the actual electricity market to incentivize low-carbon operation of resources. Secondly, in response to the massive and heterogeneous nature of flexible resources within the park, a mathematical model of adjustable resource response is established to depict resource response behavior and cost characteristics. Furthermore, an optimized operation model for industrial parks is constructed, with economic benefits and carbon emissions as the optimization objectives, to perform multi-resource collaborative scheduling and fully leverage resource complementarity. Finally, a multi-objective particle swarm optimization (MOPSO) algorithm and a comprehensive membership degree method are adopted to seek multi-objective solutions. The case study results demonstrate the significant value of the proposed method in industrial parks.

Abstract:Aiming at the problem that single chain block-chain cannot simultaneously meet the needs of weakly centralized network security verification and economic and environmental value, a three chain block-chain architecture is proposed that combines physical, economic, and environmental. Firstly, with the concept of "time-sharing energy blocks", a carbon measurement method based on source charge similarity and considering transaction information is designed. Secondly, a three chain coupled smart contract is designed. A series of processes such as energy block order placement, trading, delivery, settlement, and assessment are deployed in the smart contract. After, in response to the personalized and differentiated needs of P2P energy block trading orders, a NFBO-Token is designed to connect various links and physical devices on and off the chain, and the environmental rights of energy block carbon reduction trading are monetized in the form of EC-Token are proposed. Finally, case studies have shown that the three chain block-chain can effectively solve the network security verification, value representation, and transfer issues caused by the transaction of physical electricity assets on the block-chain, and achieve accurate tracking and accounting of indirect carbon emissions from energy blocks.

Abstract:With the continuous advancement of electricity market reforms, strategic behavior by generation companies is increasingly highlighted, especially regarding its impact on demand-side prices. The demand-side price is defined as the locational marginal price (LMP) at load nodes and is used as an indicator of the electricity-price level faced by loads. A simplified network structure of Jiangsu Province is used, and the impacts of installed-capacity structure and transmission capacity on collusive strategies of generator groups, demand-side prices, bidding outcomes, and social welfare in a provincial spot market are investigated. Four case studies (Case 1–Case 4) are constructed, and generator strategy selection, market performance, and demand-side price volatility under different installed-capacity and transmission-capacity conditions are analyzed. Under an uneven distribution of generator market shares, collusive behavior is found to significantly increase demand-side prices, reduce load demand, and aggravate consumers’ electricity burden. When transmission capacity becomes binding, collusion is intensified under system congestion; LMPs are increased through strategic bidding around congested lines, and market prices are further elevated together with generators’ profits. Social welfare is shown to be markedly lower under collusion than under perfect competition, and an additional welfare loss is observed when congestion occurs. Overall, changes in installed-capacity structure and transmission capacity are demonstrated to substantially affect generators’ pricing strategies and market efficiency, and effective measures in grid development and energy transition are required to mitigate the adverse impacts of strategic behavior on demand-side prices and to improve overall social welfare.

Abstract:Distributed smart grids enable local consumption of new energy sources while allowing their internal flexible resources to participate in electricity market transactions. However, the transaction mechanisms for distributed smart grid participation in the market remain unclear. To address this issue, an optimization method for peak shaving transactions in distributed smart grids that incorporates flexibility assessment is proposed. First, an optimization model for peak shaving transactions in distributed smart grids is established, considering flexibility evaluation. The model aims to maximize the revenue from peak shaving services for the operating entity, user satisfaction benefits, and the peak shaving effectiveness benefits for the upper-level main grid, while constraining the flexibility supply of the distributed smart grid. This simulates the participation of distributed smart grids in peak shaving services. Next, a flexibility assessment model for distributed smart grids considering ancillary service peak shaving revenues is developed. This model aims to minimize operational costs while accounting for market revenues, constrained by flexibility load operation models. It simulates internal grid operations to calculate external flexibility supply, feeding this result into the market transaction optimization model. Finally, the methodology is validated using the IEEE 33-node system. Results demonstrate that after the distributed smart grid participates in peak shaving for ancillary services, the external grid's peak-to-valley difference rate decreases from 53.3% to 36.6%, and the peak-to-valley difference reduces from 0.99 MW to 0.68 MW, effectively suppressing load fluctuations.

Abstract:As key participants in the national carbon market, the rational use of trading strategies by thermal power enterprises is conducive to reducing compliance costs and enhancing the value of carbon assets. The microstructure of the carbon market is first characterized, and trading scenarios are classified into allowance surplus, balance, and deficit based on allowance allocation relative to compliance requirements. Under each scenario, optimal strategy choices under strong-trend and weak-trend market states are examined, showing that trend-following strategies perform best in strong-trend states, while mean-reversion strategies dominate in weak-trend states. Further results indicate that a combined strategy integrating both approaches outperforms single strategies, increasing returns by 3.0%~5.4% in surplus scenarios and by 17%~33% in balanced scenarios, while reducing costs by 0.1%~6.6% in deficit scenarios. A differentiated trading strategy framework work based on market structure and market trends is thus constructed to guide thermal power enterprises in more effective participation in the national carbon market.