Abstract:Guided by the“dual carbon”strategic goal, zero-carbon parks, as a key practical vehicle for promoting the clean and low-carbon transformation of the energy system and building a new power system dominated by new energy, have been brought into a stage of rapid planning and construction. The construction of a zero-carbon park is regarded as essentially a systematic carbon reduction process that gradually evolves from low carbon to zero carbon, in which the innovation and application of carbon reduction and carbon reduction technologies are particularly critical. Based on this, an in-depth analysis from the perspective of energy-carbon coupling is conducted to systematically expound the research progress and application practice of the key carbon reduction technology system in the process of zero-carbon park construction. It is focused on in-depth research in four core technology areas：planning technology, operation optimization technology, carbon asset collaborative management technology, and key supporting technology platforms. The main challenges and core technical support faced in the current construction are sorted out, summarizes the development status and existing deficiencies of each technology are summarized, and its future development trends are looked forward to, in order to provide a reference for theoretical research and engineering practice of zero-carbon parks in my country.

Abstract:Under the goal of“dual carbon”, the high penetration of renewable energy generation brings about critical challenges such as integration difficulties and strong power fluctuations, which urgently require solutions. Electrolyzer loads with adjustable capacity can achieve bidirectional matching with renewable energy output. Meanwhile, off-grid hydrogen production yields green hydrogen with zero carbon emissions throughout its life, serving as a key driver for achieving the“dual carbon”strategy. Therefore, an optimal capacity configuration method for an off-grid wind-solar-storage hydrogen production system considering hybrid electrolyzers is proposed. First, modeling and analysis are conducted for key components on the power supply side and hydrogen production side of the system. Second, with the objective of minimizing hydrogen production costs, an optimization model for the off-grid wind-solar-storage hydrogen production system is constructed. Finally, a case study is performed based on wind and PV output data from a region in Northwest China. Simulation results demonstrate that the coordinated operation strategy of hybrid electrolyzers, compared to a single electrolyzer, can reduce hydrogen production costs, decrease the start-stop cycles of electrolyzers, and mitigate renewable energy curtailment, thereby verifying the feasibility and effectiveness of the proposed model and method.

Abstract:Feasibility assessment of zero carbon transformation in parks is an important basis for promoting the development of zero carbon parks. Considering that whether the park can achieve zero carbon transformation depends on the comprehensive effects of factors such as the park’s GDP, fossil energy consumption intensity, new energy power generation, and carbon reduction technology investment, a feasibility assessment model for zero-carbon transformation of industrial parks based on system dynamics is proposed. The model consists of four modules：fuel combustion and production process carbon emissions, sewage treatment carbon emissions, purchased power carbon emissions, and carbon sinks and CCUS technology carbon reduction. The simulation results show that the model can predict the specific time for the park to achieve zero carbon transformation under the influence of multiple factors, and provide guidance for the configuration of emission reduction strategies.

Abstract:To address the collaborative optimization challenges of microgrids containing distributed photovoltaics（PV）in cold chain logistics parks, where the refrigeration systems require 24-hour continuous operation to maintain strict temperature control（-18°C±2°C）while PV output concentrates during 10:00—15:00 and logistics operations mainly occur in early morning and evening, this temporal mismatch between power demand and PV generation leads to severe PV curtailment. Proposing a scheduling strategy that balances PV consumption rate and operational economy. This strategy tackles the issues of high PV curtailment caused by strong PV output randomness and the complexity of multi-objective cooperative optimization. A tripartite collaborative architecture of“source-load-storage”is constructed. Typical PV output scenarios and their probability distributions are generated using the k-means clustering algorithm. Downside risk constraints are employed to quantify the cost fluctuation risk under these scenario probabilities, providing a continuous regulation space spanning from risk aversion to risk neutrality. A multi-objective optimization model is established to maximize the PV consumption rate and minimize the comprehensive operational cost. A dynamic-weight ideal point method based on the rate of change of the objective functions is proposed.An improved escape algorithm is designed to solve this model. Case studies demonstrate that under a defined risk regulation level, the park’s PV consumption rate reaches 95.13% , and the daily operating cost is reduced to RMB 7 356, representing a cost reduction of 21.60% compared to pre- optimization levels. The results verify that the proposed collaborative optimization method can effectively enhance distributed PV consumption capability and operational economy. It provides a viable solution and theoretical foundation for scheduling complex microgrids containing high-penetration renewable energy and thermostatically controlled loads.

Abstract:Stackelberg game model is employed to optimize the demand response of the building microgrid in the power market. According to different types of demand response, a Stackelberg game-based optimization mechanism for carbon trading is designed for different building microgrids. Considering the actual carbon emissions of building microgrids with a high penetration of renewable energy under carbon emission constraints, a low-carbon optimal operation model for the building microgrid is established, aiming to minimize electricity costs and environmental carbon emissions. Experiments are conducted for different demand response scenarios, and analysis and verification were performed. The results show that maximizing the proportion of renewable energy through demand response can effectively improve the overall economic performance of the building and achieve a synergistic low-carbon economic effect. The proposed Stackelberg game model can maximize the benefits of agents and microgrid users in the carbon trading market, effectively enhance the accuracy of electricity consumption decisions for building users and the economic benefits for operators, and plays a vital role in building active distribution networks and a dual-carbon system. It is of great significance for developing a robust energy grid.

Abstract:With the rapid growth of renewable energy grid integrated into power grid and load increase during peak hours, the demand for peak regulation ancillary services in the power system is increasing. Therefore, an integrated energy system（IES）model based on masterslave game is proposed, and the benefits of the game parties are increased with the peak regulation demands met. Firstly, a multi-mode utilization model of hydrogen energy is established to enhance the energy coupling of IES. Secondly, an integrated demand response（IDR）model including electricity and heat is constructed with the consideration of energy consumption utility and dissatisfaction, so as to give full play to the potential of flexible load regulation. Then, a master slave game model is established with integrated energy system operator （IESO）as the leader and user aggregator（UA）as the follower, and both parties play the game with the goal of maximizing their respective revenue. Two-layer master-slave model is converted into a single-layer model through the KKT condition for obtaining optimization solution. The effectiveness of the proposed optimization scheduling model is verified by case study, and the revenue of IESO is increased by 43.60% and theutility of energy consumption of UA is increased by 2.02%.

Abstract:The peak sharing pressure on power systems is intensified by the integration of renewable energy, making massuve distributed flexible resources regarded as crucial for virtual power plant（VPP）planning and operation. A VPP model addressing renewable energy uncertainty is proposed：an improved Transformer method forecasts renewable output, a distributed resource aggregation scheme is designed, and a corresponding control strategy balancing security and economy is developed. Validation using real regional data and grid topology is used to confirm that the scheme effectively supports renewable energy utilization and system stability.

Abstract:With the deepening of“dual carbon”process, the transformation of power system to green and clean is imperative. The massive distributed resources in virtual power plant aggregate distribution network can realize the goal of power decarbonization and green transformation through flexible load regulation. Based on this, a virtual power plant economic optimization model based on near-end strategy optimization is proposed. Firstly, based on the principle of proportional sharing, a carbon flow model is constructed to track the carbon density of each node in real time. Then, the low-carbon economic dispatching optimization objectives of virtual power plant are constructed, including load adjustment cost and carbon emission cost. Finally, the proposed model is solved using the proximal strategy optimization algorithm. The example analysis shows that the proposed low-carbon model can realize the carbon flow tracking in the whole time scale on the basis of guaranteeing the power flow safety of the distribution network. Moreover, the proposed near-end strategy optimization algorithm can realize the low carbon scheduling of internal resources in virtual power plant while ensuring economy.

Abstract:Driven by the“dual carbon”goal, significant changes will occur in social and economic development, energy production, and consumption structure, leading to new characteristics in factors and trends affecting electricity de-mand forecasting. The power industry is a key area to ensure the practical achievement of the“dual carbon”goals, so research needs to be conducted on electricity demand forecasting method under the new situation. Logarithmic mean index method（LMDI）and path analysis method are used to study the influencing factors of electricity demand under the background of“dual carbon”, and extracts three carbon emission related influencing factors of electricity demand：electrification rate, clean energy generation ratio, and energy intensity. A method for predicting electricity demand based on fuzzy autoregressive distributed lag model is proposed. The regression coefficients are fuzzified on the basis of the autoregressive distributed lag model considering policy lag effects. By establishing a mini-mum fuzziness optimization model, the regression parameters with the least uncertainty are obtained, which improved the accuracy of long-term electricity demand prediction under the background of “dual carbon”. Based on the historical data of China’s socio-economy and electricity demand, and in combination with the national policy objectives, the electricity demand in China under different low-carbon paths has been predicted to verify the feasibility and effectiveness of the power demand forecasting method proposed in this article.

Abstract:In the context of the vigorous promotion of energy Internet, the formation of cooperation alliances among multiple micro grids in the region has become an important trend to improve the efficiency of regional energy utilization. A microgrid group cooperative game model under incomplete information is proposed to address the challenges of energy exchange, information asymmetry, and profit distribution during the operation of cooperative alliances. Firstly a cooperative game model for integrated energy microgrids considering energy storage sharing is established. Bayesian games are further to characterize incomplete information, are combined with cooperative games to improve the model, and operating costs are minimized while considering uncertain information risks. Finally, in the income distribution, electricity sharing indicators are design to improve the traditional Shapley value method and form a differentiated distribution strategy. Simulation results show that shared energy storage can support energy exchange between integrated energy microgrids and improve energy utilization efficiency；The Bayesian cooperative game model for microgrid group collaborative operation can be used to reduce overall external energy dependence, achieve risk return balance while incomplete information scenarios are considered, and the operating costs of participants are reduced；the improved allocation method based on the electricity sharing index can effectively enhance the benefits of contributors in the alliance and optimize the distribution of alliance benefits.

Abstract:To address the issue of renewable energy consumption responsibilities for electricity sales companies, a power purchase and sale strategy that considers both user demand response and renewable energy consumption weight is proposed. Firstly, a bilevel optimization model is constructed, incorporating user demand response and renewable energy consumption weight. The upper-level model sets consumption weight for different time periods based on the characteristics of wind and solar power output, and establishes a time-segmented power purchase and sale decision model with the goal of maximizing the electricity sales companies’revenue. The lower-level model involves users shifting and reducing their loads according to the real-time prices set by the electricity sales companies, with the objective of minimizing the users’overall electricity cost, thereby creating an optimized energy usage model for users. Secondly, the luminance attraction mechanism of the firefly algorithm is used to improve the multi-objective particle swarm optimization algorithm, and the improved algorithm is used to solve the double-layer optimization model. Finally, the effectiveness and superiority of the improved algorithm and strategy proposed are verified through a case study analysis.

Abstract:Against the backdrop of the escalating global energy and environmental crisis, accurate prediction of air conditioning energy consumption is crucial for formulating effective energy-saving policies, optimizing energy utilization, reducing energy pressure, and reducing carbon emissions, as air conditioning energy consumption accounts for a large proportion of the entire building energy consumption. A data-driven air conditioning energy consumption prediction method is proposed based on a probability model of air-conditioning occupant behavior. Firstly, based on the analysis of the relationship between air conditioning energy consumption and factors such as air-conditioning occupant behavior, environmental parameters, time, and building, a feature label system for building air conditioning energy efficiency analysis is further constructed, covering multiple dimensions such as air conditioning occupant behavior, environment, time, and building characteristics. Secondly, the air-conditioning occupant behavior probability（AOBP）model is introduced as a factor to reflect the realtime interaction between the building environment, air conditioning occupants, and energy systems. This model considers the effects of strategy, time, events, and external stimuli, thus providing a more comprehensive estimation of air conditioning usage. Finally, particle swarm optimization algorithm is utilized to optimize the long short term memory network（LSTM）and to predict energy consumption across various building types and air conditioning systems. The simulation experiment results show that the proposed data-driven air conditioning energy consumption prediction method has made significant progress in improving prediction performance, but the calculation time has also correspondingly increased.

Abstract:An operational optimization strategy for microgrid energy storage systems is developed to meet real-world user application requirements, and its effectiveness and applicability are validated using actual user data. First, a fundamental model of the energy storage system is established, providing a theoretical foundation for the operational optimization strategy. Subsequently, a multi-objective operational optimization strategy for the microgrid energy storage system is developed, focusing on economic objectives, carbon emission reduction targets, and renewable energy integration goals. The commercial optimization solver Gurobi is employed to enhance computational efficiency. Finally, the proposed optimization strategy is validated using real-world microgrid data from City A in a province of East China.The results demonstrate that the constructed energy storage system model accurately captures the operational constraints of the actual system. Compared to the user’s existing strategy, the proposed optimization strategy achieves an average reduction of 13.4676% in electricity cost expenditure. By dynamically adjusting weight factors for multi- objective optimization, the strategy enables diversified operational modes, significantly enhancing the scenario adaptability of the energy storage system’s operational strategy. Furthermore, the strategy provides decision-making support for formulating policies related to surplus electricity feed-in from us-er-side microgrids. The main innovation lies in the strategy’s user-centric design, which achieves operational flexibility through multi-objective weight allocation, improves scenario adaptability, and offers a novel approach for the practical implementation of microgrid energy storage systems.

Abstract:In order to promote the key work of carbon reduction of small or low energy consumption buildings with high quality and further optimize the energy management of smart buildings, a smart building demand response strategy for flexible load and carbon trading mechanism is proposed to optimize the demand response of smart buildings and clean energy use. Firstly, an intelligent building power-heat energy system containing distributed energy is constructed, and flexible loads such as transferable load, reducable load and transferable load are introduced into the system, and dynamic energy allocation is carried out according to the energy demand of building users. Secondly, considering the stepped carbon trading mechanism, the building carbon emission cost is optimized by adjusting the energy output, the building carbon emission is improved, the energy structure is clean and the transformation is promoted, and the economic and environmental benefits of the system are optimized. Through the comparative analysis of three scenarios, the introduction of flexible load and carbon trading mechanism in the energy system of intelligent buildings can give full play to the interaction and plasticity of the energy structure of intelligent buildings, effectively promote the consumption of clean energy, and realize the low-carbon economic operation of the energy system.

Abstract:Historical electricity load data of industrial enterprises has the characteristics of strong volatility and complex sequence, which brings challenges for accurately predicting electricity load. In order to solve these problems, a short-term load forecasting method for industry based on multi scale weight adaptation and bidirectional gated recurrent unit（MSWA BiGRU）is proposed. The proposed model is composed by a weight adaptation layer, a BiGRU layer, a feature embedding layer, and a fully connected prediction layer. Firstly, the weight adaptation layer adaptively generates the dependent thermal coefficients for different time scale load data, and then the BiGRU layer simultaneously learnes the transient fluctuation characteristics and steady-state periodic characteristics of the historical load series on multiple scales. Then, other features is embedded in the feature embedding layer. Finally, the temporal features are fused with other features to obtain the final load prediction result through the fully connected prediction layer. Experimental results on real data of electricity load in industrial and commercial enterprises show that the prediction performance of the proposed method is superior to other methods,thus the effectiveness and feasibility of this method are verified.

Abstract:While user-side carbon reduction potential and quantity-price bidding enhance market flexibility, the uncertainty in cleared quantities may trigger demand-supply mismatch. To address diverse user needs, participants are categorized into three types based on their unmet demand for uncleared quantities and demand adjustment willingness, establishing a multi-stage clearing model for electricity-carbon coupled day-ahead markets. In the first stage, the clearing model with the goal of maximizing social welfare is established, and all users carry out settlement at that stage. In the second stage, the clearing model with the goal of minimizing total system cost is established,and users with demand for unsuccessful bids carry out deviation settlement. In the third stage, the clearing model with consideration of the users’carbon intensity is established, and users agreeing to demand adjustment carry out deviation settlement. The computational results of the improved PJM5 node system show that the model not only flexibly meets user demands, but also reduces system costs, carbon emissions, and user costs.

Abstract:Virtual power plant（VPP）, as an aggregated energy system, is faced with a difficult problem that the uncertain wind power and solar power and the random charging behavior of electric vehicles affect its economy. Therefore, a virtual power plant bidding strategy that takes into account diverse un-certainties is proposed, which can improve the absorption capacity of renewable energy and in-crease the income level. Firstly, the particle swarm optimization（PSO）-long term memory（LSTM）algorithm is used to convert the wind power and EV charging demand into deterministic scenarios based on the real data and the charging capacity boundaries. Then, considering the uncertainty of group loads of renewable energy and electric vehicles, a bidding strategy model of virtual power plant is established based on the output deviation variables and the Cournot game. Finally, a numerical example is given to verify the effective-ness of the proposed model.

Abstract:To support the flexible aggregations of multiple resources, such as the distributed power, controllable load and energy storage under the novel power system, to implement the interaction between power grid and the aggregations, a virtual power plant system which can support dynamic aggregation of equipment is designed and implemented. The proposed virtual equipment packaging technology can eliminate the differences between equipment, and establish virtual units in a unified measurement standard. The equipment-level power generation forecasting and multi-load forecasting engines ensure the prediction accuracy and the flexibility of aggregations. The solution of the cloud-edge collaboration achieves the vertical aggregation of resources. Through deployment verification, the system can dynamically aggregate multiple types of resources to participate in grid interaction, and effectively improve the accuracy of new energy generation power prediction and load prediction, achieving flexible resource efficiency, precision, and flexible regulation，further ensuring the safe and stable operation of the power grid.