Abstract:Effective analysis of the potential for electric energy substitution is significant for formulating development strategies and promoting local energy conservation and emission reduction. Analyzing the development trends of electric energy substitution under different scenarios can provide a scientific basis for regional planning. An improved particle swarm optimization-support vector machine model is proposed for predicting the potential of electric energy substitution under multiple scenarios. It analyzes indicators influencing electric energy substitution potential, such as the proportion of electricity consumption, energy consumption per unit of GDP, disposable income of urban residents, CO2 emissions per unit of GDP, and quantifies these indicators. Pearson correlation coefficient method is used to screen and introduce indicators into the prediction model. Four development scenarios—baseline development, technological progress, economic development, and low-carbon environmental protection are considered for predicting the potential for electric energy substitution. Actual data from a province in southern China is analyzed, comparing results with grey wolf optimizer-support vector machine（GWO-SVM）and SVM models, validating that the proposed method demonstrates good predictive performance. The electric energy substitution potential in 2030 and 2035 under various scenarios is also analyzed, providing theoretical support for future regional electric energy substitution planning.

Abstract:Linking carbon-green certificate trading with the comprehensive demand response mechanism will help optimize user energy usage behavior and reduce carbon emissions from the integrated energy system. In this background, a method is proposed to link the carbongreen certificate trading-demand response mechanism with the cooperative game, and the alliance breakdown problem caused by the interest distribution mechanism in the cooperative game is analyzed. First, each park is the main body to form a cooperative game alliance. Second, the carbon trading mechanism, green certificate trading, comprehensive demand response mechanism and cooperative game model are linked to promote the active consumption of renewable energy in the alliance by each park in the integrated energy system. Reduce carbon emissions. Then, in order to ensure the success of the cooperative alliance, an improved benefit distribution method is proposed. Finally, through actual case simulation analysis, the method of linking the carbon-green certificate trading-demand response mechanism and cooperative game promotes the low-carbon economic operation of the integrated energy system, and through improved profit distribution methods, the enthusiasm of each park to participate in the alliance has been increased on the basis of a stable alliance.

Abstract:As the key to decarbonization development in the field of transportation, new energy vehicles have their charging demand replenishment affected by the layout planning of basic charging facilities. To address this problem, a regional electric-hydrogen generation and charging station（EHGCS）planning study based on electric-hydrogen-road-vehicle synergy is proposed. Firstly, a time-flow model is established to analyze the topology of traffic roads, and Dijkstra is used to simulate the driving paths of new energy vehicles, so as to establish an electric-hydrogen demand prediction model for new energy vehicles considering the vehicle-road-network coupling. secondly, the whole process of producing, compressing and storing hydrogen is analyzed, and the operation architecture and energy flow model of the EHGCS are constructed. Thirdly, the planning model of the regional electric-hydrogen charging station is established with the goal of minimizing the economic costs, such as investment, operation, network loss, etc., taking into account the constraints of road-network coupling, distribution network and safe operation of equipment, and solving for the optimal planning scheme by using the second-order conical relaxation technique；finally, some arterial roads of urban areas of a certain city and the nodes of the distribution network of IEEE33 are used as the examples to validate the validity and practicability of the proposed methodology.

Abstract:With the gradual progress of China’s“dual carbon”work, the carbon emission verification of residents has become increasingly important. In order to solve the problems of large analysis granularity and low verification accuracy in resident carbon emission verification, a resident carbon emission verification method based on carbon flow tracking is proposed. First, smart home is used to collect the energy consumption of residents’roof photovoltaic, energy storage and various energy consuming devices. Second, carbon flow tracking technology is adopted to realize the positive tracking of carbon emissions of household energy equipment, the reverse tracking of carbon emissions of roof photovoltaic and reverse power storage, and the two-way carbon flow allocation of loss. On this basis, the carbon emissions of residents are verified. Finally, a case study is conducted in a city in southern China, and the results show that：the proposed method can effectively improve the particle size of residents’carbon emission verification and the accuracy of carbon emission verification.

Abstract:Considering the advantages of integrated energy systems（IES）in economy and energy saving, and the important role of hydrogen storage and electric heat storage systems in power peaking, An IES for parks that includes hydrogen storage and electric heat storage systems is designed, and the capacity configuration method and optimal dispatch strategy are proposed. Firstly, the basic architecture of the system is established, and the mathematical model is established for the key equipment. Then establish a system optimization design model, in which the minimum system integrated energy cost is the optimization target. Finally, the optimization design model is solved by the bi-level solution combining particle swarm optimization（PSO）and Gurobi. Simulation results indicated that the IES designed with the proposed method can plan electricity, hydrogen, cooling and heating together to allocate resources rationally. Compared with the original energy supply system in the park, IES can reduce the energy cost of electricity, heating and cooling, improve the economics of the system,and have the potential to be promoted on the demand side. The hydrogen storage and electric heat storage system can effectively utilize the electricity in the power system valley load period, and has a strong ability to shift peaks and fill the valley.

Abstract:A two-stage low-carbon optimization scheduling model for PIES based on multi energy flow coupling and carbon electricity market synergy is established. First, the park-level integrated energy system（PIES）multi energy flow virtual carbon flow transmission mechanism based on the unified energy bus structure is studied, the concept of energy bus“carbon flow density”and the principle of carbon emission conservation within the energy storage and scheduling cycle are introduced, and the PIES multi energy supply, consumption carbon emission accounting system is designed. Second, the carbon flow density of the regional power grid is used to calculate the carbon emissions, and PIES economic dispatching is one-stage. A two-stage optimization model of low-carbon DR optimal scheduling with the signal of stepped time-sharing carbon price is constructed to reduce the total carbon emissions by using users’willingness, and load side regulation capacity. Finally, a typical PIES is used to analyze numerical example. The results show that the proposed optimization model can make timely response according to the carbon flow density trend of the regional power grid, and user side DR to minimize its own carbon emissions and achieve the goal of deep emission reduction.

Abstract:To support the safe and efficient operation, flexible and adaptive regulation capabilities of a high proportion of new energy power systems, it is necessary to simultaneously develop large-scale pumped storage energy. To scientifically and accurately quantify the benefits of pumped storage capacity with and without the influence of new energy, a concept of pumped storage capacity benefits is proposed；Secondly, a comprehensive analysis is conducted on the impact of the proportion of new energy in-stalled capacity and output characteristics on the efficiency of pumped storage capacity in different scenarios. In order to consider the impact of uncertainty in new energy on capacity efficiency. Further determine the thermal power start-up demand and pumped storage capacity benefits considering the new energy power support capacity based on meeting the power supply demand at 95% of the time；Finally, based on the planning situation of a certain region, compare and analyze the pumped storage capacity benefits corresponding different energy storage time. The research results indicate that this method can scientifically guide the construction and opti-mization of operation strategies for the scale of future pumped storage demand in the medium to long term.

Abstract:The wide area measurement system（WAMS）and synchronous phasor measurement unit（PMU）have been widely deployed in new energy stations above 40 MW according to relevant requirements. Real-time data obtained through their high-precision sampling provides conditions for the dynamic modeling and identification of new energy units. Standard units provided in the PSD-BPA transient stability program user manual is used to establish a parameter electromechanical transient model for photovoltaic power generation systems.Based on prediction errors, a closed-loop identification method uses WAMS data to identify and analyze the parameters of the photovoltaic power generation system online. On this basis, a prediction error method combined with the box-jenkins（BJ）model is proposed to identify the uniqueness of parameters. A photovoltaic power generation system model is constructed through a simulation platform, and the measured WAMS data of the photovoltaic power station in Henan power grid is fitted and analyzed, fully verifying the effectiveness of the proposed method for closed-loop identification.

Abstract:In the background of global climate change, Jiangsu Province has played a crucial role in advancing green electricity policies while facing challenges such as tightening land resource constraints and increasing pressures on renewable energy consumption. Literature and content analysis methods are used to examine 112 provincial-level green electricity policies from Jiangsu between 2016 and 2023, employing KH Coder for keyword co-occurrence network and multidimensional scaling analysis. Jiangsu’s green electricity policies have evolved through three stages：the formation and growth phase from 2016 to 2019, the adjustment phase in 2020, and the deepening and innovation phase from 2021 to 2023. The main policy issuers include the Jiangsu Provincial Development and Reform Commission, the Provincial Department of Industry and Information Technology, the Jiangsu Regulatory Office of the National Energy Administration, and the Provincial Bureau of Government Affairs. The study concludes that while these policies show strengths in market mechanisms, infrastructure development, environmental protection, and implementation, they also have significant shortcomings. These include limited capacity for inadequate inter-departmental coordination, and insufficient energy storage policies. To address these issues, the study recommends improving inter-departmental coordination, strengthening evaluation of policy effectiveness, and developing supportive energy storage policies. These actions are expected to improve policy execution efficiency and further promote the development of Jiangsu’s green electricity sector.

Abstract:Under the background of fast development of electricity demand-side response technique, the electric-power system of industrial park introduces different energy types such as gas, hydrogen to achieve comprehensive energy supply. As a result, the participation ability and intensity of demand-side response are significantly improved by the introduction of integrated energy system, and the utilization of renewable energy power is also promoted. In this study, the structure design and optimal dispatching of integrated energy system in industrial park under the background of electricity demand-side response are analyzed and researched, and the topology of integrated energy system including the thermal, electricity and hydrogen energy forms is developed. In addition, the optimal dispatching model is also established by introducing the complex constraints, in order to achieve economical operation of integrated energy system under the background of electricity demand-side response, in which the multi-energy loads are included. Experimental results show that the demand-side response participation of multi-energy loads can be effectively promoted by the introduction of integrated energy system, the operation cost can be reduced while the electric power demand during peak-period is decreased, and the utilization of renewable energy power is also significantly improved.

Abstract:With the development and popularization of distributed photovoltaics（PV）, virtual power plants have been widely applied as a novel form of electric power system integrating distributed energy resources. However, the uncertainty characteristics of distributed PV output are not fixed but depend on the aggregation scale. Firstly, virtual power plant planning and scheduling are investigated based on the decision dependent uncertainty（DDU）in distributed PV aggregation scale. Then, models for DDU and spatial correlation of distributed PV are established. An optimization risk scheduling model for virtual power plants considering DDU of distributed PV is proposed. Addressing the coupling between decision-making and uncertainty, a solution method based on affine function for the stochastic model of decision dependence probability distribution is presented. Lastly, case studies analyze the impact of DDU of distributed PV on virtual power plant scheduling results and different risk preferences of virtual power plant strategies, verifying the effectiveness of the proposed models.

Abstract:The energy consumption of the data center cooling system accounts for 40% of the total energy consumption. Reasonable air flow organization can effectively improve the cooling efficiency of the data center and improve the high energy consumption of the data center. In order to optimize air organization of data center, the impact of air supply methods, cold aisle containment, and return air vent positions on the thermal environment are first qualitatively analyzed according to the numerical simulation results. Secondly, the non-uniformity coefficient is introducedto quantitatively investigate the effects of air conditioning layout, perforated raised floor porosity, and raised floor height on air flow uniformity using the orthogonal test method. Based on a case study of a data center in East China, the conclusion is that using raised floor air supply and return air, fully enclosing the cold aisle, and placing the return air outlet above the hot aisle can improve the thermal environment performance of the data center.

Abstract:With the increasing integration of high-proportion distributed power sources, energy storage, it is necessary to introduce edge intelligent agents as intelligent entities at the terminals of distribution networks for data collection, analysis, computation, and control, thereby achieving efficient and intelligent autonomous operation of distribution networks. Traditional distribution automation systems, when undergoing top-level design, often separate business architecture and software design, which can lead to fragmentation and pose risks to the efficient and stable operation of distribution networks. Hence, an architecture for edge intelligent agent systems based on the concept of domain-driven design system architecture is proposed. Firstly, the internal architecture and implementation methods of functional modules of edge intelligent agents are analyzed. Secondly, using the domain-driven design approach, it constructs the domain model of edge intelligent agents in distribution networks, designs corresponding domain entities, state service capabilities, and domain service functions. Finally, it provides methods for constructing software platforms and software testing, and conducts verification analysis of the constructed system bycase studies.

Abstract:To fully explore the timing and weather information in load data and improve the accuracy of power load prediction, a neural network based on variational mode decomposition（VMD）and bi-directional long short-term memory（BiLSTM）is proposed. Multi-dimensional sequential power load forecasting method leverages the strengths of VMD and BiLSTM to improve the accuracy of power load prediction. Firstly, through correlation analysis of multi-dimensional weather information and time sequence information, feature vectors with high correlation are selected as inputs. Meanwhile, VMD is used to decompose the original load data into intrinsic mode functions（IMF）of different frequencies. Then, the IMF and feature vector with high correlation are input to BiLSTM neural network optimized by sparrow search algorithm（SSA）for prediction. Finally, the predicted value of IMF is superimposed to obtain the final predicted value of power load. Load forecasting data set of 2016 electrical mathematical contest in modeling is used as an example to verify. Compared with BiLSTM and VMD-BiLSTM model, VMD-SSA-BiLSTM model can fully mine timing and weather information in data, and improve the prediction accuracy of multidimensional load data.

Abstract:A hybrid model for power customer load forecasting has been introduced to tackle the challenges posed by the high dimensionality, complex features, and significant interference present in current power data. Utilizing the integrated empirical mode decomposition model, electricity consumption characteristics of power users are decomposed, separating the features into high-frequency and low-frequency components based on the zero crossing rate. Employing a multi-objective evolution-deep belief network, the low-frequency components are processed to accurately forecast the overall trends. Utilizing an enhanced long short-term memory network, the high-frequency components are processed, significantly improving the capability to handle complex nonlinear local behaviors and ensuring precise high-frequency load forecasting. Utilizing the superposition rule, the load forecasting is reconstructed to refine predictions of local fluctuations, markedly enhancing the model’s overall performance. Experimental results indicate that, compared to models such as KNN, BPNN, RNN, and LSTM, the proposed model achieves an average reduction in the mean absolute percentage error. This model demonstrates superior load forecasting accuracy and can offer insights for enhancing the safe operation and service quality of distribution networks.

Abstract:In order to improve the accuracy of photovoltaic power generation power prediction, the standard dung beetle optimization algorithm（DBO）was improved by using three strategies：Bernoulli mapping, the spiral update mechanism of whale optimization algorithm （WOA）and the optimal individual adaptive t-distribution. Through verification on 8 standard test functions, the results show that the improved algorithm has significant improvements in convergence speed and optimization ability. Furthermore, the improved dung beetle optimization algorithm was used to optimize the long short-term memory network model（IDBO-LSTM）for photovoltaic power generation power prediction, and compared with six other models. The prediction results show that IDBO-LSTM exhibits better prediction performance under 3 different weather types than other models. Compared with the DBO-LSTM model, the average absolute error rate（MAPE）of IDBOLSTM decreased by 0.08%, 3.51%, 4.02%, respectively.

Abstract:In order to solve the problem that the PV power prediction error has a serious impact on the dispatch results, an optimal storageload dispatch strategy for residents considering photovoltaic uncertainty is proposed. The affine algorithm was used to quantify the uncertainty of photovoltaic output, and a storage-load optimization scheduling model was established with the goal of minimizing the daily electricity cost and maximizing the comfort of residents, and the Gurobi solver was used to calculate the micro-energy storage charging and discharging plan and the day-ahead electricity consumption plan of residents. Through simulation verification, the proposed algorithm can fully consider the uncertainty of photovoltaic output, effectively improve the accuracy of the scheduling model, and alleviate the pressure of peak power consumption while ensuring user satisfaction.

Abstract:Leveraging the flexibility of low-voltage-side resources is a crucial means to ensure a balanced power supply and demand, as well as support the integration of renewable energy sources. Ways in which low-voltage-side resources can participate in the electricity market are currently under exploration. First, the typical approaches for low-voltage-side resource participation in the electricity market are categorized, with a particular focus on the coordination between transmission and distribution system operators when resources engage in multiple markets. Second, a detailed overview and analysis of pilot projects involving low-voltage-side resource participation in markets in North America and Europe is provided. Subsequently, the involvement of balance responsible party in Europe market is proposed and a tiered traffic-light mechanism for assessing grid operational status is introduced. Finally, taking into account the specific circumstances and current status of the Chinese electricity market, a coordination scheme and mechanism design for the involvement of low-voltage-side resources that aligns with the Chinese electricity market system is presented.