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Abstract The Snow Goose Algorithm (SGA) is a new meta-heuristic algorithm proposed in 2024, which has been proved to have good optimization effect, but there are still problems that are easy to fall into local optimal and premature convergence. In order to further improve the optimization performance of the algorithm, this paper proposes an improved Snow Goose algorithm (ISGA) based on three strategies according to the real migration habits of snow geese: (1) Lead goose rotation mechanism. (2) Honk-guiding mechanism. (3) Outlier boundary strategy. Through the above strategies, the exploration and development ability of the original algorithm is comprehensively enhanced, and the convergence accuracy and convergence speed are improved. In this paper, two standard test sets of IEEE CEC2022 and IEEE CEC2017 are used to verify the excellent performance of the improved algorithm. The practical application ability of ISGA is tested through 8 engineering problems, and ISGA is employed to enhance the effect of the clustering algorithm. The results show that compared with the comparison algorithm, the proposed ISGA has a faster iteration speed and can find better solutions, which shows its great potential in solving practical optimization problems.

Addressing issues such as high hardware costs, low recognition accuracy, and the inability to achieve fine-grained equipment classification, a non-invasive load fine-grained recognition system based on FPGA was developed and tested on a Linux system for online training. A three-dimensional (3D) image construction method based on color coding of voltage–current (V-I) trajectories is proposed to preprocess the collected voltage and current data, allowing for the distinction of features of various electrical equipment in multiple dimensions. First, high-frequency sampling data is preprocessed to extract the V-I trajectory and higher harmonic characteristics of the load. Then, the V-I trajectory is processed using RGB color coding and fused with higher-order harmonic features to construct a 3D image. This results in a 3D color V-I trajectory image that incorporates both color and harmonic features. Finally, the improved ResNet50 network is employed to identify the load characteristics, and the method is validated using the PLAID dataset and measured data. The load identification method achieves an accuracy rate of over 98%, enhancing the information conveyed by the V-I trajectory and improving the uniqueness of load characteristics, thereby enabling fine-grained equipment identification. This advancement holds significant implications for energy conservation and emission reduction in household electricity consumption, as well as for eliminating potential safety hazards associated with electrical equipment.

Hydrogen energy is a promising solution to reaching carbon emissions peak and carbon neutrality. With the development of hydrogen energy devices, coupling hydrogen energy with renewable energy for power generation can compensate for the intermittency and instability of renewable energy. Furthermore, the two-stage coordinated planning of integrated energy system (IES) and electricity-hydrogen integrated energy system (EHIES), compared to independent planning, can help generate additional benefits and reduce carbon emissions. Therefore, this paper proposes a coordinated planning of EHIES considering lifecycle carbon emissions. In the first stage, aiming to minimize investment costs, network losses, voltage deviations, and lifecycle carbon emissions, the equipment capacity and location of EHIES is determined. The life cycle assessment (LCA) method is used to quantify the lifecycle carbon emissions of the EHIES, preparing for the coordinated operation in the second stage. Subsequently, in the second stage, using carbon emissions flow to allocate users’ responsibility for emissions, the carbon emissions per unit electricity of the IES system are calculated. With the optimal cost of independent operation for IES and EHIES as the coordinated operation objective, the alternating direction method of multipliers (ADMM) is employed for sequential solution, obtaining the carbon emissions reduction amount, and iteratively updating until convergence. Finally, the effectiveness of the proposed method is verified by using the IEEE 33-bus power distribution network (PDN) and natural gas 6 node systems. The results show that compared to independent planning, the two-stage coordinated planning method reduces the overall operating cost of IES by 3.81% and decreases carbon emissions by 15.89%. This method is effective in reducing the carbon emissions of IES.

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

In recent years, there has been rapid advancement in new energy technologies aimed at mitigating greenhouse gas emissions stemming from fossil fuels. Nonetheless, uncertainties persist in both the power output of new energy sources and load. To effectively harness the economic and operational potential of an Integrated Energy System (IES), this paper introduces an enhanced uncertainty set. This set incorporates N-1 contingency considerations and the nuances of source–load distribution. This framework is applied to a robust optimization model for an Electric Vehicle Integrated Energy System (EV-IES), which includes Electric Vehicle Battery Swapping Station (EVBSS). Firstly, this paper establishes an IES model of the EVBSS, and then proceeds to classifies and schedules the large-scale battery groups within these stations. Secondly, this paper proposes an enhanced uncertainty set to account for the operational status of multiple units in the system. It also considers the output characteristics of both new energy sources and loads. Additionally, it takes into consideration the N-1 contingency state and multi-interval distribution characteristics. Subsequently, a multi-time-scale optimal scheduling model is established with the objective of minimizing the total cost of the IES. The day-ahead robust optimization fully considers the multivariate uncertainty of the IES. The solution employs the Nested Column and Constraint Generation (C&CG) algorithm, based on the distribution characteristics of multiple discrete variables in the model. The intraday optimal scheduling reallocates the power of each unit based on the robust optimization results from the day-ahead scheduling. Finally, the simulation results demonstrate that the proposed method effectively reduces the conservatism of the uncertainty set, ensuring economic and stable operation of the EV-IES while meeting the demands of electric vehicle users.

There are some problems with the fast-charging station of urban electric vehicles (EV), including blind construction, unreasonable planning location, and neglect of user requirements. This paper presents a planning method considering functional zoning and traffic influencing factors. Firstly, the planning area’s POI points and road network information are processed based on the geographic information software ArcGIS, and the urban functional zoning is divided. Then, two factors that affect the user’s time-consuming cost are studied, such as regional traffic congestion and signal waiting time. Secondly, according to the initial location of the user, the road network adjacency matrix, and the probability function of the user selecting the fast-charging station, the Floyd algorithm is used to calculate the time-consuming cost of the user to the fast-charging station. In order to reflect the profitability of the fast-charging station, the payback period function is also introduced. Finally, the MOSAPSO algorithm is used to solve the multi-objective function composed of the investment cost, operation, and maintenance cost, payback period, and user time-consuming cost of the fast-charging station. The optimized site selection and management layout of fast charging stations provide a certain reference for urban EV fast charging stations.

At present, the concept of ecological civilization has been widely recognized by the whole world, and a series of policies that guarantees the evolution of the electric vehicle (EV) industry has been implemented by multiple countries. Therefore, it is significant to predict the charging load of EVs to solve the challenges of power system planning and operation. The Monte Carlo (MC) method is preferred by many scholars in EV charging load prediction because it is very suitable for describing random characteristics with a good prediction effect. To obtain more reliable and efficient prediction results, this paper analyzes the application of parallel computing technology in MC simulation. Firstly, EVs in the region are classified according to their battery capacity. Based on the voltage change curve of lithium-ion power batteries in the process of constant current charging under different capacities, the charging power, charging time, and state of charge (SOC) of EVs are investigated. Secondly, the behavior characteristics of users and the driving parameter characteristics of EVs are studied respectively, and the probability distribution model of multi-source information is established. Thirdly, parallel computing technology in the computer field is introduced, and an improved MC method is proposed based on the multi-core CPU architecture. After fully considering the complex constant current charging process after fitting, the charging load of EVs in a region of East China is simulated. Finally, the time cost and the load forecasting results of serial and parallel methods are compared and analyzed, and the progressiveness and effectiveness of the parallel method are verified. Results show that the charging load has four peaks in a day, taxis are the main source of the peak load of the power grid, the charging load of buses fluctuates the most, and private cars are the main backup capacity to participate in V2G dispatching in the future. In addition, under the experimental conditions set in this paper, compared with the traditional serial MC method, the improved MC method based on parallel computing shows good performance with the acceleration effect improved by 7 to 12 times.

Aiming at the uncertainty in short-term load forecasting, and considering the multi-dimensionality of load data and the continuity of time series, this paper constructs a short-term power load forecasting method based on accumulated temperature effect and improved Temporal Convolutional Network. First, the common load influencing factors in the short-term forecasting process were explored, the data were cleaned and correlated, and a quantitative model of temperature variables that considered the effect of accumulated temperature was established, such as the weighted average method and the discrete correction model. Secondly, fully consider and divide the multi-dimensional influencing factors, the time series characteristic quantity is formed by taking advantage of the Temporal Convolutional Network (TCN), extracting potential relationships between time-series data and non-time-series data, and the optimal load estimation value is obtained. Finally, the output of the TCN is fused with the non-time-series data to form a new input feature matrix, and the load prediction is completed through the Back Propagation (BP) Neural Network. The calculation example shows that the TCN-BP model combined with temperature correction can reduce the average absolute error by 1.77%. Under the same forecasting method, the load forecasting accuracy obtained by the weighted average method is higher than that of the discrete correction model. Compared with the single TCN and BP models, the method proposed in this paper increases the training time, but reduces the time loss of prediction and improves the prediction accuracy.

Considering the non-linear, multi-dimensionality and time-series of load data, a short-term power load forecasting method based on TCN-DNN hybrid deep learning model is proposed. Firstly, considering the common influencing factors of short-term load, and analysing the correlation, the temperature variable quantitative model considering the accumulated temperature effect is established. Secondly, based on the deep learning, the load influencing factors are classified and processed; In order to study the internal dynamics of the data, the Temporal Convolutional Network (TCN) is used to extract and construct time-series feature vector; The outputs integrate with non-time-series data factors (date type) into new input features. Finally, the internal relationship between the characteristics and load changes is analysed as a whole by the Deep Neural Network (DNN), and the load forecasting is finally completed. Taking the actual load data of the power grid in a certain area of East China as practical example, the forecasting accuracy of the proposed method is 97.92%. The proposed method is compared with Long Short-Term Memory (LSTM), TCN, DNN and the hybrid TCN-DNN model without temperature correction, the experimental results show that the proposed method has higher optimization efficiency and forecasting accuracy.

The change of electric vehicle (EV) cluster schedulable capacity in each period under multi-time scales has strong randomness and volatility. To ensure the trip demand and the charge and discharge cost of users, and increase the reliability of load demand curve prediction, this paper proposes a dynamic charge and discharge optimization strategy that takes the charge and discharge control coefficient in each response period as the control object. According to the trip chain, the proposed method models the trip time-space distribution of EV users, sorts each response period from small to large according to the starting time, comprehensively considers the interests of users and the peak shaving demand of the power grid, and uses Quantum Particle Swarm Optimization (QPSO) to solve the multi-objective optimization of charge and discharge control coefficient for the sorted response period. The charge and discharge control coefficient is modified by introducing virtual charge time and the virtual state of charge. Compared with the traditional method, the proposed method considers the mismatch between the expected parking time of users and the actual parking time of users and can update the load demand curve in real-time due to the dynamic changes in users’ trip behavior, which is more practical. To verify the effectiveness of the method proposed in this paper, according to the simulation results of the time-space distribution of electric private car users, the load demand curves under different charge strategies, different optimization weights, and different vehicle-to-grid (V2G) responsiveness are simulated and analyzed. The results show that the proposed method can effectively reduce the peak valley difference and variance of the load demand curve under the condition of ensuring the trip demand and economic benefits for EV owners.

With the development of smart power distribution networks, the demand of smart grid user-side management is increasingly urgent. To improve the accuracy of short-term electric load forecasting for individual users, this study proposes a short-term power load forecasting model based on K-means and FCM–BP. Firstly, by analyzing the users’ electricity consumption features, K-means is applied to group users into two clusters. Secondly, for users with strong correlation at adjacent moments, local similar data is filtered out with the help of improved Fuzzy C-Mean clustering (FCM), integrating the load value of the adjacent moments into new input features. For users with weak correlation at adjacent moments, the local similar daily data are utilized as features. Finally, the feather vectors are used as input data for BP Neural Network, which is utilized to forecast the short-term load. Experimental results show that the clustering method proposed concurs with the characteristics of users’ electricity consumption behavior. With the same forecasting method, the accuracy of clustering-based load forecasting is higher than that of non-clustering load forecasting. Compared with traditional BP, RBF and GRNN Neural Network, this model has higher forecasting accuracy.

Shared energy storage offers substantial savings on construction costs and improves energy efficiency for users, yet its business model as an independent economic entity remains unclear. An optimal scheduling method for cooperative operation of shared energy storage among multiple user types is proposed in this paper, which relied on asymmetric Nash bargaining to define operational schedules and pricing strategies effectively. Initially, a cost-benefit model for shared energy storage operators, along with power generation users, demand-side consumers, and microgrid prosumers is developed. Then, a cooperative game framework is established using asymmetric Nash bargaining principles which decomposes the problem into two parts: minimizing social total cost through cooperative operation scheduling and determining service fee pricing for equitable benefit distribution. For benefit distribution, the bargaining power of users is adjusted based on their alliance contribution, ensuring revenue distribution is aligned with individual contributions and improving fairness in pricing. Subsequently, the adaptive penalty factor alternating direction multiplier method (ADMM) algorithm is employed for distributed equilibrium solving, enhancing the convergence speed and safeguarding user privacy. Finally, the economics and feasibility of the proposed cooperation framework for shared energy storage are validated through a numerical example. [ABSTRACT FROM AUTHOR]

In order to reduce the adverse effects of wind and photovoltaic power output fluctuations in the micro-grid, hybrid energy storage system, mainly super capacitor and Li-ion batteries are adopted to smooth the power fluctuations. A filtering method based on discrete Fourier transform (DFT) is applied in this paper to obtain the smoothed output which meets the power fluctuation requirements. Meanwhile, the target output of the hybrid energy storage system is determined. Taking the maximum charging/discharging power constraints and the charging state of storages (SOC) into consideration, an optimized power allocation strategy based on fuzzy control theory is proposed. Based on the strategy, an optimization model for capacity allocation with the objective of the minimum total cost of the hybrid energy storage system is established, which also takes the fluctuation rate and the constraints of the energy storage devices into concern. The genetic algorithm is used to find the optimal configuration. Furthermore, the influence of errors between its actual output and the target output on the energy storage system capacity is analyzed. The efficiency of the method has been proved through the simulation example, and the required power and capacity of storage can be reduced with certain tolerance of the smoothing goal. [ABSTRACT FROM AUTHOR]

A gradual configuration research on energy storage system (ESS) is proposed and applied for photovoltaic (PV) plant in this paper. First, the ESS is used to absorb the unbalanced energy between the solar power generation and load demand. It is found that ESS capacity will be extremely large to meet all the balancing requirements. A simple solution for this problem is to allow excess power generation. Then, the ESS is installed at a PV plant to achieve generation schedule based on power forecast. A modified probabilistic approach is adopted to study the impact on the needs for storage because of the forecast errors. Results show that the required ESS capacity can be significantly reduced when a small amount of residual PV output uncertainty has been permitted. Lastly, to be more practical and economical, utilising the ESS for power fluctuations smoothing is also included in this study. It is shown that the ESS capacity is strongly influenced by the smoothness of the output wanted, but much smaller than the two situations mentioned above. [ABSTRACT FROM AUTHOR]