In this paper, in order to improve the efficiency of data processing and the flexibility of each unit dispatching, first, the areas are divided according to the load characteristics. Second, an operating framework of distributed power system is presented based on offload strategy of mobile edge computing (MEC) and optimal allocation of computational quantity. Third, a novel hierarchical dispatching model for distributed renewable energy and energy storage systems is established based on the optimal configuration of MEC.

This paper systematically analyzes features regarding interleaved double dual boost converters to provide the basis of the controller design. First, the topological structure and working principle are introduced. Second, the voltage gain analysis, and input voltage ripple analysis output voltage ripple analysis. Furthermore, the relative controller regarding the interleaved double dual boost converter is proposed in this paper.

An optimization framework with two levels to simultaneously decide the layout and operation of the wind farm/battery energy storage is put forward in this paper.

The demand side management is taken into consideration in the optimization model.

A highly-robust co-estimation method is proposed in this paper to accurately assess the state of charge (SOC) and state of health under strong electromagnetic interference environment. The results suggest that the proposed method has strong robustness against the measurement noises on current and voltage. The average estimation errors of SOC and capacity are 1.57% and 0.11 Ahr, respectively.

This study introduces a novel method for estimating State of Health (SOH) in large-capacity batteries by combining multi-feature extraction with artificial intelligence techniques. Specifically, various Health Index sets (HIs) reflecting Incremental Capacity morphological features are extracted from the charging curves of Lithium-Ion Batteries. Subsequently, a method is proposed to fuse these HIs using an artificial neural network to achieve precise SOH estimation.

A load-side resource flexibility cluster partitioning method based on zonotope approximation is proposed to cope with the flexibility adjustment requirements brought by uncertain resources. The proposed method can fully exploit the flexible adjustment ability of load-side resources and effectively balance aggregation accuracy and calculation speed. The integration results can be used as alternative constraints to meet the access requirements for different types of stakeholders to participate in the electricity market.

This paper has introduced the multi-port medium-frequency PET topology for integrating PV generation with BS and its relevant characteristics. All parts of the structure have been elaborated in detail. The control strategy and the simulation results have verified the feasibility and availability of this topology.

This research proposes a two-level energy management model leveraging flexible load tiered demand response and energy storage systems. It optimizes economic benefits while ensuring user comfort, adjusts dynamically to the variability of renewable sources, and provides tailored incentive strategies considering user comfort. It thus represents a significant advance in demand response programs, encouraging more effective user participation and promoting optimal system operation.

To improve the development and optimize economic and equipment service life-prolonging of the island electric-hydrogen hybrid microgrid, an optimal control method that takes into account both operation cost and degradation cost is proposed. In order to verify the effectiveness of the proposed strategy, it is compared with the non-considering degradation cost strategy in power source's degradation rate and working cost. The performance of the proposed strategy demonstrates that it can effectively slow down the PEMFC degradation and is conducive to the extension of the PEMFC lifetime as well as the improvement of the system economy.

Optimal energy management of an islanded hybrid microgrid is performed by optimally scheduling the power from the storage device and the distributed generators based on their availability, bidding cost and the load demand. A deep learning algorithm say, long short-term memory (LSTM) is used to predict the uncertain parameters such as the day-ahead power from the renewable energy sources and the load demand of the islanded hybrid AC–DC microgrid. LSTM algorithm is more accurate in predicting uncertainties than artificial neural network in terms of the performance evaluation metrics. The predicted uncertain parameters are used as inputs for the optimal energy management problem and an improved grey wolf optimization (GWO) algorithm is used to solve the problem. Two different cases are considered and the results obtained with improved GWO (IGWO) algorithm is compared with other metaheuristic algorithms such as artificial bee colony, ant lion optimization, Harris Hawk's optimization, particle swarm optimization and grey wolf optimization algorithms.

Energy consumption is an important component of the high-quality development and the establishment of new productive forces in the Pearl River Delta urban agglomeration, which has led to widespread attention to the evaluation of green development. Constructing a secondary indicator system based on economic development, social security, energy utilization, and ecological environment, this study takes the nine cities in the Pearl River Delta urban agglomeration as samples and uses relevant data from 2005 to 2020 as the basis. A comprehensive evaluation model of urban agglomeration's green development of energy consumption based on radial basis function neural network is proposed and simulated using MATLAB software for quantitative measurement. The results are as follows: First, from 2005 to 2020, the green development level of the Pearl River Delta urban agglomeration steadily increased, with the average green development level increasing from 0.516 to 0.701, but there are significant differences among different cities. Second, the coupling coordination degree of central cities generally increased, while other cities were in a low-level coupling coordination stage. Third, the spatial evolution of green development in urban agglomerations is progressive, showing a distribution feature of “low in the middle and high around”. Fourth, per capita GDP, R&D expenditure, the proportion of the tertiary industry output value, and the area of arable land at the end of the year are common major obstacles to green development in urban agglomerations and internal urban circles, while the remaining major obstacles have certain differences. This study, focusing on energy consumption and green development issues, provides decision support for policy formulation and implementation in various aspects such as promoting high-quality development and the establishment of new productive forces, improving energy utilization efficiency, accelerating the transformation of traditional industries, and fostering green industries in the Pearl River Delta urban agglomeration.

Recognizing the diverse demands associated with fisheries, crop cultivation, and livestock farming loads, this paper introduces distinct reinforcement learning models tailored to address these variations. The unique contribution of this study lies in its comprehensive analysis of the AEI in conjunction with reinforcement learning, offering valuable insights into the future trajectory of reinforcement learning within the AEI domain.

A refined model for the participation of residential users in the DR uncertainty is obtained, so as to construct an optimal dispatching model with the lowest integrated cost of the grid, and is solved using an optimization solver. The experimental simulation results show that the model established in this paper is able to adjust the operation strategy according to the requirements of user comfort, enhance the enthusiasm of users to participate in DR, and at the same time reduce the dispatch security risk and economic loss caused by the uncertainty of residents' DR on the power grid.

The base configuration of the proposed 118-bus network.

Compared with the probability models, the proposed model is data-driven, that is, it can simulate wind power scenarios based on historical samples rather than probability hypothesis, and it can independently learn the space-time correlation of wind power generation in different locations. Experiments show that the cumulative distribution function curve of data generated by Wasserstein Generative Adversarial Network is highly coincident with that of real data.

This paper proposes a method for identifying distribution network line parameters considering multisource measurement. Firstly, the initial values of conductivity and susceptance are obtained through linear regression and converted into resistance and reactance, respectively. Then, based on the series parallel connection of the network end branches, a non-linear function about resistance reactance is derived. By combining the measurement data of micro phasor measurement unit and advanced metering infrastructure at multiple times, the non-linear measurement equation of the line is established, and the Levenberg–Marquardt algorithm is used to solve the non-linear function, thus achieving the identification of distribution line parameters.

First, the basic structure of a rail transit self-consistent energy system is presented. Second, considering a power transmission system with line trip-off under extreme weather conditions, a traction load-shedding model is established to obtain the maximum power exchange capability between the power transmission network and rail substations. Subsequently, an optimal hybrid energy storage (HES) planning model is proposed to minimize the total HES investment and rail transit system operation costs.

This paper proposed a joint scheduling method of peak shaving and frequency regulation using hybrid energy storage system with battery energy storage and flywheel energy storage in the microgrid.

This paper proposes a bi-level mobile energy storage (MES) pre-positioning method for the distribution network coupled with the transportation network in the context of a typhoon disaster. The method takes into account the typhoon eye and analyzes the diverse impacts of typhoons on the ‘generation-transmission-load-road’ system. The objective function incorporates the movement risk of MES and the modified closeness centrality of the distribution network and transportation network, which makes the pre-positioning scheme more reasonable.

This article proposes a method for evaluating the dominant factors of grid-connected inverters based on impedance models, which can achieve quantitative calculation of the dominant factors of system stability.

Multi-source coupling and the uncertainty in fault-induced voltage sag can diminish the accuracy of node importance identification in islanded microgrid. To address this, this paper proposes an adaptive node identification method designed for quick and accurate identification of nodes that cope with various fault scenarios.

To improve capacity utilization of distributed energy storage systems (DESS), power quality management services are quantified and integrated into an optimal bi-level sizing model, where the upper level addresses the sizing problem concerning battery and PCS capacities, while the lower level focuses on coordinating active/reactive power control of the DESS. A robust optimization approach for DESS scheduling is employed to consider uncertainties of distributed photovoltaic (PV) power generation and power quality management requirements.

This study investigates the integrated flexible operation mode of a steel plant. An illustrating method is utilised for modelling the entire steel production process and power to hydrogen process in detail for the H₂DRI-EAF steel plant, which includes natural gas, photovoltaic, wind power self-provided power plants, and carbon capture and storage systems. A mixed integer linear programming model is developed for the comprehensive scheduling of the steel mill.

A multi-level coordinated scheduling strategy is proposed for shared energy storage systems (SESS) under electricity spot and ancillary service markets to maximize the overall operational profit, promoting the accommodation capacity of renewable energy sources and enhancing power system operational flexibility. Comparative case studies have validated the superior performance of the proposed strategy for better utilization of available storage capacity and arbitrage maximization.

This paper establishes a refined process-level load model for data centres by considering the temporal and spatial shifting characteristics of computational loads, effectively reflecting the intricate interactions between these loads and servers. Based on this model, a micro-energy network is constructed that integrates renewable energy access and waste heat recovery from the data centre. This network also participates in carbon emission trading and green electricity certificate trading markets, applying an interaction mechanism.

(1) It yields the general derivation of the per-unit formulation of partial differential equations in integrated energy systems. (2) It resolves the challenge of determining the base values in integrated energy systems (IES). (3) It reduces the conditional number of the combined Jacobian matrix and improves the numerical stability caused by varying unit size widely in IES, and is effective in simulation scenarios.

The modified IEEE 30-bus network, as shown in Figure, has three thermal generators located at buses 1, 2, and 8 and two wind farms, each with a rated capacity of 75 MW. The output of wind farms is connected to buses 5 and 11, while solarunits with a rated capacity of 50 MW supply power to bus 13. As an obvious fact, outputs from wind and solar PV are uncertain, and any deficit in total output from these units must be mitigated by spinning reserve.

A power trading mechanism based on a multi-VPPs cooperative game model is designed, which can maximize the overall multi-VPPs profit. A bi-level model for low-carbon scheduling under multi-VPPs is constructed, which can take both economic and environmental benefits into account. A scenario in which a disconnection fault occurs in the transmission grids under the effect of extreme weather is simulated, allowing a quantitative assessment of its impact on multi-VPPs scheduling.

This article proposes a novel model and methodology for identifying the MRC in power system considering the interdependence with NG system, with adopting a distributionally robust (DR) approach to consider the uncertainties of renewables. A constraint-generation based algorithm is devised to solve the model, with a relax-round-polish based Alternating Direction Method of Multipliers (ADMM) algorithm to address the interaction between the two energy systems.

Herein, a bi-level optimization model is proposed to integrate the capacity remuneration mechanism (CRM), energy market (EM), and carbon emission trading (CET).

This paper proposes a short-term distributed photovoltaic power forecasting model based on lightweight convolutional neural networks. Firstly, the important factors influencing the photovoltaic output in the network are selected based on the Pearson correlation coefficients. Then, a lightweight forecasting model is constructed using the Xception convolution and attention mechanism. The model is further trained through channel pruning to generate the final lightweight model.

The two-stage reactive power optimization framework consists of day-ahead centralized optimization and real-time decentralized optimization.

Most existing long-term hydrogen planning models assume the whole year-round net load series is known in advance, which may underestimate the necessary capacity of long-term storage and cause load shedding in real-time operation. This work proposes a long-term storage planning framework robust to year-round net load fluctuation.

This article proposes corresponding photovoltaic frequency modulation strategies for different time scales, aiming to improve frequency response.

In extreme scenarios, datacentres, steel mills, and electric vehicle aggregators interact through electricity transactions. The production load is interactively regulated between the datacentre and the steel plant under load constraints.

To accurately calculate the carbon emissions of the system, the carbon emission flow model of the system is established. And carbon emission constraint derived from the carbon emission flow model is constructed for the low-carbon operation of the system. To address the uncertainty of renewable energy and load during the scheduling process, a two-stage scheduling strategy consisting of a day-ahead stage and an intra-day stage is developed.

The virtual power plant integrating the flexible resources in the distribution network can provide additional adjustment capacity for the auxiliary services of distribution network. However, the actual internal situation of distribution network including insufficient adjustable capacity of energy storage, unreasonable power allocation, and voltage overrun leads to the difficulties in optimization scheduling. Therefore, this paper proposes a power allocation optimization strategy of distributed electricity-H2 virtual power plants (EHVPPs) with aggregated flexible resources. Specifically, a distributed EHVPP division method based on the granular K-medoids clustering algorithm is proposed to realize the independent autonomy and coordinated interaction between EHVPPs, and in order to quantify the operation and regulation capacity of distributed EHVPPs, an aggregation approach of regulating feasible domains of flexibility resources based on the improved zonotope approximations is developed. Moreover, a power allocation strategy based on the flexibility weight factor is proposed to handle the calculated minimum deviation between the total active output of PV and the dispatching power command, realizing the self-consistency of distributed EHVPPs.

To achieve the fair power allocation within the wind farm (WF), an active and reactive power dispatch method for a WF considering the real-time service quality and the available power to ensure operational safety while meeting the power demand instructions from the dispatch centre. WF service quality metrics based on real-time monitoring data of key components are generated to characterize the operational stability of the wind turbines (WTs). The fair active and reactive power allocation can be realized in WFs to meet the requirements of WT equipment reliability, voltage security, and the power supply at the same time.

A “source-grid-load-storage” (SGLS) integrated project is promoted in China. Once an SGLS integrated project (SGLS-IP) is approved by a governmental organization, electricity trading among entities (power generators, prosumers and customers) with the SGLS-IP would be permitted.

This paper proposed an improved alternating direction method of multipliers optimization algorithm to protect the economic interests and privacy of integrated energy microgrids while reducing energy consumption at roughly equivalent operating costs of the distribution network under ancillary services scenarios.

This article presents a hybrid data-driven framework using an ensemble of deep learning models, including convolutional layers and long short-term memory (LSTM) networks, to enhance short-term wind power forecasting accuracy. The method effectively integrates and utilizes both high-frequency and low-frequency components of wind data, outperforming five benchmark models in real-life dataset experiments.

This paper summarizes the soiling accumulation and its impact on photovoltaic panels, the advantages and disadvantages of soiling removal methods, and analyzes the soiling removal opportunities and challenges in China's photovoltaic industry.

This paper proposes a new approach for solving the optimal power flow (OPF) problem in transmission networks using a Gradient Bald Eagle Search Algorithm (GBES) with a Local Escaping Operator (LEO). The method considers uncertainty from renewable energy sources and Vehicle-to-Grid (V2G) in the stochastic OPF problem. Monte Carlo methods estimate generation costs and study feasibility. The method's effectiveness is evaluated using an IEEE 30-bus test system.

The review promotes the use of circular economy principles. This review investigates ameliorative measures to overcome the limitations of biological treatment methods for FT effluents.

The hardware structure, operation control and energy dispatching of wind/photovoltaic/energy storage islanded microgrid based on Ethernet communication are studied and analysed. The focus is on monitoring system design. The wind/PV/energy storage microgrid system is a closed loop automatic control system with information collection, remote control, scheduling and management, energy condition monitoring.

As the first contribution, size optimization of a biomass-based HES combined with PV and diesel generator is investigated in a multi-objective manner considering techno-economic and techno-enviro-economic frameworks. To the best knowledge of the authors, in the literature, there are few investigations which focus on multi-objective optimization of biomass/diesel generator-driven HES subject to technical, environmental and economic aspects.

A sensitivity analysis is investigated to find the importance of biomass and diesel fuels cost on the different aspects of the HES.

The metaheuristic algorithms are used nowadays eliminating the possibility of getting trapped at the local optima. However, particle swarm optimization (PSO) suffers from delayed convergence, more iterations to reach the optimal point, and random parameter selection. Hence, this study employs an improved version of PSO called Phasor-PSO (P-PSO) in an MPPT controller. The proposed algorithm is parameter-less which results in reduced computational complexity and thus provides quick decision in achieving the maximum power point (MPP).

This paper proposes a optimal capacity configuration of joint system considering uncertainty of wind and photovoltaic power and demand response.The results show that the method achieves a balanced optimization of robustness and economy, effectively reduces carbon emissions and improves ability of the system to consume wind and photovoltaic power.

A new hybrid method for modeling solar power plants based on daily data. Using genetic programming to model the solar power plant. Independent solar power plant modeling from datasheet.