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The distribution of urban electricity load is highly uneven in both space and time, posing significant challenges to the problem of distribution network expansion planning (DNEP). An autonomous topology planning method for distribution network expansion is proposed to address that, driven by a learning-based decoupled optimization approach. The planning problem is decoupled from the impact of distribution network operation and used as reinforcement cost prediction for autonomous planning after the generation of topology schemes. First, a reinforcement model of the original distribution network is built that considers the actual distance calculation method of load nodes. Then an effective method for generating historical operation samples is designed, followed by the development of a learning-based reinforcement cost prediction model for the original distribution network based on operation state learning using XGBoost, which enables the prediction of said costs under different scenarios. Utilizing a distribution network topology generating method driven by the adjustable weight minimum spanning tree (AWMST) algorithm, an autonomous topology expansion planning strategy generation method employing the learning-based decoupled optimization approach is established. The feasibility and superior analysis of the proposed method is verified and analyzed on an actual urban distribution network case with simulation scenarios. The results show that the proposed method is faster and more flexible to deal with the distribution network planning problem brought about by rapid load growth in urban distribution networks. • An autonomous distribution network topology planning method is proposed. • A XGBoost learning-based reinforcement cost prediction model is established. • The topology planning scheme could be obtained by an intelligent decoupled optimization algorithm. [ABSTRACT FROM AUTHOR]

• A dynamic topology clustering method is proposed. • Optimal voltage regulation is formulated as a Des-POMDP problem. • The proposed voltage regulation algorithm can adapt topology changes. Both the high penetration of clean energy with strong fluctuation and the complicated variable operation condition bring great challenges to the voltage regulation of the distribution network. To deal with the problem, a topology-aware voltage regulation multi-agent deep reinforcement learning (MADRL) algorithm is proposed. The distributed energy storages (DESs) are modeled as agents to regulate voltage autonomously in real-time, which could fast adapt to dynamic topological scenarios. Firstly, taking the minimization of voltage fluctuation and maximization of reserve capacity as the target, the optimal voltage regulation model is established. Secondly, a topology extraction method considering voltage sensitivity is proposed for dynamic topology clustering, and the obtained typical topology is added to the observation set of agents. Then, the optimal voltage regulation model is formulated to the decentralized partially observable Markov decision process (Dec-POMDP) framework, in which only local information is required for the agent during the test process to decision-making to realize the hierarchical and partitioned control of voltage. Finally, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is used to solve the Dec-POMDP model. The feasibility and superiority of the proposed algorithm are verified and analyzed in the simulation under different scenarios. [ABSTRACT FROM AUTHOR]

This paper develops a novel solution to integrate electric vehicles and optimally determine the siting and sizing of charging stations (CSs), considering the interactions between power and transportation industries. Firstly, the origin–destination (OD) traffic flow data is optimally assigned to the transportation network, which is then utilized to determine the capacity of charging stations. Secondly, the charging demand of charging infrastructures is integrated into a cost-based model to evaluate the economics of candidate plans. Furthermore, load capability constraints are proposed to evaluate whether the candidate CSs deployment and tie line plans could be adopted. Different scenarios generated by load profile templates are innovatively integrated into the economic planning model to deal with uncertain operational states. The models and framework are demonstrated and verified by a test case, which offers a perspective for effectively realizing optimal planning of the CSs considering the constraints from both transportation and distribution networks. [ABSTRACT FROM AUTHOR]

• A novel hybrid short-term wind power forecasting method is proposed. • The automatic relevance determination algorithm is used to correct NWP data. • The spatial correlation of wind speed series between wind farms is extracted. • The corrected NWP and spatial correlation are integrated into a Gaussian process. Wind power generation rapidly grows worldwide with declining costs and the pursuit of decarbonised energy systems. However, the utilization of wind energy remains challenging due to its strong stochastic nature. Accurate wind power forecasting is one of the effective ways to address this problem. Meteorological data are generally regarded as critical inputs for wind power forecasting. However, the direct use of numerical weather prediction in forecasting may not provide a high degree of accuracy due to unavoidable uncertainties, particularly for areas with complex topography. This study proposes a hybrid short-term wind power forecasting method, which integrates the corrected numerical weather prediction and spatial correlation into a Gaussian process. First, the Gaussian process model is built using the optimal combination of different kernel functions. Then, a correction model for the wind speed is designed by using an automatic relevance determination algorithm to correct the errors in the primary numerical weather prediction. Moreover, the spatial correlation of wind speed series between neighbouring wind farms is extracted to complement the input data. Finally, the modified numerical weather prediction and spatial correlation are incorporated into the hybrid model to enable reliable forecasting. The actual data in East China are used to demonstrate its performance. In comparison with the basic Gaussian process, in different seasons, the forecasting accuracy is improved by 7.02%–29.7% by using additional corrected numerical weather prediction, by 0.65–10.23% after integrating with the spatial correlation, and by 10.88–37.49% through using the proposed hybrid method. [ABSTRACT FROM AUTHOR]

• Hydrogen-solar-storage integrated microgrid for airport electrification. • Airport energy dispatch strategy incorporated with flight schedules. • Five energy supply scenarios with techno-economic lifecycle assessment. • Sensitivity analysis of key parameters on future energy system design. • Airport microgrid presents economic and environmental benefits. Can aviation really become less polluting? The electrification of airport energy system as a micro-grid is a promising solution to achieve zero emission airport operation, however such electrification approach presents the engineering challenge of integrating new energy resources, such as hydrogen supply and solar energy as attractive options to decarbonize the present system. This paper explores the techno-economic benefits of integrating hydrogen supply, electric auxiliary power unit (APU) of aircraft, electric vehicles, photovoltaic energy (PV), and battery storage system into electrified airport energy system. The hydrogen fuel cell generation provides great flexibility to supply aircraft at remote stands, and reduces the carbon emissions caused by traditional fuel-powered APU. A mixed integer linear programming optimization method based on life cycle theory is developed for capacity sizing of hydrogen energy system, PV and battery storage, with optimization objective of minimizing the total economic costs as well as considering environmental benefits of the proposed airport microgrid system. Case studies are conducted by five different energy integration scenarios with techno-economic and environmental assessments to quantify the benefits of integrating hydrogen and renewable energy into airport. Compared with the benchmark scenarios, the integration of hydrogen energy system reduced the total annual costs and carbon emissions of airport energy system by 41.6% and 67.29%, respectively. Finally, sensitivity analysis of key system parameters such as solar irradiance, grid emission factor, elctricity price, carbon tax, unit investment cost of hydrogen energy system have been investigated to inform the design of hydrogen-solar-storage integrated energy system for future airport electrification. [ABSTRACT FROM AUTHOR]

Highlights • Dynamic gas system model to model H 2 injection into gas systems for better accuracy. • Wobbe Index and Combustion Potential to measure H 2 injection impact on gas quality. • Robust optimisation to manage H 2 mix with other gases to ensure gas quality. • Liquid petroleum gas and Nitrogen for changing Wobbe Index and Combustion Potential. Abstract A large volume of wind power is curtailed worldwide due to the intermittency and limited transportation capacity of electrical power systems. New technologies, Power-to-Gas via electrolysis, can convert excessive wind power into hydrogen to be transported by natural gas systems. However, the injection of H 2 into natural gas pipelines can cause gas quality issues due to changing gas compositions. This paper investigates the impact of injecting H 2 converted from wind power on natural gas quality. Two key indexes used to measure gas quality, Wobbe Index and Combustion Potential, are introduced to examine the impact. Then, in order to bring the two indexes into acceptable statutory ranges, H 2 is mixed with Liquid Petroleum Gas and Nitrogen. A robust optimization model, considering wind power uncertainties, is thereafter developed to manage the gas mixture, maximize H 2 injection. This paper uses the dynamic gas system model to represent real-time pipeline flows, which can better reflect gas flow features over time. The proposed method is demonstrated on a small integrated gas-electricity system. Results illustrate that excessive H 2 injection will reduce Wobbe Index but increase Combustion Potential. The robust optimization approach can effectively manage the mixture while ensuring gas quality with an uncertain wind power supply. The proposed method is beneficial to reducing renewable energy curtailment and maximizing H 2 injection, benefiting electricity system operators with low operation costs and wind power more penetration. [ABSTRACT FROM AUTHOR]

Highlights • An architecture of microgrid energy management system was established. • The architecture was designed for China in the context of Energy Internet. • Two-stage robust optimization model and algorithm were proposed. • Case study was conducted using a real case of Beijing Jinfeng Industrial Park. Abstract In order to address the practical challenges posed by the increasing penetration of distributed energy resources and electric vehicles, the evolution from traditional power systems to Energy Internet and the rapidly changing market and policy environments in China, this paper proposes an Energy-Internet-oriented architecture of microgrid energy management system for China, considering specific technical requirements, markets and policies in China. Following the proposed architecture, the robust operation of a multi-energy microgrid with renewable energy and load uncertainties is formulated as a two-stage robust optimization problem and the column and constraints generation algorithm is used to solve it. A real-life case study in China, Beijing Goldwind Industrial Park, is conducted to demonstrate the proposed architecture and method. Simulation results show that by exporting surplus generation to the external customers and bulk power grid, as an important feature of the market dimension of the proposed architecture, the revenue of the microgrid is improved significantly compared to the traditional business mode. Moreover, as important features of technology and regulation dimensions of the proposed architecture, it is shown that smart charging of electric vehicles is able to improve the revenue of the microgrid as well. Finally, the results also show that the proposed robust operation method achieves robustness at the cost of lower revenue in non-worst scenarios. [ABSTRACT FROM AUTHOR]

Network pricing is essential for electricity system operators to recover investment and operation costs from network users. Current pricing schemes are only for generation and demand that purely withdraws or injects power from/into the system. However, they cannot properly price energy storage (ES), which has the dual characteristics of injecting and withdrawing power. This paper develops a novel pricing scheme for ESs in distribution systems operated by customers to reflect their impact on network planning and operation. A novel charging and discharging methodology is designed for ESs to respond to time of use tariffs for maximising electricity cost savings. The long-term incremental cost for ES is designed based on future reinforcement horizon and short-term operation cost is quantified by system congestion. Then, a novel pricing scheme for ES is designed by integrating the two costs. The pricing signals can guide ES operation to benefit both distribution network operators and ES owners. The new methodology is demonstrated on a small system with an ES of different features and then on a practical Grid Supply Point (GSP) area. [ABSTRACT FROM AUTHOR]