Energy systems analyses are integrated elements in planning the transition towards renewable energy-based energy systems. This is due to a growing complexity arising from the wider exploitation of variable renewable energy sources (VRES) and an increasing reliance on sector integration as an enabler of temporal energy system integration, but it calls for consideration to the validity of modelling tools. This article synthesises EnergyPLAN applications through an analysis of its use both from a bibliometric and a case-geographical point of view and through a review of the evolution in the issues addressed and the results obtained using EnergyPLAN. This synthesis is provided with a view to addressing the validity and contribution of EnergyPLAN-based research. As of July 1st, 2022, EnergyPLAN has been applied in 315 peer-reviewed articles, and we see the very high application as an inferred internal validation. In addition, the review shows how the complexity of energy systems analyses has increased over time with early studies focusing on the role of wind power and the cogeneration of heat and power and later studies addressing contemporarily novel issues like the sector integration offered by using power-to-x in fully integrated renewable energy systems. Important findings developed through the application of EnergyPLAN includes the value of district heating in energy systems, the value of district heating for integration of VRES and more generally the importance of sector integration for resource-efficient renewable energy-based energy systems. The wide application across systems and development stages is interpreted as inferred validation through distributed stepwise replication. • General and EnergyPLAN-applied model validation approaches. • EnergyPLAN is highly applied with 315 articles in the journal literature (July 2022). • The high application of EnergyPLAN is seen an inferred internal validation. • Review of sector integration and multi-tool EnergyPLAN-based energy systems analyses. • EnergyPLAN's development mirrors a growing complexity in energy transition steps. 

Review and validation of EnergyPLAN

The penetration of renewable energy and multi-type energy increase in distribution system and traditional distribution system transforms into multi-energy systems. It demands an evolution for the management of the traditional distribution system operation and control. A multi-energy systems digital twin framework is proposed for the issue. The applications of system monitoring, dynamic planning, and real-time optimization are integrated with the proposed framework to facilitate the management of multi-energy systems. Additionally, a model generation automation process is implemented to guarantee the digital model in virtual space maps the physical system. The system parameter is calibrated with the automatic trigger mechanism of parameter optimization algorithm using historical operating data. The proposed model automation process and the parameter optimization algorithm was implemented based on the CloudPSS modeling and simulation platform. The model data includes geographic information, equipment information, and measurements. The implemented framework and methods were tested with a real-world multi-energy system at the Hong Feng Lake area of Guizhou, China. The testing results and system performance is analyzed. 

Automatic Modeling and Optimization for The Digital twin of a Regional Multi-energy System

Accurate photovoltaic power prediction is of great significance to the stable operation of the electric power system with renewable energy as the main body. In view of the different influence mechanisms of meteorological factors on photovoltaic power generation in different irradiation intervals and that the data-driven algorithm has the problem of regression to the mean, in this article, a prediction method based on irradiation interval distribution and Transformer-long short-term memory (IID-Transformer-LSTM) is proposed. Firstly, the irradiation interval distribution is calculated based on the boxplot. Secondly, the distributed data of each irradiation interval is input into the Transformer-LSTM model for training. The self-attention mechanism of the Transformer is applied in the coding layer to focus more important information, and LSTM is applied in the decoding layer to further capture the potential change relationship of photovoltaic power generation data. Finally, sunny data, cloudy data, and rainy data are selected as test sets for case analysis. Through experimental verification, the method proposed in this article has a certain improvement in prediction accuracy compared with the traditional methods under different weather conditions. In the case of local extrema and large local fluctuations, the prediction accuracy is clearly improved. 

Photovoltaic Power Prediction Based on Irradiation Interval Distribution and Transformer-LSTM

This paper presents an algorithm for solving the power flow (PF) problem of droop-regulated AC microgrids (DRACMs) operating in isolated mode. These systems are based on radial distribution networks without having a slack bus to facilitate conventional computations. Moreover, distributed generation units have to distribute the power and voltage regulation among themselves as a function of operating frequency and voltage droop rather than having a slack bus that regulates voltage and power demands. Based on the conventional backward/forward sweep algorithm (BFS), the proposed method is a derivative-free PF algorithm. To manage the absence of a slack bus in the system, the BFS algorithm introduces new loops, equations, and self-adaptation procedures to its computation procedures. A comparison is presented between the proposed BFS algorithm and other state-of-the-art PF algorithms, as well as PSCAD/EMTDC. In comparison to existing algorithms, the proposed approach is fast, simple, accurate, and easy to implement, and it can be considered an effective tool for planning and analyzing islanded DRACMs.

/pdf/adaptive-backward-forward-sweep-for-solving-power-flow-of-112va9d9.pdf

Adaptive Backward/Forward Sweep for Solving Power Flow of Islanded Microgrids

Microgrid load flow studies have recently attracted a lot of attention due to the introduction of independent microgrids with decentralized sources. These microgrids can be operated in grid-connected mode as well as in islanded mode. But the islanded mode operation of microgrids has some inherent limitations that cannot be addressed by conventional load flow approaches. Recent research has suggested some modifications to the traditional methods by treating system frequency as a variable parameter and leaving out the concept of slack bus. In this respect, a novel load flow method has been presented for microgrids that operate as droop-regulated distributed generations (DGs) employing distributed slack bus. The Jacobian matrix has been modified, which leads to improved convergence. Further, a comparison of the modified decoupled Newton Raphson and the modified Newton Raphson load flow techniques has been presented. Several case studies for 6-bus and 38-bus test systems, demonstrate the feasibility of the proposed load flow approach. 

A Novel Modified Decoupled Newton-Raphson Load Flow Method with Distributed Slack Bus for Islanded Microgrids Considering Frequency Variations

Simulation and analysis of integrated energy conversion and storage systems using CloudPSS-IESLab

A novel three-phase unbalanced power flow solution for islanded microgrids with distributed generations under droop controls

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Simulation and analysis of integrated energy conversion and storage systems using CloudPSS-IESLab

A novel three-phase unbalanced power flow solution for islanded microgrids with distributed generations under droop controls