Operational and Planning Strategy for Hydrogen Energy Storage in Distribution Networks Under Dynamic Transformer Capacity Expansion Scenarios

The penetration level of renewable energy (RE) including distributed generation (DG) integrated in the distribution network has been increasing in many countries. This follows widespread encouragement to use renewable energy to minimize reliance on conventional power plants to achieve net zero emissions. Malaysian energy transition targets and carbon neutral goals set by the government, lower cost of ownership of solar PV systems, and more efficient government renewable energy initiatives including Net Energy Metering (NEM) 3.0, Green Investment Tax (GITA), Large Scale Solar (LSS), and most recently the Corporate Green Power Program (CGPP) have driven the rapid development of renewable energy in the country. However, the high penetration level of distributed generation including solar PV, mini- hydro, and bio-energy has introduced several technical impacts on the operation of the distribution network including increased fault levels, voltage limit violation, reverse power flow, distribution network losses, and transformer losses. This paper analyzes the technical impacts of the high penetration level of distributed generation in medium voltage (MV) substations of the distribution network using DigSILENT PowerFactory simulation software. From the results obtained through the simulation analysis, the impact factors of fault level, voltage limit violations, reverse power flow, distribution network losses, and transformer losses have been formulated. The optimal distributed generation penetration level in distribution networks is then determined based on the highest score value of the normalized impact factor from all penetration levels.

The output of renewable energy sources is characterized by random fluctuations, and considering scenarios with a stochastic renewable energy output is of great significance for energy storage planning. Existing scenario generation methods based on random sampling fail to account for the volatility and temporal characteristics of renewable energy output. To enhance photovoltaic (PV) absorption capacity and reduce the cost of planning distributed PV and energy storage systems, a scenario-driven optimization configuration strategy for energy storage in high-proportion renewable energy power systems is proposed, incorporating demand-side response and bidirectional dynamic reconfiguration strategies into the planning model. Firstly, this paper designs a time series scenario generation method for renewable energy output based on a Deep Belief Network (DBN) to fully explore the characteristics of renewable energy output. Then, considering various cost factors of PV and energy storage, a capacity determination model is established by analyzing the relationship between annual planning costs, PV connection capacity, energy storage installation capacity, and power. Case studies are conducted on the IEEE-33 node system to compare and analyze the impact of active distribution network strategies on the planning results of PV and energy storage equipment under different scenarios. The results show that by incorporating demand-side response and bidirectional dynamic reconfiguration strategies into the active distribution network, the selection and sizing of PV energy storage can significantly improve the PV absorption capacity, achieve the lowest planning cost, and address the issue of low voltage levels during periods of excess PV output due to bidirectional reconfiguration. This improves the economic efficiency and reliability of the operation of power distribution networks with a high proportion of PV, providing a solution for energy storage planning that considers the randomness of renewable energy output.

The improvement of methods to register the commercial losses in electrical distribution networks, and especially in low voltage networks, is one of the most important tasks for power supply providers. It is rather difficult to correctly register the fact of occurrence of such losses in the network. It is objectively impossible to analyze the state of the networks based on data obtained from various points of the specified network with the required accuracy. In this regard, at present no methods have been developed for remote detection of the fact and determination of the place of commercial losses in distribution networks, that could work in the mode of integration with automated information-measuring system of fiscal electricity metering. To solve this problem a method is to be developed that allows us to establish accurately for practical purposes the volume of commercial losses in the network and determine the place of their occurrence. During the research, methods of electric power networks modeling have been used. The assumption has been made about no flow of capacitive leakage currents to ground in the network, about full compliance of the line parameters with their calculated (nominal) values, as well as the basic laws of electrical engineering science. A unique method is proposed to determine the fact and the place of commercial losses in distribution networks. In contrast to the prototypes, it is based on the analysis of data obtained from metering devices, based on the key laws of electrical engineering and it allows us to get reliable arithmetically rigorous results without using fuzzy logic. The authors have proved theoretically and practically the effectiveness of the proposed solutions, and the possibility of their application. A calculation has been made to determine the place of commercial losses in the network using an example. The proposed method to determine the fact and place of commercial losses in distribution networks of low and medium voltage levels solves the problem of inability to effectively identify the points of occurrence of commercial losses in distribution networks. The reliability of the results obtained is confirmed by mathematical rigor of the method and algorithmic nature of the procedure for analyzing the distribution network.

Distributed generation (DG) including small hydro generation, solar PV generation and bio energy installed in a certain distribution network area has led to a condition which power generation by DG within the distribution network is more than the local consumption. The condition where DG generation has excess and power flows from the distribution network back to the grid is referred as Reverse Power Flow (RPF). In this paper, an analysis of RPF has been conducted focusing on a selected distribution network with a total capacity of 20MW mini-hydro generation installed to the substation. By utilizing DigSilent simulation software, a base case study has been analyzed and a base case simulation network model was then validated by comparing the simulation results with the historical data from SCADA. Based on the validated network simulation model, several case scenarios have been studied where the capacity of the installed DG into the distribution network were increased. The impact of RPF on voltage profile at 11kV bus, transformer tap change occurrence, transformer losses and distribution network losses has been analyzed in all scenarios and the results were then presented.

The epileptic power supply from the national grid due to instability is a concern to energy consumer. This instability in power supply experienced in power distribution network could be minimized by introducing Optimized Genetic Algorithm (OGA). It is achieved by characterizing 33KV distribution network, running the load flow of the characterized 33KV distribution network, determining the distribution losses from the load flow. Minimizing the determined losses in 33kv distribution network using (OGA), and designing SIMULINK model for improving loss minimization in 33kv power distribution network using OGA. Finally, validating and justifying the percentage of loss reduction in improving loss minimization in 33kv power distribution network without and with OGA. The results obtained are conventional percentage power loss in 33KV distribution network, 75%, while that when OGA is incorporated in the system is 72.9%. With these results obtained, the percentage improvement in loss reduction in 33KV distribution network when OGA is used is 2.1%. The conventional percentage of power loss in 33KV distribution network is 80%. The percentage power loss in the distribution network now is 72.9%; hence, power loss reduction in distribution network. Unmitigated power loss was 76.7% when OGA is introduced we had 74.63%. The percentage power loss in distribution network in bus 8 is 81.7% while that when OGA is applied is 79.49%. The percentage power loss in bus 9 of 33KV distribution network is 86.7%. Finally, when optimized genetic algorithm is incorporated in the system the percentage power loss in the network was reduced to 84.36%.

Development pathway and influencing factors of hydrogen energy storage accommodating renewable energy growth

Aiming at the scenario of joint optimization of distributed optical storage in distribution network, this paper takes the maximum net profit of optical storage system as the goal. At the same time, this paper considers the high cost of energy storage battery, and combines the load characteristics, the energy storage operation characteristics and China’s electricity price policy to construct the optical storage system investment income model. On the basis of satisfying the power balance and the performance constraints of the energy storage battery, an optimization model considering the net profit of the optical storage system is established, and the YALMIP toolbox and CPLEX program are used to solve the problem in MATLAB. Finally, the results of the energy storage configuration are tested in the IEEE-33 node system. The test results prove the effectiveness of the optical storage joint optimization configuration strategy based on the net profit of the optical storage system as a technical indicator.

Purpose: This article provides available information on the role of distributed generation (DG) in the performance of a power distribution network. 
 Design/methodology/approach: The study reviewed articles about available methods for reducing technical losses in electrical distribution networks. The second step involved studying various researchers' views on renewable energy in some developing countries for introducing DG into a distribution network. The influence of DG on the economic performance of a distribution network. Finally, the study scouted for available information on the implementation of a demand response (DR) program on the performance of a distribution network in the presence of DG.
 Findings: Available information reveals that the reliability of DG for reducing the technical losses in a distribution network is higher than relying on alternating current controllers. There are indications of renewable energies in developing countries for introducing DG into a distribution network. According to the articles reviewed, the approach for the optimal location of DG did not include the combination of the voltage stability index and power loss reduction index. It is also worth considering using the power system analysis toolbox (PSAT) for DG sitting. The economic influence of DG on a distribution network's performance has not been evaluated based on the technical loss, generation cost, emission cost and reliability. It is also worth considering the benefits of demand response programs in the presence of DG.
 Research limitation: The review concentrated mainly on DG's influence in reducing technical loss. Articles relating to the effect of DG on other distribution network technical issues such as voltage stability, harmonics etc. also require attention
 Practical implications: Distribution network performance is essential for the operation of electrical gadgets. Therefore, improved distribution network performance will result in the economic development of a country.
 Originality/Value: This paper provides the platform that stimulates interest in using DG to improve the distribution network performance.

Energy storage system (ESS) can solve the problems of nodal voltage fluctuation and increase power loss in distribution network caused by high penetration of renewable energy. This paper takes the nodal voltage fluctuation and comprehensive multi-cost of ESS as the composite optimization objective, and combines multiple constraints to establish an energy storage configuration optimization model of the wind-solar-energy storage hybrid distribution network. Based on the optimization model, an improved multi-objective particle swarm optimizer (IMOPSO) is introduced to solve the problem. IMOPSO avoids the particle swarm from falling into the local optimal solution while considering the convergence speed and accuracy. Finally, through analysis of the IEEE-33 node distribution network system, the rationality of the proposed ESS configuration optimization model and the IMOPSO are verified, which can effectively reduce the nodal voltage fluctuation and power loss of the distribution network, and improve the system economy at the same time.

As the world rapidly transitions towards a sustainable energy future, the search for efficient and scalable energy storage solutions becomes increasingly vital. Hydrogen energy storage has emerged as a promising contender in this endeavor, holding the potential to revolutionize the energy landscape of tomorrow. This chapter delves into the cutting-edge trends and advancements in hydrogen energy storage, presenting a comprehensive roadmap for its integration into the global energy infrastructure. The chapter begins by providing a foundational understanding of hydrogen energy storage, elucidating the principles and mechanisms behind various storage methods. From traditional compression and liquefaction techniques to cutting-edge materials like metal hydrides and advanced nanomaterials, each storage approach is explored in detail, highlighting their advantages and limitations. Advancements in hydrogen production methods take center stage as the chapter examines the transition towards renewable-powered electrolysis and sustainable sources of hydrogen feedstock. The integration of hydrogen storage systems with intermittent renewable energy sources, such as solar and wind, is explored as a viable means of enhancing grid stability and ensuring a consistent energy supply. Addressing the challenges of infrastructure and transportation, the chapter analyzes the development of hydrogen storage and distribution networks. From on-site storage solutions to mobile storage technologies, including hydrogen fuel cells for various applications, the potential for hydrogen's widespread adoption is evaluated across different sectors, including transportation, industry, and residential use. Moreover, the chapter delves into emerging research areas that promise even greater efficiency and performance in hydrogen energy storage. Advanced materials, catalysts, and novel storage architectures pave the way for the next generation of hydrogen storage systems, promising enhanced safety, longevity, and energy density. As a roadmap for the future, the chapter outlines the crucial steps required to accelerate the adoption of hydrogen energy storage at a global scale. Policy recommendations, public-private partnerships, and investment strategies are explored to facilitate the necessary infrastructure and technological advancements. In conclusion, "Innovative Trends in Hydrogen Energy Storage: A Roadmap for Tomorrow's Energy Landscape" offers a comprehensive insight into the current state of hydrogen energy storage and its potential to transform the energy sector. As the world seeks sustainable alternatives to traditional energy sources, hydrogen emerges as a versatile and eco-friendly energy carrier, capable of shaping a cleaner and more resilient energy landscape for generations to come

For loss reduction In distributed networks a multiplicity of possibilities exist. The use of cables instead of overhead lines as well as larger diameters of the lines lower the arising losses in distribution networks. Likewise a more even load profile leads to a reduction of losses, so that in the consequence primary energy, costs and CO2-emissions can be saved. Unbalances through asymmetrical loads arise particularly within the low-voltage grids and they are responsible for substantial losses compared with the situation of symmetrical load conditions. In this paper investigations are presented, which show that considerable loss reductions can be obtained by decentralized compensation. For the computation of the losses in urban distribution networks a model is developed. The parameters (standardised load profiles, number and kind of typical consumers, line data etc..) can be varied. Results of model computations are presented.

Aiming at the issue of microgrid renewable energy consumption strategy formulation, based on the data-driven idea of artificial intelligence technology, the method of matching the image similarity of the comprehensive characteristics of source-load-storage of microgrid based on CNN (Convolutional Neural Network) and ORB(oriented FAST and Rotated BRIEF) is proposed to formulate the regulation strategy of microgrid in new dispatching cycle with the help of historical similar operation state regulation strategy. Firstly, the CNN model is used to label and classify the microgrid source-load-storage integrated characteristics images to narrow down the search scope of the historical operating state image library and improve the matching efficiency; subsequently, the ORB algorithm is used to extract the features and match the image similarity to finally mine the historical similar operating state, so as to draw on the microgrid regulation strategy of the historical similar operating state to formulate the next cycle strategy of energy storage and controllable load, so as to achieve the objective of maximizing the local consumption of renewable energy in the microgrid. The simulation analysis of a microgrid system in a typical weather day scenario shows that this method can effectively classify and match the microgrid operating state of the new dispatch period to the corresponding historical similar operating state, and the regulation strategy of energy storage and adjustable load formulated It can effectively track the fluctuations in the output of renewable energy, thereby improving the on-site consumption of renewable energy in the microgrid.

As the penetration of distributed renewable energy increases, the phenomenon of bidirectional power flow in distribution networks becomes increasingly severe. Traditional regulation devices like OLTC (on-load tap changer) and CB (capacitor bank) cannot effectively mitigate reverse power flow in distribution networks due to their limitations. The transmission capacity of the distribution network under reverse power flow is approximately 50% of the rated capacity of the OLTC, leading to issues such as voltage limit violations and high wind and solar curtailment rates. This paper proposes a method for calculating the reverse power flow delivery capacity of distribution networks, quantitatively describing the distribution network’s delivery limits for reverse power flow. Based on this, a joint optimization model for multiple distribution networks with an SOP is established. The SOP is utilized to share reverse power flow delivery capacity among multiple distribution networks, enhancing operational economy and increasing the accommodation of the DG. Finally, the method’s effectiveness and correctness are verified in the IEEE 33-node system. The results validate that while joint operation does not enhance the reverse flow transmission capacity of a single distribution network, it can, through the shared reverse flow transmission capacity approach, elevate the reverse flow transmission capacity to approximately 70% during the majority of time periods.

One of the factors that cause energy losses in distribution networks is unbalanced loads. These losses, which weigh billions of afghanis on the country's economic charter, although impossible to completely eliminate, but its study can be the beginning of inventing ways to reduce it in different categories of the system. The phase between the feeder phases and the other is the random and asynchronous behavior of the subscribers is one phase. In load distribution networks, load imbalance has two important features, one variable with the amount and severity of load imbalance and the other its dispersion along the circuit. The purpose of this study is to investigate the losses due to load imbalance in the electricity distribution network. A large part of the nationwide network waste is generated in the secondary distribution network, part of which is due to load imbalance in distribution networks. In this paper, first the various problems that cause load imbalance are described, and then the calculations of energy waste and voltage drop due to unbalanced load are performed, and in the third stage, the status of a transformer with unbalanced load is investigated. Finally, practical solutions have been proposed to reduce waste due to unbalanced electrical load in the distribution network

Controlled electric vehicle charging for reverse power flow correction in the distribution network with high photovoltaic penetration: case of an expanded IEEE 13 node test network