Optimal Microgrid Research Articles

Distributed Predefined-Time Optimization and Control for Multi-Bus DC Microgrid

Distributed Predefined-Time Optimization and Control for Multi-Bus DC Microgrid

Risk-aware microgrid operation and participation in the day-ahead electricity market

This work examines the daily bidding problem of a grid-connected microgrid with locally deployed resources for electricity generation, storage and its own electricity demand. Trading electricity in energy markets may offer economic incentives but exposes the microgrid to financial risk caused by market commitments. Hence, a multi-objective, two-stage stochastic mixed integer linear programming (MILP) model is formulated, extending prior work of a risk-neutral microgrid bidding approach. The multi-objective model minimises both expected total cost of day-ahead microgrid operations and financial risk from bidding measured by conditional value-at-risk (CVaR). Bidding curves derived as first stage decisions are always feasible under present market rules – including a limitation on the number of break points per submitted curve – while being near optimal for the microgrid’s day-ahead recourse schedule. The proposed optimisation model is embedded in a variant of the ɛ-constrained method to generate bidding curve candidates with different trade-offs between the two conflicting objectives. Moreover, scenario reduction is used to compromise accuracy of the uncertainty set for better computational performance. Particularly, the marginal relative probability distance between initial and reduced scenario set is suggested to make a decision on the extent of scenario reduction. The proposed solution procedure is tested in a computational study to demonstrate its applicability to generate optimal microgrid bidding curve candidates with different emphasis between total cost and CVaR in reasonable computational time.

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms

In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler’s laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. The optimization problem is implemented in three optimization scenarios based on real and forecasted data as well as the investigation of the battery's depth of discharge in the HMG optimization in the distribution network and its effects on the different objectives. The results including energy losses, voltage deviations, and purchased power from the HMG have been presented. Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization. Also, the outcomes proved that increasing the battery depth of discharge causes the BES to have more participation in the HMG effectiveness on the distribution network objectives and affects the network energy losses and voltage deviation reduction.

Enhancing sustainable and climate-resilient agriculture: Optimization of greenhouse energy consumption through microgrid systems utilizing advanced meta-heuristic algorithms

Greenhouses offer controlled microclimates that enable year-round cultivation, improving food security and agricultural productivity. However, greenhouses are energy-intensive, with heating accounting for a significant portion of the associated costs. This study explores optimal microgrid configurations, economic viability, and policy recommendations for sustainable greenhouse agriculture in Nigeria. An in-depth energy assessment of a reference greenhouse in a South Korean facility is conducted. Distinct climatic differences between South Korea and Nigeria are highlighted, emphasizing the need for tailored greenhouse designs and energy solutions. Shifting focus to Nigeria, this study investigates the feasibility of hybrid renewable energy systems with a focus on wind and solar power across six geopolitical zones in Nigeria. The analysis encompasses technical, economic, and policy aspects, providing a holistic perspective on renewable energy adoption. Notably, the study uses an advanced optimization model, Teaching and Learning–Based Optimization algorithm, to assess the net present cost and baseload supply reliability, offering valuable insights for investors and policymakers. The result indicates diverse energy requirements across Nigeria, with total monthly peak energy demands ranging from 5374.80 kWh in the Southeast to 17,115.76 kWh in the Northwest, and a notable variation in the Levelized Cost of Electricity (LCOE), with the lowest at $0.07327 in Kano. Specifically, in Ogun, the net present cost for the WT-PV-ESS system stood at $520,935.45, while the PV-ESS system cost was substantially lower at $500,444.41. This confirms the effectiveness of location-specific analysis and shows the suitability of photovoltaic–battery energy storage systems for Nigeria's diverse regions, with unique considerations for specific areas. Policy recommendations, including feed-in tariffs, renewable portfolio standards, net metering, research support, and market development, provide a holistic framework for the adoption of renewable energy and sustainable agriculture. Improving infrastructure, market access, and financing for smallholder farmers is integral for improving food security and standards of living in rural Nigeria. In conclusion, Nigeria can leverage renewable resources to revolutionize its energy and agriculture sectors, setting an example for a sustainable and resilient future.

Optimal multi-objective sizing of renewable energy sources and battery energy storage systems for formation of a multi-microgrid system considering diverse load patterns

Optimal microgrid design is pivotal in planning active distribution networks (ADNs) with intermittent renewable energy sources (RESs) and battery energy storage systems (BESSs). This paper introduces an innovative approach to clustering existing ADN systems, incorporating RESs and BESSs into a set of microgrids (MGs) termed a multi-microgrid (MMG). The approach accommodates diverse load patterns, considering the dynamic nature of residential, commercial, and industrial loads. The proposed methodology employs an innovative k-means algorithm that utilizes end node characteristics based on the graph partitioning method and the Silhouette technique, transforming the existing system into an MMG framework. Furthermore, an iterative pareto-fuzzy (IPF) method is suggested to determine optimal RES and BESS sizes, considering reliability, total cost, and the unutilized surplus power function. The framework integrates total line losses into the generation adequacy assessment of energy management strategy (EMS) for each MG within the MMG system. The design and performance analysis of the proposed multi-objective optimization algorithm is tested on the IEEE 33-bus distribution system. The implementation of the independent operation index (IOI) and round-trip efficiency (RTE) metrics demonstrates the effectiveness of the proposed approach in constructing an MMG, with achieved values exceeding 90 percent for IOI and 77 percent for RTE.

Operation optimization of cogeneration microgrid based on deep reinforcement learning

Abstract The deep Q learning method is adopted to transform the microgrid model into the Markov decision model to realize the coordinated management and energy optimization of the microgrid. For the problem of the Markov decision model, we cleverly divide the working state of the gas turbine into different gears to meet its requirements. To reduce the fitting difficulty and improve the result accuracy and convergence stability, a linear penalty term was added to the reward function. Considering the operating and environmental costs, in addition to the purchase and sale price of a large power grid, peak and valley electricity price and operation constraints, we further reduce the peak and valley difference and reduce the operating cost. Finally, the output of the battery and the output of the distributed power supply are obtained through simulation verification. Through the peak-shifting and valley-filling function of the battery, it can effectively deal with the fluctuation of peak and valley electricity prices, reducing the peak and valley difference of electric heating load, and providing strong support for sustainable energy management.

Energy storage capacity optimization of optical storage microgrid considering customer satisfaction and depth of energy storage discharge

Abstract The influence of the depth of battery discharge (DOD) and user satisfaction on the capacity configuration of the optical storage microgrid cannot be ignored. In this paper, the minimum comprehensive cost of an optical storage microgrid is taken as the objective function, and the model is established by considering the SOC after DOD, user satisfaction with electricity consumption, power balance, and other constraints. The numerical examples show that the proposed method can allocate suitable energy storage capacity for optical microgrids and ensure user satisfaction.

A Design and Optimization of A Renewable EnergyBased Smart Microgrid for Rural Electrification: Analysis of the Simulink Model for Different Loads

The rapid growth of renewable energy technologies has provided an opportunity to address the electrification challenges faced by rural communities. This research paper presents the design and optimization far smart micro grid system that integrates renewable energy sources to provide reliable and sustainable electricity to rural areas. The objective is to analyze the performance of the Simulink model under different load conditions, considering the system's stability, efficiency, and economic viability.

A novel distributed finite-time economic dispatch for DC microgrids with time delays

This paper delineates an advanced distributed control paradigm for finite-time generation cost optimization in DC Microgrids (MGs), taking into account time delays. Firstly, to enhance system stability with time delays, Artstein's reduction approch is utilized to convert the time-delayed system into an equivalent delay-free model. Subsequently, a finite-time consensus-based distributed algorithm for cost optimization is formulated to equilibrate the incremental costs (ICs) of all distributed generators (DGs) in a predefined convergence time, while adhering to the DGs' capacity limits. Moreover, a distributed control mechanism for finite-time voltage stabilization is developed for sustaining the balance between the generated powers and consumptions across the MG. Rigorous convergence analysis proves that the proposed controller maintains finite-time stability. The efficacy of this innovative control strategy is substantiated through a series of comprehensive case studies.Top of Form

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.

Energy-Efficient Storage System Optimization and Recent Trends in Enhancing Energy Management and Access Microgrid: A Review

Energy is a vital component of our everyday lives, driving advancements in human development, economic expansion, and efficiency. Implementing sustainable energy storage systems is an economically viable approach to improving energy efficiency and regulating the generation and consumption of power. This study extensively examines energy storage, delving into several techniques, their practical uses, and the most recent progress. It focuses on environmentally sustainable alternatives for the electricity sector, including batteries, flywheels, thermal storage, and pumped hydro storage. The influence of includes compelling case examples, such as using vanadium redox flow batteries in maritime hybrid propulsion systems and advancing portable hydrogen energy storage devices. The results are encouraging, indicating that these technologies have the potential to be adequate replacements for traditional fuel sources, particularly in isolated areas where there is a critical requirement for energy storage. Moreover, this study thoroughly examines the integration options for energy storage in microgrids. It explores the complex aspects of the models, assessment indices, and optimization methods used in designing energy storage systems. This paper explores the capabilities of software used to determine the optimal size for energy storage systems. Furthermore, this study introduces and examines uncertainty analysis in modeling energy storage devices. This state-of-the-art technology has been created to demonstrate the effectiveness of energy storage systems in microgrids, providing crucial data for future progress in the industry.

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO’s superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness.

Efficient 2-Distance Coloring Method for Maximum and Average Sparse Graphs in Resource Allocation Optimization of Microgrids

In microgrids management, optimizing resource allocation and enhancing efficiency are critical challenges. This paper introduces the 2-distance coloring technique as a novel approach to address these issues by focusing on the coloring problem of sparse graphs. Through rigorous theoretical derivation, we establish the minimum number of colors required for 2-distance coloring in sparse graph scenarios. Specifically, we demonstrate that for a graph G with an average maximum degree less than 2+17/20 and a maximum degree of 6, it can be list 2-distance colored using no more than 11 colors. This means that, given any set of 11 colors for each vertex, a valid 2-distance coloring can be achieved, ensuring no two adjacent vertices share the same color. This finding is pivotal, as it sets a precedent for the number of resources needed to efficiently manage and allocate resources in microgrids systems through 2-distance coloring. The application of this technique in microgrids promises to revolutionize resource distribution, reducing overlap and maximizing efficiency across the network.

Techno-economic assessment of hybrid renewable energy system with multi energy storage system using HOMER

Energy storage systems (ESS) address the uncertainty of renewable energy sources (RES) in renewable energy based isolated microgrids. One type of ESS is unable to provide 100 % supply reliability. Moreover, there are either excess energy or reliability issues due to a lack of energy management techniques. Therefore, this paper proposes hybrid RES (HRES) with multiple-ESS (MESS), including battery and hydrogen storage system (HSS). The techno-economic analysis and optimal design of HRES-based microgrids, which include wind turbine (WT), solar photovoltaic (PV), fuel cell (FC), electrolyzer, HSS, and battery energy storage (BES) are performed using HOMER. The design objectives are to minimize the net present cost (NPC) and cost of energy (COE) in the proposed HRES with MESS. The analysis is performed for a multi-energy storage system. The results show that WT/PV/FC/Electrolyzer/HSS/BES/converter is the optimal microgrid with the lowest NPC of $838832, $679605, and COE of 0.232 $/kWh, 0.189 $/kWh at capacity shortages of 0.0 % and 1.0 %, respectively. The excess energy and unmet loads are also the lowest for the system at the same capacity shortage. The sensitivity analysis shows the impact of uncertain techno-economical parameters on NPC and COE.

Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850.

Optimal Economic Research of Microgrids Based on Multi-Strategy Integrated Sparrow Search Algorithm under Carbon Emission Constraints

In the global transition towards sustainable energy, microgrids are emerging as a core component of distributed energy systems and a pivotal technology driving this transformation. By integrating renewable energy sources such as solar and wind power, microgrids not only enhance energy efficiency and reduce reliance on traditional energy sources but also bolster grid stability and mitigate the risk of widespread power outages. Consequently, microgrids demonstrate significant potential in improving the reliability of power supply and facilitating flexibility in energy consumption. However, the operational planning and optimization of microgrids are faced with complex challenges characterized by multiple objectives and constraints, making the reduction in operational costs a focal point of research. This study fully considers an operational model for a microgrid that incorporates distributed energy resources and comprehensive costs, integrating a battery storage system to ensure three-phase balance. The microgrid model includes photovoltaic power generation, wind power generation, fuel cells, micro-gas turbines, energy storage systems, and loads. The objectives of operating and maintaining this microgrid primarily involve optimizing dispatch, energy consumption, and pollution emissions, aiming to reduce carbon emissions and minimize total costs. To achieve these goals, the study introduces a carbon emission constraint strategy and proposes an improved Multi-Strategy Integrated Sparrow Search Algorithm (MISSA). By applying the MISSA to solve the operational problems of the microgrid and comparing it with other algorithms, the results demonstrate the effectiveness of the carbon emission constraint strategy in the microgrid’s operation. Furthermore, the results prove that the MISSA can achieve the lowest comprehensive operational costs for the microgrid, confirming its effectiveness in addressing the operational challenges of the microgrid.

Inner-outer layer co-optimization of sizing and energy management for renewable energy microgrid with storage

Conventional microgrid optimization schemes fall short in achieving global optimality for both sizing and scheduling aspects. In response to the demand for simultaneous optimization, this paper presents a novel inner-outer layer framework that includes an outer layer dedicated to sizing optimization and an inner layer focused on Energy Management System (EMS) operational optimization. This approach has a distinctive mechanism of two-layer interaction, where the outer layer focuses on the size assignment and renewal, while the inner layer is constrained by the size assignment and provides feedback on the corresponding operational cost to the outer layer. It enhances the capability of global optimization since the optimal sizes are searched within a fixed space. To establish the range of initial generation values, the framework commences with a pre-optimization phase. Based on the convexity nature of optimization problems, a combined solution methodology involving Rolling Horizon Optimization (RHO) and Particle Swarm Optimization (PSO) is implemented. The case study demonstrates that the algorithm exhibits a good convergence within 30 generations, reaching a global satisfactory solution with a population size of 50. Furthermore, our analysis of optimal sizes across diverse scenarios fills the research gap in understanding the relationship between optimal sizes and operational scenarios. These results underscore that the impact of the time-of-day tariff strategy is significant, and it is risky to determine the optimal sizes by rule-based strategies. Finally, the comparison to other co-optimization methods shows that the proposed framework can reduce the annual total cost by up to 23.1%, which highlights its greater capability of global optimization.

Optimal Microgrids in Buildings with Critical Loads and Hybrid Energy Storage

This research aims to optimize and compare the annual costs of energy services in buildings with critical loads and analyze case studies for hospitals and higher education institutions in the United States. Besides electricity and natural gas costs, the study considers all the infrastructure costs of capital amortization and maintenance. In addition, it studies energy resilience improvement due to distributed generation, including solar photovoltaic, solar thermal, internal combustion engine, and fuel cell sources. The optimization considers the electrical consumption, the heating and cooling demands, and the operational strategy of the energy storage systems. To simulate real scenarios, energy tariffs were modeled and considered, and final optimization results were produced. Some of the microgrid load was considered critical to model resilience benefits. The results show that if favorable energy tariffs are applied, the benefits of increasing energy resilience represent a novel market with high potential in facilities with significant critical loads. This methodology can be used in similar scenarios, adapting each particular load profile and critical load to provide a combined optimal solution regarding resilience and economic benefits.

The Interaction between Short- and Long-Term Energy Storage in an nZEB Office Building

The establishment of near-autonomous micro-grids in commercial or public building complexes is gaining increasing popularity. Short-term storage capacity is provided by means of large battery installations, or, more often, by the employees’ increasing use of electric vehicle batteries, which are allowed to operate in bi-directional charging mode. In addition to the above short-term storage means, a long-term storage medium is considered essential to the optimal operation of the building’s micro-grid. The most promising long-term energy storage carrier is hydrogen, which is produced by standard electrolyzer units by exploiting the surplus electricity produced by photovoltaic installation, due to the seasonal or weekly variation in a building’s electricity consumption. To this end, a novel concept is studied in this paper. The details of the proposed concept are described in the context of a nearly Zero Energy Building (nZEB) and the associated micro-grid. The hydrogen produced is stored in a high-pressure tank to be used occasionally as fuel in an advanced technology hydrogen spark ignition engine, which moves a synchronous generator. A size optimization study is carried out to determine the genset’s rating, the electrolyzer units’ capacity and the tilt angle of the rooftop’s photovoltaic panels, which minimize the building’s interaction with the external grid. The hydrogen-fueled genset engine is optimally sized to 40 kW (0.18 kW/kWp PV). The optimal tilt angle of the rooftop PV panels is 39°. The maximum capacity of the electrolyzer units is optimized to 72 kW (0.33 kWmax/kWp PV). The resulting system is tacitly assumed to integrate to an external hydrogen network to make up for the expected mismatches between hydrogen production and consumption. The significance of technology in addressing the current challenges in the field of energy storage and micro-grid optimization is discussed, with an emphasis on its potential benefits. Moreover, areas for further research are highlighted, aiming to further advance sustainable energy solutions.