Microgrid Planning Research Articles

A tri-level stochastic model for operational planning of microgrids with hydrogen refuelling station-integrated energy hubs

Energy hubs are efficient energy systems in which multiple energy carriers are converted, conditioned and stored to supply multiple forms of energy demands such as electricity, gas and heat. On the other hand, the penetration of fuel cell vehicles in transportation sector is increasing. The role of hydrogen refuelling stations is to inject hydrogen into fuel cell vehicles. A hydrogen refuelling station may absorb its required electricity from an energy hub. The operational planning of the microgrids with hydrogen refuelling station-integrated energy hubs has not been addressed before; therefore, the main goal of this research is to develop a hierarchical stochastic framework for operational planning of isolated microgrids with hydrogen refuelling station-integrated energy hubs, considering the uncertainties. In a hierarchical framework, the players are not obliged to submit their models to a central agent, so the privacy of players is preserved; moreover, it is computationally inexpensive. Mixed-integer linear programming models are used for hydrogen refuelling stations and energy hubs, while a mixed-integer quadratic programming model is used for modeling microgrid. CPLEX and GUROBI solvers are respectively used for solving the developed models. SCENRED module is used for scenario reduction. The studied microgrid is a renewable-rich 69-bus radial network. The findings approve the efficiency of the proposed methodology. The impact of batteries and wind generators on the operation of energy hubs has been evaluated.

Optimal planning and operation of heterogeneous autonomous and grid-connected microgrids based on multi-criteria techno-economic, environmental, and social indices

Sustainable energy transition involves the execution of recent technologies as a means of ensuring energy access and security. However, the increasing adoption of such technologies, particularly in the presence of diverse constraints, poses significant challenges from a planning perspective. In this context, the multi-objective analysis offers valuable insights for decision-making that balances mutual benefits, as relying solely on a single objective may increase the risk level for stakeholders engaged in coordinated decisions. Based on the real dataset, this study presents a comparative multi-criteria techno-economic, environmental, and social evaluation of site-specific unified standalone and grid-connected hybrid microgrids. The study employs a modified Last Cluster Mean Carried Forward approach for data processing, incorporating the Proprietary Derivative-free Algorithm and Original Grid-Search Algorithm to ensure a standardized comparison. Results reveal the substantial advantages of grid-connected systems over standalone counterparts, with reductions of 12.54 % to 63.73 % and 68.93 % to 89.13 % in terms of Net Present Cost and Levelized Cost of Energy, respectively. Grid-connected systems exhibit superior adaptability, recovering 52.3 % to 98.1 % surplus energy with a Renewable Fraction averaging 77.1 % to 87.9 %. However, these systems were hindered by frequent interruptions and required a minimum capacity shortage of 2.5 % to 3.5 %. Furthermore, grid-connected systems have proved feasible when carbon emissions, forests required to absorb the emissions, and the Social Cost of Carbon Emissions are considered in the range of 4.65–67.13 kiloton (metric), 423.32–6108.53 ha, and $0.24-$3.42 million, respectively. Social analysis and sensitivity analyses are performed to justify the robustness and adaptability. Lastly, the findings are followed by policy recommendations and results validation by comparing prevailing government tariffs and other studies.

Utility-Scale Grid-Connected Microgrid Planning Framework for Sustainable Renewable Energy Integration

Microgrids have emerged as a crucial focus in power engineering and sustainable energy research, with utility-scale microgrids playing a significant role in both developed and developing countries like the Philippines. This study presents a comprehensive framework for utility-scale microgrid planning, emphasizing the sustainable integration of renewable energy resources to the distribution grid. The framework addresses the operational modes of grid-connected and islanded microgrids, emphasizing the seamless transition between these modes to ensure a continuous power supply. By leveraging local distributed energy resources, the microgrid aims to reduce dependence on the main transmission grid while enhancing resilience and reliability. The proposed planning framework not only eases the economic burden of constructing renewable energy sources but also aids distribution utilities in maximizing local resources to achieve sustainable energy goals. Through a detailed network analysis and modeling, the framework provides a robust foundation for optimizing the energy mix and enhancing the overall system performance. This research contributes to advancing microgrid technology as a key driver towards achieving UN Sustainable Development Goals, particularly in promoting clean and affordable energy access.

Optimizing Microgrid Planning for Renewable Integration in Power Systems: A Comprehensive Review

Optimizing Microgrid Planning for Renewable Integration in Power Systems: A Comprehensive Review

Voyage scheduling and energy management co-optimization in hydrogen-powered ships

The maritime sector is striving to lessen its dependence on fossil fuels, motivated by both economic factors related to fuel efficiency and the policies established by the International Maritime Organization (IMO). Consequently, there has been a growing focus on developing and deploying all-electric ships (AES) as a more sustainable alternative to conventional fossil fuel-powered vessels. AES presents several advantages over conventional ships, including zero emissions, enhanced energy efficiency, and greater sustainability. However, the implementation of AES also brings new challenges, such as the necessity to manage a hybrid microgrid and optimize voyage scheduling. Hybrid fuel cell (FC)-battery energy storage (BES) technology can significantly contribute to addressing these requirements. To tackle these challenges, this article introduces a comprehensive framework for the optimal planning of hybrid microgrids and voyage scheduling in AES. The framework incorporates multi-objective optimization techniques based on genetic algorithms to achieve a balance between cost and voyage time. The proposed framework is applied to a case study of a ship operating on a fixed route between two ports. The simulation results indicated that the total cost per voyage, associated with the objective function of cost minimization and voyage time reduction, has decreased by 12% and increased by 40%, respectively, when compared to the regular ship speed profile. Additionally, the travel time has been reduced by half an hour and 2 h, respectively, in comparison to the regular ship speed profile. To verify the results, the optimization problem for cost and voyage scheduling is also solved by Mayfly Algorithm (MA) separately. Thus, it can be illustrated the effectiveness of the framework in enhancing energy efficiency and fuel economy while preserving the ship's performance.

Precise Positioning of Distributed Power Supply in Smart Microgrids for High-latitude Areas

This paper focuses on the precise micro-location of distributed power sources in smart microgrids at high latitudes. It first clarifies the strategic position of microgrids in power systems, especially in complex environments at high latitudes, and emphasizes the urgency of their location. Subsequently, it systematically reviews the research trends of smart microgrid and distributed power supply domestically and internationally, revealing the limitations of existing research and problems to be solved. The unique climate adaptability and power supply-demand characteristics of microgrids in this region are deeply analyzed according to the characteristics of high latitude, clarifying the key role of distributed power supply. The core discussion revolves around examining the application potential and limitations of mainstream distributed energy sources such as solar energy, wind energy, and energy storage devices in a specific environment. The aim is to provide a theoretical basis and practical guidance for scientific planning and optimal layout of smart microgrids in high-latitude regions while promoting innovation and development in this field.

Optimal planning of energy microgrid with multi-objective functions in independent Mode

Energy storage systems play a crucial role in managing the uncertainties associated with power generation from renewable sources like wind turbines (WTs) and photovoltaic (PV) systems. This article presents on determining the most effective sizing of energy resources within a microgrid, which includes hydrogen storage, PV, battery systems, and WT in independent mode of main grid. The study aims to minimize installation costs, maximize the penetration of WT and PV systems in meeting demand, and reduce load shedding. To tackle the intricacies of the planning process, the research utilizes the modified sunflower optimization (MSFO) algorithm. Through comparative experiments, the study confirms the efficacy of this method, showcasing its capacity to deliver enhanced outcomes. The findings indicate that the proposed method efficiently optimizes resource allocation at an optimal pace.

Hybrid energy-based electric vehicles charging station integrated with INVELOX wind turbine: A case study of Kermanshah

Global warming from fossil fuels is a growing concern for governments. A potential solution is using renewable energy microgrids, which reduce dependence on the power grid. On the other hand, Electric vehicles are a solution to combat environmental pollution as they emit no harmful pollutants compared to conventional vehicles. However, charging these vehicles can lead to congestion, reliability, and resiliency issues for power systems. To address this issue, renewable energy sources are being utilized to ease the burden of electric vehicles on power grids. Therefore, this paper assumes a proposed microgrid's planning and management in Kermanshah city and uses unique renewable energy sources such as the INVELOX wind turbine and common photovoltaic panels by utilizing Homer software. Results show that for the first scenario, excluding PV panels, the NPC is $432,173. Adding PV panels reduces the optimized NPC by 53 % to $282,523. Sensitivity analysis highlights the impact of inflation, fuel prices, and PV longevity. In the second scenario with the INVELOX wind turbine, the NPC without PV is $373,199, with $26,070 in annual repairs. Adding PV panels decreases the NPC to $253,405 and maintenance to $14,288, showing significant cost reductions. Novel technologies like INVELOX and PV panels enhance microgrid cost efficiency and environmental impact.

Optimal planning and partitioning of multiple distribution Micro-Grids based on reliability evaluation.

Power system researcher have turned to Micro-Grids (MG) for higher reliability, greater flexibility, lower operating costs and losses, and lower CO2 emissions at the distribution system. This paper presents a single-level stochastic optimization framework for planning and partitioning of a distribution system including Multiple Micro-Grids (MMGs). The main objective is to minimize the total cost of the system including investment, operation, total losses and reliability costs of the distribution network. The proposed model takes into account the viewpoints of MG owners and distribution system operators, simultaneously. The voltage stability index is introduced to identify the optimal site of MG investment. To deal with uncertainties caused by renewable generations, the Firefly Algorithm (FA) and probability-tree method is used to create various operation scenarios of Photo-Voltaic (PVs) and Wind Turbines (WTs). This model is solved through the genetic algorithm in MATLAB, and to evaluate its effectiveness, numerical studies have been carried out on the experimental IEEE distribution network with four specified locations for investing MGs and seven Tie Switches (TS) for network partitioning. Simulation results reveal that optimal locations for MG investment are determined in such a way all MGs connect to the buses near the beginning of the feeder and as a result, load point reliability is improved, total active power losses are reduced, and the energy program becomes more optimized.

Developing a cooperative approach under normal and contingency conditions for generation expansion planning of microgrids

AbstractThe issue of generation expansion planning in microgrids has become a challenging issue in electricity industry for two reasons: load growth and uncertainties in renewables' generation. Therefore, this issue is considered here. Here, the modelling of generation expansion planning problem has been developed in a network of microgrids in a decentralized manner, considering normal and contingency conditions. On the other hand, in order to further develop the study considered, decentralized generation expansion planning model of microgrids by considering contingency conditions has been addressed in a cooperative approach to minimize total costs. In developed model, investment decisions are made at the higher level and operational constraints has been considered at lower one. Also, case studies are defined in three different scenarios: islanding operation of microgrids as first scenario and peer‐to‐peer trading of microgrids in non‐cooperative and cooperative approaches as second and third scenarios, respectively. The results of simulations have shown that by facilitating the transactions between microgrids, their total costs are reduced. The costs of the whole set of microgrids in the non‐cooperative scenario are reduced by 9.4% compared to the islanding scenario; and the costs are reduced by 7.5% in the cooperative scenario compared to the non‐cooperative scenario.

Machine Learning Models for Solar Power Generation Forecasting in Microgrid Application Implications for Smart Cities

In the context of escalating concerns about environmental sustainability in smart cities, solar power and other renewable energy sources have emerged as pivotal players in the global effort to curtail greenhouse gas emissions and combat climate change. The precise prediction of solar power generation holds a critical role in the seamless integration and effective management of renewable energy systems within microgrids. This research delves into a comparative analysis of two machine learning models, specifically the Light Gradient Boosting Machine (LGBM) and K Nearest Neighbors (KNN), with the objective of forecasting solar power generation in microgrid applications. The study meticulously evaluates these models’ accuracy, reliability, training times, and memory usage, providing detailed experimental insights into optimizing solar energy utilization and driving environmental sustainability forward. The comparison between the LGBM and KNN models reveals significant performance differences. The LGBM model demonstrates superior accuracy with an R-squared of 0.84 compared to KNN’s 0.77, along with lower Root Mean Squared Error (RMSE: 5.77 vs. 6.93) and Mean Absolute Error (MAE: 3.93 vs. 4.34). However, the LGBM model requires longer training times (120 s vs. 90 s) and higher memory usage (500 MB vs. 300 MB). Despite these computational differences, the LGBM model exhibits stability across diverse time frames and seasons, showing robustness in handling outliers. These findings underscore its suitability for microgrid applications, offering enhanced energy management strategies crucial for advancing environmental sustainability. This research provides essential insights into sustainable practices and lays the foundation for a cleaner energy future, emphasizing the importance of accurate solar power forecasting in microgrid planning and operation.

Sustainable urban transformations based on integrated microgrid designs

The impacts of natural hazards on infrastructure, enhanced by climate change, are increasingly more severe emphasizing the necessity of resilient energy grids. Microgrids, tailored energy systems for specific neighbourhoods and districts, play a pivotal role in sustaining energy supply during main grid outages. These solutions not only mitigate economic losses and well-being disruptions against escalating hazards but also enhance city resilience in alignment with Sustainable Development Goal (SDG) 11. However, disregarding socioeconomic factors in defining microgrid boundaries risks perpetuating inequalities and impeding progress towards other SDG 11 targets, including fair democratic participation. Our approach integrates social and technical indicators to bolster urban microgrid planning. Through a case study in a US county, we illustrate how integrated microgrid planning effectively intertwines urban resilience, well-being and equity while promoting sustainable development. This study underscores the importance of integrated microgrid planning for sustainable and resilient urban transformation amid environmental and societal challenges.

Stochastic optimization for capacity configuration of data center microgrid thermal energy management equipment considering flexible resources

The rapid development of Internet and cloud computing technologies has led to the expansion of the scale of data centers, resulting in a continuous increase in the demand for data processing and storage. This not only results in higher energy consumption but also makes thermal management a key factor of energy management in data centers. A capacity configuration method is proposed for the core thermal management equipment of data center microgrids, electric boilers, cooling systems, and heat pumps. This method not only considers the uncertainty factors of the source and load but also effectively utilizes the characteristics of various flexible resources in data centers, such as the adjustable ability of different types of batch processing loads, air thermal inertia, and waste heat recovery, significantly improving the energy utilization efficiency and enhancing the system’s economy and flexibility. In addition, a wind power scenario generation method based on conditional least squares generative adversarial networks was proposed to improve the generation quality of wind power scenarios, and a random optimization model was constructed. Considering a certain province’s data center microgrid as a case study, the effectiveness of the model was verified, and a sensitivity analysis of the relevant parameters provided important references for microgrid planning. This study provides a new perspective and tool for the thermal management of data center microgrids and comprehensive utilization of renewable energy, which is of great significance for promoting the improvement of data center energy efficiency and sustainable development.

A Novel Model for Battery Optimal Sizing in Microgrid Planning Considering Battery Capacity Degradation Process and Thermal Impact

A Novel Model for Battery Optimal Sizing in Microgrid Planning Considering Battery Capacity Degradation Process and Thermal Impact

Application of improved Wolf pack algorithm in planning and operation of multi-microgrid systems with electric vehicles

Abstract With the rapid growth of renewable energy sources and the widespread use of electric vehicles (EVs), the planning and operation problems of multiple microgrids (MMGs) have become more complex and diverse. This paper develop an MMG model with multiple renewable energy sources and small-scale EVs, aiming to maximize the use of renewable energy sources and realize the charging demand of EVs, and highlighting the potential role of EVs in MMGs. In addition, the paper underscores the indispensable role of measurement technology in microgrids and the impetus that microgrid development provides for advancements in measurement technology. To this end, this paper proposes an improved Wolf pack algorithm (IWPA) based on the standard Wolf Pack Algorithm (WPA) with a spiral search approach and chaotic updating of individuals to improve the global search capability of the algorithm and the complexity of solving the scheduling problem. Through simulation experiments on ten standard test functions and examples, it is verified that the IWPA algorithm improves the search accuracy by 2.8%–6.8% and 13.9%–18.3% in the worst and best cases, respectively, in comparison with other algorithms, and it also has a faster convergence speed. Meanwhile, this paper proposes a load interval pricing strategy for the shortcomings of time-of-use pricing strategy and traditional real-time pricing strategy, which is simulated under grid-connected operation, isolated grid operation, and multi-microgrid cooperative operation modes, and the simulation results of the arithmetic example show that this strategy can effectively reduce carbon emissions, and IWPA can effectively coordinate renewable energy, EVs, and other energy resources to achieve efficient energy management of MMGs and supply-demand balance.

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.

Optimal load scheduling strategy for isolated building-integrated DC microgrid using binary PSO to maximize user comfort and minimize peak-to-average ratio

Due to the growth of electricity consumption in residential and commercial buildings, there is a growing burden on utilities in urban areas. Sustainable buildings are being developed by integrating renewable energy generation to make an eco-friendly environment. An isolated building-integrated (IBI) DC microgrid fed by a hybrid solar PV (photo voltaic) generator-thermo electric generator (TEG) is one viable option for obtaining self-sustainable energy for the building. The battery is an important component in such IBI DC microgrid fed by the hybrid solar PV generator-TEG for providing reliable power to the loads. However, the operation and planning of the IBI DC microgrid are challenging, and therefore it needs a suitable load scheduling strategy. This paper proposes the optimal load scheduling strategy (OLSS) under demand response to schedule the household loads in the building. The proposed OLSS aims to balance two conflictive objectives, user comfort and peak-to-average ratio (PAR), using a binary particle swarm optimization algorithm (BPSO). The proposed OLSS optimizes user comfort and PAR and reduces peak demand and battery usage. It also helps in the optimum utilization of power generated from hybrid solar PV generator-TEG by shifting the loads during sun hours. Reducing battery usage reduces the conversion loss and extends the battery life. To validate the proposed OLSS, the investigations are carried out using the data obtained across different seasons. The results show that the proposed OLSS schedules the household loads with the desired user comfort and reduces PAR by 22 % in future buildings.

Reliability-Based Planning and Partitioning of Multiple Micro-Grid Considering Demand Side Response Program

Reliability-Based Planning and Partitioning of Multiple Micro-Grid Considering Demand Side Response Program

Optimizing power systems and microgrids: A novel multi-objective model for energy hubs with innovative algorithmic optimization

This article delves into the dynamics of power system and microgrid planning, emphasizing the emerging significance of energy hubs. The study introduces a pioneering multi-objective model that integrates economic considerations and microgrid pollution mitigation. The focus is on renewable sources such as wind and solar, coupled with advanced energy storage systems like E-fuel. Sophisticated probabilistic modeling techniques address uncertainties in renewable resources, complemented by a demand-side management program for cost reduction during peak hours. Transforming into an optimization problem, an innovative algorithm inspired by African vulture foraging behaviors provides a distinctive approach to energy landscape optimization. In a comparative analysis with established models, this approach demonstrates proficiency in simulating market behavior and providing optimal strategies for profit maximization. In a simulation analysis of four scenarios, the impact of risk assessment, a novel electronic fuel storage market, and an innovative energy storage solution on operational costs and pollution emissions is scrutinized. Notably, the proposed method excels in Case 4, showcasing a significant reduction in both operational costs and emissions. Case 4 demonstrates a substantial reduction in operational costs to $1745.732 and pollutant releases to 10753.621 kg, outperforming existing models with lower operational costs and pollutant releases.

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.