Sizing Of Microgrid Research Articles

Intelligent energy management system of hydrogen based microgrid empowered by AI optimization technique

Microgrids powered by hydrogen often face challenges in effectively managing energy over an extended duration due to the intermittent nature of renewable energy sources (RES) and fluctuating loads. This research emphasizes the use of AI technologies, including machine learning to improve the efficiency of intelligent energy management system (IEMS), hydrogen storage, fuel cell consumption, and power system quality improvement. A novel approach integrating Isolation Forest Ensemble (IFE) and Modified Water Wave Optimization (MWWO) has been proposed to enhance energy management and to improve the power quality. This method combines the population size with precision and accuracy. The effectiveness of this study has been assessed against established methods (AVO, PSO & IPSO, MFO, SAG & XGBoost, MILP, and QTLBO) concerning numerous factors including component sizing, economics, reliability, and efficiency of microgrid. The precision level of the proposed technique is approximately 83 %. The primary benefit of this research is the improvement of power quality and cost-efficiency in hydrogen based microgrids, attained through optimized and intelligent energy management system. This article furnishes evidence indicating the superiority of the anticipated approach. This study utilizes MATLAB/Simulink and PyCharm framework for its investigation and projection.

Application of hydrogen storage in polygeneration microgrids: Case study of wind microgrid in India

Renewable energy based stand-alone microgrids are highly promising to supply electricity to isolated communities and remote locations. However, both buffer and long-term energy storage are essential due to the highly intermittent nature of renewable sources. By employing hydrogen energy storage with fuel cell and electrolyser in these microgrids, multiple outputs such as electricity, heat, water, and fuel, can be achieved. Design, development and optimization of efficient energy storage system remain a key challenge for the stand-alone microgrid technology. One must ensure that the microgrid satisfies the required electric load but is not over-designed, which is achieved by limiting the two performance parameters i.e., unmet electrical load fraction and excess electricity fraction. The capacities of wind turbine, electrolyser and hydrogen storage are optimized corresponding to the limiting values of unmet electrical load (fUL) and excess electricity (fEX) below 10 %. For the location of Ladakh which has rich availability of wind resource, the optimum size of microgrid is observed to be smallest as WT-20 kW, El-20 kW, H2-15 kg with fUL and fEX as 6.4 % and 8.9 % respectively. The Levelised Cost of Energy (LCOE) is also minimum as 0.89 $/kWh for the location of Ladakh. The distribution of wind resource has significant impact on the size of hydrogen storage. Uniform wind energy with higher velocity round the year reduces the capacity of hydrogen energy storage. Kanyakumari has non-uniform wind resource across different months of the year corresponding to which the capacity of H2 storage is as high as 370 kg.

Optimal sizing of islanded microgrid using pelican optimization algorithm for Kutubdia Island of Bangladesh

This study proposes an optimal design approach, based on the Pelican Optimization Algorithm (POA), to configure the optimal sizing of design variables on an islanded microgrid: photovoltaic (PV) modules, wind turbines (WT), diesel generators (DG), and batteries in Kutubdia, Bangladesh based on optimal life cycle cost (LCC) and cost of energy (COE). Additionally, the economic analysis of three independent battery technologies, notably lead acid, lithium-ion, and nickel-iron are carried out to find the economically feasible technology, to ensure uninterrupted power supply. Moreover, reliability and sensitivity analyses of the optimized microgrid using POA were conducted for various reliability indices and variable interest rates. Results show that proposed POA method provides the optimal island microgrid configuration with lead acid (LA) batteries (PV/WT/LA/DG) based on a minimum LCC of $8334901, COE of 0.1080$/KWh and greenhouse gas emission amount of 19664 kgs/year. Furthermore, the outcomes generated by the POA are compared with genetic algorithm, particle swarm optimization, moth flame optimization algorithm, whale optimization algorithm and grey wolf optimization. It is found that POA method achieves more competitive results compared to other optimization techniques due to its ability to adjust parameters, fast convergence speed, and straightforward computations.

Systematic Literature Review of Heuristic-Optimized Microgrids and Energy-Flexible Factories

Decentralized renewable energy generation and consumption through microgrids, coupled with short- and long-term storage systems and enhanced demand flexibility, represent a promising strategy for mitigating grid stress and reducing emissions in the industrial sector. However, transitioning into a sustainable industry often poses challenges in terms of economic feasibility. This review surveys current optimization approaches and simulation functionalities to enhance feasibility. It follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, covering 1066 studies from 2016 to 2023 across three research areas: optimal system sizing of microgrids (OSS), optimization of electrical energy distribution to storage systems and consumers (EED), and energy flexibilization of factories (EF). As a result, 24 filtered sources from these areas were analyzed. Quantitative analysis indicated that evolutionary and swarm-inspired metaheuristics are predominantly applied in OSS, whereas exact linear problem solvers are favored for EED and EF optimization. A range of functionalities is available, and approaches often prioritize individual functionalities, such as load forecasting, dynamic electricity pricing, and statistical representation of energy generation, rather than comprehensively integrating them. Furthermore, no current approach simultaneously integrates optimization and simulation models across all three research areas.

Multi-objective optimal sizing and design of renewable and diesel-based autonomous microgrids with hydrogen storage considering economic, environmental, and social uncertainties

The multifaceted nature of human society necessitates the consideration of various indicators when planning and designing energy systems. The success of energy systems is contingent on their ability to reliably meet demands while satisfying different objectives that align with societal needs. This paper presents the multi-objective optimal design and configuration of hydrogen-storage-based microgrids to reliably meet electric load demands in remote regions while considering economic, environmental and social uncertainties. A comprehensive Mixed Integer Linear Programming (MILP) was adopted to model the microgrid consisting of renewable energy systems (RES), hydrogen energy storage system (HESS), battery energy storage system (BESS), and diesel generator (DG). Advanced Interactive Multidimensional Modelling System (AIMMS) was used to perform the deterministic and robust optimisation with special consideration on cost-greenhouse emissions minisation and employment generation maximisation. This study further performs a comparative analysis between hydrogen-based microgrids and lithium-ion battery-based microgrids in terms of the key objectives. In overall performance, the configuration consisting of photovoltaic (PV), wind turbine (WT), fuel cell (FC), electrolyser (EL) and low-pressure tank (LPT) outplays the other configurations considered in terms of total job factor, levelised cost of electricity, renewable energy penetration factor, carbon emission reduction capability. Considering both deterministic and robust solutions of the configuration, the levelised cost of electricity ranges between 0.131 and 0.169 $/kWh, the total lifetime cost varies from $2,002,100 to $6,784,740, and the job provision factor ranges between 0.339 and 0.447. Nevertheless, there is a need for further reduction of the investment cost for its continued technological and market penetration. This is a good basis for the adoption of hydrogen-based storage systems with renewable energy systems.

Optimal sizing of off-grid microgrid building-integrated-photovoltaic system with battery for a net zero energy residential building in different climates of Morocco

An optimal sizing of an off-grid microgrid system composed of photovoltaic (PV)/building integrated photovoltaic (BIPV)/battery energy storage installation is undergone for Net Zero Energy Residential Building blocks across six different climates of Morocco. The Particle Swarm Optimization algorithm is used to find the optimal sizing of the system, by considering the hourly spatiotemporal variations in both solar energy availability and energy demand, with the lowest Total Annualized Cost as the objective function and capacities of BIPV and battery as decision variables. The methodology adopted focuses on main load fulfillment through direct PV and BIPV power supply, backed by battery energy storage technology, to continually guarantee self-sufficiency. A key metric, the load cover factor, is introduced to quantify the ratio by which the load demand is satisfied by the solar PV and BIPV systems. The findings show that the optimal sizing of the BIPV system can help to improve the load cover factor by 0.68–2.58 %. Moreover, integrating BIPV system with PV system and Battery leads to a reduction in the Levelized Cost of Energy with approximately 8.7–20.72 %, as opposed to utilizing only the PV system and battery. Depending on the local climate, the levelized cost of energy ranges from $0.366/kWh in Ouarzazate city up to $0.664/kWh in Ifrane city. Lastly, this holistic approach aims to transform the building from its traditional role as an energy consumer to a carbon-free energy prosumer.

Optimal planning and designing of microgrid systems with hybrid renewable energy technologies for sustainable environment in cities.

Although hybrid wind-biomass-battery-solar energy systems have enormous potential to power future cities sustainably, there are still difficulties involved in their optimal planning and designing that prevent their widespread adoption. This article aims to develop an optimal sizing of microgrids by incorporating renewable energy (RE) technologies for improving cost efficiency and sustainability in urban areas. Diverse RE technologies such as photovoltaic (PV) systems, biomass, batteries, wind turbines, and converters are considered for system configuration to obtain this goal. Net present cost (NPC) is this study's objective function for optimal sizing microgrid configuration. For demonstration, we assess the technical, economic factors, and atmospheric emissions of optimal hybrid renewable energy systems for Putrajaya City in Malaysia. The required solar radiation data, temperature, and wind speeds are collected from the NASA surface metrological database. From the quantitative analysis of simulations, the biomass-battery-based system has optimal economic outcomes compared to other systems with an NPC of around 1.07M$, while the cost of energy (COE) is 0.118 $/kWh. Moreover, environmentally safe nitrogen oxide emissions, carbon monoxide, and carbon dioxide concentrations exist. The grid-tied RE technology boasts cost-effectiveness, with an NPC of 348,318 $ and a COE of 0.0112 $/kWh. This study aids decision-makers in formulating policies for integrating hybrid RE systems in urban areas, promoting sustainable energy generation.

Reliability-based sizing of islanded multi-energy microgrid: a conic chance-constrained optimization approach

AbstractThis paper presents a methodology to optimally design a multi-energy microgrid with thermal and electric loads considering $$N-1$$ N - 1 and probabilistic regulation reserves. This methodology consists of a chance-constrained optimization that determines the optimal sizing of the microgrid. Microgrid operations are rigorously considered by modelling hourly thermal and electric demand patterns as well as technology production schedules over a year. Such schedules include both electric and thermal power balances, ramp constraints, $$N-1$$ N - 1 and regulation reserves, among others. To ensure a reliable microgrid design and operation, reserve constraints have been proposed to deal with both $$N-1$$ N - 1 generation contingencies, and forecast errors. $$N-1$$ N - 1 reserves guarantee that a sudden outage of any of the electric generators is strategically covered by the remaining generators in order to avoid load shedding. Additionally, nonzero-mean random forecast errors of electric load and solar production are addressed by a set of chance constraints able to schedule asymmetric up and down regulation reserves. Their levels are high enough to cover hourly random forecast errors (or intermittencies) with a high threshold probability. The proposed methodology results in a mixed integer second-order cone program. Results of microgrid designs with and without reliability reserves are carefully analysed and compared. Neglecting reliability constraints leads to a lower-cost design at the expense of exposing the microgrid to unsafe operation. Finally, sensitivity analysis to study the optimal portfolio sizing with respect to electric BESS investment cost, solar production forecast mean error, and random intermittency threshold probability are performed.

Optimal sizing of multi-energy microgrid with electric vehicle integration: Considering carbon emission and resilience load

To address the intermittency of renewable energy sources, global warming, and increasing load demands, this paper proposes the optimal sizing of a multi-energy microgrid (MEMG) consisting of electrical, thermal, cooling, and hydrogen networks. The system integrates multi-energy storage and EVs along with the resilience load to facilitate a robust operation scheme. The paper introduces an improved resilience backup mechanism for EVs using hybrid storage. Then, random samples for stochastic parameters are generated with Monte Carlo Simulations (MCS). To this end, a mixed-integer linear programming-based model is developed to minimize cost, emissions, and load shed. Then, a CPLEX solver is applied to solve it efficiently. The optimal MEMG with the hydrogen network reduces cost by 4% and emissions by 40%. The case study validates that hybrid storage (BES-HST-TST) can effectively reduce the electricity grid purchase to zero, making MEMG self-sufficient while yielding the least annual system cost (ASC) of 3855562$ and decreasing emissions to 8385 kg, resulting in economic savings, environmental sustainability, and increased utilization of renewables. Notably, V2G can save 0.7546% of MEMG extra cost incurred due to EVs integration and significantly reduces CO2 emissions by 4%. A novel finding is that the proposed hybrid storage backup mechanism can effectively minimize the extra cost of keeping the resilience load by retaining 31.46% of backup in hydrogen form. It mitigates risk associated with power outages while achieving the least ASC of 3858933$ and CO2 emissions of 8043 kg. These results can help realize a green, cost-effective, and efficient energy system.

A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions

The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies.

Techno-economic optimization for isolated hybrid PV/wind/battery/diesel generator microgrid using improved salp swarm algorithm.

The main objective of this study is to develop a new method for solving the techno-economic optimization problem of an isolated microgrid powered by renewable energy sources like solar panels, wind turbines, batteries, and diesel generators while minimizing greenhouse gas emissions. An Improved Salp Swarm Algorithm (ISSA) with a position adaptation mechanism for the salp leader that involves a leader salp that moves about depending on both food availability and its previous position has been proposed to overcome the convergence problem. In the original SSA, as the approach converges, it can no longer find optimal solutions and becomes trapped in a local minimum. Three Microgrid System (MS) configurations are discussed: PV/WT/BESU/DG, PV/BESU/DG, and WT/BESU/DG. The proposed method seeks to find a middle ground between technical criteria and environmental concerns when deciding on PV, WT, BESU, and DG sizes. The findings indicate that the proposed ISSA approach gives superior results compared to other well-known algorithms like the original SSA, the Ant Lion Optimizer (ALO), the Dragonfly Approach (DA), and the Moth-Flame Optimization Algorithm (MFO), which, after significant investigation, has been proven to help determine the appropriate microgrid size. With PV sizes of 10, 9 WT, 24 BESU, and 3 DG, the PV/WT/BESU/DG configuration offers the highest level of cost-effectiveness with Cost of Energy (COE) of 0.2109 $/kWh, Net Present Cost (NPC) of 376,063.8 $, Loss of Power Supply Probability (LPSP) of 4%, Renewable Energy Fraction (REF) of 96%, and CO2 emission of 12.4457 tons/year. ISSA is brought up as a possible solution to both the problem of rising energy prices and the difficulties inherent in microgrid design.

A review on the microgrid sizing and performance optimization by metaheuristic algorithms for energy management strategies

A growing concern over climate change and the depletion of conventional energy resources have led to the urgent need for sustainable and resilient energy solutions. The optimization of the size of renewable sources is crucial to maximizing their effectiveness. In contrast to conventional single-objective optimization, the multi-objective technique aims to achieve a trade-off between energy cost and power supply reliability. Due to this need, microgrids (MG) have emerged as a promising paradigm, allowing for localized and decentralized energy generation and distribution. Consequently, the conventional techniques for modelling and optimizing exhibit numerous limitations as the power grid continues to produce substantial volumes of high-dimensional and diverse data types. This review paper examines the use of metaheuristic algorithms in the context of multi-objective energy optimization for hybrid renewable energy-integrated microgrids. A comparative analysis of diverse metaheuristic algorithms for microgrid optimization is provided in this paper, which emulates natural phenomena, such as evolutionary processes and swarm dynamics. Based on the findings of case studies, it can be concluded that trade-offs exist between various objectives, eventually leading to the development of both resilient and efficient microgrid designs. By reviewing sustainable energy solutions, and advocating microgrids as viable alternatives to conventional centralized power systems, the review enhances the advancement of sustainable energy solutions.

Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids

This study investigates the optimization of the size of a solar-wind hybrid microgrid using Particle Swarm Optimization (PSO) to improve energy production efficiency, economic feasibility, and overall sustainability. By using past solar and wind resource data, load demand profiles, and system component specifications, the PSO algorithm effectively maximized the capabilities of solar panels and wind turbines. The findings indicate a significant rise in daily energy production, with a 15% enhancement in solar panel capability and a 12% boost in wind turbine capability. The increased energy production plays a crucial role in dealing with the natural irregularity of renewable resources, hence enhancing the resilience and self-reliance of the microgrid. The economic calculations demonstrate significant improvements in the economic feasibility of the microgrid designs. The Levelized Cost of Energy (LCOE) undergoes a significant 10% decrease, suggesting a more economically efficient energy generation. Moreover, the payback time for the original expenditure is reduced by 15%, indicating faster returns on investment. The economic improvements highlight the practical advantages of using PSO for microgrid size, in line with the goal of creating sustainable energy solutions while minimizing economic costs. The improved performance of Particle Swarm Optimization (PSO) is shown by a thorough comparison study with other optimization approaches, such as Genetic Algorithms (GA) and Simulated Annealing (SA). The superior convergence rate of PSO, together with a 15% enhancement in solution quality relative to GA and SA, underscores the efficiency and efficacy of PSO in traversing the complex solution space associated with microgrid size. PSO’s comparative advantage makes it an effective tool for tackling the intricacies of integrating renewable energy, highlighting its potential for extensive use in microgrid design and optimization. The sensitivity evaluations demonstrate that the solutions optimized by the PSO are resilient even when important parameters vary, thereby highlighting the stability and dependability of the approach. In addition to technical and economic factors, the study evaluates the environmental consequences and social aspects of the optimum microgrid designs. The land use efficiency has seen a 10% enhancement, demonstrating the optimum application of area for renewable energy infrastructure. In addition, there is a 7% improvement in community approval, which demonstrates the algorithm’s ability to effectively handle social aspects and promote a comprehensive and socially acceptable approach to renewable energy projects.

Optimum sizing of stand-alone microgrids: Wind turbine, solar photovoltaic, and energy storage system

The growing energy demand and rising fossil fuel expenses in isolated and remote regions have increased interest in renewable energy sources (RESs). However, RESs such as photovoltaics (PVs) and wind turbines (WTs) are intermittent and fluctuating; hence reliability is a major concern. The hybridization of RESs with energy storage systems can effectively address their fluctuations and intermittency and enhance overall efficiency; however, the corresponding system cost is a primary concern. Thus, determining the optimal sizing of a hybrid system is the major challenge. Previous studies have suggested metaheuristic algorithms that rely on specific parameters to find an optimal solution. To address this issue, a hybrid algorithm, namely JGWO, which combines JAYA and grey wolf optimizer (GWO), has been proposed. The aim is to solve the unit sizing problem of a hybrid system (PV-WT-battery) that can meet consumers’ demands at the lowest total annual cost (TAC) while considering the maximum allowable probability of power supply loss (LPSPmax) to ensure system reliability. The JGWO is compared to JAYA, GWO, genetic optimization algorithm (GOA), and teaching-learning based optimization (TLBO) in reliability and TAC. Findings reveal that JGWO outperforms existing algorithms at LPSPmax values of 0%, 1%, 3%, 2%, and 5%, yielding TAC of 66542, 61102, 50247, 43046, and 34464 USD, respectively, for a hybrid system (PV-WT-battery). The results also show that the proposed hybrid PV-WT-battery system is more cost-efficient than PV-battery and WT-battery systems, regardless of the LPSPmax value.

Optimal techno-economic feasibility analysis of a grid-tied microgrid considering demand response strategy

Integrating microgrids into the existing power system increases utility grid reliability and efficiency. Optimal sizing and configuration of Microgrid (MG) components are essential at the design stage for minimizing the overall cost and emissions of the MG. This paper presents a novel mathematical model for optimizing the configuration and operation of a grid-connected microgrid exploiting Demand-Side Management (DSM). This paper studies the impact of Demand Response (DR)-based DSM on optimal MG design from the customers' and utility's perspectives, considering both economic and technical performance metrics. A comparative performance study of six DR programs is investigated in order to verify the effectiveness of the proposed Renewable Generation-based Dynamic Pricing (RGDP)-based DR. In this paper, the sizing problem of MG is formulated as a bi-objective optimization in order to fulfil two objective functions: minimizing the total annual cost and minimizing life cycle emissions. An advanced evolutionary algorithm named the Dandelion Algorithm is utilized in order to solve the complex and constrained nonlinear optimization problem. The results demonstrate that considering RGDP-based DR in the MG sizing problem reduces the installation and operating costs of the MG without load shedding. The proposed DR strategy satisfies the customers' needs with high efficiency and low cost.

Tilos Island's ideal microgrid size for wind, solar, and batteries

This article describes a power plant that is versatile regarding its modeling and associated with a Multiple Objectives Particle Swarm optimization in order to determine the optimal size of each component of the power plant. The simulation is appropriate for a variety of power sources, storage devices and loads. The method is utilized on a Wind Turbine/ Photovoltaic Device/ Battery System setup located in Tilos, Greece. The optimization is intended to reduce the expense of the system and the energy derived from alternative sources that are not renewable. The results produce a Pareto front that represents the expense of the equipment and the degree of autonomy of the micro-grid. The most effective solution to a specific expense associated with energy importation is demonstrated as an example.

A comparison of different metaheuristic optimization algorithms on hydrogen storage-based microgrid sizing

Microgrids (MGs) with a high penetration of renewable energy are becoming increasingly popular, mainly due to the need for a sustainable and environmentally friendly power system. However, the stochastic characteristic of renewable energy sources makes it a considerable challenge when designing a microgrid. Appropriate installation of energy storage systems (ESSs) such as battery and hydrogen storage systems are needed to counter the intermittent nature of energy sources. This study presents a comparison and evaluation of eight different metaheuristic approaches for optimizing the size of a hydrogen storage-based microgrid, with the aims of minimizing the microgrid’s cost and ensuring the ability to regulate the energy flow within the system. In addition, the optimization algorithm considers the power of the photovoltaic (PV) system, electrolyzer, fuel cell, and the capacity of the battery and hydrogen tank as decision variables. Results of numerical simulations proved that, under the above problem framework, the particle swarm optimization algorithm outperforms the rest. The algorithm is able to produce an optimized microgrid with a 25.3% lower annual system cost compared to the worst-performing algorithm. Its ability to escape the local optimum solution is also showcased.

Quantifying agricultural productive use of energy load in Sub-Saharan Africa and its impact on microgrid configurations and costs

The use of advanced energy technologies for agricultural purposes—such as irrigation, refrigeration, crop processing, and egg incubation—has the potential to increase crop yield, reduce vulnerability to changing precipitation patterns, increase shelf life, strengthen income and employment opportunities in rural areas, and reduce emissions by displacing fossil fuel-based technologies. These productive uses of energy (PUE) in remote areas could potentially be powered by microgrids that additionally serve otherwise unelectrified communities, most of which are located in rural Sub-Saharan Africa. In this paper, we use high-resolution geospatial data to estimate the end-use electricity demand for a range of agricultural PUE across Sub-Saharan Africa, and we share these data in an open-access mapping tool. Next, we use REopt®, a techno-economic optimization model of energy systems, to determine the cost and system sizing implications of incorporating agricultural PUE into microgrid designs in Kenya and Zambia. We estimate the upper bound of agricultural PUE demand for irrigation, milling, shelling, refrigeration, and egg incubation across Sub-Saharan to be 16.8 TWh/yr. We find that incorporating local agricultural PUE into microgrid system designs increases the required system sizing while having minimal impact on the levelized cost of energy of these systems. Our analysis is the first to demonstrate the PUE potential in the agricultural sector at a 10x10-kilometer resolution across Sub-Saharan Africa and to show, at scale, how site-specific PUE can impact the cost and sizing of microgrids that are otherwise deployed to serve local household and community load.

Stochastic, resilience-oriented optimal sizing of off-grid microgrids considering EV-charging demand response: An efficiency comparison of state-of-the-art metaheuristics

The resilient microgrid (MG) capacity planning and optimisation problem is widely recognised as a non-deterministic polynomial time-hard (NP-hard) problem. Accordingly, metaheuristics – top-level algorithms inspired by various natural and physical processes – can be utilised to determine the near optimality in designing MGs. However, a comprehensive review of the mainstream literature has shown that the performance of several metaheuristics has not yet been evaluated. In response, this paper first systematically benchmarks the efficiencies of previously unexplored metaheuristics in MG sizing applications against the well-established metaheuristic in the literature, namely the particle swarm optimisation (PSO) algorithm. To this end, the metaheuristics are separately integrated into a novel MG sizing method, which is aware of the optimal demand response capacity procured from electric vehicle (EV)-charging loads. Two grid-independent, 100%-renewable MGs are modelled, which enable the reliable and robust supply of electrical loads in areas far removed from the grid. Furthermore, an advanced EV-charging demand response program is integrated into the overall method, whilst quantifying various sources of time-series data uncertainty and considering specific resilience constraints. The simulation results yielded from three real-world isolated community case studies in Aotearoa-New Zealand confirm the effectiveness of the proposed stochastic, resilience-oriented, EV-charging demand response-addressable MG sizing method. Importantly, the comprehensive statistics-based performance evaluations indicate that new metaheuristics have the potential to outperform the PSO by up to ∼6% in MG sizing applications. This indicates the potentially significant implications of using advanced metaheuristics for improving the economics – and, therefore, rolling out – capital-intensive grid-isolated 100%-renewable MGs.

Planning third generation minigrids: Multi-objective optimization and brownfield investment approaches in modelling village-scale on-grid and off-grid energy systems

Access to reliable and sustainable electricity is still precluded for a large share of global population living in rural areas of developing countries, especially in sub-Saharan Africa. Hybrid microgrids are considered a suitable solution for providing affordable and reliable access to electricity to isolated communities. Properly planning and sizing such systems is although an aspect that can greatly influence the sustainability of the intervention, and the arrival to the market of the third generation minigrids poses new challenges to the process. Three main challenges are identified as pivotal for the proper sizing of new generation microgrids: arrival of the main grid, inappropriateness of Net Present Cost as only objective function in the strategy selection process, and necessity to operate on already existing minigrids. Such aspects are addressed in this work by proposing a methodological advancement to an existing open-source microgrid sizing model: a grid outage model alongside the definition of new constraints and variables for the optimization problem with grid-connected microgrids, a multi-objective optimization option, and a brown-field optimization option. The new version of the model is tested on real life case studies in rural Rwanda (greenfield) and Mozambique (brownfield), proving the profitability of grid-connected and grid-extension solutions for sufficiently low connection distances. Sensitivity analyses are performed to assess variations in system size, cost and CO2 emissions with respect to microgrid and grid connection parameters.