Wind Sources Research Articles

Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory

In the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon measurement across all operational aspects of distribution networks, reduce their carbon emissions through controlling unit operations, and ensure stable and safe operation. First, we propose a method for measuring carbon emission intensity on the source and network sides of active distribution networks with network losses, allowing for the calculation of total carbon emissions throughout the operation of networks and their equipment. Next, based on the carbon flow distribution of distribution networks, we construct a low-carbon dispatch model and formulate its optimization problem within a Markov Decision Process framework. We improve the Soft Actor–Critic (SAC) algorithm by adopting a Gaussian-distribution-based reward function to train and deploy agents for optimal low-carbon dispatch. Finally, the effectiveness of the proposed model and the superiority of the improved algorithm are demonstrated using a modified IEEE 33-bus distribution network test case.

Nested active regions anchor the heliospheric current sheet and stall the reversal of the coronal magnetic field

During the solar cycle, the Sun's magnetic field polarity reverses due to the emergence, cancellation, and advection of magnetic flux towards the rotational poles. Flux emergence events occasionally cluster together, although it is unclear if this is due to the underlying solar dynamo or simply by chance. Regardless of the cause, we aim to characterise how the reversal of the Sun's magnetic field and the structure of the solar corona are influenced by nested flux emergence. From the spherical harmonic decomposition of the Sun's photospheric magnetic field, we identified times when the reversal of the dipole component stalls for several solar rotations. Using observations from sunspot cycle 23 to present, we located the nested active regions responsible for each stalling and explored their impact on the coronal magnetic field using potential field source surface extrapolations. Nested flux emergence has a more significant impact on the topology of the coronal magnetic field than isolated emergences as it produces a coherent (low spherical harmonic order) contribution to the photospheric magnetic field. The heliospheric current sheet, which separates oppositely directed coronal magnetic fields, can become anchored above nested active regions due to the formation of strong opposing magnetic fluxes. Further flux emergence, cancellation, differential rotation, and diffusion, then effectively advects the heliospheric current sheet and shifts the dipole axis. Nested flux emergence can restrict the evolution of the heliospheric current sheet and impede the reversal of the coronal magnetic field. The sources of the solar wind can be more consistently identified around nested active regions because the magnetic field topology remains self-similar for multiple solar rotations. This highlights the importance of identifying and tracking nested active regions to guide the remote-sensing observations of modern heliophysics missions.

A frequency control strategy with DC microgrid systems for EV stations

ABSTRACT Fluctuations in power supply and demand lead to frequency deviations, which have detrimental effects on the operation of electrical devices and equipment. Therefore, the implementation of effective frequency control mechanisms is essential for enhancing the strength and dependability of microgrid systems. Research aims to develop a frequency control mechanism for an electrical grid system that integrates both wind and photovoltaic (PV) energy sources. The proposed system utilises a Doubly Fed Induction Generator (DFIG) system with frequency controller for regulating frequency and balancing source and demand of power in the grid. To optimise the solar power output, a Luo converter is implemented, which offers high efficiency with improved conversion of power. Additionally, an adaptive neuro fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller is engaged to ensure optimal regulation of photovoltaic system. DC link obtained from the PV system is then utilised for powering DC loads and charging electric vehicles (EVs) effectively. In order to accomplish this, a bidirectional converter is used, which permits power flow in both ways and facilitates effective EV charging from the microgrid system. The validation of research is conducted on MATLAB software, and the results proves greater converter efficiency of 94.93%, with reduced total harmonic distortion (THD) of 1.24%.

Optimization of Hybrid Energy Systems Based on MPC-LSTM-KAN: A Case Study of a High-Altitude Wind Energy Work Umbrella Control System

This paper presents an optimization method for hybrid energy systems based on Model Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov–Arnold Networks (KANs). The proposed method is applied to a high-altitude wind energy work umbrella control system, where it aims to enhance the stability and efficiency of energy utilization. The work umbrella system integrates wind and solar energy sources, with energy stored in a battery and used to control the umbrella’s operations. The MPC framework is employed to optimize control actions by solving a finite-horizon optimization problem, ensuring the battery State of Charge (SOC) remains within an optimal range. The LSTM network provides accurate predictions of environmental conditions, including wind speed and solar irradiance, which are essential for MPC’s decision-making process. To address complex nonlinearities in the system, the KAN is utilized to model and approximate these dynamics, refining the LSTM predictions. The integration of these advanced control strategies enables the system to handle varying operational conditions and maintain optimal performance. The case study demonstrates the effectiveness of the MPC-LSTM-KAN approach, revealing improvements in the SOC stability, energy efficiency, and operational endurance of the high-altitude wind energy work umbrella system. The results indicate that this hybrid optimization method offers a robust solution for managing hybrid energy systems in dynamic environments.

A new integrated modelling and control of ternary pumped storage hydro power generation for small signal stability studies

Pumped storage hydro power generation (PSHPG), a reliable renewable energy source with an energy storage feature, can impart efficient damping torque to power system oscillations to improve small signal stability with appropriate modelling and additional compensation through the governor. But ternary PSHPG (T-PSHPG) has the unique feature of hydraulic short circuit (HSC) mode, where both pumping and generating modes operate simultaneously. This study presents a novel fourth-order modified Heffron Phillips (MHP) model of T-PSHPG in coordination with a multi-band Power system stabilizer (MB-PSS) employing 2-way penstock, where primary penstock circulates water from the upper reservoir to the lower reservoir through the turbine and secondary penstock divides primary penstock, thereby creating short-circuit path through the pump. Also, this model includes additional reactive power droop control to maintain the voltage profile. A new state space matrix has been developed for coordinating T-PSHPG in HSC mode with MB-PSS. The control parameters of the proposed coordinated model are set by a new multi-objective differential evolution-improved particle swam optimization (DE-IPSO) algorithm. With variable load, random solar and wind source penetrations, it has been found that the proposed model can provide adequate damping actions. The sensitivity of the proposed model has been observed by varying critical model parameters by ±50 %. For all conditions, the settling time of speed variation is within 2.672 s to 5.951 s and the minimum damping ratio lies within 0.105 to 0.455. The minimum damping ratio is 0.32 and the settling time is found to be 3.1 s for system response subject to sudden solar power variation, which has been observed by shifting system eigenvalues toward the left half of the complex plane. Time and frequency domain parameters with sensitivity analysis predict system oscillations being damped effectively with consistency for the proposed modelling and control strategy. The results are verified in real-time with OPAL-RT real-time digital simulator. It has been justified by the present study that appropriate modelling of T-PSHPG along with coordinated control action provided by MB-PSS can handle critical power system oscillations efficiently and effective damping torque can be imparted in HSC mode.

Recovery of electrical power from exhaust air—role of vertical axis wind turbine

AbstractThis article explores the potential of regeneration of power from unnatural wind sources with the help of vertical axis wind turbines (VAWT). Researchers are searching for urban as well as industrial wind sources, which can be useful as the natural wind source to obtain electrical energy and utilize it. The wind sources considered here are the exhaust of the cooling or ventilation fans used in buildings, industries, and other places. The Darrieus VAWT extracts wind power from the exhaust air and reduces the power consumption of the concerned electrical drives. These uncommon energy sources have an inherent capability to recover a significant amount of energy without polluting the environment with less payback period. Recovered energy can be suitably converted into electrical energy and may be fed back to the power grid without any pollution, thus minimizing the total energy consumption of the building and reducing the cost operations. A detailed study of experimental models with different types of assembly and turbine models is conducted here with power outputs and operational behaviors on the existing systems. Various parameters and factors for existing and new applications are in detail that show the system has no negative impact on regular operation.

Porous Monolithic Perovskite Structures for High-Temperature Thermochemical Heat Storage in Concentrated Solar Power (CSP) Plants and Renewable Electrification of Industrial Processes

A novel approach towards thermal energy storage of surplus renewable energy (RE) is introduced via a hybrid thermochemical/sensible heat storage concept implemented with the aid of porous structures made of redox metal oxides, capable of reversible reduction/oxidation upon heating/cooling in direct contact with air, accompanied, respectively, by endothermic/ exothermic heat effects and demonstrating fully reversible dimensional changes under cyclic operation. The proposed modular storage units can be heated during the day to a level exceeding the metal oxide’s reduction onset temperature either by hot air streams from air-operated Concentrated Solar Power (CSP) tower plants or via surplus/cheap RE-electricity from photovoltaics, wind, or other renewable sources (“charging”/energy storage step). When this RE sources become non-available or upon demand, the fully charged system can transfer its energy to a controlled airflow that passes through the porous oxide block and initiates the exothermic oxidation of the reduced metal oxide. Thus, a hot air stream is produced which can be used to provide electricity or exploitable heat for industrial processes. The present work elaborates on the operating principles and the potential application of this concept and reports progress in the preparation and shaping of reticulated porous ceramics (RPCs also known as “ceramic foams”) from CaMnO3-based perovskite compositions and their preliminary testing with respect to cyclic reduction-oxidation.

Fault Diagnosis of Wind Turbine Blades with Continuous Wavelet Transform based Deep Learning Model Using Vibration Signal

Wind Turbines are the most crucial devices in wind energy conversion systems to increase energy generation efficiency from wind sources. Blade failure in wind turbines is due to the induced vibrations of change in ecological variables. It is necessary to periodically inspect the state of the wind turbine blades to enhance safety by decreasing downtime, lessening the likelihood of unplanned failures requiring extensive repair, and progressively increasing power generation with reduced logistical costs. The primary goal of the proposed research is to adopt a hybrid strategy that combines convolutional neural network models and continuous wavelet transform. The bump wavelet-based continuous wavelet transform with a convolutional neural network model is employed to classify the faulty wind turbine blades based on the extracted vibration signals of turbine blades. This approach distinguishes between different states of blade faults affecting wind turbine blades during operational phases. The research considers blade faults in the horizontal axis wind turbine, including blade bending, erosion, and the connection looseness between the hub and blade. The cross-validation and hold-out methods are used to validate the classification accuracy and are compared with other existing popular methods. The hold-out method has a better classification accuracy of 97.916%.

Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.

Conceptual hybrid energy model for different power potential scales: Technical and economic approaches

This research attempts to address the gap between the theoretical fundamentals of hybrid renewable energy systems and their practical implementation at different scales through a new Conceptual Hybrid Energy Model (COHYBEM). The main objective was to develop a multi-variable model to allow a new complete and comprehensive techno-economic analysis of the performance of possible hybrid renewable power systems at different scales. The purpose is to evaluate the influence of critical parameters by changing key parameters in the developed model and identifying their impacts. It covers big data analyses, simulation and optimization of hybrid energy solutions, combining wind, solar and hydropower energy sources with the energy storage technology of pump hydropower storage. The research also denoted the Pareto front with the increasing power installed, for the maximum efficiency and total satisfied demand by Wind + PVSolar and by Hydro converges to a higher percentage, while a minimum waste by Wind + PVSolar is also progressing towards the increasing scales. In terms of investment costs for the 243 analyzed case studies, it varies between 45 k€ to 2.1 M€, resulting in a net present value (NPV) between 18 and 600 k€ and a payback period around 6–17 years depending on the power scale analyzed.

Dynamic Optimization and Placement of Renewable Generators and Compensators to Mitigate Electric Vehicle Charging Station Impacts Using the Spotted Hyena Optimization Algorithm

The growing adoption of electric vehicles (EVs) offers notable benefits, including reduced maintenance costs, improved performance, and environmental sustainability. However, integrating EVs into radial distribution systems (RDSs) poses challenges related to power losses and voltage stability. The model accounts for hourly variations in demand, making it crucial to determine the optimal placement of electric vehicle charging stations (EVCSs) throughout the day. This study proposes a new approach that combines EVCSs, distribution static compensators (DSTATCOMs), and renewable distributed generation (RDG) from solar and wind sources, with a focus on dynamic analysis over 24 h. The spotted hyena optimization algorithm (SHOA) is employed to determine near-global optimum locations and sizes for RDG, DSTATCOMs, and EVCSs, aiming to minimize real power loss while meeting system constraints. The SHOA outperforms traditional methods due to its unique search mechanism, which effectively balances exploration and exploitation, allowing it to find superior solutions in complex environments. Simulations on an IEEE 34-bus RDS under dynamic load conditions validate the approach, demonstrating a reduction in average power loss from 180.43 kW to 72.04 kW, a 72.6% decrease. Compared to traditional methods under constant load conditions, the SHOA achieves a 77.0% reduction in power loss, while the BESA and PSO achieve reductions of 61.1% and 44.7%, respectively. These results underscore the effectiveness of the SHOA in enhancing system performance and significantly reducing real power loss.

Smart Monitoring of Microgrid-Integrated Renewable-Energy-Powered Electric Vehicle Charging Stations Using Synchrophasor Technology

With the growing concern over climate change and energy security, the Government of India expedited enhancing the share of renewable energy (RE) derived from solar, wind and biomass sources within the energy blend. In this paper, a techno-economic and environmental analysis of a microgrid-integrated electric vehicle charging stations fueled by renewable energy is proposed for a typical area in the State of Karnataka, South India. The power transaction with the grid and the sell-back price to the national grid were investigated. Carbon emissions were also assessed, and 128,406 CO2 kg/Yr can be saved in the grid-connected mode. Also, in this work, different scenarios such as injecting active power, reactive power, and active and reactive power, and injecting active and absorbing reactive power to the grid are comprehensively assessed. Out of four types, type 3 (inject real and reactive power) provides significant reduction in power losses by up to 80.99%. The synchrophasor-technology-based monitoring method is adopted in order to enhance the microgrid system’s overall performance. The execution times for different cases with distributed generators (DGs) and electric vehicle charging stations (EVCSs) for conventional systems and micro-phasor measurement units (µPMU) were observed to be 19.07 s and 5.64 s, respectively, which is well accepted in the case of online monitoring.

Green carbon and the chemical industry of the future

The pressing need to mitigate climate change and drastically reduce environmental pollution and loss of biodiversity has precipitated a so-called energy transition aimed at the decarbonization of energy and defossilization of the chemical industry. The goal is a carbon-neutral (net-zero) society driven by sustainable energy and a circular bio-based economy relying on renewable biomass as the raw material. It will involve the use of green carbon, defined as carbon derived from terrestrial or aquatic biomass or organic waste, including carbon dioxide and methane emissions. It will also necessitate the accompanying use of green hydrogen that is generated by electrolysis of water using a sustainable source of energy, e.g. solar, wind or nuclear. Ninety per cent of the industrial chemicals produced in oil refineries are industrial monomers that constitute the precursors of a large variety of polymers, many of which are plastics. Primary examples of the latter are polyolefins such as polyethylene, polypropylene, polyvinyl chloride and polystyrene. Polyolefins are extremely difficult to recycle back to the olefin monomers and discarded polyolefin plastics generally end up as the plastic waste that is responsible for the degradation of our natural habitat. By contrast, waste biomass, such as the lignocellulose contained in forestry residues and agricultural waste, constitutes a renewable feedstock for the sustainable production of industrial monomers and the corresponding polymers. The latter could be the same polyolefins that are currently produced in oil refineries but a more attractive long-term alternative is to produce polyesters and polyamides that can be recycled back to the original monomers: a paradigm shift to a truly bio-based circular economy on the road to a net-zero chemical industry. This article is part of the discussion meeting issue ‘Green carbon for the chemical industry of the future’.

Combined Economic Emission Dispatch Including Variable Energy Resources

Sustainable energy solutions are becoming more and more necessary as the world's energy needs increase as a result of population expansion and industrialization. Using renewable energy sources has become essential to meeting these needs and reducing the negative effects on the environment. The increasing incorporation of renewable energy sources (RES) like wind and solar into microgrid systems poses a notable obstacle to attaining optimum power dispatch because of their intrinsic unpredictability. The combined economic emission dispatch (CEED) issue may become inefficient as a result of this fluctuation, especially in islanded microgrid systems. In particular, in areas with significant RES potential, resolving this problem is essential to improving the sustainability and dependability of the energy supply. In this work, the optimization of the CEED issue in an islanded microgrid system with wind, solar, and thermal energy sources is the main emphasis. By employing a weighted sum approach and a Butterfly Optimization Algorithm (BOA), the research aims to provide an efficient solution to the multi-objective CEED dilemma. The proposed method outperforms traditional optimization techniques, offering a more robust framework for integrating RES into microgrids. This research reveals a number of limitations that affect the effectiveness of energy dispatch systems, such as thermal unit ramp rates and operating restrictions. Subsequent investigations have to concentrate on delving deeper into these limitations and devising tactics to augment the flexibility of optimization algorithms such as the Butterfly Optimization Algorithm (BOA) approach.

Multi-Objective Short-Term Operation of Hydro–Wind–Photovoltaic–Thermal Hybrid System Considering Power Peak Shaving, the Economy and the Environment

In recent years, renewable, clean energy options such as hydropower, wind energy and solar energy have been attracting more and more attention as high-quality alternatives to fossil fuels, due to the depletion of fossil fuels and environmental pollution. Multi-energy power systems have replaced traditional thermal power systems. However, the output of solar and wind power is highly variable, random and intermittent, making it difficult to integrate it directly into the grid. In this context, a multi-objective model for the short-term operation of wind–solar–hydro–thermal hybrid systems is developed in this paper. The model considers the stability of the system operation, the operating costs and the impact in terms of environmental pollution. To solve the model, an evolutionary cost value region search algorithm is also proposed. The algorithm is applied to a hydro–thermal hybrid system, a multi-energy hybrid system and a realistic model of the wind–solar–hydro experimental base of the Yalong River Basin in China. The experimental results demonstrate that the proposed algorithm exhibits superior performance in terms of both convergence and diversity when compared to the reference algorithm. The integration of wind and solar energy into the power system can enhance the economic efficiency and mitigate the environment impact from thermal power generation. Furthermore, the inherent unpredictability of wind and solar energy sources introduces operational inconsistencies into the system loads. Conversely, the adaptable operational capacity of hydroelectric power plants enables them to effectively mitigate peak loads, thereby enhancing the stability of the power system. The findings of this research can inform decision-making regarding the economic, ecological and stable operation of hybrid energy systems.

Study on Process of Converting Wind Energy to Electricity

Wind is a renewable energy, wind is product natural from the change of air temperature, air pressor and the rotate of planet. Wind is an old source of energy so the humans was use it for wind mill, sailing, air transport or drying and so on. Now a day, many countries are focusing on research study about wind turbine to generate electricity. The purpose of this research is to study the process of converting wind energy into electricity by taking wind source from a fan with a radius of 17 cm, the fan speed is about and the wind turbine power source is a small motor model ESPL-3423-D120. The study shows that the efficiency of wind turbines from the conversion of kinetic energy into electricity is about 15%. For radius of wind turbines 15 cm, we can convert wind power into electric power about 0.8954 W; For radius of wind turbines 17 cm, we can convert wind power into electric power about 1.1454 W; we also notice that the wind power is change by a radius of wind turbine and wind speed

Prospects of green hydrogen production in the Philippines from solar photovoltaic and wind resources: A techno-economic analysis for the present and 2030

As global temperatures continue to rise, reducing greenhouse gas emissions is more important than ever – demanding an urgent transition to renewable energy systems. In this study, the potential for green hydrogen production from solar and wind sources in the Philippines is explored. Renewable energy systems can yield higher decarbonization by producing green hydrogen, potentially an ‘energy vector’ for industrial, power, and transportation applications, thereby representing a promising solution for the nation's energy transition. This study explores the competitiveness of green hydrogen production in the Philippines through a comprehensive techno-economic analysis to predict the levelized cost of hydrogen (LCOH) derived from solar- and wind-powered electrolysis. The 2030 projections explore three scenarios, each representing varying research and development intensities resulting in different electrolyzer costs. The resulting LCOH values underscore the need for economic policy interventions to slash hydrogen costs to a competitive level. The present and near-future costs and attributes of electrolysis technologies still fall short compared to fossil fuel-based pathways. Furthermore, sensitivity and uncertainty analyses are conducted to investigate subsidy and policy requirements, and random changes to costs. The findings of this work provide a comprehensive understanding of the potential fluctuations and challenges in the economic landscape in the Philippines.

Solar Wind Structures from the Gaussianity of Magnetic Magnitude

The heliosphere is permeated with highly structured solar wind originating from the Sun. One of the primary science objectives of Parker Solar Probe (PSP) is to determine the structures and dynamics of the plasma and magnetic fields at the sources of the solar wind. However, establishing the connection between in situ measurements and structures and dynamics in the solar atmosphere is challenging: most of the magnetic footpoint mapping techniques have significant uncertainties in the source localization of a plasma parcel observed in situ, and the PSP plasma measurements suffer from a limited field of view. Therefore, it lacks a universal tool to self-contextualize the in situ measurements. Here we develop a novel time series visualization method named Gaussianity Scalogram. Utilizing this method, by analyzing the magnetic magnitude data from both PSP and Ulysses, we successfully identify in situ structures that are possible remnants of solar atmospheric and magnetic structures spanning more than 7 orders of magnitude, from years to seconds, including polar and midlatitude coronal holes, as well as structures compatible with supergranulation, “jetlets” and “picoflares.” Furthermore, computer simulations of Alfvénic turbulence successfully reproduce the Gaussianization of magnetic magnitude, supporting the observed distribution. Building upon these discoveries, the Gaussianity Scalogram can help future studies to reveal the fractal-like fine structures in the solar wind time series from both PSP and a decades-old data archive.

Techno-Economic Analysis of Solar, Wind, and Biomass Hybrid Renewable Energy Systems to Meet Electricity Demand of a Small Village in Bihar State of India

This study examines the potential use of Hybrid Renewable Energy Systems (consisting of photovoltaic, wind, bio, and diesel sources) both with and without the inclusion of battery storage in the eastern region of India. An evaluation is conducted to determine the economic viability of several system configurations, and the most efficient system is selected using HOMER software. The investigation focused on six distinct scenarios to meet the energy needs of a village community. The goal was to satisfy a daily load need of 1093.7 kWh, with a peak demand of 153.63 kW. The study examined many factors, such as system efficiency, financial viability, and ecological consequences. The primary aim of the research was to compare the power costs associated with different designs of HRES. Detailed techno-commercial assessments were carried out to examine the energy production, consumption, and financial impacts of each scenario. This research provides valuable insights for individuals and organizations seeking reliable and long-lasting energy solutions by analyzing the potential benefits and drawbacks of implementing HRES in rural areas. An evaluation is conducted to determine the energy contribution of each element within an RES, as well as the influence of HRES on energy expenses and net present value. The findings of this study reveal that the optimized hybrid system comprises 133 kW photovoltaic arrays, a 130-kW wind turbine, a 0.2 kW biogas generator, a 100-kW diesel generator, a 540-kWh battery bank with nominal capacity, and a 58-kW converter. This system has a minimum COE of 0.347$/kWh and NPC of $1.71M. The research offers useful insights for designers, scholars, and policymakers on the existing design constraints and policies of biomass-based hybrid systems.

Integrated Optimal Energy Management of Multi-Microgrid Network Considering Energy Performance Index: Global Chance-Constrained Programming Framework

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed.