Microgrid Setup Research Articles

Practical solutions for microgrid energy management: Integrating solar forecasting and correction algorithms

Nowadays, the shift towards renewable energies is happening at an exponential pace. Microgrids are an integral part in integrating renewable energies into the global energy mix. Due to the volatility of renewables, such as solar or wind, proper energy management is needed to avoid generation or load curtailment. This issue is addressed throughout the literature using a wide variety of strategies, ranging from classical linear to nature inspired metaheuristic optimization algorithms. This paper goes beyond the simulation phase of different optimization algorithms and addresses the optimal energy management problem by proposing a novel framework to integrate optimization strategies into the daily operation of microgrids. The proposed framework showcases 3 practical methods for controlling the distributed generators, as well as a communication scheme which facilitates the data transfer between the machine running the optimization strategy and the energy management system of the microgrid. The framework is demonstrated on a small-scale islanded microgrid setup, located at the Technical University of Cluj-Napoca. The microgrid setup consists of a photovoltaic array, a battery energy storage system and two dispatchable generators. A two-stage optimization strategy is used, consisting of a day-ahead stage, which uses the solar forecast provided by the Global Forecast System, prior to each day, to issue a working plan for the dispatchable generators, and an intra-day stage, which uses hourly solar forecasts, issued during the day, to adjust the operation of the microgrid and minimize the error between the day-ahead solar forecast and the actual weather conditions. Mixed integer linear programming is used to solve the optimization problem and validate the correct operation of the proposed framework over two case studies. The results show that the microgrid works as intended, in both scenarios, as well as the benefits of using an intra-day correction stage.

Effective dynamic energy management algorithm for grid-interactive microgrid with hybrid energy storage system

Microgrids offer an optimistic solution for delivering electricity to remote regions and incorporating renewable energy into existing power systems. However, the energy balance between generation and consumption remains a significant challenge in microgrid setups. This research presents an adaptive energy management approach for grid-interactive microgrids. The DC microgrid is established by combining solar PV with a battery-supercapacitor (SC) hybrid energy storage system (HESS). The proposed approach integrates the frequency separation strategy with a rule-based algorithm to ensure optimal power sharing among sources while maintaining the safe operation of storage units. Specifically, the battery meets steady-state energy demands, the SC addresses transient power requirements, and the grid support is tailored to system needs. The method employs the dq reference frame technique to control the grid inverter (VSC). The key merits include efficient power allocation, fast regulation of the DC link voltage irrespective of load or generation variations, seamless transition between scenarios, and introduction of a straightforward battery state of charge (SOC)-based coefficient for allocating power between the battery and the grid while enhancing the power quality within the grid. Moreover, safety measures prevent the SC from overcharging, the battery from high current, overcharging, and deep discharging, potentially extending their lifespan. Validation and implementation of the method are conducted using MATLAB/Simulink.

Bids and asks: Blockchain-based peer-to-peer trading platform

In residential microgrids, rooftop solar power generation is becoming increasingly common. A new concept in electricity markets, such as peer-to-peer (P2P) auctions, is also emerging. In these markets, consumers and prosumers can exchange locally generated power directly with each other without relying on an intermediary. This approach promotes sustainable development. However, data security is a major concern in energy trading. Consequently, the use of blockchain technology in power markets has become more widespread. Blockchain can facilitate the trading of power from P2P sources. This paper presents the design and algorithms for automating energy trading through smart contracts, utilizing Home Energy Management Systems (HEMS) and Smart Meter Gateways (SM) for prosumers, consumers, and utilities. Simulations across various microgrid setups validated the peer-to-peer (P2P) energy market on the blockchain network. This framework increases profits for prosumers and consumers trading with each other, the necessity of blockchain operators for updating smart contracts and managing participants, the economic impracticality of current high gas costs on Ethereum, and the benefit of a closer bidding range for profit generation in P2P energy markets.

Dual layer energy management model for optimal operation of a community based microgrid considering electric vehicle penetration

This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

Adaptive protection coordination in microgrid based on nature inspired meta-heuristic optimization algorithm

Ensuring a robust protection system is crucial for safeguarding the integrity of the overall system against abnormalities. Incorporating distributed generation (DG) into the distribution network can introduce fluctuations in fault current levels and directions, potentially causing mismatches in the response of the existing coordination system. This study proposes an adaptive protection coordination scheme designed to accommodate both grid-connected and standalone modes, addressing various fault scenarios. Utilizing a hybrid WCMFO algorithm, optimal relay settings are determined to facilitate effective coordination within a microgrid setup. The proposed method has been analyzed on 9 bus Canadian benchmark system integrated with four DGs. The performance of the proposed method is compared to other optimization techniques to demonstrate its effectiveness. System modelling is conducted using MATLAB/Simulink, and validation is further carried out using industrial ETAP software on a test microgrid system. The analysis extends to evaluating the enhancement in overall system reliability, quantified in terms of energy not supplied (ENS).

An advanced graph algorithm-based protection strategy for detecting kilometric and cross-country faults in DC microgrid

Low voltage DC microgrids (LVDC) are on rise, because of increase in usage of electronics-based utility loads. However, the protection and safety aspects of these grids remain unresolved due to the fault current magnitude, unnecessary tripping, and blinding of protection. In this paper, an adaptive differential protection system incorporated with an advanced graph algorithm is proposed for DC microgrid. This graph algorithm is a combination of Fenwick tree and Bidirectional Dijkstra algorithm. Fenwick tree algorithm is used to determine the network configuration and Bidirectional Dijkstra algorithm is used to determine the least distance from the fault location to the nearest distributed generations. The developed protection scheme is applied to a 7 bus, 400 V DC microgrid setup using Real Time simulator (control hardware in loop) to detect and clear the faults such as kilometric faults, and cross-country faults. In terms of efficiency, the proposed algorithm demonstrates superiority over the conventional algorithm by detecting and rectifying faults in 384 μs.The results depict the robustness of the protection setup in clearing the faults rapidly with minimum network disconnection.

DC-Link Dynamics Examination of the Parallel-Connected Three-Phase Inverters in Islanded and Grid-Connected Modes

This paper presents a comprehensive analysis of an AC microgrid setup, consisting of parallel three-phase inverters. It introduces a novel controller that integrates the tasks of controlling the DC-link voltage and implementing a current limiting approach. The design is informed by the characteristics of lead compensators and its main objectives are twofold: i) to regulate the DC-link voltage in order to minimize unwanted power exchange among the parallel inverters, and ii) to ensure the current-limiting capabilities of all inverters, regardless of the microgrid's operational state. This includes both standalone and grid-connected modes, voltage sags in the grid side, as well as transitional intervals between the two. In contrast to existing DC-link voltage control methods that do not incorporate a current limiting strategy, the proposed solution effectively restrains the dq axis current values in all operating conditions. This is crucial for preserving system stability, avoiding abrupt changes in DC-link voltage and current during sudden grid modifications and transitions. To validate the efficacy of the proposed controller, a series of simulations were carried out using Matlab/Simulink to evaluate its performance and compare it with an established method. The results explicitly demonstrate that the proposed controller successfully stabilizes the system, minimizes fluctuations in DC-link voltage, and restricts the dq axis current for each inverter without the potential of protection relay tripping.

Integration of cascaded coordinated rolling horizon control for output power smoothing in islanded wind–solar microgrid with multiple hydrogen storage tanks

This paper presents a strategy based on the hierarchical rolling horizon control, also called model predictive control (MPC), for efficiently managing a hydrogen-energy storage system (HESS) within an islanded wind–solar microgrid. An electrolyzer uses electricity generated from renewable sources to produce clean hydrogen, which is then re-electrified by a fuel cell as needed to meet the microgrid’s loads. The main contribution lies in the incorporation of multiple hydrogen storage tanks in the HESS, distinguishing it from existing literature, which typically focuses on a single tank. The incorporation of multiple tanks in the HESS enables the storage of large volumes of hydrogen for long-term use, allowing the microgrid to operate autonomously without interaction with the utility grid. In order to ensure optimal performance, the selection of the most suitable device for operation at each time-step is crucial. The proposed control strategy takes into account the economic and operational costs, degradation aspects, and physical constraints of the HESS, while simultaneously ensuring the tracking of reference demands and with the highest priority smoothing out the variations of renewable energy sources. Numerical simulations and a lab-scale microgrid setup demonstrate that the controller effectively manages the HESS thus satisfying economic constraints and optimizing device costs, even when deviations occur between the predicted and real-time scenarios. Furthermore, the inclusion of multiple hydrogen tanks allows the microgrid to both mitigate fluctuations in renewable power sources and effectively meet load demand.

Integrated Control and Energy Flow Management for Hybrid Grid-Connected Photovoltaic/Wind Systems with Battery Storage Using Fuzzy Logic Controllers

This paper focuses on the control and management of an energy conversion system within a microgrid setup comprising multiple renewable energy sources and a storage system, all connected to the AC-grid through DC/DC, AC/DC, and DC/AC converters. Various control objectives are defined, which depend on factors such as energy availability, load demand, storage system state of charge, and grid availability. To address these scenarios, an energy flow management system is developed in this work. The primary roles of this management algorithm include ensuring the safety of all microgrid components, enhancing the storage system’s lifespan, and improving energy efficiency. To achieve these objectives, multiple control loops are designed based on fuzzy logic controllers ensuring appropriate system operation modes. The effectiveness of this control and energy management strategy is evaluated using the MATLAB/Simulink/Simpower environment, demonstrating its capability to meet the specified objectives.

Energy Management Model for a Remote Microgrid Based on Demand-Side Energy Control

The internet of things is undergoing rapid expansion, transforming diverse industries by facilitating device connectivity and supporting advanced applications. In the domain of energy production, internet of things holds substantial promise for streamlining processes and enhancing efficiency. This research introduces a comprehensive monitoring and energy management model tailored for the University of Cuenca’s microgrid system, employing internet of things and ThingSpeak as pivotal technologies. The proposed approach capitalizes on intelligent environments and employs ThingSpeak as a robust platform for presenting and analyzing data. Through the integration of internet of things devices and sensors, the photovoltaic system’s parameters, including solar radiation and temperature, are monitored in real time. The collected data undergo analysis using sophisticated models and are presented visually through ThingSpeak, facilitating effective energy management and decision making. The developed monitoring system underwent rigorous testing in a laboratory microgrid setup, where the photovoltaic system is interconnected with other generation and storage systems, as well as the electrical grid. This seamless integration enhances visibility and control over the microgrid’s energy production. The results attest to the successful implementation of the monitoring system, highlighting its efficacy in improving the supervision, automation, and analysis of daily energy production. By leveraging internet of things technologies and ThingSpeak, stakeholders gain access to real-time data, enabling them to analyze performance trends and optimize energy resources. This research underscores the practical application of internet of things in enhancing the monitoring and management of energy systems with tangible benefits for stakeholders involved.

Comparative Double Auction Approach for Peer-to-Peer Energy Trading on Multiple microgrids

Peer-to-peer (P2P) energy trading is one of the most effective methods to increase the usage of Renewable Energy (RE) resources in the distribution network and reduce losses by eliminating long transmission and distribution lines. This research aims to enhance the efficiency of P2P energy trading by examining the suitability of four distinct double auction mechanisms: Average, McAfee, Trade Reduction and Vickrey-Clarke-Groves (VCG). We conducted a systematic evaluation of these mechanisms across various microgrid (MG) types. The study algorithm integrates user preferences, bidding strategies and time-of-use tariffs, allowing participants to indicate their willingness to pay for different energy qualities and specific time periods. Notably, both the Average and VCG mechanisms emerged as the most effective across a majority of MG setups. Specifically, the average mechanism was found to be optimal for a consumer-centric MG, while the VCG mechanism was predominantly advantageous during non-peak hours trading. However, it was observed that P2P energy trading from MG to MG was inefficient due to the lesser number of peers. In conclusion, this work offers a comprehensive solution that adeptly identifies and recommends the most fitting auction mechanisms for diverse MG configurations and usage timings, paving the way for more efficient P2P energy trading.

Experimental investigation of a novel smart energy management system for performance enhancement of conventional solar photovoltaic microgrids

Solar photovoltaic microgrids are reliable and efficient systems without the need for energy storage. However, during power outages, the generated solar power cannot be used by consumers, which is one of the major limitations of conventional solar microgrids. This results in power disruption, developing hotspots in PV modules, and significant loss of generated power, thus affecting the efficiency of the system. These issues can be resolved by implementing a smart energy management system for such microgrids. In this study, a smart energy management system is proposed for conventional microgrids, which consists of two stages. First power production forecasting is done using an artificial neural network technique and then using a smart load demand management controller system which uses Grey Wolf optimiser to optimize the load consumption. To demonstrate the proposed system, an experimental microgrid setup is established to simulate and evaluate its performance under real outdoor conditions. The results show a promising system performance by reducing the conventional solar microgrids losses by 100% during clear sunny conditions and 42.6% under cloudy conditions. The study results are of relevance to further develop a smart energy management system for conventional microgrid Industry and to achieve the targets of sustainable development goals.

A Carbon Reduction and Waste Heat Utilization Strategy for Generators in Scalable PV—Diesel Generator Campus Microgrids

The increased unavailability of electricity from the National Utility in South Africa, coupled with the extreme conditions of rural areas and general lack of infrastructure, leads to the setup of unique microgrids to utilize the conditions available. One such unique microgrid, a scalable photovoltaic (PV)-Diesel generator microgrid, is situated in the Phuthaditjhaba district on the University of the Free State (UFS) Qwaqwa campus in South Africa. Waste heat and greenhouse gas (GHG) emissions are considered inherent by-products of campus hybrid PV—Diesel generator microgrids with high utilization opportunities for both heat exchange and carbon offsets. This paper presents confirmation that available waste heat from a typical rural campus microgrid can be stored through the use of a rock bed thermal energy storage (TES) system. It was identified that, through the temperature profile of the stored waste heat, thermal energy can be utilized through deferable (time-independent) and non-deferable (time-dependent) strategies. Both utilization strategies are dependent on the type of application or applications chosen through demand-side management. Carbon emission reduction takes place through the reduction of diesel consumption due to the utilization of waste heat for applications previously served by diesel generators. Design novelties are presented using the concept of rock bed TES within a microgrid setup.

Optimizing Hybrid Microgrid Power Systems for Local Power Distribution: A Study on Combined Photovoltaic and Fuel Cell Systems in the Philippines

In pursuit of energy self-sufficiency and meeting the growing energy demand, the Philippine government has formulated its Energy Road Map for the year 2040, aiming to strengthen, continue, and accelerate the adoption of renewable energy (RE) across the archipelago. This paper presents a proposed multiple microgrid system integrated into an existing distribution system, utilizing renewable energy sources. The proposed model involves the conversion of a section of the distribution system into a microgrid setup, comprising photovoltaic (PV) energy and fuel cell (FC) technologies connected to a 13.2 kV distribution grid. A modified three-phase three-level voltage-sourced converter (VSC) is employed to control the inverter. The proposed modifications result in improved operational efficiency compared to conventional approaches. Various operating cases are considered, each with a designated power source operating according to a predefined schedule. A unified controller is employed across all operating cases, ensuring system stability. Simulation and experimental results conducted through MATLAB/Simulink demonstrate the impact of VSC in terms of voltage regulation, frequency stability, and accumulated power losses. They revealed that voltage regulation for understudy cases ranged from 0.1 to 4.5%, microgrid frequencies were between 59.1 and 60.08 Hz, and power distribution losses were at 1.2–3.3% of the generated power.

A Distributed Control Scheme for Cyber-Physical DC Microgrid Systems

An innovative distributed secondary control technique for balanced current sharing and voltage regulation for an off-grid DC microgrid setup is presented in this research. The droop control scheme is conventionally used for current sharing amongst distributed sources (DSs) in a microgrid. However, this method has two major drawbacks. Firstly, due to the line resistance of each DS, the output voltage is different for each DS, and the output current-sharing property deteriorates. Secondly, the droop action increases the DC bus voltage variation. To address these drawbacks, a fuzzy-based distributed secondary controller is proposed. The proposed controller in each DS simultaneously ensures balanced current sharing and sustains DC bus voltage at the reference value by using a communication network to interact with one another. The required circumstance to guarantee the proposed controller’s stability is provided. The stability analysis is beneficial to inform the choice of control parameters. The real-time simulation outputs demonstrate the proposed control scheme’s robustness in achieving the control objectives under varying operating conditions.

Integrated model for optimal energy management and demand response of microgrids considering hybrid hydrogen-battery storage systems

Hybrid hydrogen-battery (HHB) based energy storage technologies have recently become matters of significant interest to enable sustainable and net zero-emission microgrids characterized with high penetration levels of variable renewable energy sources (RES) such as solar and wind. In this regard, there are multiple objectives and constraints controlling the scheduled decision-making framework that manages these microgrid resources while supplying the demand. As such, this paper introduces an integrated energy management system (IEMS) to concurrently optimize the operation schedule of the HHB storage system and provide demand response (DR) via adequate shifting slots for the elastic loads of a microgrid setup. The proposed IEMS aims to optimally manage the microgrid’s energy by minimizing three conflicting objective functions: the electricity and battery degradation costs, customers’ discomfort, and peak-to-average ratio (PAR). The proposed IEMS is developed for microgrids containing a mix of variable RES, an HHB storage system, and a local power consumption considering dynamic grid tariff. The IEMS is formulated as a mixed-integer nonlinear problem and is solved using a multi-objective artificial hummingbird optimizer (MOAHA). To account for the variability and uncertainty of RES, the proposed IEMS is tested under four scenarios: good, bad, average weather scenarios, and a forecasted weather profile based on a stochastic model of RES. The performance of the proposed IEMS is evaluated via comprehensive comparative analyses with the conventional energy management strategy (CEMS) without and with consideration of the HHB storage system and DR. Moreover, the performance of MOAHA has been assessed versus the state-of-the-art multi-objective particle swarm optimizer (MOPSO). For the purpose of these comparisons, four quality metrics have been utilized to quantify the benefits of the proposed IEMS: renewable contribution factor (RCF), greenhouse gas (GHG) emission, loss of power supply probability (LPSP), and saving cost (SC). The results show that the proposed IEMS yields better-optimized values that help the microgrid operator identify the sweet spot of the conflicting objectives, where it has increased the customer saving to be (82.95%, 29.65%, 33.33%, and 33.00 %), (73%,23.94%, 25.49%, and 31.65%) and (10.69%,3.62%, 1.59%, and 1.67%) compared to the CEMS without/with including HHB. and IEMS based-MOPSO over the four studied scenarios, respectively. Also, the results show the superiority of the proposed IEMS in improving the identified four quality metrics compared to the CEMS without/with considering HHB storage systems and IEMS-based-MOPSO. Accordingly, integrating HHB and implementing optimized IEMS based-MOAHA with considering the DR approach plays a vital role in providing a profit for the customer, protecting the environment from emissions, and enhancing the system’s reliability with considering the weather uncertainty.

Establishing and Operating Experiential Benchmark Setups of dc Microgrids: A Guideline

Increased attention is given to microgrids for their numerous advantages over conventional power networks. However, there are still a limited number of real microgrid networks that are currently in service, and this raises shortages in expertise on microgrids testing and evaluation before actual deployment. Low-cost, rapid, realistic, and flexible testing are the key challenges that face the microgrids’ validation. This article focuses on the importance of experiential validations to reduce the risks and ensure the desired microgrids performances. It highlights the major challenges for establishing and operating realistic and accurate experimental microgrid testbeds. It also provides guidelines and recommendations for the validation of microgrid studies. An experimental dc microgrid setup showcase is demonstrated, and helpful tips and remarks are suggested. Different case studies on the developed dc microgrid’s setup are presented, and the authors’ experiences and analyses are shared. The experimental results show the accuracy of microgrid testbeds and their expansion capabilities as well as their effectiveness in testing various microgrid conditions.

Performance Enhancement of an Economically Operated DC Microgrid With a Neural Network–Based Tri-Port Converter for Rural Electrification

The DC Microgrid sounds familiar in recent days for its independent grid operation and energizing small communities without relying on the central grid. The sudden change in energy demand in the microgrid can negatively impact its performance and operation. Energy management is the only optimal solution to the energy production of microgrids. This article -discusses an economically operated DC microgrid for rural electrification with a tri-port converter based on the radial basis function neural network (RBFNN)-based intelligent control strategy to provide enhanced performance to the microgrid. The advantage of the proposed system is that it provides optimal energy management solutions during dynamic loading conditions and enhances the operation of the microgrid. The outstanding aspect of the proposed system is that it boosts the conversion operation and effectively manages the battery energy storage system to supply energy to the domestic loads and supply power to the grid during excess power generation. In the assessment, the rural regions of Tamilnadu and Andhra Pradesh, India, have been considered to enhance the microgrid setup. The performance evaluation of the proposed system has been conducted and validated using an experimental setup. The assessment also discusses the economic and environmental analysis in using the proposed system. The results support the performance and efficiency of the proposed model.

PowerNet: Multi-Agent Deep Reinforcement Learning for Scalable Powergrid Control

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet, in which each agent (DG) learns a control policy based on (sub-)global reward but local states and encoded communication messages from its neighbors. Motivated by the fact that a local control from one agent has limited impact on agents distant from it, we exploit a novel spatial discount factor to reduce the effect from remote agents, to expedite the training process and improve scalability. Furthermore, a differentiable, learning-based communication protocol is employed to foster the collaborations among neighboring agents. In addition, to mitigate the effects of system uncertainty and random noise introduced during on-policy learning, we utilize an action smoothing factor to stabilize the policy execution. To facilitate training and evaluation, we develop PGSim, an efficient, high-fidelity powergrid simulation platform. Experimental results in two microgrid setups show that the developed PowerNet outperforms the conventional model-based control method, as well as several state-of-the-art MARL algorithms. The decentralized learning scheme and high sample efficiency also make it viable to large-scale power grids.

Planning and Operational Aspects of Individual and Clustered Multi-Energy Microgrid Options

With the restructuring of the power system, household-level end users are becoming more prominent participants by integrating renewable energy sources and smart devices and becoming flexible prosumers. The use of microgrids is a way of aggregating local end users into a single entity and catering for the consumption needs of shareholders. Various microgrid architectures are the result of the local energy community following different decarbonisation strategies and are frequently not optimised in terms of size, technology or other influential factors for energy systems. This paper discusses the operational and planning aspects of three different microgrid setups, looking at them as individual market participants within a local electricity market. This kind of implementation enables mutual trade between microgrids without additional charges, where they can provide flexibility and balance for one another. The developed models take into account multiple uncertainties arising from photovoltaic production, day-ahead electricity prices and electricity load. A total number of nine case studies and sensitivity analyses are presented, from daily operation to the annual planning perspective. The systematic study of different microgrid setups, operational principles/goals and cooperation mechanisms provides a clear understanding of operational and planning benefits: the electrification strategy of decarbonising microgrids outperforms gas and hydrogen technologies by a significant margin. The value of coupling different types of multi-energy microgrids, with the goal of joint market participation, was not proven to be better on a yearly level compared to the operation of same technology-type microgrids. Additional analyses focus on introducing distribution and transmission fees to an MG cooperation model and allow us to come to the conclusion of there being a minor impact on the overall operation.