Renewable Energy Sources In Microgrids Research Articles

Reinforcement-Learning-Based Virtual Inertia Controller for Frequency Support in Islanded Microgrids

As the world grapples with the energy crisis, integrating renewable energy sources into the power grid has become increasingly crucial. Microgrids have emerged as a vital solution to this challenge. However, the reliance on renewable energy sources in microgrids often leads to low inertia. Renewable energy sources interfaced with the network through interlinking converters lack the inertia of conventional synchronous generators, and hence, need to provide frequency support through virtual inertia techniques. This paper presents a new control algorithm that utilizes the reinforcement learning agents Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG) to support the frequency in low-inertia microgrids. The RL agents are trained using the system-linearized model and then extended to the nonlinear model to reduce the computational burden. The proposed system consists of an AC–DC microgrid comprising a renewable energy source on the DC microgrid, along with constant and resistive loads. On the AC microgrid side, a synchronous generator is utilized to represent the low inertia of the grid, which is accompanied by dynamic and static loads. The model of the system is developed and verified using Matlab/Simulink and the reinforcement learning toolbox. The system performance with the proposed AI-based methods is compared to conventional low-pass and high-pass filter (LPF and HPF) controllers.

Optimal design and sizing of renewable energies in microgrids based on financial considerations a case study of Biskra, Algeria

Integration of renewable energy sources in microgrids is a challenging process, where a wealth of metrics should be optimized together to achieve an optimal design. This paper presents a comprehensive approach for selecting the best microgrid structure including a versatile renewable energy source (RES), the proposed microgrid systems are considered using investment costs evaluation and total power generation needs. Through the analysis of the economic feasibility and guiding decision-making on microgrid projects, three system configurations have been examined at the Algerian site of Biskra using the HOMER software. In this work, photovoltaics, photovoltaics/wind, and photovoltaics/wind/diesel projects were explored in respect to the specificities of the targeted region. The Net Present Cost (NPC) was found to be 11.7 M$,13.3 M$, and 9.45 M$ respectively, likewise, Levelized Cost of Energy (LCOE) were 0.19 $/kWh, 0.728 $/kWh, and 0.188 $/kWh respectively. Where a comparative analysis was carried out as a sensitivity analysis to find out the effect of different economic and technical conditions on costs and parameters. Cash flows were also forecasted, and financial criteria such as the Internal Rate of Return (IRR) (13%, 15%), Net Present Value (NPV) (1 M$ to 10 M$) for three projects, and Discounted Payback (DPb) (6 to 9 years). In addition, Monte Carlo simulation is used to assess the risks associated with the project's net present value, this is done to maintain the robustness of project decision-making outcomes. The findings emphasise the importance of financial analysis in the design and sizing of RES microgrid.

Microgrid Frequency Regulation Based on a Fractional Order Cascade Controller

Nowadays, the participation of renewable energy sources (RESs) and the integration of these sources with traditional power plants in microgrids (MGs) for providing demand-side power has rapidly grown. Although the presence of RESs in MGs reduces environmental problems, their high participation significantly affects the system’s whole inertia and dynamic stability. This paper focuses on an islanded MG frequency regulation under the high participation of RESs. In this regard, a novel fractional order cascade controller (FOCC) is proposed as the secondary frequency controller. In the proposed FOCC controller structure, a fractional order proportional-integral controller is cascaded with a fractional order tilt-derivative controller. The proposed FOCC controller has a greater degree of freedom and adaptability than integer order controllers and improves the control system’s efficiency. The adjustable coefficients of the proposed controller are tuned via the kidney-inspired algorithm. An energy storage system equipped with virtual inertia is also employed to improve the system inertia. The proposed FOCC controller efficiency is compared with proportional-integral-derivative (PID), tilt-integral-derivative (TID), and fractional order proportional-integral-derivative (FOPID) controllers under different disturbances and operating conditions. The results demonstrate that the presented controller provides better frequency responses compared to the other controllers. Moreover, the sensitivity analysis is performed to show the proposed controller robustness versus the parameters’ changes in the system.

Advanced energy management strategy for microgrid using real-time monitoring interface

Recently, the integration of renewable energy sources in microgrids has seen a significant rise due to their attractive prices, reliability etc. However, besides the techno-economic benefits, the renewable energy sources are intermittent, and the high penetration of renewable sources into the microgrid poses design and operation challenges. Indeed, an efficient energy management strategy (EMS) is required to govern power flows across the entire microgrid. This paper introduces an advanced EMS design with a real-time monitoring interface for the effective operation of the hybrid microgrid and data analysis. The proposed advanced EMS model uses a real-time monitoring interface, and it provides the optimum operation and control in terms of balanced power supply and voltage profile with stable frequency. We designed the microgrid, which comprises hybrid sources such as solar and wind power sources, Li-ion battery storage system, backup electrical grids, and AC/DC loads, considering the functional constraints of a microgrid energy management and stability. In addition, the battery energy storage is effectively managed through the performance control of battery charging and discharging using an efficiency controller. The proposed system control is based on the optimum power supply of loads through the available renewable sources and the battery State of Charge (SOC). The microgrid measurement data is transmitted through the Python platform and a graphical user interface (GUI) software developed for data analysis. The simulation results using Matlab Simulink and Python platforms demonstrate the relevance and effectiveness of the proposed EMS and monitoring interface for the stable and reliable operation of the developed hybrid microgrid.

A Review on Renewable Energy Sources in Microgrid

The utilization of RES to address electrical energy requirements is gaining traction as a potential solution to this issue of electrical energy shortages. The use of renewable energy sources in electrical generation systems is done in a variety of ways, including in microgrid systems. Microgrid technologies have massive benefits for both the user as well as the electric utility provider. There has been a lot of microgrid development in numerous nations since micro grids have a lot of benefits, such as higher power quality and being more ecologically friendly. Power generation, microgrid architecture, power electronics, control systems, and security devices are all areas of microgrid innovation. In this paper, it is demonstrated that persistence as well as artificial neural networks have certain qualities that make them useful to investigators of controllers at various levels of control that micro grids must have in order to be cost-effective, productive, as well as capable of meeting energy power quality and quantity requirements. Keywords: Renewable energy sources, Microgrid, persistence, Artificial Neural Network

Analysis of a linear programming‐based decision‐making model for microgrid energy management systems with renewable sources

With the increasing share of renewable energy sources in microgrids, systems enhancing the power flexibility at the demand side have become mandatory in microgrid architecture. Thus, the electrical energy storage system has been mostly integrated into microgrids although its capital and operating costs are very high. Hence, diversifying microgrid energy storage based on the end-energy usage can improve the reliability, operating cost, and power demand flexibility of the microgrid. The hereby study integrates an absorption chiller and electrical heat pump coupled to a heat storage into a renewable energy resources-based microgrid and develops its centralized energy management model for both off-grid and on-grid operation modes. The model considers the current energy level of energy storages in the microgrid, current, and forecasted information state to compute the vector-valued decision minimizing a realistic microgrid operating cost. The operating cost includes the energy purchasing/generation, heating/electrical storage, and penalty cost due to load shedding. Hence, such an objective function leads to maximizing the consumption of the generated renewable power, minimizing the energy storage and load shedding cost. The load shedding has been allowed to increase the flexibility in microgrid energy management. However, a high penalty has been imposed on each electrical, heating, or cooling load shedding decision. The decision vector components are thermal and electrical power flow between each energy source to loads and energy storage systems. The problem has been modeled as a Linear Programming with forecasted multi-parametric inputs at different prediction horizons. The effect of the charging/discharging rate and capacity of energy storages, coefficient of performance of absorption chiller and heat pump, prediction horizon, and energy level of storage devices provided to the myopic energy management model has been evaluated for better integration of renewable sources and thermal storage systems into microgrids. Suggestions have been made to improve the microgrid self-consumption, energy efficiency, and myopic decision-making model using the energy level of storage devices obtained from the full look-ahead optimization model. Highlights The optimization problem combining electricity, heating, and cooling is formulated The electrical and thermal storage charging/discharging rate significantly affect the power supplied by the renewable power source decision of the microgrid energy management model. Accurate energy storage level information into the myopic energy management model improves the model decisions. The absorption chiller coefficient of performance greatly impacts the cooling and electrical loads shedding.

Design of a Boost Converter with MPPT Algorithm for a PV Generator Under Extreme Operating Conditions

Photovoltaic generators (PVGs) are one of the most popular renewable energy sources (RESs), which achieve 47% of RES in microgrids. The aim of this work is to design and simulate a PVG system with a rated power of about 1,621 kW at the standard test conditions (STC), i.e., 1,000 W/m2 and 25ºC. The main components of the proposed PVG are 12 PV panels connected in series (the peak power of a PV panel at STC is about 135 W). A DC-DC boost converter is proposed for implementing the maximum power point tracking (MPPT) algorithm. The proposed MPPT algorithm is tested under extreme conditions; a wide range of change in temperature, irradiance, and load variations. The boost converter is designed to verify stable power flow from the PVG to the load. The calculated and the simulation results using MATLAB/Simulink are in good agreements and the maximum efficiency of the implemented MPPT algorithm is about 99%.

A Center of Mass Determination for Optimum Placement of Renewable Energy Sources in Microgrids

The objective of this article is to determine the location and size of the renewable energy sources (RESs) to be installed in microgrid networks that have multiple distributed generators. Since RESs have faster dynamics and low inertia, microgrid sources could encounter large disturbances in their power production when RESs are deployed. When the disturbance magnitude exceeds a certain limit, the stable operation of the microgrid can be compromised. In this study, the impact of installing a certain RES capacity on a microgrid is numerically evaluated. A novel methodology is developed to accurately determine the location and size of a newly installed RES in a microgrid. The proposed methodology considers the complete system dynamics in the process of locating and sizing the installed RES. The stability of the microgrid is examined using a modified microgrid load flow model. The proposed method automatically checks for the optimum RES placement to ensure minimal effect on microgrid stability. The effectiveness of the proposed methodology is verified experimentally on a microgrid testbed.

Provision of Frequency Stability of an Islanded Microgrid Using a Novel Virtual Inertia Control and a Fractional Order Cascade Controller

Nowadays, the renewable energy sources in microgrids (MGs) have high participation to supply the consumer’s demand. In such MGs, the problems such as the system frequency stability, inertia, and damping reduction are threatened. To overcome this challenge, employing the virtual inertia control (VIC) concept in the MG structure could be considered as a viable solution to improve the system frequency response. Hence, this work proposes a novel modeling for VIC in an islanded MG that provides simultaneous emulation of the primary frequency control, virtual inertia, and damping. To show the efficiency of the proposed technique, a comparison is made between the dynamic performance of the proposed VIC and conventional VIC under different scenarios. The results indicate that the proposed VIC presents superior frequency performance in comparison with conventional VIC. In addition to VIC modeling, a new cascade controller based on three-degrees of freedom and fractional-order controllers (FOCs) is proposed as an MG secondary controller. The effectiveness of the proposed controller is compared to tilt-integral-derivative and FO proportional-integral-derivative controllers. The Squirrel search algorithm is utilized to obtain the optimal coefficients of the controllers. The results demonstrate that the proposed controller improves the MG frequency performance over other controllers. Eventually, the sensitivity analysis is performed to investigate the robustness of the proposed controller in the face of the variations of the parameters.

Current-Control Inverter Schemes for a Grid-Connected PV Generator

Photovoltaic Generators (PVGs) are one of the popular renewable energy sources (RESs), which achieve 47% of RES in microgrids. The main components of the proposed system are: (i) a PVG considering extremum operating conditions, the irradiance change is 400-1000 W/m2 and the temperature change is 25-60 °C; (ii) a DC-DC boost converter used for regulating the variable DC-link voltage via an inner voltage-loop controller (iii) a single-phase H-bridge inverter controlled by an outer current-loop controller for implementing the maximum power point tracking (MPPT) algorithm at grid side; (iv) an inductive power filter for improving the total harmonic distortion (THD) of the grid current. In this paper, the grid current is controlled to follow the maximum power point (MPP) current considering the losses when transferring the DC power to the grid. Two current-loop controllers are implemented, which are: (i) hysteresis current controller (HCC) and PI current controller (PICC). The proposed design is validated in MATLAB/SIMULINK and the simulation results show significant improvement in power quality, Total Harmonic Distortion (THD) of the output grid current is less than 2% and the power factor is close to unity.

Optimal power scheduling of renewable energy sources in micro-grid via distributed energy storage system

This research is mainly focusing on the optimal power management by controlling the charging and discharging modes of the battery storage of the micro-grid (MG). A droop-based controller or battery controller is proposed for this work in order to optimize the power management of the battery. Charging and discharging modes are controlled by the droop-based characteristic where it will perform as a battery controller for the battery storage. Furthermore, the charging and discharging rates will depend on the signal from the MG at power secondary and its signal will be read by the battery controller and choose either to charge or discharge the battery storage where it will suffice the energy demand by loads in the micro-grid. The simulation results show the effectiveness of the controller to control sharing the power based on the desired energy from the battery storage to the loads at the MG. Moreover, all the critical cases have been advised, such as sudden decrease or disturbance of any generating unit. The result has been observed that due to sudden decrease or disturbance of any generating unit, the battery controller manages to control the charging and discharging rate based on the insufficient energy caused by the disturbance to fulfil the demand at MG.

Synchrophasor based islanding detection for microgrids using moving window principal component analysis and extended mathematical morphology

Extensive deployment of non-dispatchable renewable energy sources in microgrids (MGs) has led to frequent fault events, DG and line trips and is arduous to discriminate islanding events (IEs) from other transient conditions. For enhanced grid security, time synchronised data from phasor measurement units is utilised. However, data handling at the control centre is tedious as system variables monitored often exhibit non-linear characteristics that impose a problem to the operator in discriminating between islanding and non-IEs. To cope with this, a zero non-detection zone, reliable and precise islanding detection method for data-intensive grid-connected MG, is proposed. Voltage phasors, frequency and rate of change of frequency from various locations are processed through moving window principal component analysis (MWPCA) cascaded with extended mathematical morphological filter (EMMF). MWPCA reduces data dimensionality and Q statistics obtained are passed to EMMF, which acts as a non-linear filter. Further, the islanding detecting factor identified all IEs within the prescribed time limit with minimum false alarms. Accuracy, precision and reliability demonstrated by using a case study model using DIgSILENT are encouraging. It can be adopted by operators to run MG securely. Further, testing of the proposed method on a typical utility feeder shows promising results.

Probabilistic optimal coordinated planning of molten carbonate fuel cell-CHP and renewable energy sources in microgrids considering hydrogen storage with point estimate method

The ever-growing augmentation of Renewable Energy Sources (RES) and Combined Heat and Power (CHP) units in microgrids (MG) exacerbates schedule management requirements in such systems. In this paper, a modified hybrid Bird Mating Optimization Differential Evolution (BMO-DE) algorithm is developed to address the mixed integer nonlinear programming problem of MG units’ probabilistic optimal planning. The proposed MG structure is consisted of MCFC-CHP, Wind Turbines (WT) and Photovoltaics (PV). The presented stochastic model schedules MG units coordinately, while the strategy of MCFC-CHP hydrogen storage is taken into account. The proposed solution models the uncertain parameters of electricity price, wind speed and sun irradiation using the 2m + 1 Point Estimate Method (PEM). The modeling of uncertain parameters results in more accurate planning and operation of the entire system. The proposed solution is applied on a 33-bus distribution network. In this manner, the total power loss decreased to 358.5 kW and the total operation cost is 3.1349 × 104$, which shows 18% decrease compared to MINLP method and 25% decrease compared to GA-PSCAD method of the total operation cost using the BMO-DE method. The results also justified the usage of CHPs while supplying thermal loads due to increased efficiency and profit.

IEC 61850 Modeling of UPFC and XMPP Communication for Power Management in Microgrids

Integration of distributed renewable energy sources in microgrids presents many challenges to grid such as voltage, stability and power management issues. In literature different control strategies to improve the performance of microgrids integrated with renewable energy sources proposed. FACTS based controller such as Unified Power Flow Controller (UPFC) has also been employed in microgrids to improve the power management and transient stability. Coordinated control strategies between UPFC controller and DERs have been developed. However, the underlying communication for realizing these strategies has not been discussed. This paper presents IEC 61850 and XMPP based communication for coordinating UPFC and DERs in microgrid for its stable operation. IEC 61850 information model for UPFC controller is developed. Furthermore, the XMPP protocol is utilized to provide the scalability for communication network in microgrids with high penetration of DERs. Additionally, XMPP also provides improved security along with the scalability. IEC 61850 message exchanges over XMPP communication between different components of microgrid for power management is demonstrated.

Distributed Consensus Based Algorithm for Economic Dispatch in a Microgrid

Economic dispatch problem (EDP) is a fundamental optimization problem of power systems. With the penetration of renewable energy sources in microgrids, this paper proposes a distributed algorithm based on consensus theory to solve the EDP with a quadratic cost function. The method takes advantage of the fact that incremental costs need to be equal for all buses at optimal output power values. Thus, the incremental cost of each bus is selected as a consensus variable and the local mismatch between total demand and generation is assigned as a feedback variable to meet demand constraints. Unlike the existing related works, the feedback gains for the feedback variables are different and time-varying. Using multi-parameter perturbation theory and graph theory, the convergence of the algorithm is proved. Furthermore, the upper bounds of the feedback gains are given theoretically, and we obtain that the algorithm converges to the optimal solution at a rate governed by the second largest eigenvalue of the system matrix. Meanwhile, the algorithm is a fully distributed algorithm without a leader or a virtual command node. The simulation results illustrate the effectiveness of the algorithm even though there is a demand change and generator damage. Some simulation results intuitively illustrate how the convergence speed changes with the feedback gains.

Improving Microgrid Frequency Regulation Based on the Virtual Inertia Concept while Considering Communication System Delay

Frequency stability is an important issue for the operation of islanded microgrids. Since the upstream grid does not support the islanded microgrids, the power control and frequency regulation encounter serious problems. By increasing the penetration of the renewable energy sources in microgrids, optimizing the parameters of the load frequency controller plays a great role in frequency stability, which is currently being investigated by researchers. The status of loads and generation sources are received by the control center of a microgrid via a communication system and the control center can regulate the output power of renewable energy sources and/or power storage devices. An inherent delay in the communication system or other parts like sensors sampling rates may lead microgrids to have unstable operation states. Reducing the delay in the communication system, as one of the main delay origins, can play an important role in improving fluctuation mitigation, which on the other hand increases the cost of communication system operation. In addition, application of ultra-capacitor banks, as a virtual inertial tool, can be considered as an effective solution to damp frequency oscillations. However, when the ultra-capacitor size is increased, the virtual inertia also increases, which in turn increases the costs. Therefore, it is essential to use a suitable optimization algorithm to determine the optimum parameters. In this paper, the communication system delay and ultra-capacitor size along with the parameters of the secondary controller are obtained by using a Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm as well as by considering the costs. To cover frequency oscillations and the cost of microgrid operation, two fitness functions are defined. The frequency oscillations of the case study are investigated considering the stochastic behavior of the load and the output of the renewable energy sources.

Impact of a Periodic Power Source on a RES Microgrid

The aim of this article is to highlight the impact of a periodic power source, such as a tidal turbine, on the operation and sizing of an autonomous hybrid microgrid with photovoltaic panels and storage. The technique of hill climbing (repeated local search) is used to find the optimum combination of Renewable Energy Sources (RES) and storage units with respect to the required capital cost for various load curves and weather conditions. To model the operation of the microgrid devices, analytical and phenomenological models, have been used, which take into account the specifications of actual commercial devices. Six different case studies are presented, with and without a tidal generator, which are based on six different sets of electrical consumption data corresponding to the Euripus campus of the National & Kapodistrian University of Athens (NKUA) in Psachna, Evia, Greece, and respective meteorological and tidal current data from the region. The results show that tidal energy may be used in a RES microgrid, where applicable, to satisfy the base load requirements, leading to a reduction in installed capacities of intermittent RES and storage, accompanied with cost reduction, especially in cases where a high load factor is observed or may be achieved, through demand response mechanisms. Such a hybrid microgrid configuration may be appropriate for regions where low velocity tidal and marine currents exist along with substantial solar and/or wind energy potential, such as the Mediterranean coast line and islands.

Distributed Control System With Aperiodic Sampled Time-Delayed Data for Batteries and Renewable Energy Sources in Microgrid

This paper proposes an aperiodic sampled and time delayed data based distributed control system for frequency restoration, state of charge balancing and voltage restoration in a microgrid with distributed batteries and PV systems. Lyapunov–Krasovskii functionals and linear matrix inequalities are used to calculate the control system parameters and ensure its stability. The proposed control method is scalable since the stability depends only on the largest and smallest/second smallest eigenvalues of the communication graph Laplacian matrices. Real-time digital simulator is used to verify the proposed control method on an IEEE 13 node test feeder with variable loads.

Evaluating efficiency of renewable energy sources in planning micro-grids considering uncertainties

Nowadays, the renewable energy sources (RES) are widely utilized in micro-grids due to technical development and emission increase, which make the planning the micro-grids integrated the RES very important. To obtain the optimal planning strategy and evaluating efficiency of the RES in micro-grids, a mixed integer programming (MIP) planning framework for a grid-connected micro-grid is presented in this study. The understudy micro-grid consists of the wind turbines and photo-voltaic systems, which are connected to utility grid through the point of common coupling (PCC). The objective function is minimizing the life cycle cost of object comprising of the investment and operation cost of the RES, the energy purchased cost from the utility grid, the emission taxes cost and the replacement cost or residual value of equipment at the end of the planning period. The uncertainties of load, electrical price, wind speed and solar radiation are taken into consideration and then a combinable model with the clustering technique is utilized to integrate them. Finally, numerical simulations for a test grid-connected micro-grid are made to validate the effectiveness of the proposed model and show efficiency of the RES to can apply to practical micro-grids.

Energy management systems for hybrid distributed generation sources in grid connected and stand-alone micro-grids

This paper proposes energy management systems for micro-grids. In recent years, the use of renewable energy sources in micro-grids has become an effective means of power decentralization especially in remote areas where the extension of the main power grid is an impediment. But a mixture of renewable energy sources and conventional generation poses serious challenges in the operation and control of micro-grids as a result of the uncertainty associated with renewable sources. Therefore, excellent energy management with regards to the power production, control, reliability, and consumption is needed in the power system. The main objective of the energy management system is to minimize the system cost as well as meeting the demand. In order to take into account the uncertainties of distributed generators (DGs) and load consumption, demand response participation and load shedding is taken into account. Two different types of algorithms are used to solve the smart energy management system of the micro-grid. To minimize system costs, which includes unit commitment and demand response, a genetic algorithm is used. To further balance the supply and demand during extreme cases, load shedding is employed through the use of the artificial neural network. The simulation results displayed corroborate the merit of the proposed method.