Standalone Microgrid Research Articles

Rule-Based Power Management and Quality Enhancement in a Standalone Microgrid

This paper focusses on the challenges associated with solar PV-diesel generator (DG)-battery storage (BS) based microgrid operating in a standalone mode. The multi-objective and coordinate control of converters is designed and implemented to acheive power management, while maintaining operation of DG in fuel economy zone (FEZ), and increase loadability of the DG with nonlinear load. Additionally, the total harmonic distortion of DG current is maintained within 5% as per IEEE 519 standard. Moreover, the control prevents flow of negative sequence currents in the DG during single phase/double phase unbalanced faults. The state of charge (SOC) of BS is also regulated to prevent over-charging/over-discharging by regulating MPPT/off-MPPT operation of the solar PV array. The hardware implementation of control is done on the laboratory set-up to test the efficacy of the control, which concludes satisfactory performance under various scenarios, and enhanced performance as compared to contemporary algorithms.

Dynamic and Model Predictive Controllers for Frequency Regulation of an Isolated Micro—Grid with Electrical Vehicles and the ESS Integration

Electric vehicles (EVs) improve the performance of Energy Storage Systems (ESS). The inclusion of electric vehicles in the microgrid system has attracted considerable attention due to the emerging potential of vehicle-to-grid options. The proper coordination among ESS & EVs has a high capacity to tolerate frequency discrepancies and the functioning of the MG (Micro-Grid). In this research, two innovative control techniques-To address the issue of frequency regulation in the standalone MG, Model Predictive Control (MPC) and Dynamical Droop Control (D2C) are linked to affect the structure of ESS and EVs following extensive RES inclusion or a significant change in load demand. The D2C protects EV energy in line with system demands by preserving the least amount of power for anticipated EV demand. Combined MPC and D2C settings are tuned using a complex evolutionary technique to further improve performance. In MATLAB/Simulink, a single MG (micro-Grid) is modeled and evaluated using the controlling strategies indicated above. As well, a range of case papers is taken into account to confirm the integration of the D2C and MPC for a single MG's modulation scheme. Moreover, the MPC outperforms both fuzzy-based proportional-integral (FPI) and proportional-integral (PI) controllers in terms of performance results.

Novel Renewable Energy Source in Standalone Microgrid Application with Island Load Management

Renewable energy sources, which use natural sources to generate energy, are currently replacing conventional energy sources to fulfil the world’s expanding energy demands. However, photovoltaic and wind energy, which are among these types of energy sources, are not steady and not available for 24 hours in a day. As an alternative, it is possible to extract and use hot water from the earth all the time. Therefore, this study proposes a thermoelectric generator that uses hot water from the hot spring area to generate free electricity as a new renewable energy source in a microgrid application. In this system, the microgrid is standalone without a battery storage system because the thermoelectric generator is in operation for 24 hours in a day (day and night). In this way, the cost of the battery is eliminated by removing it from the system. In this paper, a novel generation method is proposed in microgrid applications as a renewable energy source. In addition, load management system was implemented to control three types of residence loads. The main advantage of this study is to generate electricity from renewable sources like photovoltaic and thermoelectric to demand without much wasting energy by controlled loads according to the proposed load management algorithm. In this study, the microgrid and energy management control is modeled with MATLAB/Simulink. The results showed that the essential load is always active through thermos electric generator besides the absence of battery storage system.

Development of a Control Strategy for the Hybrid Energy Storage Systems in Standalone Microgrid

The intermediate energy storage system is very necessary for the standalone multi-source renewable energy system to increase stability, reliability of supply, and power quality. Among the most practical energy storage solutions is combining supercapacitors and chemical batteries. However, the major problem in this kind of application is the design of the power management, as well as the control scheme of hybrid energy storage systems. The focal purpose of this paper is to develop a novel approach to control DC bus voltage based on the reference power's frequency decomposition. This paper uses a storage system combined of batteries and supercapacitors. These later are integrated in the multi-source renewable energy system to supply an AC load. This technique uses the low-pass filters' properties to control the DC bus voltage by balancing the generated green power and the fluctuating load. The hybrid storage system regulates power fluctuations by absorbing surplus power and providing required power. The results show good performances of the proposed control scheme, such as low battery current charge/discharge rates, lower current stress level on batteries, voltage control improvements, which lead to increase the battery life.

Demand-Side Management of Self-Sustained Droop Based Standalone Microgrid Using Conservation Voltage Reduction Strategy

Self-sustained microgrid (MG) carries the potential to replicate a conventional grid network with a smooth and robust control scheme to generate and disseminate power with the integration of diverse renewable sources by employing the voltage source inverter (VSI). The power generation capability is the primary function of VSI in an inverter-based distributed generation (DG). In addition, it is also operated to perform the task of demand-side management with the utilization of the conservation voltage reduction (CVR) strategy. In a conventional distribution network, the CVR strategy is exploited by voltage deduction within permissible range with the support of open-loop schemes, such as tap changers, capacitors, or close-loop schemes, such as supervisory control and data acquisition system. However, the CVR's applicability in a standalone MG is still a viable approach to be delved into and hence, this article intends to employ CVR by utilizing the conventional P-f droop and V-I droop principle in inverter-based DG. The proposed strategy produces voltage compensation according to the degree of voltage or frequency variation and therefore, the degree of voltage reduction is not fixed but it varies with the disturbance. The proposed control mechanism relies on the deviation of the voltage generated by the V-I droop and frequency output provided by the P-f droop during the overloading and peak demand condition. In addition to it, a control methodology is devised to check the coordination between CVR and load shedding strategy to shed load during overloading conditions. The performance testing of the proposed control methodology is being validated through real-time Opal-RT device.

Neuromorphic deep learning frequency regulation in stand-alone microgrids

Frequency instability has been a growing problem in recent years due to the rising penetration of distributed generating systems in the format of Microgrids (MGs), which are powered by renewable energy sources (RESs) with an unpredictable nature. The provision of an effective load frequency control (LFC) to an MG model under such conditions contributes significantly to the restoration regarding the unconfigured power system’s stability. An MG structure is employed, which combines renewable energy sources like solar and wind, as well as biologically renewable sustainable energy sources like wastewater, agricultural and domestic wastes, and contains appropriate battery systems and is suited to achieve both energy and waste control in this study. In addition, redox flow battery (RFB), an innovative battery storage system with a fast dynamic response in this MG structure, has been considered as one of the energy storage devices. A nonlinear integral backstepping (NIB) controller is adopted to stabilize the frequency deviation of the integrated MG system with RFB under various level of load disturbances and randomness of RESs. In particular, a spike neural network (SNN) based on neuromorphic platform is proposed to adjust the coefficients of NIB controller. Furthermore, real time analyses are performed with OPAL-RT to validate the feasibility of the proposed strategy from a systematical viewpoint. As a result of this study, the proposed controller for load disturbance, RES power changes, and contingency circumstances in bio renewable MG is determined to be flexible enough to satisfy the efficient frequency regulation for these grids.

Integrated Energy Management System for Microgrid based on Renewable Energy Sources

An effective energy management strategy is crucial to ensure highest system reliability, stability, operation efficiency and cost-effective operation of renewable energy sources based standalone microgrid. This paper presents an efficient energy management system for microgrid incorporated with Photovoltaic system, PMSG based wind turbine and energy storages including battery, fuel cell-Electrolyzer. Implemented hybrid modified invasive weed optimization with perturbed and observed method for PV systems to harvest maximum energy during partial shading condition. A sliding mode controller is implemented for boost converter to work as maximum power point tracker for wind turbine. Three solar plants and three wind farms are considered in this paper to establish 1 MW microgrid. Each wind farm is established with multiple wind turbines and similarly each solar plant having multiple PV modules. Each wind turbine and solar plant has their own inverter to synchronize at point of common coupling (PCC). Effective controllers are proposed to supply quality power at PCC under linear status and nonlinear status of single and three phase loads. Small size battery is considered to work under transient time and electrolyzer-fuel cell set will be working under steady state condition to reduce the cost of the system. TS-Fuzzy based controllers are designed for all the converters and implemented hardware-in-loop on a Real Time Simulator (RTS) by using OPAL RT technology/modules. The results unveiled that the RTS precisely emulate the dynamics of the microgrid with proposed controllers.

Energy Management and Optimization of a Standalone Renewable Energy System in Rural Areas of the Najran Province

Electricity demand is increased due to the exponential rise in consumption in the residential, industrial, and transportation sectors as the human comfort level increases with each passing year. Most countries rely on conventional fuels, especially natural gas, to fulfill their energy demand, which increases the risk of reservoir depletion of conventional fuels. In this regard, the adoption of energy generation through free and environment-friendly renewable energy resources such as wind turbines and solar photovoltaics is increasing rapidly. However, the fluctuating and intermittent nature of renewable resources requires the integration of advanced control mechanisms and power electronics to ensure power quality and system resilience towards disturbances. Standalone microgrids comprising renewable source-based distributed generation systems are becoming the ideal solution to fulfill the need for decentralized small residential communities. This research work focuses on the optimal design and techno-economic analysis of a standalone microgrid for a small residential community in Sharurah City in the Kingdom of Saudi Arabia. HOMER Pro and MATLAB/Simulink have been used for the optimal design of microgrids and energy management using intelligence-based control mechanisms in between generation and load, respectively. The electricity price obtained from the optimized microgrid system is 0.18 SAR/kWh, which is quite low in comparison to generation through a base system (0.38 SAR/kWh) and a diesel generator, respectively. A simulation of the optimized system was modeled and verified using MATLAB/Simulink. The obtained results show the robustness of the presented system against the change in load demand and meteorological conditions.

Fuzzy-based cooperative interaction between stand-alone microgrids interconnected through VSC-based multiterminal converter

This paper proposes a cooperative strategy for the flexible integration of stand-alone AC microgrids using an intermediate multiterminal DC link based on voltage source converters. The strategy manages cooperative power exchange between microgrids through a knowledge-based fuzzy inference system that relies on the current operating conditions of the microgrids. By determining the amount of power to be exchanged between microgrids to improve global reliability, the cooperative strategy achieves an overall reduction in load loss and an increase in the use of available renewable energy. Additionally, the multiterminal DC link provides complete isolation between microgrids, allowing for independent AC bus voltage regulation and management. The effectiveness and expandability of the proposed cooperative energy exchange strategy for enhancing the overall operation of multiple integrated microgrids have been fully verified by simulation and experimental validation.

MSTOGI-FLL-ROGI Based Frequency Adaptive Control for a Single Stage SPVA-BES-BDC-DG Set Connected Standalone Microgrid for Remote Areas

The operation of a standalone microgrid (MG) is severely affected by the presence of local nonlinear loads at the point of interconnection (PIC), which tend to degrade the power quality of the MG. Moreover, in MGs connected with diesel generator (DG) sets, the frequency varies with loads, which leads to poor power quality due to erroneous estimation of the fundamental components. To solve these problems, this paper presents a mixed second and third order generalized integrator with a frequency locked loop (MSTOGI-FLL) prefilter cascaded with a reduced order generalized integrator (ROGI) filter-based control for a solar photovoltaic array (SPVA), a battery energy storage (BES), and a synchronous reluctance diesel generator set (SynRDG) based standalone microgrid. The control improves the operation of the MG by providing enhanced harmonic attenuation and DC offset rejection from the DG currents and PIC voltages during distorted or single-phase load conditions without degrading the dynamic performance of the filter. The control makes the filter adaptive to the DG set frequency, which enhances the filter performance. A power management scheme (PMS) is presented to utilize the SPVA-BES combination and it minimizes the DG set operation. The presented control is compared with other GI-based controls in terms of dynamic performance, frequency tracking, and filtering capability. The MG performance is analyzed under several dynamic conditions using simulation and hardware results.

An Online Prime Dual Approach for Energy Cost Management in Standalone Microgrid with Optimized PMU

The integration of renewable energy sources, battery banks, AC Loads, and DC Loads into standalone microgrids poses a significant challenge for energy management. This study aims to address these challenges by proposing an energy cost management model based on the online prime dual approach. The proposed model uses a real-time adaptation of PMU points, voltage range, and frequency variations to determine the energy deficit in existing standalone microgrids and battery bank charging levels. To account for energy savings, the study considers automatic scheduling of event sequences to prevent unwanted activities from being renewed every hour. The proposed model shows a significant improvement in power sharing, energy cost, and PMU controllability with response time and accuracy. The study presents the percentage of power sharing with energy costs of the proposed energy monitoring in standalone microgrids with minimal PMU locations for different expanses ranging from 20% to 90%. The cost savings of various modes can range from Rs. 5 to Rs. 7 for all expanses within a wide control range.

Evaluation of a hybrid power system based on renewable and energy storage for reliable rural electrification

Microgrids system consisting of single or multiple energy resources and storage is used to provide electricity to remote rural areas. Subsequently, they can be worked in both grid-connected and islanded modes. Microgrids can provide stable energy solutions to areas where grid extension is either costly or not feasible. The system's long-term economic stability and sustainability must be considered throughout the microgrid's design. Renewable energy sources have a variety of objectives and possible restrictions. The current plan suggests a rural microgrid with integrated solar, diesel, and battery systems. The proposed microgrid for the rural area of Uttarakhand (India) is given a techno-economic and feasibility analysis. For the hilly region of India, a standalone microgrid is created to handle the home peak load of 13kW. Moth flam optimization (MFO) is utilized to optimize the proposed energy framework, and the results are compared with those of a genetic algorithm (GA) and particle swarm optimization (PSO). The optimal hybrid system's net current and energy costs are calculated to be $58372.00, with the cost of energy per unit being $0.14/kWh. Sensitivity analysis and robustness viability have been performed for the configuration. The finding suggested that the energy framework using MFO is 18% and 16% less expensive than GA and PSO, respectively. This research adds new direction to critical sustainable development among developed countries.

Forced oscillation suppression using extended virtual synchronous generator in a low-Inertia microgrid

Effects of forced oscillations (FOs) in well-damped power systems are relatively smaller than those of microgrids (MGs) in which the severity of the FOs may be intensified by converter interfaced generators (CIGs). According to the distinct system characteristics, the FOs in MGs will be challenging problems in future research topics. Without proper controls of the CIGs, the FOs may be exhibited extremely higher amplitude, resulting in the MG instability. Such influences will be exacerbated in a low-inertia MG, which can trigger critical frequency oscillation and system collapse. This paper introduces an extended virtual synchronous generator (VSG) with virtual forced components to attenuate the dynamic FO effects in the presence of a low-inertia MG. Contrastive scenarios, i.e., periodic, combined, full sine, and high-frequency FOs are conducted to validate the performances of VSG control in both stand-alone and interconnected MG environments. Numerical results verify that the extended VSG control provides promising benefits in a low-inertia MG not only for the sake of MG stability improvement but also the further FO suppression.

Energy Management in a Standalone Microgrid: A Split-Horizon Dual-Stage Dispatch Strategy

Microgrid technology has recently gained global attention over increasing demands for the inclusion of renewable energy resources in power grids, requiring constant research and development in aspects such as control, protection, reliability, and management. With an ever-increasing scope for maximizing renewable energy output, there is also a need to reduce the curtailment of power on both the generation and demand sides by increasing forecasting accuracies and using resources more effectively. This paper proposes a dual-stage dispatch employing a novel “split-horizon” strategy, in a bid to enhance energy management in a standalone microgrid. The split-horizon is essentially the considered time horizon split into equal operational periods of the dual-stage dispatch. The proposed strategy utilizes a custom-designed novel variant of the inertia-weight-based particle swarm optimization (PSO), termed customized PSO, to perform the optimal schedule and dispatch operation by benefitting from the simplicity of PSO and customization as per the considered objectives. A modified IEEE 34-node test system is derived into a standalone microgrid with added distributed energy resources to test the proposed strategy, while another standalone microgrid, a modified IEEE 69-node test feeder, is also considered for scalability. Furthermore, the validation of the strategy is performed appropriately with a case study while also validating the proposed optimization algorithm. It is observed that the proposed energy management strategy provides approximatelya 7% reduction in costs.

Conservation Voltage Reduction Strategy for Autonomous Microgrid with Improved Voltage-CurrentDroop-Based Inverter Control Framework

In a conventional AC distribution system, the conservation voltage reduction (CVR) strategy is widely employed to lower down the voltage of specific load to lower the power consumption. The wide applicability of demand side management (DSM) using CVR in a stand-alone microgrid through VSI-based energy sources is a thrust area, that is, not examined yet and needs to be explored. The fast dynamics and flexible control are the characteristics of the voltage-current droop method which further increases the inertia of the voltage source inverter. For utilizing these advantages of this droop method, it is required to determine the accurate droop gain to properly coordinate the distribution of power among DGs. In this paper, the voltage-current droop method is utilized to carry out the function of DSM, and a modified droop computation method for voltage-current droop is formulated to determine the impedance from initial of the DG point to the downstream end for multifeeder network. As most of the droop control techniques emulate the conventional power grid such as Q-V droop control which reduces voltage with the increase of reactive power, the research prospect is very high in devising the new droop computation method for voltage-current droop for accurate control of power. In addition to it, the work is extended to apply the benefits of voltage-current droop to execute DSM strategy in standalone MG. Moreover, the capability of the proposed estimation of droop parameter is implemented on a standalone 5-bus single-feeder multi-DGs network, and furthermore, the scheme is applied to IEEE-9 bus multifeeder multi-DGs network to show the applicability of the proposed scheme. The simulation results produced from MATLAB/Simulink are compared with the decentralized power-based droop method and conventional voltage-current droop technique to analyse the performance of the devised scheme.

Battery Lifetime Extension in a Stand-Alone Microgrid With Flexible Power Point Tracking of Photovoltaic System

In stand-alone dc microgrids (dcMGs), battery energy storage systems (BESSs) are conventionally used for regulating the dc-link voltage, causing a continuous battery operation. Though operating the photovoltaic (PV) system at its maximum power point (MPP) yields minimum battery discharge current, the opposite is true for battery charging current. Therefore, reducing the battery charging current based on its state-of-charge (SoC) and the amount of available PV surplus power (which can be treated as virtually stored energy) is an opportunity for improving the battery life. The main objective of the control strategy proposed in this article is to prolong the battery lifetime by reducing the charging current and keeping the battery SoC at lower values if the PV power is enough to supply the loads. Additionally, the PV system is used as the primary asset to regulate the microgrid voltage. The dynamic performance of the proposed control strategy is validated with experimental tests under various operating conditions. Furthermore, its effectiveness in prolonging the battery lifetime is evaluated using an aging model of a lithium-ion (Li-ion) battery (without loss of generality) by simulated case studies.

Battery state of charge based improved adaptive droop control for power management of a microgrid having large scale renewable generation

Power management in a standalone microgrid having large scale renewable generation is generally based upon droop control strategies. However, the renewable sources such as photovoltaics (PV), which are interfaced with microgrid through power electronic converters have low inertia. Because of low inertia, the transient power variation during switching events like change in load may be very large and may result into variation in voltage and frequency beyond permissible limits. In this paper an adaptive scheme for power management of a standalone microgrid is proposed, which is based on available state of charge (SoC) of the batteries to mitigate these issues. The proposed battery SoC based adaptive droop control for power management allows smooth transition of distributed energy resources (DERs) from maximum power point tracking (MPPT) mode to adaptive droop mode and vice versa. The transient behaviour is handled with the help of batteries and supercapacitor (SC), which is further improved with the help of fuzzy logic based proportional integral controller (FLC-PI). A significant drop in SC power during switching events is obtained with FLC-PI. The test microgrid is composed of multiple PV generation at different locations as primary source of generation, multiple batteries energy storage system (ESS) at different locations, fuel cell (FC), supercapacitor (SC) and a dynamic load.

Climate influence on the optimal stand-alone microgrid system with hybrid storage – A comparative study

In the efforts to combat global warming, diversified social, political and technical strategies have been proposed and partially carried out. The vision to have a 100% renewable energy-based electricity system has been one of the long-term strategies in Germany. It is well known that renewable energy can only be utilized to the full extent when its down-side characteristics of intermittency and variability can be compensated by a practical storage system. Due to its long-term storage capacity in comparison to the battery storage, in recent years, hydrogen storage system has been undergoing a technological development and deployment boom, especially for the microgrid systems. This research evaluates the techno-economic feasibility of a 100% hybrid renewable energy-based system with different scenarios of energy storage systems for an off-grid microgrid system in two distinct climate regions within Germany, namely Hamburg and Munich. The “HOMER Pro” software was used to analyse the economic and environmental impact amongst the case studies. The aim of this theoretical study is to answer the questions: 1) Under different climate conditions which type of energy storage (i.e. battery, hydrogen tank, and hybrid battery-hydrogen tank) can offer the overall flexibility and economic advantages? 2) For a specific climate condition, how is the hybrid storage system deployed to achieve the optimum cost-effective scenario? 3) How would climate uncertainties affect the future energy plan for different climate zones?

DEVELOPMENT OF INTELLIGENT CONTROL STRATEGY FOR POWER QUALITY IMPROVEMENT OF HYBRID RES

The major problem in renewable energy system is that the variation in power generation from time to time because of the intermittent nature of the renewable sources. In this paper presents the Artificial Neural Network (ANN) based intelligent control strategy for hybrid standalone microgrid system for varying wind, varying solar irradiation, varying load and symmetrical and asymmetrical fault conditions is presented. A dynamic model of hybrid standalone microgrid consisting wind and solar PV renewable sources with maximum power point tracking control algorithm is developed in MATLAB/Simulink software. A solar PV panel, wind energy conversion system and dynamic model of hybrid energy storage consisting Lithium-ion batteries (Li-Ion) and supercapacitor is connected with the DC Bus. The DC link is connected through the power electronic interfacing circuit and converters connected to a source for a diverse electricity generation. The main objective of this thesis is to improve the power quality of the hybrid microgrid system by solving the voltage sag and swell problem and reduce total harmonics distortion occurring due to various symmetrical and asymmetrical condition. The simulation studies have been carried out to determine system performance with different scenarios of the sources such as typical solar radiation, temperature, wind, battery and supercapacitor charge or discharge conditions The proposed strategy gives better performance characteristics and reduces the harmonics of the system compared to the conventional solution.

Microgrids Imitate Nature for Improved Performance—Use of Nature-Inspired Optimization Techniques in Future Power Systems

There is a constant push towards increasing use of renewable energy-based distributed generators around the globe. While they provide a clean and sustainable source of energy, they employ technologies that are unknown to traditional power systems. These generators are interfaced via inverters that lack the inertia of large synchronous machines. This manifests itself as a more volatile frequency profile that is susceptible to disturbances. This phenomenon is more amplified in stand-alone microgrids which are utilized as a popular electrification alternative in isolated or underserved communities. One solution approach takes its inspiration from nature, e.g., behavior of bees, butterflies, or ants. When employed in a suitable way, animals’ natural behavior helps optimize interaction between different renewable-energy based generators and create a more stable microgrid. There are different approaches to stabilizing such systems with novel optimization approaches. Some of them optimize the ratio between generators that utilize rotating machines and inverters. Penetration of renewable energy generation is about increasing the share of inverter-interfaced generators in the system without causing stability issues. Since renewable energy resources are intermittent and not dispatchable, it is important to create a diverse portfolio where the overall system achieves some stability. For instance, if a local grid is fed by PV panels, wind generation and a small-scale hydroelectric power plant, the varying nature of these resources may complement each other. On a sunny day, PV output might be very high, and wind may not be so significant. On the other hand, on a rainy day, clouds may reduce PV output while precipitation may feed the local hydro power plant. Similarly, wind generation might complement others on a windy day. While the idea is easy to comprehend qualitatively, finding the correct ratio is not trivial. Furthermore, there are many factors at play that are independently changing and impacting the outcome. For different sites, the available renewable energy resources, their profiles as well as the local load conditions would be different. Therefore, a systematic approach is required to optimize these systems at planning, operation and control levels. Nature-inspired optimization algorithms seem to have an edge in doing just that.