Stand-alone Mode Of Operation Research Articles

Control Performance Assessment of a Zero Harmonic Distortion Grid-Forming Converter for Medium Voltage Islanded Microgrids

The world is currently witnessing a rapid transformation in the production and utilization of electrical energy. The traditional centralized generation model for electric power is swiftly evolving into a more decentralized system, known as Distributed Generation (DG), which incorporates renewable energy sources situated closer to end-users. This shift towards DG has paved the way for the emergence of grid-forming converters, which play a pivotal role in enhancing voltage and frequency stability within microgrids (MGs) and isolated applications. This study focuses on assessing the performance of the Zero Harmonic Distortion (ZHD) in both stand-alone and parallel operation modes. The distinctive feature of this converter lies in its inherent ability to generate a sinusoidal voltage source without the need for capacitive filtering components, which can adversely affect cost, efficiency, and size while potentially contributing to resonance problems. This is achieved through a judicious combination of harmonic cancellation within a three-winding transformer and the utilization of Selective Harmonic Elimination Pulse Width Modulation (SHE PWM) dismissing a closed-loop control structure. Simulation and hardware-in-the-loop results presented in this work demonstrate the satisfactory performance of the ZHD grid-forming converter.

A novel RMPC strategy for three-phase inverters operating in grid-connected and standalone modes

One of the main features of microgrids is the capability of operating in both grid-connected (GC) and standalone (SA) modes. This paper presents a novel dual mode robust model predictive control (RMPC) strategy for a three-phase inverter with an LCL filter in both GC and SA operating modes under the filter’s parameter uncertainty. At first, a disturbance observer gain is obtained by solving a linear matrix inequality (LMI), which is determined to preserve the stability of the algorithm. The designed disturbance observer takes into account the polytopic uncertainty of system parameters. A performance index with two weighting matrices is then defined and solved in an infinite horizon by turning it into an optimization problem under LMI constraints. The performance of the control strategy highly depends on the weighting matrices. Hence, an optimization algorithm is formulated to ascertain the best matrices values for both GC and SA operation modes. Given the nonlinearity issue, particle swarm optimization (PSO) is employed to derive the optimal weighting matrices offline. To evaluate the proposed control strategy’s effectiveness, simulations and experiments are performed under several scenarios in both GC and SA operating modes. The results reveal the proposed control strategy’s powerfulness compared to other techniques in the presence of grid voltage and load current disturbances for both inverter operating modes.

Data-driven optimization for microgrid control under distributed energy resource variability

The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid’s real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

Intelligent learning approach for transition control and protection of solar PV integrated electric vehicle charging station

Recently, the control of distributed generations (DGs) with electric vehicles (EVs) parking lot charging systems has received much attention for supporting emergency local loads during grid outages. To achieve optimal transition control of grid-connected (GC) and standalone (SA) modes, an intelligent learning-based algorithm is implemented for optimal handling of the disturbance in the system by injecting a fault condition over certain cycles during a weak/strong grid operation. Here, a fuzzy Q-learning (FQL) based algorithm is implemented for detecting and monitoring the distributed grid condition during different scenarios and a seamless transitioning control in GC and SA modes of operation. In this context, the implementation of designed FQL algorithm enables dynamic adjustments of control signals in response to real-time uncertainties in two major applications (grid synchronization and islanding detection mode). The FQL algorithm focuses on real-time decision-making in response to sudden fault scenarios, addressing the problem under various modes of operation. To validate the proposed FQL controller accuracy, a real-time hardware-in-loop (HIL) simulation and their experimental validation is carried out of a 16kWp grid integrated PV sub-system with different load profiles. The performance evaluation of the FQL controller is investigated under linear and nonlinear local load scenarios.

Mitigation of severe false data injection attacks (FDIAs) in marine current turbine (MCT) type 4 synchronous generator renewable energy using promoted backstepping method

False data injection attack (FDIA) is kind of the most important and prevalent cyber attacks, influencing in power systems and renewable energy resources in microgrids (MGs). The destructive effects caused by FDIAs in the voltage components of synchronous generator with full converter in AC microgrid (ACMG) renewable energy production would seriously damage the DC link voltage, network frequency and also, the active power. The extracted voltage components in the d and q axes to control the grid side converter (GSC) as the main control section, are the important signals for power control process of the synchronous generator with wind or marine current turbine (MCT). In this regards and due to the nonlinear behavior caused by cyber attacks in the generator control process, the robust performance of modified nonlinear backstepping method would be an appropriate alternative, instead of the conventional linear control including PI controllers. Thus, a remedial action against various FDIAs caused in the voltage control commands of the GSC is proposed. Moreover, promotion of the backstepping control by combining with the switching terms, adopted from the higher order super twisting sliding mode control (HOSTSMC), the performance of the proposed method would be precisely robust. To verify the proposed method, severe FDIAs by injecting various constant values, periodic and also, noisy signals are our case studies which are simulated via MATLAB software in the weak grid connect and standalone operation modes.

Techno-Economic Analysis and Optimization of Hybrid Renewable Energy System with Energy Storage under Two Operational Modes

Access to cheap, clean energy has a significant impact on a country’s ability to develop sustainably. Fossil fuels have a major impact on global warming and are currently becoming less and less profitable when used to generate power. In order to replace the diesel generators that are connected to the university of Debre Markos’ electrical distribution network with hybrid renewable energy sources, this study presents optimization and techno-economic feasibility analyses of proposed hybrid renewable systems and their overall cost impact in stand-alone and grid-connected modes of operation. Metaheuristic optimization techniques such as enhanced whale optimization algorithm (EWOA), whale optimization algorithm (WOA), and African vultures’ optimization algorithm (AVOA) are used for the optimal sizing of the hybrid renewable energy sources according to financial and reliability evaluation parameters. After developing a MATLAB program to size hybrid systems, the total current cost (TCC) was calculated using the aforementioned metaheuristic optimization techniques (i.e., EWOA, WOA, and AVOA). In the grid-connected mode of operation, the TCC was 4.507 × 106 EUR, 4.515 × 106 EUR, and 4.538 × 106 EUR, respectively, whereas in stand-alone mode, the TCC was 4.817 × 106 EUR, 4.868 × 106 EUR, and 4.885 × 106 EUR, respectively. In the grid-connected mode of operation, EWOA outcomes lowered the TCC by 0.18% using WOA and 0.69% using AVOA, and by 1.05% using WOA and 1.39% using AVOA in stand-alone operational mode. In addition, when compared with different financial evaluation parameters such as net present cost (NPC) (EUR), cost of energy (COE) (EUR/kWh), and levelized cost of energy (LCOE) (EUR/kWh), and reliability parameters such as expected energy not supplied (EENS), loss of power supply probability (LPSP), reliability index (IR), loss of load probability (LOLP), and loss of load expectation (LOLE), EWOA efficiently reduced the overall current cost while fulfilling the constraints imposed by the objective function. According to the result comparison, EWOA outperformed the competition in terms of total current costs with reliability improvements.

Investigation of the feasibility of microgrid under three operational configurations using whale optimization algorithm

PurposeThis study aims to investigate the feasibility of proposed microgrid (MG) that comprises photovoltaic, wind turbines, battery energy storage and diesel generator to supply a residential building in Grindelwald which is chosen as the test location.Design/methodology/approachThree operational configurations were used to run the proposed MG. In the first configuration, the electric energy can be vended and procured utterly between the main-grid and MG. In the second configuration, the energy trade was performed within 15 kWh as the maximum allowable limit of energy to purchase and sell. In the third configuration, the system performance in the stand-alone operation mode was investigated. A whale optimization technique is used to determine the optimal size of MG in all proposed configurations. The cost of energy (COE) and other measures are used to evaluate the system performance.FindingsThe obtained results revealed that the first configuration is the most beneficial with COE of 0.253$/KWh and reliable 100%. Furthermore, the whale optimization algorithm is sufficiently feasible as compared to other techniques to apply in the applications of MG.Originality/valueThe value of the proposed research is to investigate to what extend the integration between MG and main-grid is beneficial economically and technically. As opposed to previous research studies that have focused predominantly only on the optimal size of MG.

Optimization Technique for Renewable Energy Storage Systems for Power Quality Analysis with Connected Grid

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function’s preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system’s performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation’s results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode.

The Design and Processor-In-The-Loop Implementation of a Super-Twisting Control Algorithm Based on a Luenberger Observer for a Seamless Transition between Grid-Connected and Stand-Alone Modes in Microgrids

The abrupt transfer from grid-connected (GC) to stand-alone (SA) operation modes is one of the major issues that may threaten the stability of a distributed generation (DG) system. Furthermore, if the islanding mode happens, it is vital to take into consideration the load voltages or load current waveforms as soon as feasible. This paper develops an advanced control technique based on a super-twisting sliding mode controller (ST-SMC) for a three-phase inverter operating in both the GC and SA modes. This control scheme is proposed to ensure a smooth transition from the GC to SA mode and enhance the load voltage waveforms under the islanding mode. In addition, to minimize the operational costs of the system and the complexity of the studied model, a digital Luenberger observer (DLO) with a proper design is adopted for estimating the inverter-side current. The control scheme of the whole system switches between a current control mode during the GC mode and a voltage control mode during the SA mode. The super-twisting control algorithm is applied to the outer voltage control loop involved in the cascaded voltage/current control scheme in the SA mode. Simulation tests of a three-phase inverter are performed for the purpose of assessing the suggested control performance by using the PowerSim (PSIM) software and comparing it with a classical PI controller. Furthermore, a processor-in-the-loop (PIL) implementation in a DSP board TMS32F28335 while debugging is conducted using code composer studio 6.2.0. The obtained results show efficient control properties, such as a smooth transition among the microgrid (MG) operating modes, as well as effectiveness and robustness during both the GC and SA operation modes.

Household resilience realized by photovoltaic and battery energy system in natural hazard-triggered blackouts: Evidence from Japan

Photovoltaic (PV) panels, increasingly popular due to decarbonization, are expected to increase household resilience during natural hazard-triggered blackouts. Their effectiveness is also expected to improve with the addition of storage batteries. However, PV ownership does not necessarily lead to electricity use in blackouts; even when used, the actual use of electrical appliances is unclear. We examined households that owned PV panels and experienced a natural hazard-triggered blackout to determine what barriers and facilitating factors existed for using PV electricity, which electrical appliances could be used with PV, and whether the benefits of PV changed with storage batteries. A questionnaire survey targeted households in Japan that experienced blackouts caused by the 2018 Hokkaido Eastern Iburi earthquake and the 2019 Typhoon Faxai, respectively, and their responses (n = 282 and 259, respectively) were analyzed. Descriptive statistics showed that while it was sometimes necessary to switch PV systems to stand-alone operation mode during blackouts, most respondents who did not use PV systems did not know how to operate them, which was a barrier to their use. The main facilitating factors were preparation like advance explanations from the sales staff and the regular manual check for use in case of blackouts. Bayesian item response theory models demonstrated that PV electricity enabled households to use a variety of electrical appliances, including those related to food and communication (i.e., refrigerators and cell phone chargers), whose needs increased during the blackouts. The probability of each appliance being used increased in households with batteries. The above results were commonly confirmed regardless of hazard type. These findings will improve the disaster resilience of households equipped with PV panels and batteries, as well as spread the installation of such equipment by highlighting their benefits.

The development and applications of a controllable lander for in-situ, long-term observation of deep sea chemosynthetic communities

Understanding the evolution of chemosynthetic communities and environmental changes near fluid seepages in the deep sea requires in-situ long-term observation data. However, in-situ detection or sampling for the investigation of cold seeps, hydrothermal vents, and nearby chemosynthetic ecosystems, by manned submersibles and remotely operated vehicles (ROVs), has great restrictions in terms of time. The observation parameters of a free-fall mode Lander cannot be adjusted in real time because there is no communication channel once the Lander is separated from the vessel. A long-term ocean observation platform (LOOP), that uses a new controllable mode for launching and recovery with the aid of a research vessel and submarine vehicles, has been developed and used in the cold seep area of the South China Sea. The LOOP can be operated in an online real-time control mode allowing landing site selection and adjustment of observation parameters during the launching process, with subsequent switched to an offline stand-alone operation mode for long-term, continuous observation. The effective observation times were 375 days and 414 days, respectively, during the 2016 and 2018 deployments in the cold seep area in the South China Sea. Results of these deployments show that salinity and dissolved oxygen parameters have strong spatial heterogeneity in both the horizontal and vertical directions within the cold seep vent. The spatial heterogeneity of environmental parameters may be one of the main driving factors for the uneven spatial distribution of chemosynthetic communities in cold seep areas. Overall, the LOOP provides an innovative and controllable launching and recovery mode and is expected to become a universal underwater observation platform for in-situ, long-term, and continuous data acquisition.

An accurate dynamical model of induction generator utilized in wind energy systems

Due to the advantages of a self-excited induction generator (SEIG), it plays a main role in sources of renewable energy, such as wind turbines (WT). The regulation of terminal voltage and frequency is poor under variable rotor speed and load conditions at stand-alone operation mode. The generator terminal voltage depends on the excitation capacitance which can be controlled by a capacitor bank and static voltage compensator. The dynamical model of the machine is described by differential equations in D-Q axes transformations of the synchronously rotating frame. Many models of analysis are proposed in the literature. In those models, several approximations are used to simplify the process of calculations, such as neglecting the iron core resistance, stray load resistance, stator and rotor leakage reactance, and magnetic saturation. In this work, a comprehensive dynamic model of the SEIG-WT is performed to analyze the system performance under transient and steady-state conditions. This dynamic model considers the effect of all machine parameters variation. New analytical formulas are used for to accurate calculation of minimum and maximum values of excitation capacitance and generator rotor cut-off and maximum speed. The dynamic model results are partially compared with experimental results, and accurate agreement is shown.

Analysis of Grid-Interactive PV-Fed BLDC Pump Using Optimized MPPT in DC–DC Converters

In solar photovoltaic (PV) system-based Brushless DC (BLDC) motors for water pumping application, the role of DC/DC converters is very important. In order to extract the maximum power from the PV array, an efficient DC/DC converter is essential at the intermediate stage. In this work, different DC/DC converter topologies suitable for BLDC motors are proposed. The converters are supported by an optimized maximum power point tracking system to provide a reliable operation. Recent optimization algorithms such as fuzzy logic, perturb and observe, grey wolf, and whale optimization are implemented with the PI controller in maximum power point tracking to maximize the conversion efficiency. The obtained results using SEPIC, LUO, and interleaved LUO converters provide a comparative study in the case of converter output, motor parameters, and grid output. The performance analysis on three different converters and multiple optimization methods are carried out. By analyzing the performance of different converter topologies, the interleaved LUO converter outperforms the other two converters with the results of a voltage gain ratio of 1:22, conversion efficiency of 98.3%, and grid current THD of 2.9%. Moreover, regarding the power quality aspect, the total harmonic distortion of the grid current is maintained below the IEEE-519 standard. In addition, the developed system has an advantage of operating both in stand-alone and grid-connected operation modes.

Feasibility study of a standalone hybrid energy system to supply electricity to a rural community in South Sudan

Despite promising solar potential in South Sudan, rural electrification has long been an issue for the country's growth and development, as well as addressing climate change and fuel cost limits. This study aims at the feasibility analysis of a hybrid energy system for a rural community in the Southern part of South Sudan without access to electricity. Over a year, typical energy consumption profiles were generated based on the energy needs of the community. HOMER pro program was used to configure and optimize the system, and six different combinations were simulated and analyzed economically and technically based on the standalone mode of operation. The PV/DG/Battery design offers the lowest Net Present Cost (NPC) and Cost of Energy (COE), with a 22.94% return on investment due to the substantial solar potential. The study also found a modest wind speed in rural areas. In the rural community, the proposed configuration system can help provide electricity access with an 85% renewable fraction. The results of this study's six hybrid systems/combinations can serve as a reference benchmark for stakeholders, demonstrating the possibilities of boosting renewables, changing configuration settings, and lowering the cost of electricity.

An E-STATCOM Based Solution for Smoothing Photovoltaic and Wind Power Fluctuations In a Microgrid Under Unbalanced Conditions

This manuscript presents a novel control architecture for the E-STATCOM (STATCOM integrated with Energy Storage System (ESS)) that is common for both the Stand-Alone (SA) as well as the Grid-Connected (GC) modes of operation of a microgrid operating under unbalanced conditions. The proposed control architecture fulfils the aim of regulating the E-STATCOM in such a manner so as to smoothen the power fluctuations of a nearby renewable energy source (solar, wind, etc) and also to maintain the voltage and frequency at the Point of Common Coupling (PCC) within the normal range of operation. Hence, the resultant real power injected by the renewable energy source along with the E-STATCOM is constant in magnitude, irrespective of whether the microgrid is in the GC or the SA mode of operation. Dynamically varying limits are introduced to provide inherent VSC over-current protection. In order to illustrate the performance of these control architectures, a model of the IEEE 34 node power distribution network that is being supplied by a solar Photo-Voltaic (PV) Distributed Generation (DG) unit, a wind DG unit and two identical E-STATCOMs (for compensating the power fluctuations from the PV and wind DG units) has been considered for study in PSCAD/EMTDC.

Techno-economic feasibility analysis of optimally sized a biomass/PV/DG hybrid system under different operation modes in the remote area

This paper presents an integrated research framework for the techno-economic feasibility and optimal design of a hybrid energy system for a remote area. A mixed-integer linear programming model is developed for finding optimal solutions to satisfy electricity demand and ensure sustainability. The cost-efficiency and environmental benefits of the hybrid system under the stand-alone and grid-connected operation modes are evaluated and compared. Suifenhe, a remote border area in Northeast China was taken as the case study to verify this model. The results suggest that a grid-connected hybrid system consisting of solar power (28.17 MW), biomass generator (8.71 MW), and battery storage (79.51 MW) is the best economically feasible option to satisfy local electricity demand with the levelized cost of electricity of 0.1498 $/kWh. To achieve 100% electricity self-sufficiency, diesel generators would become the main component satisfying 70% of total demand due to the cost advantage and flexibility. Besides, a 100% renewable energy system is currently not economically feasible due to extremely high costs and considerable electricity supply surplus. Future decreasing PV capital cost and implementing carbon price would facilitate the development of renewable energy and the decarbonization of the power system.

Control of single-phase photovoltaic H6 inverter in grid-connected and stand-alone modes of operation

Control of single-phase photovoltaic H6 inverter in grid-connected and stand-alone modes of operation

Control of single-phase photovoltaic H6 inverter in grid-connected and stand-alone modes of operation

This paper proposes a control strategy for single-phase transformerless photovoltaic H6 inverter with capability of operation in both grid connected and stand-alone modes. This control strategy enables seamless transition between two modes without any reconfiguration of control structure and need of any external communication. A multi-loop control structure including an inner voltage and current control with PR controller and outer power control with droop controller is utilised to make the inverter operate in both modes. In grid-connected mode, the power control loop regulates reference voltage and frequency for voltage control loop, to adjust power flow of inverter to the main grid while in islanded mode, reference voltage and frequency are determined according to droop characteristic of inverter to supply local load demand. The simulation and experimental result are used to verify the performance of the proposed control.

Resilience Enhancement at Edge Cloud Systems

It is becoming common practice to push interactive and location-based services from remote datacenters to resource-constrained edge domains. This trend creates new management challenges at the network edge, not least to ensure resilience. These challenges now need to be investigated and overcome. In this paper, we explore the use of open-source programmable asset orchestration at edge cloud systems to guarantee operational resilience and a satisfactory performance level despite system incidents such as faults, congestion, or cyber-attacks. We discuss the design and deployment of a new cross-level configurable solution, Resilient Edge Cloud Systems (RECS). Results from appropriate tests made on RECS highlight the positive effects of deploying novel service and resource management algorithms at both data and control planes of the programmable edge system to mitigate against disruptive events such as control channel issues, service overload, or link congestion. RECS offers the following benefits: i) the switch automatically selects the standalone operation mode after its disconnection from the upper-level controllers; ii) deployment of edge virtualized services is made, according to client requests; iii) the client requests are served by edge services and the related traffic is balanced among the alternative on-demand routing paths to the edge location where each service is available for its clients; iv) the TCP traffic quality is protected from unfair competitiveness of UDP flows; and v) a set of redundant controllers is orchestrated by a top-level multi-thread cluster manager, using a novel management protocol with low overhead.

Optimal scheduling of the combined power and desalination system

In this paper, the scheduling model is proposed that addresses the operational optimization of the combined power and desalination (CPD)system, which is described as a mixed integer non-linear programming (MINLP) problem with the maximizing total economic benefits and the time-dependent electricity price is considered. The typical day of each season is selected as one scheduling cycle. The results showed: The multi-stage flash (MSF) system keeps running in the total scheduling cycle and the water production rate varies between 2,000 t/h and 4,000 t/h. The reverse osmosis (RO) system only keeps running in the Spring. In the other seasons, a part group’s module of RO system would be shutdown following the increasing demand of electricity. The higher electricity price, the more electricity is supplied to the users and the less water is produced. Vice versa, less electricity and more water are produced. On stand-alone operation mode, the largest fluctuation range of electricity varies from 360 to 510 MW in Summer. The least range varies from 270 to 330 MW in Spring. On the scheduling mode, the difference of the largest and least value varies between 10 to 20 MW. The operation stability and efficiency will be improved by the optimal scheduling of the CPD system.