Photovoltaic Generation Units Research Articles

Satin bowerbird algorithm with an adaptive constriction factor for enhanced photovoltaic integration in distribution feeders

—This paper introduces an enhanced Satin bowerbird optimizer (SBO), specifically tailored for the integration of photovoltaic distributed generation (DG) units into radial distribution systems. The upgraded SBO version involves two pivotal adjustments. Firstly, the positional updating mechanism of the newly generated bower is modified to enable exploration around the iteration's elite bower. Secondly, an adaptive constriction factor is introduced, progressively decreasing during iterations to concentrate the search in promising areas. These modifications significantly amplify the exploration capacity of each new bower in the SBO algorithm. The proposed upgraded SBO aims at minimizing costs related to CO2 emissions from the grid and those linked with photovoltaic units, in addition to energy losses. The variability of photovoltaic DG units is represented using the Beta Probability Density Function (PDF) to portray distinct solar irradiation conditions experienced daily. The enhanced SBO version undergoes testing on a practical Nigerian distribution system, the Ajinde 62-bus network, and a standard IEEE 69 nodes system. Simulation results underscore the effectiveness of the upgraded SBO version, revealing substantial reductions in energy losses and emissions. Specifically, for the Ajinde 62-bus network, the proposed SBO version achieves a noteworthy 31 % reduction in the combined yearly costs of energy losses and emissions compared to the initial case. Additionally, for the IEEE 69-bus system, it attains a considerable reduction of 35 %. Furthermore, the simulation results illustrate the competitive performance of the suggested SBO version when compared to Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms, as well as the standard SBO.

Power reserve control for utility-scale PV power plants under cloud conditions

The photovoltaic generation growth has posed challenges for the control and operation of contemporary power systems, degrading the system frequency response and stability margins. The decrease of the system equivalent inertia and the performance deterioration of the primary frequency control are the main causes of these problems. Such challenges can be overcome by the provision of ancillary services from photovoltaic power plants. However, the provision of ancillary services based on active power, such as inertial response, frequency regulation, and curtailment, requires the de-loaded operation of the photovoltaic power plants. In such context, this work proposes a de-loaded control approach (or, equivalently, power reserve control) for large photovoltaic power plants under shading conditions caused by cloud movements. This kind of shading condition poses a significant challenge for the de-loaded control, as it causes large and fast variations in the maximum power point of the multiple photovoltaic generation units that compose the photovoltaic power plant. A test system corresponding to a 100 MWac photovoltaic power plant with 25 photovoltaic generation units of 4 MWac is employed to evaluate and validate the proposed control approach. The stochastic distribution and movement of clouds, as well as the solar irradiance over the photovoltaic power plant are generated by an approach based on the fractal geometry. The proposed control was effective and presented a good performance under different cloud coverage levels.

Research on fast frequency response to AC grid of a battery‐free MVDC system incorporating a high proportion of distributed photovoltaic generation units with reserve capacity

AbstractWith the fast development of renewable energy, system operators intend to require renewable energy generation to have more grid support capabilities. This paper studies the grid‐connected control strategy of a battery‐free medium‐voltage direct current (MVDC) system with distributed photovoltaic generation units (DPVGUs), aiming to realize the rapid grid support function to AC grid frequency. Firstly, a scheme for the MVDC system can keep some reserve capacity is provided. At this moment, DPVGUs execute a master‐slave coordinated control strategy, that is, the master DPVGU adopts an improved DC constant voltage control strategy to maintain the MVDC bus voltage, and the remaining DPVGUs adopt an improved droop control strategy to quickly provide corresponding power. Secondly, when the frequency deviation of AC grid exceeds the deadband of primary frequency control, fast frequency response (FFR) control strategy of the modular multilevel converter is proposed, aiming to actively transmit some or all of the reserved power of the MVDC system to the AC grid and can quickly provide frequency support to AC grid. In FFR control strategy, an additional control strategy is added on the vector control strategy. Finally, simulation studies are carried out in RTDS to verify the effectiveness of the proposed control scheme.

Optimizing energy payment, user satisfaction, and self-sufficiency in flexibility-constrained smart home energy management: A multi-objective optimization approach

Smart homes have assumed a pivotal role within energy communities, offering sophisticated home energy management systems tailored to end-users preferences across technical, economic, social, and environmental facets. Amid the proliferation of smart appliances and advanced communication infrastructure, these domiciles play a crucial part in optimizing energy utilization and fostering sustainable practices. Notably, they create an enabling environment for the integration of renewable energy resources, but their widespread adoption may potentially challenge the flexibility of the overarching energy system. In this paper, a novel flexibility-constrained smart home energy management framework is proposed aiming at optimizing energy payment, end-user satisfaction, and end-user self-sufficiency preferences. Diverse technologies including Combined Heat and power (CHP) units, PhotoVoltaic (PV) generation units, electrical energy storage systems, and electric vehicles are considered in the smart home where the PV unit uncertainty is modeled by a scenario-based approach. The lexicographic technique as a Multi-Criteria Decision Making (MCDM) approach is utilized to find an appropriate compromise among different targets. The proposed model is structured as a Mixed-Integer Linear Programming (MILP) problem that is solved by the CPLEX solver in the GAMS environment. The results reveal a significant disparity in energy payments when the flexibility limit falls below 40 % of the predefined amount. The results demonstrate that altering the priority order of the objective functions has led to significant changes in energy payment, end-user satisfaction, and end-user self-sufficiency, with variations of 27.4 %, 100 %, and 56.64 %, respectively.

A proposed fault identification-based fuzzy approach for active distribution networks with photovoltaic systems

Recently, the issue of protection in active distribution networks has been greater than before due to mushrooming use of photovoltaic (PV) generation units. PV systems coupled to power electronics devices and their control strategies causes significant changes to fault current characteristics leading to maloperation of fault identification schemes. Therefore, this paper proposes a robust self-adaptive fault identification scheme that incorporates the presence of PV systems and considers the effect of uncertainty arising due to their high penetration levels. The real-time voltage and current measurements are captured locally by advanced one-end measuring facilities, considering the uncertain temperature and irradiation conditions, of solar generation units. First, the proposed scheme employs wavelet transform, statistical alienation concept, and fuzzy logic to identify the fault criteria. Then, it uses fuzzy concept-based the system features to obtain the optimal protection scheme setting. For simulation analysis, a 10 MW solar farm-connected to a real-world active distribution system is emulated in PSCAD-EMTDC simulator to create intensive study cases, and the proposed scheme is implemented in MATLAB software. Several fault parameters are examined in the scheme comprising different fault types, fault locations, and transition resistances. Also, the uncertainty arising from PV plants and different loading conditions are taken full consideration in the developed scheme. Simulation results have verified the validity of fault identification scheme; nonetheless, the computational burden is kept reasonable. The proposed scheme is investigated under white gaussian and impulsive noise. Furthermore, sensitivity analysis and comparative study are conducted in terms of accuracy, high decision speed, and sampling frequency variation. The proposed scheme succeeds to detect and classify internal faults for both on/off-grid operation modes and differentiate them from external ones in the case of on-grid mode. The proposed scheme has dominance performance including high speed fault detection within, a half cycle, and provides accuracy more than 98 %.

Prospective life cycle environmental impact assessment of renewable energy-based methanol production system: A case study in China

Integrating renewable electricity and green hydrogen with CO2 utilization to produce chemicals has drawn much attention due to its low carbon emission characteristics. The goal of this paper is to investigate the environmental sustainability of producing a steady hourly output of methanol by utilizing electricity and hydrogen produced via renewable energy resources, and CO2 captured from a coal-fired power plant located in Inner Mongolia, China. Six renewable energy-based electricity and hydrogen systems are considered based on photovoltaic, wind turbines and combinations thereof, including battery and grid technologies. The environmental impact indicators of these system are compared comprehensively through life cycle assessment approach, including not only global warming potential but also fossil fuel depletion, water & soil & air pollution indices and human health index. The results indicate that the most environmentally friendly system is Case B2 which uses photovoltaic to produce hydrogen and electricity, with battery for energy storage and excess electricity sold back to grid. It shows apparently the best sustainability performance in global warming potential, which is 0.105 kg CO2-eq per kg methanol. In general, the proposed six systems outperform conventional methanol production systems in terms of global warming potential and abiotic depletion potential, with reductions ranging from 74% to 92% and 51.1%–73.8% respectively, but they have high levels in other environmental sustainability indicators such as human toxicity, eutrophication potential, and ozone layer depletion potential due to the photovoltaic electricity generation unit. The findings highlight that it is essential to consider a comprehensive range of environmental sustainability indicators when developing renewable energy-based methanol systems on a large scale.

Assessment and mitigation of the influence of rising charging demand of electric vehicles on the aging of distribution transformers

The world is currently moving towards more environmentally friendly modes of transport. As a result, the sales of electric vehicles (EVs) are growing exponentially, and the new charging-associated loads are influencing the operation of the power distribution networks and their elements. Given the stochastic nature of the additional load from EVs, it is difficult to predict such a load with analytical methods. In this regard, a fuzzy-logic-based tool to assess the influence of the charging load of EVs on the transformers’ aging was developed in the MATLAB-Simulink environment, and the impact of different EV penetrations was simulated and assessed. The fuzzy-logic-based model incorporates the influence of ambient temperature, power quality, and overloads. It comprises a diagnostic part, which warns a user about possible issues by providing actual information about the transformer's state, and a tuning part, which is aimed at optimizing the transformer's load level and the power factor at the secondary voltage side. Moreover, the effectiveness of photovoltaic generation units, shunt capacitor banks and battery energy storage systems, installed at the secondary voltage side, for distribution transformers’ service life extension were analyzed. The results show that, for high EV penetration levels, a combined grid reinforcement strategy allows to reduce the loss of life of the transformer by around 3–5 times.

Efficient Integration of Photovoltaic Solar Generators in Monopolar DC Networks through a Convex Mixed-Integer Optimization Model

The problem regarding the optimal siting and sizing of photovoltaic (PV) generation units in electrical distribution networks with monopolar direct current (DC) operation technology was addressed in this research by proposing a two-stage convex optimization (TSCO) approach. In the first stage, the exact mixed-integer nonlinear programming (MINLP) formulation was relaxed via mixed-integer linear programming, defining the nodes where the PV generation units must be placed. In the second stage, the optimal power flow problem associated with PV sizing was solved by approximating the exact nonlinear component of the MINLP model into a second-order cone programming equivalent. The main contribution of this research is the use of two approximations to efficiently solve the studied problem, by taking advantage of convex optimization models. The numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness of the proposed approach when compared to multiple combinatorial optimization methods. Two evaluations were conducted, to confirm the efficiency of the proposed optimization model. The first evaluation considered the IEEE 33-bus grid without current limitations in all distribution branches, to later compare it to different metaheuristic approaches (discrete versions of the Chu and Beasley genetic algorithm, the vortex search algorithm, and the generalized normal distribution optimizer); the second simulation included the thermal current limits in the model’s optimization. The numerical results showed that when the maximum point power tracking was not regarded as a decision-making criterion, the expected annual investment and operating costs exhibited better performances, i.e., additional reductions of about USD 100,000 in the simulation cases compared to the scenarios involving maximum power point tracking.

Design and Implementation of an Integrated Control Scheme for GaN-Based Multiple Power Converters

In response to the rapid changes in the international energy environment, developing renewable energy (RE)-based distributed generation (DG) and various smart micro-grid systems is crucial for creating a robust electric power grid and new energy industries. In this aspect, there is an urgent need to develop hybrid power systems suitable for coexistent AC and DC power grids, integrated by high-performance wide ban gap (WBG) semiconductor-based power conversion interfaces and advanced operating and control strategies. Due to the intrinsic feature of variation in RE-based power generation, the design and integration of energy storage devices, real-time regulation of power flow, and intelligent energy control schemes are key technologies for further promoting DG systems and micro-grids. This paper investigates an integrated control scheme for multiple GaN-based power converters in a small- to medium-capacity, grid-connected, and RE-based power system. This is the first time that a complete design case demonstrating three GaN-based power converters with different control functions integrated with a single digital signal processor (DSP) chip to achieve a reliable, flexible, cost effective, and multifunctional power interface for renewable power generation systems is presented. The system studied includes a photovoltaic (PV) generation unit, a battery energy storage unit, a grid-connected single-phase inverter, and a power grid. Based on system operation condition and the state of charge (SOC) of the energy storage unit, two typical operating modes and advanced power control functions are developed with a fully digital and coordinated control scheme. Hardware of the GaN-based power converters and digital controllers are designed and implemented. The feasibility and effectiveness of the designed controllers and overall performance of the proposed control scheme are verified with results from simulation and experimental tests on a 1-kVA small-scale hardware system.

Risk constraint techno-economic assessment of photovoltaic-penetrated distribution network considering hydrogen vehicles in a multi-market environment

Challenges arising from increasing the penetration roof-top photovoltaic (PV) system are mostly related to the uncertainties concerned with PV generation units, which significantly impact the optimal operation of the power grid and may impose economic risks on the network operator. This article analyzes the real-time and day-ahead financial risk of a PV penetrated smart distribution grid with hydrogen-powered vehicles demands. The proposed approach models the uncertainties using a scenario-based method and evaluates the resultant risk of these uncertainties by utilizing the downside risk constraint (DSRC) approach. Compared to other risk-measures, DSRC method is a convex non-equilibrium approach and provides a closed-loop solution which is incorporated explicitly as a constraint in the problem. Besides, due to the linearity of the proposed DSRC method, there is no increased computational burden of the final risk-based model. The uncertainties involved in the model include PV generation, electrical demand, real-time market price, and behavior of hydrogen-powered vehicles. The studied test system is the standard IEEE 33 bus distribution network which 14 buses have installed PV with capacity of 50-150% of their peak load. The results validate a 100% reduction of expected downside risk (EDR), while the total cost is increased only by 3.8%.

Energy Trading Strategy of Distributed Energy Resources Aggregator in Day-Ahead Market Considering Risk Preference Behaviors

Distributed energy resources aggregators (DERAs) are permitted to participate in regional wholesale markets in many counties. At present, new market players such as aggregators participate in China’s power market transactions. However, studies related to market trading strategy have mostly focused on centralized wind power and PV generation units. Few studies have been conducted on the decision-making strategies for DERAs in China’s power market. This paper proposes an auxiliary decision-making model for distributed energy systems to participate in the day-ahead market with more reasonable trading strategies. Firstly, the Gaussian mixture model (GMM) is used to deal with the uncertainties of wind power and photovoltaic (PV) output in the distributed energy system. Secondly, the information gap decision theory (IGDT) is used to deal with the uncertainty of price fluctuations in the spot electricity market. Thirdly, according to the different risk preferences of the DERAs facing market price fluctuation, the robust decision model and opportunity decision-making model in the day-ahead market are constructed, respectively. Finally, to deal with the irrational behavior of the DERAs’ perception of “gain” and “loss” with market risks in China’s two-tier market environment, the prospect theory and the marine predator’s algorithm (MPA) are employed to obtain a day-ahead trading decision scheme for DERA. The analyses show that RDES with robust preference can withstand greater price volatility in the day-ahead market; they will reduce the bidding expectations and increase the system operating cost to improve the achievability of the expected revenue. However, DERAs under the opportunity strategy is more inclined to sell electricity to the market and offset system operating costs with revenue. The proposed model can provide strategic reference for DERAs with different risk preferences to bid in day-ahead market and can improve the level of aggregators’ participation in electricity trading.

A Novel State-of-Charge-Based Droop Control for Battery Energy Storage Systems to Support Coordinated Operation of DC Microgrids

A modern dc microgrid often comprises renewable energy sources (RESs), such as photovoltaic (PV) generation units, battery energy storage systems (BESSs), and local load, and it is also connected to the utility grid through a point of common coupling (PCC). While most existing approaches have to rely on communication links to achieve the desired control performance, this article proposes a novel control strategy without resorting to the communication links. This is achieved by assigning BESSs as master units and regulating the dc bus voltage with a novel state-of-charge (SoC)-based droop control, where the BESSs coordinate the slave units (e.g., RES and utility grid) with the aid of the dc bus signaling (DBS) technique to avoid overcharging and overdischarging of these BESSs. In the proposed droop control, the reference voltage for these BESSs is designed for coordinated operation between BESSs and utility grid, and it is maintained constant in the normal SoC range, which can reduce dc voltage variation. Droop coefficients designed for SoC balance of BESSs are dynamically adjusted based on their own SoC values. Furthermore, the preset maximum deviation between the reference voltage and the dc bus voltage ensures the reliable coordinated operation. Real-time hardware-in-loop (HIL) tests considering three different scenarios are conducted to validate the effectiveness of the proposed method.

Efficient Day-Ahead Dispatch of Photovoltaic Sources in Monopolar DC Networks via an Iterative Convex Approximation

The objective of this research is to propose an efficient energy management system for photovoltaic (PV) generation units connected to monopolar DC distribution networks via convex optimization while considering a day-ahead dispatch operation scenario. A convex approximation is used which is based on linearization via Taylor’s series expansion to the hyperbolic relations between voltages and powers in the demand nodes. A recursive solution methodology is introduced via sequential convex programming to minimize the errors introduced by the linear approximation in the power balance constraints. Numerical results in the DC version of the IEEE 33-bus grid demonstrate the effectiveness of the proposed convex model when compared to different combinatorial optimization methods, with the main advantage that the optimal global solution is found thanks to the convexity of the solution space and the reduction of the error via an iterative solution approach. Different objective functions are analyzed to validate the effectiveness of the proposed iterative convex methodology (ICM), which corresponds to technical (energy losses reduction), economic (energy purchasing and maintenance costs), and environmental (equivalent emissions of CO2 to the atmosphere in conventional sources) factors. The proposed ICM finds reductions of about 43.9754% in daily energy losses, 26.9957% in energy purchasing and operating costs, and 27.3771% in CO2 emissions when compared to the benchmark case in the DC version of the IEEE 33-bus grid. All numerical validations were carried out in the MATLAB programming environment using the SEDUMI and SDPT3 tools for convex programming and our own scripts for metaheuristic methods.

Applying the Crow Search Algorithm for the Optimal Integration of PV Generation Units in DC Networks

This paper presents an efficient master–slave methodology to solve the problem of integrating photovoltaic (PV) generators into DC grids for a planning period of 20 years. The problem is mathematically formulated as Mixed-Integer Nonlinear Programming (MINLP) with the objective of minimizing the total annual operating cost. The main stage, consisting of a discrete-continuous version of the Crow search algorithm (DCCSA), is in charge of determining the installation positions of the PV generators and their corresponding power ratings. On the other hand, at the slave level, the successive approximation power flow method is used to determine the objective function value. Numerical results on 33- and 69-bus test systems demonstrate the applicability, efficiency and robustness of the developed approach with respect to different methodologies previously discussed in the scientific literature, such as the vortex search algorithm, the generalized normal distribution optimizer and the particle swarm optimization algorithm. Numerical tests are performed in the MATLAB programming environment using proprietary scripts.

Leader Harris Hawks algorithm based optimal controller for automatic generation control in PV-hydro-wind integrated power network

• A novel attempt has been put together to design a Model Predictive Control based on the leader Harris Hawks Optimization approach for a PV integrated LFC system. Further, a combined AVR-LFC loop has been designed for a PV, wind and hydro integrated hybrid power system. • The validation of the controller's better performance has been obtained through a thorough comparative analysis where the transient response attributes from the proposed controller have been compared with the exiting controllers available in the literature. Also, the convergence mobility of the proposed LHHO optimizer has been compared with HHO, WOA, and PSO algorithms. • To further strengthen the frequency stability of the network, the authors have proposed and implemented the virtual inertia and capacitive energy storage devices in association with the LHHO-MPC controller. • The robust nature of the proposed controller algorithm has been assessed under different test case scenarios where the step load perturbation (SLP) values have been varied randomly, and the stochastic load model has been considered. Also, various nonlinearities and reduced inertia cases have been presented to show the efficiency of the proposed LHHO-MPC controller working in coordination with CES and VI units. The work in this manuscript aims at designing a novel control strategy based on the Model Predictive Controller aided with Leader Harris Hawks Optimization (MPC-LHHO) algorithm for the regulation of frequency and voltage in renewable penetrated power systems. Herein, two cases have been considered where the first case discusses the frequency stabilization in a thermal power system linked with the solar photovoltaic (PV) plant. Subsequently, the second case features the simultaneous voltage and frequency regulation in a hybrid interconnected power system including thermal, wind, diesel, photovoltaic, and hydro generation units. Additionally, for both cases, the proposed MPC-LHHO algorithm has been evaluated in coordination with the capacitive energy storage and virtual inertia units. Furthermore, the stability analysis of the controlled PV-thermal system has been conducted using eigenvalues and pole-zero plots. In both cases, the effectiveness of the proposed algorithm has been tested after considering diverse scenarios including random loading, nonlinearities, time delays, reduced inertia and stochastic variations in load. For all these cases the presented algorithm has given a stable response and has ensured proper regulation of voltage and frequency in the renewable integrated power system network.

A multi-objective hunter-prey optimization for optimal integration of capacitor banks and photovoltaic distribution generation units in radial distribution systems

A multi-objective hunter-prey optimization for optimal integration of capacitor banks and photovoltaic distribution generation units in radial distribution systems

Development of Nottingham Arc Model for DC Series Arc Modeling in Photovoltaic Panels

DC series arc faults are known as a great threat to photovoltaic (PV) systems. Due to component aging and the high level of dc voltage, the occurrence of dc series arc fault is a serious concern in PV generation units. This article deals with driving effective dc series arc models from the original Nottingham arc model using the recorded practical data in a PV system. The original Nottingham arc model has three constant parameters and suffers significant inaccuracy for modeling the series arc faults. To avoid the inaccuracy in the modeling, at first, a Nottingham model is developed that is relevant to voltage and current of the fault with three parameters including one constant model order for exponents of the current and two time-series coefficients. Also, to further improvement in the model's accuracy, a two-fold Nottingham model is proposed that is relevant to voltage and current of the fault with five parameters including two constant model orders for exponents of the current and three time-series coefficients. Finally, a full-time-variant Nottingham model is proposed that mathematically contains three time-series parameters including one time-variant model order of the current exponent. In the full-time-variant Nottingham model, the optimal parameters corresponding to each sliding window of data are utilized. In all the proposed models, the arc coefficients are estimated using a new formulation using the least-squares error technique. Verifying the effectiveness of the proposed models through actual data, the accuracy of the proposed models is compared with the original Nottingham model.

Distributed Secondary Control for Battery Management in a DC Microgrid

This research presents the design and simulation of a distributed secondary control based on a consensus algorithm for the efficient management of an isolated DC microgrid (MG-DC) that secures the distribution of active power according to the capacities of each storage unit, reducing duty cycles and extending its life cycle. The balance of powers is fulfilled through a photovoltaic (PV) generation unit and an energy storage system (ESS) based on batteries. The PV Boost converter has a maximum power point tracking (MPPT) controller based on the perturb and observe (P & O) method. In contrast, a Buck–Boost converter is integrated into each battery, which is bidirectionally controlled through a local controller and a primary droop control that balances the required power at the loads. It produces a voltage deviation on the DC bus. To compensate for this deviation, a distributed secondary control strategy based on consensus is proposed to restore the voltage while managing the power sharing according to the capacities of each battery. It allows for the improvement of its life cycle, which is shown in the state of charge (SOC) index, thus extending its life cycle. The controllers are evaluated for solar re-source changes, load changes, and different storage capacities.

Optimal Placement of Photovoltaic Distributed Generation Units in Radial Unbalanced Distribution Systems Using MATLAB and OpenDSS-Based Cosimulation and a Proposed Metaheuristic Algorithm

In this paper, an improved slime mould algorithm (ISMA) is proposed for finding the optimal location and sizing of photovoltaic distributed generation units (PVDGUs) in unbalanced distribution systems (UDSs). The proposed method is developed by changing the control variable update mechanism of the original slime mould algorithm (SMA). Total power losses on distribution lines and voltage deviation index of all buses are minimized under the consideration of various constraints. The location and sizing of PVDGUs found by ISMA are added into the cosimulation between MATLAB and OpenDSS for reaching other remaining parameters of UDSs. In addition, sunflower optimization (SFO), social-ski drive (SSD), cuckoo search algorithm (CSA), salp swarm algorithm (SSA), bonobo optimizer (BO), and SMA are also run for finding PVDGUs placement solution on the unbalanced three-phase IEEE 123-bus test feeder. As a result, the proposed ISMA can reduce the power loss up to 78.88% and cut the voltage deviation up to 1.4779 pu while that of others is from 69.10% to 78.87% and from 1.5759 to 1.4996 pu. Thus, ISMA should be used to place PVDGUs in UDSs meanwhile the cosimulation between MATLAB and OpenDSS should be applied as a power flow calculation tool for UDSs.

MPC‐based DC microgrid integrated series active power filter for voltage quality improvement in distribution system

SummaryThis paper presents a DC microgrid integrated series active power filter (Se‐APF) to improve the voltage profile of the utility distribution system under sag, swell, and harmonics scenario. The DC microgrid encompasses a solar photovoltaic generation unit and a composite energy storage unit (CESU). A lithium‐ion battery and supercapacitor as a CESU are envisioned in this work. Compared to conventional Se‐APF supported by the capacitor/battery storage, the integrated DC microgrid prolonged the operational features of the Se‐APF for voltage quality improvement and feeding the localized dc loads or charging the CESU occasionally from the PV generation system. Further, a power management algorithm is implemented to coordinate the operations of the sources in the DC microgrid to accomplish the above‐mentioned features. The Se‐APF and associated power converters in the DC microgrid are regulated by a nonlinear controller based on optimal switching vector‐model predictive control algorithm. The proposed control method can be used to help distribution networks with voltage fluctuations by offering much faster and more precise voltage regulation. The feasibility and effectiveness of the proposed control strategy are verified in MATLAB/Simulink platform and then validated in a real‐time simulation platform using Opal‐RT Lab real‐time simulator OP4510.