Operation Of Microgrid System Research Articles

Cost-effective sizing method of Vehicle-to-Building chargers and energy storage systems during the planning stage of smart micro-grid

Demand side management (DSM) is a great challenge for new power systems based on renewable energy. Vehicle-to-Building (V2B) and Energy Storage Systems (ESS) are two important and effective tools. However, existing studies lack the sizing method of bidirectional chargers and ESSs. This study has proposed a cost-effective sizing method of V2B chargers and ESSs during the planning stage. By developing a linear model that clusters electric vehicle users based on mobility patterns and employing mixed integer linear programming for day-ahead control strategies, the method minimizes the dynamic payback period of initial investments. Tested in an office park featuring photovoltaic generation, the optimal configuration of 50% V2B chargers and 1 ESS significantly reduces cumulative peak-hour load and peak power by 51.3% and 42.4%, respectively. The price and rated power of EV chargers on the optimal sizing result are also investigated, providing guidance for the design and operation of micro-grid systems. Furthermore, the study suggests further exploration into actual data acquisition, real-time control strategy enhancement, and comprehensive user behavior for broader application.

Energy Management Strategy for DC Micro-Grid System with the Important Penetration of Renewable Energy

This paper presents an energy management strategy using a Stateflow controller related to DC microgrids with the important penetration of renewable energy. The increase in world electricity demand is one of the principal drivers of the exhaustion of fossil fuels and increased greenhouse gas emissions. To solve these problems, several countries have adopted actions for widespread renewable energy deployment, which includes wind energy, solar power, biomass power, tidal, and hydropower. These sources are considered as significant in delivering clean energy and reducing greenhouse gas emissions for sustainable improvement. As these sources play an increasingly vital role in the global energy landscape, the efficient management of these intermittent sources is essential for grid stability and sustainability. This paper aimed to develop an energy management strategy for DC microgrids to supply power to a DC microgrid system. The main objective of this paper was to implement an energy management system to ensure the proper operation of DC microgrid systems utilizing Simulink blocks available in MATLAB/Simulink 2020b software. The simulation results demonstrated that the developed energy management algorithm was unconditionally reliable, ensuring the proper operation of the microgrid systems. Additionally, the results demonstrated that the energy management strategy exhibited robust performance across different scenarios, effectively balancing energy generation and consumption while ensuring the reliable operation of the microgrid system. Moreover, the developed algorithm model presents another advantage, as it enables users to access and to change any control parameters within the DC microgrid. By comparing these results with the literature, the developed energy management algorithm provides safety and the automatic control of the microgrid.

Stochastic scheduling of energy sharing in reconfigurable multi-microgrid systems in the presence of vehicle-to-grid technology

On the one hand, the inherent intermittency in demands and renewable energy sources (RES) frequently bring challenges such as overload or surplus generation within microgrids. On the other hand, electric vehicle aggregations (EVAs) have garnered substantial attention as a pivotal strategy to address climate change and serve as a sustainable substitute for petroleum-based vehicles. However, the uncoordinated deployment of EVAs within microgrids, especially in the face of the intermittent nature of RES, poses a potential threat to the secure operation of microgrid systems. To tackle the mentioned issues, this research concentrates on interconnecting a group of scattered microgrids to create a multi-microgrid system. In more detail, by developing an energy management strategy to reconfigure the interconnections among microgrids, the efficient exchange of power among these multi-microgrid systems is facilitated, addressing the variability in load demands amidst the stochastic generation patterns of RESs. Besides, grid-to-vehicle (G2V) and vehicle-to-grid (V2G) concepts of EVAs are synchronized within the reconfigurable microgrid structure to enhance the flexibility of the model. To evaluate the model under realistic situations, a scenario-based method is also employed to reflect the effects of uncertainties on the model. The proposed approach, characterized by its mathematical convexity, allows for employing efficient solvers like CPLEX, ensuring the attainment of a feasible global solution within a finite timeframe. The effectiveness of the proposed method is demonstrated through its implementation on a modified 33-bus test system operated as multi-microgrid system. The results show the effectiveness of the proposed approach as a promising tool for optimizing the operation of reconfigurable multi-microgrid systems in the presence of EVAs, leading to operational cost reduction and voltage profile enhancement.

Simultaneous Damping and Frequency Control in AC Microgrid Using Coordinated Control Considering Time Delay and Noise

The incorporation of converter-based generating sources in utility-scale microgrids causes frequency instability and low-frequency oscillations (LFOs), which is also a reason for degeneration in system stability. Damping frequency control is an essential part of alternating current (AC) microgrid system operation and control. Sudden changes in load, variations in renewable power outputs due to changes in solar insolation or wind speed, and so on factors cause the system frequency to deviate from the nominal value. Therefore, the role of a frequency controller is to maintain the dynamic stability in an AC microgrid by retaining the system frequency at the nominal value. Again, AC microgrids with high renewable power penetration face even more difficulty in maintaining frequency stability because of their poor inertial response. The research presented here proposes a novel approach for grid-connected AC microgrid oscillation damping and frequency control that simultaneously takes into consideration time delay and noise. To improve the frequency response and dampen LFOs by providing the voltage and frequency within the specified range, a coordinated technique-based control approach is adopted. In the developed hybrid control, the frequency controller relies on active power modulation, while the power oscillation damping controller is dependent on reactive power modulation. To improve stability and reduce communication consequences such as noise and signal latency (time delay), the developed power oscillation damping controller and frequency controller are coordinated along with the robust linear quadratic Gaussian controller. The comparative investigation of the effectiveness of the coordinated control technique is employed in the software of MATLAB/Simulink for grid-connected AC microgrid. The outcome of the simulation illustrates the superior effectiveness of the suggested controller over the traditional droop controller for huge power flows under disturbance with various operating conditions, under/overfrequency events, time delay, and noise. This grid encouragement capability for AC microgrids is anticipated to lead to novel possibilities for generating revenue.

Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids

This paper introduces a novel model for optimizing microgrid systems by integrating multi-purpose renewable energy (MEM) and cutting-edge technologies, including electric vehicles (EVs). The proposed MEM demand response programs encompass various energy sources such as wind energy, multi-carrier energy storage technologies, boilers, combined heat and cooling units, EVs, P2G (power-to-gas), and demand response capabilities. The primary objective is to minimize the total operational cost of the microgrid system. A distinctive aspect of the proposed method lies in considering the prices of all energy carriers as unknown variables. Market prices are integrated into the modeling process, incorporating scenarios with reasonable probabilities and taking into account demand-side management programs. Moreover, the model allows for customizable programming of different parts of the multi-energy microgrids, with a focus on maintaining convexity principles for the operating area of each CHP (combined heat and power) unit. To tackle the unique complexity of this optimization problem, a developed blue whale optimization algorithm is proposed. This algorithm builds upon the main whale optimization algorithm (WOA), a promising population-based optimization approach. However, the effectiveness of WOA heavily relies on the careful setting of exploration and exploitation parameters, which may lead to being trapped in local optima. To address this challenge, the paper introduces a self-adaptation modification based on wavelet theory to enhance the WOA's performance. The proposed model and optimization method are thoroughly evaluated through simulation studies for different scenarios, showcasing their efficacy in achieving cost-efficient and sustainable microgrid operation. The combination of MEM and emerging technologies like EVs, along with the improved optimization algorithm, marks a significant contribution to advancing the planning and operation of microgrid systems towards low-cost, reliable, and environmentally-friendly energy solutions.

Optimal operation of microgrid systems considering user energy storage behavior

Optimal operation of microgrid systems considering user energy storage behavior

A modified differential evolution algorithm for frequency management of interconnected hybrid renewable system

In recent decades, the insufficiency in power production has led to the incorporation of renewable energy sources into microgrid systems. However, ambiguity in the generation of renewable sources and load variation impacts on the system frequency influencing the stable operation of microgrid system. To augment the stable operation of the system an intelligent controller is required for continuous electric power. The implementation of a modified differential evolution (MDE)-based cascaded PIDFN controller using ITAE is proposed in this work. In order to demonstrate the robustness and efficacy of the proposed MDE optimization technique is compared with DE, TLBO, IWO and PSO.MDE-based cascaded PIDFN controller is implemented for governing the frequency in two-area interconnected microgrid system. The system is substantiated over load perturbations, system uncertainties, communication delay and real time data of solar irradiance and wind speed and action of UPFC in MATLAB<sup>®</sup>/SIMULINK environment.

Adaptive sliding mode control based on fuzzy logic for hybrid smart microgrid energy management system

ABSTRACT In this paper, an adaptive sliding mode controller (ASMC) based on fuzzy logic is applied to control hybrid smart microgrid power systems under uncertainty. The chattering effects can occur if not adjusted properly due to the sliding mode amplitude control law. The approximation of the ASMC law function is performed by Fuzzy logic, and chattering effects are reduced through a low pass filter. The Lyapunov theory proves the stability of the control system and is also applied to stabilise the system’s control mechanism. The hybrid micro-grid system operation, which consists of distribution generation, loads, PWM IGBT inverter and low pass filter, is simulated using MATLAB 2017b. The reference speed is compared with the hybrid microgrid real speed, which was 1.0 p.u. and gradually controlled through ASMC-fuzzy until the speed desired is achieved for power supply stability. A comparison of results between the simulated ASMC-Fuzzy and PI controller indicated a drastic reduction of THD, which satisfies the required ‘below 5% THD level according to IEEE 1547 standard’ required for operation.

Efficient energy management framework for enhancing the techno-economic-environmental performance of grid-connected microgrids under uncertain conditions

Due to the limited availability of fossil fuels and environmental concerns over greenhouse gas emissions, micro-grid systems (MGSs) have become crucial for integrating a high penetration of renewables. Nonetheless, the complexity of the operation of MGS increases in the actual operating process due to the irregular nature of renewables, electrical loads (load demands), and uncertain market prices. The core aim of this paper is to suggest a bi-objective optimization model for managing energy efficiently in a grid-connected MG while considering the unsteadiness of electrical load demands, market prices, and renewable output powers. Also, the suggested model tries to improve the MG’s scheduling while considering the wind turbine, photovoltaic, loads, and market pricing uncertainty. MG operators can develop strategies that balance cost savings with risk management and ensure long-term sustainability by considering the uncertainty of the different generation/demand resources and markets. The suggested optimization model includes two distinct objective functions – minimizing the 24-hour horizon’s total projected cost of the grid-connected MG and reducing MG emissions during the considered period. Further, a simultaneous reduction of the overall operating cost and the pollutant emission MG over 24 h is presented. The general algebraic modeling system (GAMS) has been used to solve the presented multi-objective problem. The findings clearly demonstrate the efficacy of the suggested energy management (EM) methodology for improving the techno-economic-environmental performance of grid-connected MGs under uncertain situations.

Performance improvement of Hybrid System based DFIG-Wind/PV/Batteries connected to DC and AC grid by applying Intelligent Control

One of the main causes of CO2 emissions is the production of electrical energy. Therefore, many researchers goal’s is to develop renewable power systems. This paper proposes a new intelligent control development of hybrid PV–Wind-Batteries. Neuro-Fuzzy Direct Power Control (NF-DPC) is invested in order to enhance system performance and generated currents quality. An improved MPPT algorithm based on Fuzzy Controller (FC) is invested for PV power optimization. In addition, a new Modified Fuzzy Direct Power Control (MF-DPC) is developed and applied to the grid side converter to control the active and reactive power by monitoring the involved active power flow and providing a unit power factor by imposing a zero reactive power. An Energy Management Algorithm (EMA) is developed to maintain energy balance, meet the DC load demand, mitigate fluctuations caused by weather condition variations (wind speed and solar irradiance), and minimize battery overcharge and deep discharge. To test the proposed hybrid microgrid system operation, the different parts of the system are modeled, the wind turbine associated to the DFIG, the photovoltaic system as well as the battery storage system. Furthermore, the associated power converters with their control strategies are also presented. Global system simulation, using MATLAB/Simulink, is carried out to validate the effectiveness of both EMA and control techniques. The obtained results show significant reduction of active/reactive power ripples and THD by about 64%, 72%, and 50%, respectively. The EMA ability to manage the energy flow, produced and requested by the load. The THD rate of all injected currents is less than 4%, meaning that the proposed controls will increase the used equipments’ life span, minimize their maintenance and then reduce the hybrid power system cost.

Optimal scheduling of distributed generators for efficient microgrid system operation for different electricity market pricing and grid participation

Optimal scheduling of distributed generators for efficient microgrid system operation for different electricity market pricing and grid participation

Modeling and optimization of micro grid supply and demand system for renewable thermal energy

The economic benefit of microgrid is the key factor to realize environment-friendly power supply. With the wide application of new energy power generation, scholars have carried out a lot of research on microgrid models, but the existing models cannot reflect the important role of renewable energy in microgrid operation. Therefore, the author proposes a power generation and storage model based on renewable heat energy and microgrid. In order to minimize the cost and meet the requirements of CO2 emission limitation, the cost of power generation, storage and production in the microgrid is analyzed in detail. At the same time, the optimization method of renewable energy microgrid system operation is also studied. First of all, by analyzing the characteristics of the microgrid system, a mathematical model and simulation calculation system are established to help understand the microgrid system. Secondly, the author has carried out a separate study on the energy storage in the microgrid system, and listed the benefits and costs of the energy storage process. In the grid-connected state, the performance of each unit in the microgrid system is better, the system is more stable and safe, and the operating cost is reduced again..

Role of optimal sized dual energy storage based microgrid for ancillary service support to utility grid

Electrical energy storage system (ESS) has been used worldwide as ancillary support to the grid, aiding in frequency regulation and grid stability. Intermittent renewable energy sources (RESs) can be effectively integrated into the grid by maintaining power balance using different types of energy storage. However, the power requirement during small frequent, and large persistent power variations in the load demand can be more effectively controllable with the combination of the different energy storage according to their operational characteristics. This paper evaluates the suitability of a dual energy storage system (DESS) integrated in a grid connected microgrid (MG) system for providing ancillary services to the utility grid. The DESS is a combination of battery bank storage (BBS) and pump hydro storage (PHS). The BBS is very fast responding and it can counter the immediate variations in the power whereas, the PHS system can counter the large variations of the power as needed in the MG system. Four different operating cases have been studied to obtain the optimal size of the DESS for optimizing not only the operational cost but also for the best combination size of the storage systems to counter the variability in power generations and the load demand in the power system. Day-ahead electricity market scheduling has been performed on the MG to control the net power demand variations on the distribution feeder of the utility grid. The simulation results show that the higher capacity of the DESS does not always provide the optimal operational cost of the MG system during the grid-connected mode. Therefore, optimal sizing of the storage system also plays an important role in providing the optimal solution. It has been found that the optimal size of the individual energy storage and the DESS is different for microgrids supporting or not supporting the grid. The study enables the system operator (SO) to choose the best combination of storage systems for the supportive and non-supportive operation of microgrid system.

Optimal Microgrid System Operating Strategy Considering Variable Wind Power Outputs and the Cooperative Game among Subsystem Operators

In recent years, the microgrid system (MGS) has become an important method for the consumption of renewable energy sources (RES). In the actual operation process, the uncertainties of RES add to the complexity of the MGS operation. Furthermore, the MGS is often operated by multiple subsystem operators. The benefit distribution among subsystem operators is an important factor affecting the overall stable operation of the MGS. In order to resist the interference of the above factors, a two-stage optimization method is proposed in this paper, which includes a bi-level robust optimization (BRO) model in the overall scheduling stage of the MGS and a nucleolus-based cooperative game (NCG) model in the internal cost allocation stage among the subsystem operators. The simulations demonstrated the following outcomes: (1) the P2G device can reduce the operating cost of the MGS by converting electricity into natural gas when the electricity price is low; (2) the two-stage optimization method can ensure the stable operation of the MGS by resisting the disturbance of uncertain wind power outputs in the overall scheduling stage and realizing a reasonable cost allocation among the subsystem operators in the internal cost allocation stage.

Low-Carbon Robust Predictive Dispatch Strategy of Photovoltaic Microgrids in Industrial Parks

With the flexible integration of local renewable energy with the smart distribution network system, the problems of high operating costs and power shortage can be effectively solved. However, taking the industrial park microgrid with high penetration photovoltaic as an example, due to the uncertainties and fluctuations arising from the meteorological conditions and the load demands, the safe and reliable operation of the microgrid system has been threatened significantly. Operators often need to pay additional unnecessary costs to maintain stable operations of the microgrid. Therefore, in this study, a dispatch strategy based on robust model predictive control considering low-carbon cost is designed to reduce the adverse effects of uncertainties. First, a low-carbon energy management scheme is formulated based on short-term source and load forecast information in which a two-stage robust optimization solution method is used to generate the optimal dispatch scheme under the worst scenario. Then, an intraday real-time strategy with a closed-loop feedback mechanism is formed based on the model predictive control. Finally, the feasibility of the proposed strategy is simulated and analyzed based on the measured data of the photovoltaic microgrid in the industrial park. The results show that compared with the general intraday scheduling strategy and the day-ahead robust strategy, the proposed strategy can effectively get low-carbon scheduling plans considering the uncertainty of source and load while efficiently balancing the robustness and economy of the grid-connected industrial park photovoltaic microgrid system operation.

Robust soft computing control algorithm for sustainable enhancement of renewable energy sources based microgrid: A hybrid Garra rufa fish optimization – Isolation forest approach

A novel hybrid Garra rufa Fish optimization (GRFO) – isolation Forest (iForest) soft computing approach is proposed in this paper for optimizing the controller parameters in an isolated test microgrid. The test microgrid comprises of two PV units and one wind generator synchronized via Voltage Source Converters (VSC) in the ac side. Each VSC is regulated based on power generation in individual micro sources. The operation of VSC is the key factor maintaining the stability of the micro grid. Each VSC is controlled by outer power loop and inner current regulation loop employing PI controllers. The performance of the VSC is enhanced using the proposed hybrid GRFO – iForest algorithm for tuning the PI controller gains. The performance of the proposed approach is then compared with conventional tuning PI controller and optimization techniques like Ant Lion Optimization (ALO) and Modified Ant Lion optimization based Artificial Neural Network (MALANN). The gain parameter of the proposed controller is optimally tuned and the controller provides reliable sustainable microgrid system operation. The proposed approach is simulated in MATLAB/Simulink and validated using OP-4500 Real Time Simulator environment. Based on the results, the performance of the GRFO-iForest approach is superior during steady-state and transient operation. It enhances the sustainability of the microgrid by restoring normal operating conditions after a small physical disturbance and the microgrid remains stable with the optimized regulator. The stability of the overall system is proved with the mathematical model.

Optimized Integration of Hybrid Renewable Sources with Long‐Life Battery Energy Storage in Microgrids for Peak Power Shaving and Demand Side Management under Different Tariff Scenario

Herein, a novel cost‐effective demand side management and peak power shaving are demonstrated by optimized scheduling of renewable energy source integrated grid‐connected hybrid microgrid and vanadium redox flow battery (VRFB) storage. To promote the waste to energy for the rural and urban communities, the biogas energy source is integrated along with the combined solar photovoltaic (PV) and wind energy sources. As a long life and scalable battery storage solution, VRFB storage is adopted for peak shaving and microgrid performance reliability. Power generation from the renewable sources, VRFB charge–discharge, and grid power usage are scheduled considering two practical electricity tariff profiles, thus making the overall microgrid system operation cost‐effective and efficient. The optimized cost of energy management is determined considering the operation and maintenance cost of energy source and battery storage, grid tariff profile, and power import and export. The performance of the overall control scheme is experimentally validated by a grid‐connected hybrid microgrid consisting of 10 kWp solar PV, 1 kW wind turbine, 15 kVA biogas engine generator, and 1 kW 6 h VRFB storage. The proposed energy management scheme is scalable and a generalized one that claims to be suitable for large‐scale renewable energy integrated power systems as well.

A cost-efficient sizing of grid-tied hybrid renewable energy system with different types of demands

An optimum energy management strategy is proposed for a hybrid microgrid system's operation with various types of demands. To endorse minimizing system's project lifetime costs which include capital, replacing, operating, and maintaining cost, exchanged power cost with the grid, and cost of purchased natural gas, an optimization for minimizing operational cost of hybrid microgrid consisting of photovoltaic arrays, wind turbines, battery energy storage, fuel cell, and reformer is presented. To guarantee the benefits of the developed methodology, two different hydrogen production methods are considered. Utilizing battery energy storage is inevitable for mitigating intermittency of renewable energy resources in microgrid system with high level of integration of these components. To assess the optimum energy management strategy of the microgrid system, each source's model is developed to interrelate the energy altering between electricity and hydrogen. In order to ascertain the optimum capacity of each DER unit and the energy management within the system, a meta-heuristic Particle Swarm Optimization algorithm (PSO) is used. It is concluded from the results that by utilizing microgrid waste, there is about 8.7%, and by utilizing municipal waste, there is about 27% reduction in the fuel consumption in the employed reformer. It is observed that the cost of energy is reduced by 19% in the case of municipal waste utilization.

A Comprehensive Review on Control Techniques for Power Management of Isolated DC Microgrid System Operation

The present research investigations in power management fraternity are focused towards the realization of smart microgrid (MG) technologies. Due to intrinsic advantages of Direct Current (DC) system in terms of compatibility with power generation sources, modern loads and storage devices, DC MG has becoming popular over Alternate Current (AC) system. A secondary voltage and current control schemes of DC MG system are mainly based on the distributed consensus control of Multi-agent system (MAS) to balance generation and the demand. The basic concern of the cooperative control of MAS is consensus, which is to design a suitable control law such that the output of all agents can achieve synchronization. The distributed consensus algorithm requires much less computational power and information exchange in between the neighbor’s agent. Meanwhile the other controllers such as model predictive control (MPC) requires accurate dynamic models with high computational cost and it suffers from lack of flexibility. The hierarchical consensus control technique is classified into three levels according to their features, namely primary, secondary, and tertiary. MAS is a popular distributed platform to efficiently manage the secondary control level for synchronization and communication among the power converters in autonomous MGs. In this article, various primary control techniques for local voltage control, voltage restoration in the secondary control level and tertiary control for energy management techniques are discussed. With this, the key emphasis to reduce the voltage deviation and disturbances in a heterogeneous DC MG network solutions are discussed. Furthermore, to analyze the system response and the charging and discharging characteristics of the battery unit, the developed second order heterogeneous consensus controller is compared with the traditional homogeneous consensus control and droop control methods. Finally a detailed discussion on simulation case study using heterogeneous consensus control method over the traditional methods are provided using MATLAB/Simulink platform.

Decentralized Control Strategy for an AC Co-Phase Traction Microgrid

High speed and heavy loads have become more prevalent in the traction power supply system recently. To ensure system operating stability, better power quality, and sufficient power capacity, improvements are needed over the conventional traction system. Inspired by the concept of a microgrid (MG), an AC co-phase traction MG system was proposed. Substations were connected to the traction grid as distributed generators operate in islanded mode. Droop control was adopted as the primary control to stabilize the system’s operating frequency and voltage. Considering the operating features of the substation and locomotive load, a de-centralized secondary control strategy was proposed for AC co-phase traction MG system operation with enhanced resiliency. The proposed control strategy could increase system stability and prevent circulation currents between substations. Moreover, the proposed de-centralized coordination between substations does not rely on communication, which promotes the system’s “plug-and-play” functionality. Stability analysis was undertaken and the proposed controller was proved to be exponentially stable. The dynamic response of the proposed controller was validated using comprehensive case studies in MATLAB/Simulink.