Microturbine Research Articles

A Rule-Based Modular Energy Management System for AC/DC Hybrid Microgrids

Microgrids are considered a practical solution to revolutionize power systems due to their ability to island and sustain the penetration of renewables. Most existing studies have focused on the optimal management of microgrids with a fixed configuration. This restricts the application of developed algorithms to other configurations without major modifications. The objective of this study is to design a rule-based modular energy management system (EMS) for microgrids that can dynamically adapt to the microgrid configuration. To realize this framework, first, each component is modeled as a separate entity with its constraints and bounds for variables. A wide range of components such as battery energy storage systems (BESSs), electric vehicles (EVs), solar photovoltaic (PV), microturbines (MTs), and different priority loads are modeled as modules. Then, a rule-based system is designed to analyze the impact of the presence/absence of one module on the others and update constraints. For example, load shedding and PV curtailment can be permitted if the grid module is not included. The constraints of microgrid components present in any given configuration are communicated to the EMS, and it optimizes the operation of the available components. The configuration of microgrids could be as simple as flexible loads operating in grid-connected mode or as complex as a hybrid microgrid with AC and DC buses with a diverse range of equipment on each side. To facilitate the realization of diverse configurations, a hybrid AC/DC microgrid is considered where the utility grid and interlinking converter (ILC) are also modeled as separate modules. The proposed method is used to test performance in both grid-connected and islanded modes by simulating four typical configurations in each case. Simulation results have shown that the proposed rule-based modular method can optimize the operation of a wide range of microgrid configurations.

Modeling and Analysis of Micro-Turbine Integrating PV Generation Systems

Abstract. As a matter of fact, in the emerging field of distributed generation, micro turbines (MT) are seen as having significant potential in power generation especially in recent years. In reality, it can provide flexibility as well as robustness for PV generation system with great performances. Therefore, with this in mind, this paper proposes a hybrid power system integrating MT into PV generation system. To be specific, this study investigates as well as discusses the operating principles of MT. On this basis, a coordinated PQ control strategy for the hybrid PVMT power generation system is proposed. At the same time, the inertia enhancement brought by the MT is analyzed validating the potential and feasibility of the proposed strategy. According to the analysis, the current limitations as well as future improvements are discussed in the meantime. Overall, these results shed light on guiding further exploration of MT integrating PV generation systems and pave a novel path for designs.

Optimizing Real-Time Scheduling for Post Islanding Energy Management Using African Vulture Optimization Algorithm on Hybrid Microgrids Environment

Microgrids (MG) are small-scale energy systems that use distributed energy storage and sources. Hybrid microgrids are transforming energy management by incorporating various energy resources like wind, solar, and battery storage. Effective scheduling of this resource is vital to minimize the costs and maximize energy autonomy. Advanced scheduling algorithm optimizes the operation of hybrid microgrids, which dynamically adjusts the energy consumption and generation to satisfy the demand while ensuring power balancing. This scheduling strategy has been instrumental in improving the sustainability and resilience of MGS, which paves the way for an environmentally friendly and more reliable energy future. They can operate on islanded or grid-connected modes. The optimization of hybrid MG scheduling is paramount in the field of post-island management to ensure effective energy sustainability and distribution. Using metaheuristic approaches like simulated annealing or genetic algorithms allows the finetuning of scheduling parameters to increase energy utilization while reducing environmental impact and costs. Therefore, the study presents a Real-Time Scheduling for Post Islanding Energy Management using African Vulture Optimization Algorithm (RTSPIEM-AVOA) in Hybrid microgrid environment. The RTSPIEM-AVOA approach is utilized to improve its functioning by determining the most efficient scheduling of the installed generation unit. The AVOA can handle complex optimization issues while avoiding local optima solutions because of the balance among the exploitation and exploration stages. A microturbine (MT) system, battery storage, a fuel cell (FC), photovoltaic (PV), and wind turbine (WT) make up the suggested MG system. This work looks at three scenarios: PV and WT operating at normal generation, PV and WT operating at their maximum power, and WT operating at its rated power. Consider the two objective functions of minimizing pollutant emissions and reducing operational costs. According to the experimental results, the RTSPIEM-AVOA technique outperforms other models in microgrid scheduling by efficiently optimizing it to satisfy the community's changing needs while transitioning to a greener and more sustainable energy future.

Maiden application of mountaineering team-based optimization algorithm optimized 1PD-PI controller for load frequency control in islanded microgrid with renewable energy sources

Load Frequency Control (LFC) is essential for maintaining the stability of Islanded Microgrids (IMGs) that rely extensively on Renewable Energy Sources (RES). This paper introduces a groundbreaking 1PD-PI (one + Proportional + Derivative-Proportional + Integral) controller, marking its inaugural use in improving LFC performance within IMGs. The creation of this advanced controller stems from the amalgamation of 1PD and PI control strategies. Furthermore, the paper presents the Mountaineering Team Based Optimization (MTBO) algorithm, a novel meta-heuristic technique introduced for the first time to effectively tackle LFC challenges. This algorithm, inspired by principles of intellectual and environmental evolution and coordinated human behavior, is utilized to optimize the controller gains. The effectiveness of the proposed methodology is rigorously evaluated within a simulated IMG environment using MATLAB/SIMULINK. This simulated IMG incorporates diverse power generation sources, including Diesel Engine Generators (DEGs), Microturbines (MTs), Fuel Cells (FCs), Energy Storage Systems (ESSs), and RES units like Wind Turbine Generators (WTGs) and Photovoltaics (PVs). This paper employs the Integral Time Multiplied by the Squared Error (ITSE) and Integral of Time Multiplied By Absolute Error (ITAE) indicators as the primary performance metrics, conventionally used to mitigate frequency deviations. To achieve optimal controller parameter tuning, a weighted composite objective function is formulated. This function incorporates multiple components: modified objective functions related to both ITSE and ITAE, along with a term addressing overshoot and settling time. Each component is assigned an appropriate weighting factor to prioritize specific performance aspects. By employing distinct objective functions for different aspects of control performance, the derivation of optimized controller gains is facilitated. The efficacy and contribution of the proposed methodology are rigorously demonstrated within the context of RES-based IMGs, featuring a comparative analysis with well-known optimization algorithms, including Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). These algorithms are used to optimize the 1PD-PI controller, resulting in three control schemes: 1PD-PI/MTBO, 1PD-PI/WOA, and 1PD-PI/PSO. The effectiveness of these control schemes is evaluated under various loading conditions, incorporating parametric uncertainties and nonlinear factors of physical constraints. Three case studies, presented in eight scenarios (I-VIII), are utilized to comprehensively assess the efficiency, robustness, and sensitivity of the proposed approach. This analysis extends beyond the time domain, considering the stability evaluation of the proposed control scheme. Simulation results unequivocally establish the superior performance of the MTBO algorithm-optimized 1PD-PI controller compared to its counterparts. This superiority is evident in terms of minimized settling time, reduced peak undershoot and overshoot, and enhanced error-integrating performance characteristics within the system responses. Improvements are observed in both the high range and within the 80–90% range for criteria such as overshoot, undershoot, and the numerical values of the objective functions. This paper underscores the practicality and effectiveness of the 1PD-PI/MTBO control scheme, offering valuable insights into the management of frequency disturbances in RES-based IMGs.

Optimal Scheduling of Renewable Sources Based Micro Grid With PV and Battery Storage Using Crayfish Optimization Algorithm

ABSTRACTEnvironmental concerns and energy security are pressing issues of the 21st century, with a heavy reliance on fossil fuels causing significant environmental pollution and resource depletion. To mitigate these problems, it is crucial to explore and implement alternative clean energy sources. This manuscript proposes a novel crayfish optimization algorithm (COA) for optimal scheduling in a hybrid power system that incorporates various renewable energy sources, like battery energy storage systems (BESS), fuel cells (FC), wind turbines (WT), micro turbines (MT) and photovoltaic (PV) panels. The importance of the work lies in its ability to optimize the entire operating costs of a grid‐connected microgrid while improving the accuracy and efficiency of energy management. The COA method addresses economic dispatch problems and manages energy within the grid‐connected microgrid, accounting for high levels of uncertainty. The proposed approach, tested using MATLAB Simulink, achieved a cost value of 252, outperforming existing methods such as GTO, PSO, SSA, and ALO. This illustrates the potential of the proposed technique to provide more cost‐effective and efficient energy management solutions in hybrid power systems.

Gas emission optimization and optimal energy management of a microgrid operating by renewable and sustainable generation systems

The economic operation of electric energy generating systems is one of predominant problems in energy systems. Due to the need for better reliability, high energy quality, lower losses, lower cost and clean environment, the application of renewable and sustainable energy sources such as wind energy, solar energy, fuel cells, etc. in recent years has become more widespread mainly. In this work, one of most general of all swarm intelligence algorithms inspired by collective intelligent behavior of natural or artificial systems called the Particle Swarm Optimization (PSO) is applied to solve the Gas emission Optimization (GEO) and the Optimal Energy Management (OEM) problems of a micro-grid (MG) operating by Renewable and Sustainable Generation Systems (RSGS). Our main goal is to minimize nonlinear objective function of an electrical micro-grid taking into account of equality and inequality constraints. The PSO approach was examined and tested on a standard MG system composed of different types of RSGS such as wind turbines (WT), photovoltaic systems (PV), fuel cells (FC), micro turbine (MT), diesel generator (DEG) and loads with energy storage systems (ESS). The results are promising and show the effectiveness and robustness of proposed approach to solve the GEO and the OEM problems. The results of proposed method have been compared and validated with those known references published recently.

Optimizing virtual power plant allocation for enhanced resilience in smart microgrids under severe fault conditions using the hunting prey optimization algorithm

The rapid expansion of renewable energy sources (RESs), such as photovoltaic (PV), wind turbine (WT), micro turbine (MT), and fuel cell (FC), has presented both opportunities and challenges to power systems, particularly in terms of environmental sustainability and economic feasibility. While RESs offer benefits like reduced environmental pollution, decreased power losses, and enhanced power quality, their intermittent nature and uncertainties pose challenges, resulting in variable generation and instability in distribution systems. To address these challenges, the concept of aggregating distributed energy resources (DERs), battery energy storage system (BESS), electric vehicles (EVs), and controllable loads into a virtual power plant (VPP) managed by an energy management system (EMS) has emerged. This study aims to determine the optimal location and size of VPPs within radial distribution system (RDS) while considering network resilience to severe weather events. The problem is formulated as an optimization task with dual objectives: minimizing the operating cost of VPPs and reducing energy not supplied (ENS) during natural disasters such as floods and earthquakes. To address this optimization problem, a novel meta-heuristic optimization algorithm called the hunting prey optimization algorithm (HPOA) is applied. HPOA serves various functions within the RDS, specifically targeting the optimization of VPP location, VPP resource management, and the objective functions. A case study is conducted, incorporating RESs, BESS, and EVs, and compared against existing algorithms such as BESA and SMA using a standard IEEE 85-bus RDS. Simulation results conducted in MATLAB demonstrate that the proposed HPOA algorithm effectively determines the optimal size and location of VPPs, leading to improved economic, operational, and resilience indices in the network.

A Hybrid Wild Horses Optimization (WHO) and Dwarf Mongoose Optimization (DMO) method for optimum energy management in SG system

AbstractA hybrid technique is proposed for the energy management (EM) of smart grid (SG) systems. The proposed method integrates the Wild Horse Optimization (WHO) and dwarf mongoose optimization (DMO) methods; hence, it is named the WHO–DMO approach. The Micro‐grid (MG)‐tied system is a combination of battery, micro turbine (MT), photovoltaic (PV), and Wind Turbine (WT). The key aim of the proposed approach is to manage the resources and power of the SG model and reduce the cost of electricity. The objective of the system is to improve load demand. The WHO method is enhanced by the DMO method, which minimizes the objective of the system. Access to power demand, state of charge (SOC), and renewable energy sources (RESs) for storing elements are considered constraints of the system. The unit of renewable power systems relies on batteries as energy sources to stabilize and sustain stable and consistent output power throughout the operation. The proposed technique is done in MATLAB platform and its implementation is calculated using the existing methods. From the simulation, the proposed method has less cost and higher power than the existing methods.

A frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning

The construction of microgrid has fully promoted the large-scale access of distributed power and the rapid development of electric vehicles (EVs). And microgrid system faces operational issues such as strong random disturbances from distributed power sources and loads, as well as unexpected events during operation. These issues can lead to unstable microgrid frequency, excessive discharge of EVs, and increased control costs. To solve the issues, a frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning (EDRL) is proposed in this paper. First, consider the effect of the vehicle-to-grid (V2G) process on the minimum time required to fully charge an EV, and the impact of the output distribution of micro turbines (MTs) on the regulation cost, and based on the coupling relationship between generator terminal voltage regulation and system frequency control, a multimicrogrid comprehensive control model is constructed. Second, in order to deal with engineering tasks with deceptive rewards and sparse rewards such as integrated frequency control, evolutionary algorithms and deep reinforcement learning algorithms are combined to effectively assist the training process to jump out of local optimal solutions and approach optimal control strategies. Meanwhile, the algorithm is improved based on the novelty search and intelligent partition strategy, so that the strategy has better convergence characteristics and can reduce the cost of information transmission and computational complexity under the premise of ensuring the control effect. Furthermore, the state space, action space and reward function of the controller are defined. Finally, the simulation results show that the proposed controller has the ability of coordinated control, can effectively reduce the adjustment cost of MT unit and the unnecessary discharging of EVs while ensuring the frequency regulation requirements of each submicrogrid, which is far superior to PID control, fuzzy control, and traditional deep reinforcement learning control.

A reinforcement learning approach using Markov decision processes for battery energy storage control within a smart contract framework

With the increasing penetration of renewable energy sources (RESs), the necessity for employing smart methods to control and manage energy has become undeniable. This study introduces a real-time energy management system based on a multi-agent system supervised by a smart contract, employing a bottom-up approach for a grid-connected DC micro-grid equipped with solar photovoltaic panels (PV), wind turbines (WT), micro-turbines (MT), and battery energy storage (BES). Each unit is controlled and managed through a distributed decision-making structure. The BES agent is governed by an intelligent structure based on a reinforcement learning model. To facilitate interaction and coordination among agents, a tendering process is employed, where each agent, under its supervised control structure, presents its offer for the tendered item at each time period. The tendering organization allocates demand using the first-price sealed-bid algorithm among bidders to optimize energy cost in the Microgrid. The proposed approach offers a real-time intelligent system capable of ensuring fault tolerance, stability, and reliability in the Microgrid. The main achievement of this study is the development of a robust real-time energy management system that integrates various renewable energy sources and battery storage while ensuring efficient operation and resilience in the face of faults or disruptions.

Enhancing microgrid sustainability: Dynamic management of renewable resources and plug-in hybrid electric vehicles

This paper addresses the challenges inherent in Microgrids (MGs), Renewable Energy Sources (RES), and Plug-in Hybrid Electric Vehicles (PHEVs), presenting a pioneering approach for a sustainable energy future. The focal point is a novel Multi-Stage Dynamic Energy Management (EM) framework, strategically designed to optimize the use of non-dispatchable RES and PHEVs while reducing dependency on the Utility Grid (UG). The research integrates Solar Photovoltaic arrays (SPVs), Wind Turbines (WTs), Micro Turbines (MTs), Battery Energy Storage Systems (BESS), PHEVs, and loads, forming a comprehensive MG to enhance overall efficiency and resilience. The core innovation lies in the dynamic EM framework, minimizing UG dependency and adapting to stochastic RES generation and PHEV charging patterns. The study underscores the significance of demand response in enhancing energy efficiency and grid reliability. Demonstrating effective RES utilization, the research showcases the potential for a greener and more resilient energy infrastructure. Through advanced simulations, the proposed framework's performance is rigorously evaluated under various scenarios, including high RES penetration. The results not only affirm the framework's effectiveness but also highlight its potential to revolutionize MG management by reducing grid dependency and maximizing renewable resource utilization.

Analyzing flexibility options for microgrid management from economical operational and environmental perspectives

Power grids have undergone a massive transformation due to increasing number of renewable/non renewable distributed generators, storage technologies and electrical vehicles. This paper focuses on the flexible energy management of grid connected microgrid (MG) in order to analyze the effects of the flexibility options such as electric vehicle with demand response (EV-DR), battery storage system (BSS), hydrogen storage system (HSS) and dynamic line rating (DLR) in presence of renewable sources. In addition, microturbine (MT), fuel cell (FC), biomass (BIO), geothermal (GEO), and diesel engine (DIE) whose output is controllable are also considered as flexible sources. In this paper, optimum operation of grid-connected MG is modeled as a mixed-integer linear programming problem considering uncertainties of wind turbine (WT) and photovoltaic (PV) sources. A day-ahead forecasting of irradiance and wind speed are performed with Decision Tree Regression (DTR) and Long Short Term Memory (LSTM) algorithms for sensitive calculation of PV and WT output. The cost function merges operating costs including fuel, operation & maintains (O&M), depletion costs with emission cost of CO2, SO2, and NOx. Obtained numerical results show that total cost of the MG is reduced by 9.40% and emission cost is diminished by 5.59% with inclusion of all considered flexibility options. Further, load factor is improved from 0.7932 to 0.8236 with 100% flexibility condition for MG.

An efficient day-ahead cost-based generation scheduling of a multi-supply microgrid using a balancing composite motion optimization (BCMO) approach

The efficient management and optimal scheduling of generation resources in multi-supply microgrid (MG) are essential for ensuring cost-effective and reliable power supply. Renewable energy resources such as wind turbines (WTs), photovoltaic (PV) systems, micro turbine (MT) batteries, and fuel cells (FCs) are utilized. This paper presents Balancing Composite Motion Optimization (BCMO) for day-ahead cost-based generation scheduling in multi-supply MG. The BCMO approach combines the advantages of composite motion optimization techniques to minimize the overall generation cost. The proposed method considers demand forecasts, renewable energy availability, and real-time pricing to optimize generation schedule. The microgrid is modeled as mathematical optimization problem, considering multiple generation sources, including renewable energy, conventional generators, and energy storage systems, that are used to optimize the above factors. The objective function is formulated to minimize the total generation cost while satisfying demand requirements and operational constraints. By considering the cost of generation from different sources and availability of renewable energy, the proposed approach enables the microgrid to operate economically efficiently. The objective function is formulated to minimize the total generation cost while satisfying the demand requirements and operational constraints. By considering the cost of generation from different sources and the availability of renewable energy, the proposed approach enables the microgrid to operate in an economically efficient manner. The performance of the proposed system is executed on the MATLAB working platform and compared with various approaches. The best BCMO solution in cold sunny, warm sunny, cold cloudy days and warm cloudy are 290.8821, 358.7908, 261.324, and 2265.055., respectively.

Optimal scheduling of a dairy industry based on energy hub considering renewable energy and ice storage

The use of energy in industrial processes is a vital aspect of their proper functioning and productivity. However, with the increasing demand for energy and the need for more sustainable energy sources, managing energy efficiently has become crucial for industries. In this context, energy management systems (EMS) have emerged as a solution to integrate different energy sources and optimize their use. This work proposes a model for the optimal scheduling of a dairy industry based on an energy hub, considering the integration of renewable energy sources and Ice Storage. The proposed model aims to minimize daily operating costs by satisfying the electrical and thermal demands of the industrial process while considering the availability and costs of different energy sources. The model takes into account the use of microturbines (MT), photovoltaic (PV) sources, wind generation (WT), electrical storage systems (ESS), and energy from the electrical grid for the generation of electrical energy, and a boiler and an Ice Storage for thermal energy. A case study is conducted in this paper at the dairy farm belonging to UDLA in Nono, Ecuador, where the advantages of the proposed energy hub model for the dairy industry are analyzed. The suggested scheduling optimization model is solved using Pyomo library with a mixed integer linear programming method showing the benefits of including Ice Storage Systems and renewable energy.

Optimal economic-emission load dispatch in microgrid incorporating renewable energy sources by golden jackal optimization (GJO) and Mexican Axolotl optimization (MAO)

This article proposes the use of hybrid technique to achieve balanced and compromised solution among the generation cost and the emission of pollutants. Microgrids (MGs) are the restricted power energy system that transmitted, generated, and distributed. The renewable energy sources (RESs) are used in their fullest extent. Advantages of MG include reducing cost and transmission losses. Operated in different modes like wind turbine (WT), microturbine (MT) and fuel cell (FC). The proposed technique used to execute the golden jackal optimization (GJO) and Mexican Axolotl optimization (MAO) named as GJO–MAO technique. The objective of the technique is to solve dissimilar optimization issues in MG reduces the computational cost and maximize performance. The objectives of economic dispatch are based on fractional scheduling and restricted environment. Three different scenarios, low-voltage MG system are investigated. GJO–MAO techniques used to optimize various issues on MG by using renewable energy. The proposed technique performance is done in the MATLAB. When the time-of-use (TOU) energy market price strategy with the fixed pricing approach, the economic dispatch is calculated by time-of-use electricity market pricing method, generating cost decreases by 18.5%, 13.5% if the FP-related combined economic emission dispatch (CEED) is examined, and 15% after evaluating the environmental-constrained-economic-dispatch (ECED). The MG producing cost targets for ECED and ECD are according to renewable energy sources. The best and most system is used for finding a fair compromise between the cost of generating and emission. The smallest values of implementation time and standard deviation of superiority and robustness are achieved.

Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control

AbstractSystem reliability and stability can be significantly improved by the interconnected operation of multimicrogrids, and electric vehicles (EVs) provide a more flexible solution for frequency control, which also present challenges for frequency control. Therefore, a load frequency control (LFC) strategy for multimicrogrids with vehicle to grid (V2G) dependent on learning‐based model predictive control (MPC) is proposed. First, a controller‐interconnected multimicrogrid topology is proposed; thus, a multimicrogrid consisting of microturbines (MTs), distributed power sources, and EVs and their random power constraints is established. Second, a control parameter adaptive algorithm based on learning‐based MPC is designed. The real‐time frequency offset and EV station output power boundary are used as the state set, adjustable parameters of the MPC controller are used as the action set, and reward function is set with frequency deviation so that the adaptive adjustment of the weight parameters of the MPC controller is realised. Additionally, the improved MPC controller designed in this paper can transform the frequency control process into an optimization problem, which is well adapted to various random constraints in the control process. In addition, the deep deterministic policy gradient (DDPG)‐MPC double‐layer controller can prevent machine learning controller failure. Finally, the simulation results show that, compared with traditional control and MPC algorithms, the learning‐based MPC controller applied to the controller interconnection structure can exchange information between submicrogrids. Moreover, based on the experience accumulated in the prelearning process, the controller parameters can be updated according to the environmental state in real time, thereby significantly improving the robustness and rapidity of the multimicrogrid frequency control process. Meanwhile, compared with a traditional DDPG controller, the proposed controller with double‐layer coupling structure can better ensure the safe operation of the multimicrogrid system when the machine learning agent fails and cannot output actions normally.

Impact of energy storage devices on microgrid frequency performance: A robust DQN based grade-2 fuzzy cascaded controller

This article intended an advanced deep-Q network (A-DQN) based algorithm for the optimal design of a robust grade-2 fuzzy cascaded controller (G2-FCC) concerning frequency control of an autonomous AC microgrid under several electrical disturbances. The proposed AC microgrid is equipped with a hydraulic electrolyzer fuel cell (HE-FC) that empowers electrically to a variety of local consumers. Apart from the HE-FC, the proposed microgrid model is associated with few fractional megawatts (MW) rated power generating sources such as diesel generators (DEG), micro-turbines (MT), solar power stations (SPS), wind power stations (WPS) and geothermal to maximize the total power of a system. However, to increase system performance and energy quality some energy-storing devices such as ultra-capacitors (UC), flywheel (fw), and battery energy management systems (BEMS) are integrated into the system. Most renewable energy sources such as solar and wind power plants are associated with high uncertainties and exhibit low inertia which severely affects the grid frequency. The proposed fuzzy G2-FCC controller dominates the frequency disturbance gracefully and shows its superiority over a few standard approaches like fuzzy Proportional Integral Derivative (PID) and PID controllers. The energy-storing components also have shown their potential in regard to frequency profile improvement of the microgrid system. Finally, the suggested A-DQN algorithm is proved as the most effective tool to optimal design the parameters of the G2-FCC controller. Finally, it is verified that the suggested G2-FCC controller is found to be the most effective tool to improve the settling time of ΔF by 154.54 and 249.64 % over well-existing grade-1 fuzzy PID and PID approaches, respectively.

Multi-Objective Operational Cost Management with Minimum Net Emission of a Smart Microgrid

This research article considers a microgrid (MG) with various distributed generation sources (DGs) such as microturbine (MT), fuel cell (FC), wind turbine (WT), and solar cell (PV) along with the battery storage system. This article aims to simultaneously minimize operational costs and emissions for a daily schedule, while considering uncertainties such as load demand, market prices, and electricity generated by different renewable energy sources (RESs). This article proposes the slime mould algorithm (SMA) to solve this complex optimization problem. Initially, SMA is tested for three scenarios considering the minimization of operational cost and emission, respectively, as two single objectives. In Scenario A, a reduction of 1.95% in operational cost and 4.57% in emissions is observed compared to the Grasshopper Optimization Algorithm (GOA). In scenarios B and C, SMA reduces operational cost by 0.78% and 2.65%, respectively, while also reducing emissions by 6.27% and 0.28%, respectively, when compared to GOA. The results obtained by other algorithms are worse than those obtained by GOA. This study also showed that SMA effectively considers multi-objective functions by using the weighted sum method and the fuzzy decision-maker to approach the Pareto-optimal solution, achieving a good tradeoff between operational cost and emissions in each scenario.

Dual−Layer Distributed Optimal Operation Method for Island Microgrid Based on Adaptive Consensus Control and Two−Stage MATD3 Algorithm

Island microgrids play a crucial role in developing and utilizing offshore renewable energy sources. However, high operation costs and limited operational flexibility are significant challenges. To address these problems, this paper proposes a novel dual−layer distributed optimal operation methodology for islanded microgrids. The lower layer is a distributed control layer that manages multiple controllable distributed fuel−based microturbines (MTs) within the island microgrids. A novel adaptive consensus control method is proposed in this layer to ensure uniform operating status for each MT. Moreover, the proposed method can achieve the total output power of MTs to follow the reference signal provided by the upper layer while ensuring plug−and−play capability for MTs. The upper layer is an optimal scheduling layer that manages various forms of controllable distributed power sources and provides control reference signals for the lower layer. Additionally, a two−stage twin−delayed deterministic policy gradient (MATD3) algorithm is utilized in this layer to minimize the operating costs of island microgrids while ensuring their safe operation. Simulation results demonstrate that the proposed methodology can effectively reduce the operating costs of island microgrids, unify the operational status of MTs, and achieve plug−and−play capability for MTs.

RETRACTED: Optimal management of microgrid, considering various renewable and storage units of electrical-thermal generations and demand response program

This paper presents a model for the optimal operation of Micro-Grids (MGs) based on multi-agent systems. The studied MG including Micro-Turbine (MT) with Fuel Cell (FC) as the Combined Heat and Power (CHP) unit, Rubbish Incinerator (RI), Wind Turbine (WT), boiler, reformer, anaerobic digestion, as well as the electrical, thermal, and hydrogen storage units to supply both electrical and thermal loads. Also, the input fuels of MT and FC are natural gas and hydrogen, respectively. The wet waste has been used to supply hydrogen requirements for FC operation by anaerobic digestion and reformer. Also, municipal solid waste, which is separated from wet waste, is used in the RI unit to produce electrical energy. Hence, the MG also has power exchange with the upstream network. The proposed model schedules not only the output day-ahead electric power of units but also the exchanged power with the upstream network, to reduce the operation cost. For better evaluation of the problem, the uncertainties of WT, loads, and electricity price are considered and modeled by using probability distribution functions and Monte Carlo simulation. According to electrical load including vital and responsive loads, the Demand Response (DR) program has been considered in the MG operation. The simulation results show that the proposed model for the optimal operation of MG could minimize the operating cost by about 30%, under different conditions.