Microturbine Research Articles

Optimal energy management of micro-grid using sparrow search algorithm

In this paper, a recent metaheuristic approach of sparrow search algorithm (SSA) is employed to manage the operation of microgrid (MG) in an optimal manner. Two optimization problems are formulated in this work, the first one is a single objective problem that aims at minimizing either the total operating cost or the total emission from the system. The second problem is multi-objective comprising both total operating cost and total emission simultaneously. A new version of SSA is proposed to manage the energy of MG optimally. The considered MG comprises photovoltaic modules (PV), wind turbine (WT), fuel cell (FC), micro-turbine (MT), batteries (BSS), and grid. The obtained results using the proposed SSA are compared to other programmed approaches of krill herd optimizer (KH), Harris hawks optimizer (HHO), artificial ecosystem-based optimizer (AEO), Fuzzy-self adaptive particle swarm optimization (FSAPSO), and antlion optimizer (ALO). Non-parametric analysis using Friedman and Kruskal–Wallis ANOVA tests are applied for evaluating the proposed algorithm performance statistically. At solving the single objective problem, the proposed SSA achieved minimizing in cost and emission with 1.44% and 54.76% respectively compared to KH. Regarding the multi-objective problem, the proposed multi-objective SSA (MOSSA) succeeded in saving about 42.78% in operating cost and reducing the emission by 0.118% compared to ALO. The main findings demonstrated the robustness of the proposed SSA in managing the operation of the constructed MG.

Two-Cored Energy Management System for Industrial Microgrid

Energy systems have to deal with energy cost and environmental concerns such as greenhouse gas emission. Industrial buildings considered as Microgrid (μG) with heavy load worsen these issues even more. Further, cyber-attacks on the data communication channel between utility and customer is also a potential threat and may alter the data as well as the confidentiality of it, resulting in an inaccurate result. To address these problems, this paper proposes two-cored Building Energy Management System (BEMS) for Industrial Microgrid (IμG) with first cored termed as the energy layer concentrating on energy cost and emission reduction, while second cored termed as security layer provides the un-authorized intrusion detection and prevention system (IDS/IPS) for cyber secure communication of data. The μG under consideration contains national grid, Natural Gas (NG), solar Photovoltaic (PV) as input carriers; electrical energy at output ports; electric vehicle (EV) fleet; battery bank; solar PV panel as non-dispatchable Distributed Energy Resources (DERs) and Internal Combustion Engine (ICE), Fuel Cell (FC) and Micro Turbine (MT) as dispatchable DERs. Energy layer optimization problem has been solved in MATLAB using flower pollination algorithm for µG energy consumption cost and emission reduction. To develop and analyze the security layer, Linux operating system based Smooth-sec software has been used. Devised security layer continuously monitors the network traffic between customers and BEMS as well as BEMS and utility server. During monitoring it distinguish the licensed user or malicious attacker to detect and prevent possible internal and/or external intrusions in the communication channel. Results show that EMS reduces energy cost and emission in addition to cyber security from internal threats. Proposed two-cored control may be manufactured for utilities to realize its benefits for industrial customers in a smart energy distribution system. Keywords: Demand Response, Energy Management System, Flower

The Selection of the Most Cost-Efficient Distributed Generation Type for a Combined Cooling Heat and Power System Used for Metropolitan Residential Customers

Distributed generation (DG) using renewable energy sources is of widespread interest. For example, modern centralized conventional fossil fuel power generation commonly adds DG using renewable energy resources to the grid. Therefore, in these changes, it is necessary to optimize renewable energy systems to increase energy efficiency and reduce emissions. In previous studies, meta-heuristic algorithms were used to optimize DG location and capacity, but different types of DG systems and integrated energy hub conditions were not considered. Determining the most effective DG type for an integrated energy hub is critical. Accordingly, this study presented a methodology for selecting the most cost-efficient DG for metropolitan residential customers of energy hubs. In this paper, we model energy hubs for residential customers and the most cost-efficient DG type using MATLAB and HOMER software, considering microturbine (MT), photovoltaic (PV), wind turbine, and fuel cell (FC) power sources. For this purpose, the energy hub was modeled as a combined cooling heat and power (CCHP) system and selected a specific metropolitan area as a testbed (Atlanta, USA). For practical simulation, the total active power of the Atlanta community was measured by multiplying the average load profile data of residential houses collected by open energy information (OpenEI). The first case study showed that optimal-blast MTs without absorption chillers (AbCs) were the most cost-efficient compared to other optimal-blast DG systems without AbCs. Additional second case studies for optimal and full-blast MTs with AbCs were performed to verify the results for energy consumption, costs, and emissions savings. As a result, full-blast MTs with AbCs comprise the most cost-efficient DG type in the CCHP system for metropolitan residential customers, reducing energy consumption, cost, and emissions.

Load frequency control of a diverse energy source integrated hybrid power system with a novel hybridized harmony search-random search algorithm designed Fuzzy-3D controller

ABSTRACT The manuscript addresses a robust three-dimensional fuzzy-PID controller (Fuzzy-3D PID) to control frequency of a diverse energy source integrated hybrid power system under various loadings. In order to obtain stability over system frequency under any disturbances, a secondary control loop called load frequency control (LFC) loop is required in the system. LFC tries to monitor power generation irrespective of load demand. Further, a novel hybridized harmony search and random search (hyHS-RS) algorithm is proposed to optimally design the suggested Fuzzy-3D PID controller. The proposed hybrid network is incorporated with integration of different diverse energy sources such as wind-plant (WP), solar-plant (SP), diesel-generator (DEG), and micro-turbine (MT) along with conventional power-generating sources like thermal system, hydro system, and nuclear power plant. The wind-plant and solar-plant exhibit large dynamics and low inertia which degrades the system frequency seriously. The proposed Fuzzy-3D PID controller makes necessary control actions over disturbed frequency and trying to amend steady nominal frequency in the system. It has been observed the percentage improvement in ITAE with proposed hyHS-RS over basic harmony search (HS) and random search (RS) are 54.5% and 201.6% respectively. Finally, proposed hyHS-RS designed Fuzzy-3D PID controller justifies its superiority through different comparative studies.

An efficient approach based power flow management in smart grid system with hybrid renewable energy sources

This manuscript proposes an efficient hybrid method for power flow management (PFM) in smart grid (SG) system with hybrid renewable energy sources (HRES), such as photovoltaic (PV), wind turbine (WT), micro turbine (MT), fuel cell (FC), diesel generator (DG), battery energy storage (BES). The proposed hybrid method is the consolidation of turbulent flow of water-based optimization (TFWO) and battle royale optimization (BRO), hence it is called TFW-BRO. The major objective of the proposed approach is “to cost reduction and control the power flow (PF) of the system. The control signal of the voltage source inverter is generated by TFWO depending on the variation of power transmission between the source and load side. The control signal is assured using BRO via the parallel implementation against active with reactive power type. The power balance constraints, capacity constraints are used for define the objective function. Depending on the variation of PF, the TFW-BRO method optimizes the control parameter of the controller. Finally, the proposed method is activated in Matrix Laboratory (MATLAB)/Simulink site, and then the performance is likened with other existing methods. The statistical analysis such as mean, median, standard deviation for the proposed technique is 0.9681, 0.9062 and 0.1099 respectively.

System modeling of micro‐grid with hybrid energy sources for optimal energy management—A hybrid elephant herding optimization algorithm‐adaptive neuro fuzzy inference system approach

AbstractThis manuscript proposes a hybrid method for system modeling and optimal allocation of low cost micro‐grid. The proposed hybrid method is the joint execution of the enhanced elephant herding optimization algorithm (EHOA) and adaptive neuro fuzzy inference system (ANFIS) named as EHO‐ANFIS. By using MG inputs, such as solar photovoltaic, wind turbine (WT), micro turbine (MT), fuel cell (FC), and battery energy storage system. EHO optimizes micro‐grid configuration in minimal fuel costs based on needed load requirement. Here, learning phase of ANFIS is utilized for predicting the load requirement. EHOA reduces operation and maintenance costs, emission cost on the basis of the predicted load requirement. The proposed method is executed in MATLAB/Simulink site and the robustness of the proposed method is compared with different existing methods. In the proposed method, the maximal generated power of photovoltaic represents 6 kW, wind turbine indicates 7.8 kW, micro turbine denotes 11.8 kW, FC implies 6.8 kW, and battery refers 3 kW. By utilizing genetic algorithm, the generated power of photovoltaic signifies 7 kW, WT implicates 6 kW, MT implicates 4 kW, FC refers 7 kW, and battery implies 14 kW. The proposed method has minimal cost effective depending on its load demand. The computational time of the proposed technique under 100, 250, 500, and 1000 trails is 5600, 14 000, 28 000, and 56 000 s.

Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market

This paper presents a stochastic planning algorithm to plan an operation of a multi-microgrid (MMG) in an electricity market considering the integration of stochastic renewable energy resources (RERs). The proposed planning algorithm investigates the optimal operation of resources (i.e., wind turbine (WT), fuel cell (FC), Electrolyzer, photovoltaic (PV) panel, and microturbine (MT)) and energy storage (ES). Various uncertainties (e.g., the power production of WT, the power production of PV, the departure time of electric vehicle (EV), the arrival time of EV, and the traveled distance of EV) are initially forecasted according to the observed data. The prediction error is estimated by fitting the forecasted data and observed data using a Copula method. A Cournot equilibrium and game theory (GT) are applied to model the real-time electricity market and its interactions with the MMG. The proposed algorithm is examined in a sample MMG to determine the operation of uncertain resources and ES. The obtained results are compared with a baseline and the other conventional optimization methods to verify the effectiveness of the proposed algorithm. The obtained results authenticate the importance of modeling the interaction between the MMG and electricity market, especially under the high integration of uncertain RERs, resulting in above 8% cost reduction in the MMG.

A cost-oriented resource scheduling of a solar-powered microgrid by using the hybrid crow and pattern search algorithm

The tendency towards more and more employing renewable energies, particularly at the distribution system level and the residential sector has significantly increased. The growing penetration level of distributed generation (DG) units based on renewable energies has brought several challenges into power system operation and management. Thus, developing and proposing efficient energy management systems is regarded as a critical task in the area of power system operation. This issue is more highlighted in microgrids (MGs), that are mainly based on DG units. Accordingly, a day-ahead energy management system is proposed, which is operating in the grid-connected mode and equipped with a photovoltaic (PV) panel, a wind turbine (WT), and a microturbine (MT), besides the energy storage system. A detailed mathematical model was used for the PV panel and different weather circumstances and their impacts on the solar power generation and MG day-ahead resource scheduling problem are investigated. Afterward, a hybrid optimization algorithm, obtained by combining the crow search algorithm and pattern search method, known as (HCS-PS) has been proposed to deal with the problem for a 24-h scheduling period. The results, obtained from simulation show that the suggested HCS-PS method is capable of resulting in more superior solutions compared to some other well-established optimization algorithms. Moreover, the uncertainties of the problem due to the intermittent renewable power generation, load demand, and market price, have been successfully handled by deploying a scenario-based stochastic optimization technique.

Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

Present power systems face problems such as rising energy charges and greenhouse gas (GHG) releases. These problems may be assuaged by participating distributed generators (DGs) and demand response (DR) policies in the distribution system (DS). The main focus of this paper is to propose an energy management system (EMS) approach for campus microgrid (µG). For this purpose, a Pakistani university has been investigated and an optimal solution has been proposed. Conventionally, it contains electricity from the national grid only as a supply to fulfil the energy demand. Under the proposed setup, it contains campus owned nondispatchable DGs such as solar photovoltaic (PV) panels and microturbines (MTs) as dispatchable sources. To overcome the random nature of solar irradiance, station battery has been integrated as energy storage. The subsequent nonlinear mathematical problem has been scheduled by mixed-integer nonlinear programming (MINLP) in MATLAB for saving energy cost and battery aging cost. The framework has been validated under deterministic and stochastic environments. Among random parameters, solar irradiance and load have been taken into consideration. Case studies have been carried out considering the demand response strategies to analyze the proposed model. The obtained results show that optimal management and scheduling of storage in the presence of DGs mutually benefit by minimizing consumption cost (customer) and grid load (utility) which show the efficacy of the proposed model. The results obtained are compared to the existing literature and a significant cost reduction is found.

Network constrained optimal performance of DER and CHP based micro-grids within an integrated active-reactive and heat powers scheduling

Nowadays, different types of dispatchable and non-dispatchable distributed energy resource (DER) units, combined heat and power (CHP) plants as well as heat only units are integrated into a micro-grid (MG) platform. These facilities can help MG operators to satisfy the required electricity and heat demands of controllable and non-controllable loads even with no power exchange between the external main grid and the MG. This paper introduces a network constrained optimal operation model based on an integrated active-reactive and heat powers scheduling in MG for both grid-connected and isolated modes. The DERs like fuel cell (FC) units, diesel generators (DGs) and micro turbines (MTs) are motivated to take part in providing reactive power to the MG based on a reactive power payment function. Meanwhile, the dependency of produced heat and active power by CHP units through feasible operating region as well as the dependency of produced active and reactive powers by DER units through capability curves are taken into account in the optimal economic dispatch of the MGs. The main goal of this paper is to find the optimal economic dispatch of active and reactive powers as well as heat energy for DER and CHP based micro-grids in which network constraints such as AC power flow limitations are taken into consideration. The proposed model is examined on a 33-bus test MG for grid-connected and isolated modes and the simulation results are presented to investigate and analyze the different features of the proposed method.

Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework

Renewable energy sources prevail as a clean energy source and their penetration in the power sector is increasing day by day due to the growing concern for climate action. However, the intermittent nature of the renewable energy based-power generation questions the grid security, especially when the utilized source is solar radiation or wind flow. The intermittency of the renewable generation can be met by the integration of distributed energy resources. The virtual power plant (VPP) is a new concept which aggregates the capacities of various distributed energy resources, handles controllable and uncontrollable loads, integrates storage devices and empowers participation as an individual power plant in the electricity market. The VPP as an energy management system (EMS) should optimally dispatch the power to its consumers. This research work is proposed to analyze the optimal scheduling of generation in VPP for the day-ahead market framework using the beetle antenna search (BAS) algorithm under various scenarios. A case study is considered for this analysis in which the constituting energy resources include a photovoltaic solar panel (PV), micro-turbine (MT), wind turbine (WT), fuel cell (FC), battery energy storage system (BESS) and controllable loads. The real-time hourly load curves are considered in this work. Three different scenarios are considered for the optimal dispatch of generation in the VPP to analyze the performance of the proposed technique. The uncertainties of the solar irradiation and the wind speed are modeled using the beta distribution method and Weibull distribution method, respectively. The performance of the proposed method is compared with other evolutionary algorithms such as particle swarm optimization (PSO) and the genetic algorithm (GA). Among these above-mentioned algorithms, the proposed BAS algorithm shows the best scheduling with the minimum operating cost of generation.

Imperialist competitive algorithm optimized cascade controller for load frequency control of multi-microgrid system

ABSTRACT Control of multi-energy source-based microgrid is a complex task due to the presence of unpredictable renewable energy sources (RES). This paper proposes a cascade structure of proportional derivative with (1+ PI) controller (C-PDF(1+ PI)) for load frequency control of an AC multi Micro-grid (MG). The MG contains a thermal power unit, Diesel Engine Generator (DEG), Micro Turbine (MT), Fuel Cell (FC), and uncontrolled solar and wind sources. Diverse storage device units like Battery Energy Storage System (BESS), Fly Wheel Energy Storage System (FESS), and Electric Vehicles (EV) are also included in the studied MG system. The proposed C-PDF(1+ PI) controller consists of a first stage Proportional Derivative controller with filter (PDF) and one plus proportional-integral (PI) controller in the second stage. The Imperialist Competitive Algorithm (ICA) is applied to optimize the controller parameters. To confer superiority of the C-PDF(1+ PI) controller, its responses are related with PI, PID, and PIDF controllers. It is observed that the ITAE improvement in percentage with the proposed approach is 70.64%, 53.23%, and 47.10% compared to PI, PID, and PIDF controller while considering the simultaneous variations in load, solar, and wind powers. To justify the efficacy of the ICA technique, it is compared with other well-established techniques like the Genetic algorithm (GA) and the Cuckoo Search algorithm (CSA). It is observed that for the identical system and C-PDF(1+ PI) controller, the ITAE value improvement in percentage by ICA compared to GA and CSA are 31.94% and 15.93%, respectively, while considering all the three uncertainty like changes in load condition, change in solar power irradiation and changes in wind power conditions. Finally, it is conferred that the proposed ICA optimized C-PDF(1+ PI) controller significantly improves microgrid dynamic performance under various operational conditions.

Optimal energy management of micro grid connected system: A hybrid approach

In this manuscript, a hybrid method for optimum energy management of micro grid (MG) connected system is proposed. The MG connected sources are Photovoltaic (PV) array, Wind Turbine (WT), Fuel Cell (FC), Micro Turbine (MT), Diesel Generator (DG), battery storage (BESS). The proposed technique is the consolidation of Artificial Neural Network (ANN), Artificial Bee Colony (ABC), and Firefly Algorithm (FA). The proposed method is utilized to maintain the power flow (PF) amid the energy sources and grid. Initially, the predicted load requirement based on the ANN technique predicts the load requirement and ABC optimizes the MG configuration. Finally, the FA technique helps to reduce the FC including operation with maintenance cost. The proposed method is executed in MATLAB/Simulink site and the performance is analyzed with different existing methods like online management, ABC, ABC-ABC, and ABC-Gravitational Search (GS). Here, the maximal power generation of photovoltaic is 5.9 kW, wind turbine is 5.9 kW, micro turbine is 4 kW, FC is 4 kW, DG is 6 kW, and battery is 0.5 kW. The total cost of ABC, ABC-ABC, ABC-GS, and the proposed technique is about 4.1$/h, 3.9$/h, 3.3$/h, and 2.7$/h. Optimum micro grid combination using proposed method, overall cost comparison using different methods, statistical evaluation of MG management, cost accuracy percentage (CAP) of proposed and existing techniques, efficiency under various number of trails are also analyzed in this work. The comparison results demonstrate that the proposed technique has minimal cost-efficient based on its load requirement.

Characteristics Analysis of Archimedes Screw Turbine Micro Hydro Power Plants with Variations in Water Discharge

Electrical energy has a very important role in human life, but the electricity supply still does not fully meet the needs of the community. Many potential energy resources can be converted into electrical energy. One of the potential energy resources is the flow of water that can be used for micro-hydropower plants for remote areas. This paper presents the results of research on micro-hydro power plants using Archimedes screw turbines. This power generation system has several advantages, namely low maintenance costs, does not damage the water ecosystem, does not require extensive land. The purpose of this research is to study the characteristics of the Archimedes micro-hydro power plant on variations in water discharge. These characteristics include mechanical characteristics such as the maximum torque and rpm and electrical characteristics such as voltage, current and electric power. The determination of characteristics is carried out on the prototype of the micro hydropower plant Archimedes screw turbine with the specifications of the turbine screw having 2 blades, the turbine elevation angle of 30°, 2.5: 1 gearbox ratio using four variations of water discharge, namely 1.18 l/s, 2.33 l/s, 2.55 l/s, and 2.64 l/s. The determination results of the micro hydropower plant Archimedes screw turbine characteristics show that an increase in water flow will cause an increase in mechanical characteristics such as large torque and rpm and electrical characteristics such as voltage, current and electric power. However, the increase in the magnitude of these characteristics is not directly proportional to the increase in water discharge.

Scenario-Based Two-Stage Stochastic Scheduling of Microgrid Considered as the Responsible Load

This article presents a novel linear programming (LP) based two-stage stochastic approach for microgrids (MGs) under uncertainties. In this regard, the day-ahead programming of dispatchable resources in MG was modeled, considering the uncertainties in demand loads, and upstream network electricity price. Moreover, the inherent stochastic nature of wind and solar resources as well as the environmental aspects were modeled to find a realistic solution. Also, real-time pricing (RTP), demand response (DR) program considering the energy storage system (ESS) as a DR option was implemented on an MG as a smart customer. The extensive form of the two-stage stochastic recourse model was properly implemented for dispatchable and non-dispatchable resources. Furthermore, scenario generation and reduction procedures were realized with autoregressive and moving average (ARMA) model-based time-series (TS), and backward reduction (BR) method by the Kantorovich distance (KD), respectively. The simulations on a grid-connected MG, including micro-turbine (MT), fuel-cell (FC), wind-turbine (WT), photovoltaic module (PV), and ESS were reported in operational cases based on one month of real recorded data for wind speed, solar irradiance, demand load, and upstream network electricity price. The results for the next day in real-time confirmed the accuracy of the developed optimization methodology.

Energy management system of low voltage dc microgrid using mixed-integer nonlinear programing and a global optimization technique

Integration of a group of distributed generators (DGs) and loads in the form of a microgrid (MG) enhances the supply reliability, reduces the amount of greenhouse gasses (GHG) emission, and reduces the generation cost. However, this integration requires a refined energy management system (EMS). The MG-EMS dispatches the power between DGs to optimize either of the generation cost, the amount of GHG emissions, or both simultaneously. Minimum cost scenario needs an accurate generation cost model for each DG. In this paper, generation cost model includes the no-load cost and nonlinear behavior of losses inside the DG. In addition, the start/stop cost of DGs and power losses within the MG are also considered. The proposed optimization problem becomes a non-convex mixed integer nonlinear (MINL) problem which is hard to solve. The paper suggests using branch and reduce optimization navigator (BARON) algorithm, a global optimization technique. Minimum cost and minimum emission scenarios are single objective problems, while operation at reasonable emission and cost is a multi-objective scenario. Results of the single objective scenarios are exploited to determine feasible constraints to optimize the multi-objective scenario using constraints approach. Eventually, the capability of an energy storage system (ESS) to compensate generation shortage and minimize the emission is also investigated. The study is carried out on a DC microgrid comprised of photovoltaic (PV), fuel cell (FC), microturbine (MT), diesel generator (DE), battery ESS, and Egyptian grid load profiles over four seasons of the year.

Optimization of the integrated power and desalination plant with algal cultivation system compromising the energy-water-environment nexus

This article examines the exhaust waste heat recovery potential of a microturbine (MT) for seawater desalination using an organic Rankine cycle (ORC). Waste heat from microturbine is exploited using organic Rankine cycle (ORC) and the power generated is delivered to the reverse osmosis (RO) plant. The brine of the reverse osmosis plant is sent to a membrane distillation (MD) plant for further treatment after preheating using the excess heat from the turbine. Also, biological capture of CO2 through cultivation of microalgae has been considered by using flue gases from the microturbine. Plant integration is performed on the basis of the energy-water-environment nexus concept. In this regard, a multi-objective optimization approach is implemented in this work considering economic and environmental points of views. The first objective function is to maximize the annual profit of the system. The second objective function is an environmental index which is defined as the CO2 emission per desalinated water production. Minimization of this index is considered as the second objective function. The results showed that the proposed integrated ORC-RO-MD-CO2 capture plant can considerably improve the system both economically and environmentally.

Economic scheduling of a smart microgrid utilizing the benefits of plug-in electric vehicles contracts with a comprehensive model of information-gap decision theory

In this paper, a hybrid optimization approach is proposed to smart microgrid (SMG) operators to schedule the existing energy resources optimally to meet the load demand of the system. The proposed hybrid optimization is structured based on information-gap decision theory (IGDT) and two-stage stochastic programming to minimize the operational cost of the system in day-ahead (DA) and real-time (RT) power markets. The uncertainty of the day-ahead market price is tackled with the IGDT. The IGDT is furnished with robustness and opportunity functions leading to various risk-averse and risk-taker decision-making strategies. The problem is structured as bi-level programming, while by implementing the concept of envelop bounds, it is transferred into a single-level optimization problem. On the other hand, the uncertainties of the operation of the real-time market are modeled using scenarios with stochastic programming. The studied SMG is equipped with a photovoltaic system, a wind turbine, two microturbines (MTs), and battery storage. The results determine the scheduling of day-ahead exchange power and scheduled PEV contracts as here-and-now decisions of the optimization. However, the traded energy in the real-time market, MTs’ dispatch, and the charging/discharging plan of battery storage are wait-and-see decisions made by the second stage of the programming. The results are obtained and discussed from the perspective of risk-neutral, risk-averse, and risk-taker decision-makers. As an example of performance, when the uncertainty horizon is increased by 10% in the robustness mode, the operation cost is increased by about 27.5%. In comparison, the same amount of increment of uncertainty horizon leads to cost-saving about 33.3% addressing the positive aspect of uncertainty using opportunity function.

Optimal energy scheduling strategy for multi-energy generation grid using multi-agent systems

Due to high environmental concerns and ever-increasing fossil fuel costs, generation of clean energy and its optimal management considering the demand side responses are very essential to upsurge the productivity and reliability of the distribution grids. Hence, a kind of distributed microgrid identified as multi-energy generation grid (MEGG) has been proposed in this paper. The anticipated MEGG integrated with renewable energy resources, fuel cells (FCs), an alternative grid-supporting generator (AGSG) connected with boiler, micro-turbine (MT), and thermal system, wastes burning power plant (WBPP), and energy storage system (ESS) that investigate the optimal resource management in a grid under various operational conditions to minimize the emissions and operational costs. The problem is supervised by agents-based technique considering the generation capabilities and demand side responses. The three-layered technique is presented for the distributed optimal resource management based on multi-agent systems (MAS) framework, where different agents operate and perform tasks autonomously. An optimal energy management scheduling strategy is accomplished considering actions that guarantee the maximum use of generated renewable energy, eradicate the excess use of WBPP and FC, minimize AGSG operational time, and full-load demands satisfaction during the entire day. Similarly, coordination between the MEGG and central power grid has also been accomplished for the purpose to exchange power. For validation, three scenarios have been presented which shows that the combined utilization of distributed energy resources can profusely generate electricity having less emissions, recover the generation costs in peak hours, and enhance the reliability of the entire grid.

Real-time monitoring and operation of microgrid using distributed cloud–fog architecture

In this paper, a new distributed multi-agent framework based on the three layers’ fog computing architecture is developed for real-time microgrid economic dispatch and monitoring. To this end, the changes of load at any time will be tracked by the proposed technique, considering unit sudden exits and entries. Moreover, to make the system more realistic, different renewable energies, including photovoltaics (PVs), wind turbines (WTs), fuel cells (FCs), and microturbines (MT) are considered in the proposed technique. To overcome the complexity of the problem, by using advantages of fog computing, a new fast consensus-based optimization algorithm is used, which is modified based on the fuzzy adaptive leader technique. Finally, the proposed technique is simulated and tested on microgrids with 6 and 14 buses, respectively. Simulation results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.