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Renewable energy is a promising solution to combat the scarcity of electricity, particularly in isolated and rural areas. Microgrids (MG) can be employed for installing systems with different energy sources, such as renewable energy components and conventional energy sources like utility grids or grid-connected inverter systems. The amount of energy produced by renewable sources depends on their location, which has implications for energy production. This research aims to explore MG and their challenges for efficient operation. The study discusses various AI models used by researchers to mitigate problems associated with MG planning. Additionally, the paper presents a case study based on the most beneficial ML tool like clustering to gain insights into an existing MG system. The paper also delves into the issues related to PV, a connected distributed energy resource (DER), such as forecasting, and predictive management to reduce maintenance costs, and how AI tools can address them. Furthermore, forecasting methods such as LSTM and GRU models are discussed because of the stochastic nature of PV production. [ABSTRACT FROM AUTHOR]

Nowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs. [ABSTRACT FROM AUTHOR]

In the restructured electricity market, microgrid (MG), with the incorporation of smart grid technologies, distributed energy resources (DERs), a pumped-storage-hydraulic (PSH) unit, and a demand response program (DRP), is a smarter and more reliable electricity provider. DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines. Better bidding strategies, prepared by MG operators, decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources (RES). But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate. To solve these issues, this study suggests non-dominated sorting genetic algorithmII (NSGA-II) for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources. The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions. TOU-based DRP is used, especially considering the time of outages along with the time of peak loads and prices, to enhance the reliability of MG and reduce costs and emissions. [ABSTRACT FROM AUTHOR]

The energy management issue of microgrids typically adopts demand response programs and reconfiguration of distribution networks to improve the technical and financial characteristics of microgrids. This manuscript proposes an energy management optimization in micro grids using IoT by applying the GBDT-JS technique to account for the uncertainty introduced by renewable energy sources and electric demands. The proposed technique is a combination of gradient boosting decision tree algorithm (GBDT) and jellyfish search (JS); hence, it is called GBDT-JS technique. The proposed method is to include maximizing the benefits of microgrids and reducing power fluctuations to the main grid. This proposal presents a GBDT-JS to develop the suitable adaptive day-ahead real-time energy dispatch under the occurrence of operating uncertainty based on the energy management system (EMS). Energy dispatch is achieved to satisfy the many operating criteria based on optimum solutions which are further implemented, for example minimum power fluctuation and operating cost. The main aim of the works contributions is shortened as below: (1) The optimum GBDT system is decided day-ahead depending on the predictive microgrid system levels consider a many operating objects. (2) JS technique is improved a day-to-day basis, adopted to execute the energy dispatch, and directly present in the uncertainty system. The GBDT-JS technique is done on MATLAB, and their performance is compared to different existing techniques, such as slime mould optimization algorithm, side-blotched lizard algorithm, and jellyfish search (JS). [ABSTRACT FROM AUTHOR]

Renewable solar energy is the future of all other resources because of its reliability and availability all over the earth. Optimization of the energy consumption and utilization of internet of things (IoT) devices deployed in such systems poses significant challenges. Axis tracker panel is the scope for the next decade to increase the performance of the existing panels. This research focuses on the development of intelligent energy optimization algorithms for IoT devices. The integration of renewable energy sources and IoT devices in solar-microgrid energy systems offers promising solutions for sustainable and efficient energy management. The proposed whale optimization algorithm (WOA) takes into account dynamic factors, including varying energy availability and fluctuating demand patterns, to optimize the overall performance. Leveraging real-time data from IoT sensors and smart meters, the algorithms balance energy generation and consumption, prioritize critical loads, and incorporate energy forecasting techniques to handle fluctuations in renewable energy production. Moreover, they integrate demand response mechanisms and dynamic pricing models to encourage flexible energy consumption patterns and minimize operational costs. The results of this study demonstrate the significant potential of the WOA algorithm in enhancing the sustainability of microgrid energy systems, paving the way for a greener and more reliable energy future. [ABSTRACT FROM AUTHOR]

Abstract Due to the advantages of micro‐grids including power losses and voltage drops reduction, reliability improvement and reduction in transmission network cost, numerous clean micro‐grids including renewable resources such as wind, tidal and solar are developed in different countries of world. The investment cost of the renewable energy‐based generation units especially tidal barrages is high, and to generate the cost‐effective electricity from the renewable resources, the optimization process should be performed. In this paper, optimal planning of a micro‐grid containing energy storage device and new mixture of renewable resources including tidal barrage and photovoltaic system is performed. For this purpose, optimal characteristics of barrage type tidal plant including number of turbines, sluices and hydro‐pumps, turbine tip and hub diameters and sluice width are determined to maximum energy of tidal plant is generated. Then, number of photovoltaic systems and batteries are obtained to supply the required load in minimum cost. To optimize the objective functions, different heuristic approaches including particle swarm, genetic and imperial competitive algorithms (ICAs) are applied, in the paper. To study the effectiveness of the proposed approach, optimal planning of a micro‐grid is performed. It is deduced from the numerical results that the particle swarm method has performed best in determining the optimal solution.

Renewable energy sources have been widely disseminated around the world. However, due to weather fluctuations, energy storage systems are needed to supply the periods in which the renewable sources are absent. The reservoir of a hydroelectric plant is an example of energy storage that meets the demand even with climatic variations. However, in order to be able to use hydropower, an adequate land topology is necessary and the flooding of a large reservoir. The present work proposes a hybrid microgeneration composed of solar photovoltaic and hydropower in a parallel and complementary way. The daytime demand will be supplied by solar energy and the night time demand by stored water energy in a small adequate reservoir, and the grid will be the backup of the system. The methodologies used in the modeling, the system topology and the proposed control will be presented in this paper. This study highlights the feasibility of the proposal in some situations where there is no consumption or exchange of energy with the grid. The proposed topology can also be used as an off-grid system. [ABSTRACT FROM AUTHOR]

Smart grids and their technologies transform the traditional electric grids to assure safe, secure, cost-effective, and reliable power transmission. Non-linear phenomena in power systems, such as voltage collapse and oscillatory phenomena, can be investigated by chaos theory. Recently, renewable energy resources, such as wind turbines, and solar photovoltaic (PV) arrays, have been widely used for electric power generation. The design of the controller for the direct Current (DC) converter in a PV system is performed based on the linearized model at an appropriate operating point. However, these operating points are everchanging in a PV system, and the design of the controller is usually accomplished based on a low irradiance level. This study designs a fractional-order proportional-integrated-derivative (FOPID) controller using deep learning (DL) with quasi-oppositional Archimedes Optimization algorithm (FOPID-QOAOA) for cascaded DC-DC converters in micro-grid applications. The presented FOPIDQOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter. In addition, the proposed model develops a FOPID controller using a stacked sparse autoencoder (SSAE) model to regulate the converter output voltage. To tune the hyper-parameters related to the SSAE model, the QOAOA is derived by the including of the quasi-oppositional based learning (QOBL) with traditional AOA. Moreover, an objective function with the including of the integral of time multiplied by squared error (ITSE) is considered in this study. For validating the efficiency of the FOPID-QOAOA method, a sequence of simulations was performed under distinct aspects. A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques. [ABSTRACT FROM AUTHOR]

The contribution of Renewable Energy Resources (RER) in the process of power generation is significantly high in the recent days since it paves the way for overcoming the issues like serious energy crisis and natural contamination. This paper deals with the renewable energy based micro-grid as it is regarded as the apt solution for integrating the RER with the electrical frameworks. As the fixed droop coefficients in conventional droop control approaches have caused various limitations like low power-sharing and sudden drops of grid voltage in the Direct Current (DC) side, the Harmonized Membership Fuzzy Logic (MFL) droop control is employed in this present study. This proposed droop control for the hybrid PV-wind-battery system with MFL assists in achieving proper power-sharing and minimizing Total Harmonic Distortion (THD) in the emergency micro-grid. It eradicates the deviations in voltage and frequency with its flexible and robust operation. The THD is reduced and attains the value of 3.1% compared to the traditional droop control. The simulation results of harmonized MFL droop control are analogized with the conventional approaches to validate the performance of the proposed method. In addition, the experimental results provided by the Field Programmable Gate Array (FPGA) based laboratory setup built using a solar photovoltaic (PV) and wind Permanent Magnet Synchronous Generator (PMSG) reaffirms the design. [ABSTRACT FROM AUTHOR]

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

There has been a steep increase in the global micro-grid market. The micro-grid provides integration of different types of renewable and nonrenewable technologies. The integration of an efficient energy storage system is essential to handle the intermittency problems associated with renewable energy sources (RES). The majority of micro-grids make use of battery storage systems (BSS). There are several drawbacks associated with BSS viz. large size, low life cycle, and high cost. In this respect, the hydrogen-based storage system (HSS) has attracted the attention of system planners as an effective alternative. The HSS provides requisite support to curb the variability of RES and helps to maintain system reliability. The HSS produces hydrogen when excess power is available from RES. The stored hydrogen can further be put to several other applications besides generation of electric power. Thus, HSS is emerging as a promising technology that can be used alone/in conjunction with BSS to increase the viability of micro-grid through its diversified applications. The objective of this paper is to present a comprehensive review on different aspects of HSS based micro-grids. Various problems affiliated with traditional storage systems have been discussed to assert the importance of HSS in micro-grid applications. The review is initiated with an overview of HSS comprising of electrolyzer, hydrogen storage unit and fuel cell. The micro-grid planning and economic optimization approaches with particular reference to HSS based micro-grids are discussed in detail. Finally, a summary of literature survey identifying research scope in HSS based micro-grid has been discussed. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems (HRES) combine two or more renewable energy systems and are an interesting solution for decentralized renewable energy generation. The exergy and exergo-economic approach have proven to be useful methods to analyze hybrid renewable energy systems. The aim of this paper is to present a review of exergy and exergy-economic approaches to evaluate hybrid renewable energy systems in buildings. In the first part of the paper, the methodology of the exergy and exergo-economic analysis is introduced as well as the main performance indicators. The influence of the reference environment is analyzed, and results show that the selection of the reference environment has a high impact on the results of the exergy analysis. In the last part of the paper, different literature studies based on exergy and exergo-economic analysis applied to the photovoltaic-thermal collectors, fuel-fired micro-cogeneration systems and hybrid renewable energy systems are reviewed. It is shown that the dynamic exergy analysis is the best way to evaluate hybrid renewable energy systems if they are operating under a dynamic environment caused by climatic conditions and/or energy demand. [ABSTRACT FROM AUTHOR]

To become sustainable, the production of electricity has been oriented towards the adoption of local and renewable sources. Distributed electric and thermal energy generation is more suitable to avoid any possible waste, and the Micro Gas Turbine (MGT) can play a key role in this scenario. Due to the intrinsic properties and the high flexibility of operation of this energy conversion system, the exploitation of alternative fuels and the integration of the MGT itself with other energy conversion systems (solar field, ORC, fuel cells) represent one of the most effective strategies to achieve higher conversion efficiencies and to reduce emissions from power systems. The present work aims to review the results obtained by the researchers in the last years. The different technologies are analyzed in detail, both separately and under a more complete view, considering two or more solutions embedded in micro-grid configurations. [ABSTRACT FROM AUTHOR]

Energy management becomes essential when distributed energy sources such as solar, wind, and fuel cell are connected in a micro-grid. In this paper, using a combination of two powerful neural network tools and fuzzy logic, intelligentization, and adaptation of droop control along with voltage and current control as one of the most common methods of decentralized control is done. One of the essential features of this method is its fast performance and the need for telecommunication infrastructure. In this paper, we provide a comprehensive control system that enables proper operation in both upstream and island network modes for both AC and DC microgrids. The proposed method is simulated to evaluate its effectiveness. The proposed structure, by adapting the control system by ANFIS structure, can properly distribute power between distributed products in a brilliant way and without the need for operators and costly telecommunications infrastructure in the face of severe disturbances such as change. The state between the connection and the island or fault occurrence ensures the stability of both the AC and DC parts of the microgrid. The results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a simulation of an insulated micro-grid system based on the three-level converters control for energy management. Different renewable power sources (wind turbine and Photovoltaic (PV) energy systems) are used to energize the micro-grid. However, a battery energy storage system (BESS) and a variable diesel generator are also used to improve the reliability of the system. The contribution of this research is focused on the power control method based on improving the quality of energy transfer, mastering dynamic interactions and maximum energy production from renewable energies to reduce the fuel consumption by the diesel. Firstly, the proposed control model for each renewable energy was carried out through simulation in the environments of Matlab and Simulink to test the robustness and performance. The second part of this research is dedicated to managing the sharing of power between load, generators, and storage systems by extracting the references of power. The three-level PWM rectifiers for variable speed diesel generators was used to maintain and control the DC bus voltage of the isolated micro-grid. The results obtained from simulations show a good correlation between static and dynamic systems even for fluctuating sun power and wind speed. [ABSTRACT FROM AUTHOR]

With the rapid depletion of fossil fuels and its detrimental environmental concerns, renewable energy sources (RES) have been widely adopted in the ship power system for improving the energy flexibility at the seaport and making it easier to create a green maritime transit corridor. However, the problem of load frequency control in RES-based micro-grid systems is inescapable because of its low inertia, sporadic characteristics of RES and random load demand. Therefore, this paper presents a novel fractional order proportional integral-one plus tilt-derivative PIλ-(1 + TD) cascade controller for frequency regulation of seaport hybrid micro-grid (SHMG) system consisting of diverse RES including bio-diesel generator, fuel cell, organic Rankine cycle-based solar thermal power, wind turbine generator, sea wave energy and energy storage system. This study introduces a flow direction algorithm (FDA) to optimize the suggested hybrid cascaded PIλ-(1 + TD) controller with integral time square error serving as the objective function. The dominance of the FDA-optimized PIλ-(1 + TD) controller is proved over proportional-integral-derivative (PID), fractional-order PID (FOPID), tilt-integral-derivative (TID) and cascaded PI-PD control scheme by examining the dynamic behavior of SHMG when a variety of sea-shore loads are used as significant disruption to the considered system. Apart from a multi-step variation of load and renewable energy generation, various realistic scenarios such as random load variations, wind speed fluctuations, solar irradiance changes, availability uncertainty of RES and generating/storage units, communication time-delay and ± 30% model perturbations are also considered to analyze the efficacy and resiliency of PIλ-(1 + TD) controller. The hybrid electrical energy source, flywheel and plug-in hybrid electric vehicles are used to support the SHMG for meeting the transient and steady-state power need. Heat pumps and freezers are also considered in SHMG to further advance the system's performance. The advocated PIλ-(1 + TD) controller achieves a substantial reduction of almost 30% to 38% in settling time, 16% to 27% in undershoot and 76% to 87% in overshoot compared to contemporary PID, FOPID, TID and cascaded PI-PD controllers. Furthermore, the robustness of the proposed frequency control strategy is also validated when SHMG is integrated into the main grid. Comparative analyses of signal strength induced by different controllers are also worked out to ensure fair comparison and prevent actuator saturation. Finally, the Bode-based relative stability of the proposed controller is analyzed compared to its contemporaries. • FDA-tuned hybrid cascaded PIλ-(1 + TD) frequency controller for seaport hybrid μG powered by renewable energies • Interaction of seaport micro-grid to the sea/shore side and utility grid. • Exceptional dynamic performance across various operational scenarios. • ESS and controllable loads contribute significantly in frequency control. • Enhanced system stability while ensuring the optimum utilization of RES. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources continues to increase, the stability of the grid is being challenged. Solutions are needed to curb fluctuations in renewable energy generation and ensure a stable electricity supply. As a clean and efficient secondary energy source, hydrogen energy plays an important role in the power system. However, the development of hydrogen energy storage systems is currently limited by technology and costs, making it difficult to scale up. How to invest to make hydrogen storage economical is worth studying. This paper constructs a regional microgrid system, considering the economics of investment by renewable energy generators (REGs) and integrated energy operators (IEOs), and the sensitivity of the payback period under conditions of market adjustment and technological progress. A combination of the Gurobi and Particle Swarm Optimization (PSO) algorithm is used to solve the investment optimization model, and Shapley is used to allocate multi-intelligence benefits. The results show that: (1) the investment returns and payback periods of REG are within a reasonable range; (2) IEO is hardly economical in the presence of market price changes and technological advances. On this basis, this paper constructs a win-win investment model for REG and IEO and explores the distribution of benefits under their cooperation model. The results show an increase in return on investment of 0.34% for REG and 1% for IEO following investments by REG in partnership with IEO. [ABSTRACT FROM AUTHOR]

Nowadays, electricity consumption in industrial and domestic sectors is growing increasingly. In recent years, due to the expansion of consumption and increased concern about environmental pollution resulting from the use of fossil fuels to generate electricity and running out of the resources, using renewable energies such as wind and solar has been considered. One of the problems in these resources is the inherent uncertainty as well as accidental nature of these sources, which complicates planning and forecasting of resources. In this paper, optimal scheduling of micro-grid connected to the main grid with renewable energy resources is studied with the help of mixed-integer linear programming in general algebraic modeling system (GAMS) software. A diesel generator is used in cooperation with other renewable resources so as to control the load instability. Furthermore, stochastic programming and probabilistic scenarios are used in order to model the uncertainty of wind and solar resources. Solar power is predicted to be in the interval of hour 6th to hour 19th. At the outset and also at the end of day, power generation has the minimum amount, while the maximum amount of power generation can be found at hour 13th. Battery charging occurrence is in the case of low load, which is existent for the grid in all the scenarios (nights: at hours 2nd to 5th; days: at hours 13th to 15th). Selling the energy to the main grid is performed at the times in which the denoted price possesses the peak value (at hours 8th to 12th and 19th to 22th). Finally, operation cost of the micro-grid using the proposed method is compared with genetic algorithm, which confirms the efficiency of the method. [ABSTRACT FROM AUTHOR]

The centralized power grid bears a heavy burden in a time when consumers expect an uninterrupted reliable power supply, a reduction in carbon emissions, increased efficiency within the national grid, and power supplied to remote communities. One such structure is used to implement this type of control that is based on smallscale Distributed Generation (DGs), at a single house or a small building level: micro-grid (μG). A μG is an autonomous, sustainable hybrid framework that uses renewable and non-renewable energy resources to supply continuous electricity to the load. Taking this scenario into account, the three sources of generation that have been utilized in the proposed μG are Photovoltaic (PV) array, Fuel Cell (FC), and Wind Turbine (WT) in this research paper. The active and reactive power of all three generation resources has been controlled using various controllers, i.e., integral, proportional-integral, proportional derivative, proportional integral derivative, fractional-order proportional-integral, Fractional Order Proportional Integral Derivative (FOPID), and Sliding Mode Controller (SMC). An advanced optimization technique based on a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm has been utilized to optimize all these controllers. The integral square error has been taken as the objective function for both optimization algorithms. Finally, a graphical and tabular comparative analysis of all optimized controllers along with their control parameters and performance indexes has been evaluated to find the best optimal solution using MATLAB/Simulink. The performance of SMC has been surpassed the performance of all other optimized controllers for the power stability analysis. Moreover, a smart switching algorithm has been introduced for switching between the generation resources following the load demand and cost of the system to operate the μG more economically. Finally, a case study has been performed in which the smart switching algorithm has been utilized to switch to the best available generation resource in case of any fault at the generation side to provide uninterrupted power to the attached loads. [ABSTRACT FROM AUTHOR]

In renewable energy micro-source applications, a wide range of voltage conversion of the step-up converter is an essential part to equalize the typical low voltage of micro-source with DC-bus voltage of Inverter or motor drives. In order to meet this purpose, an ultra-voltage gain step-up DC-DC converter is proposed in this paper. The proposed converter has made an arrangement of the regenerative-boost fed switched inductor and capacitor configurations. Thereby, due to such an organization of structure, the proposed converter can develop an extremely high voltage-gain even at lower duty ratios. In addition, it has the advantage of lower switching stress across all power semi-conductive diodes. Furthermore, this paper describes the steady-state analysis and comparative features of the converter with existing recent literature. Finally, to validate theoretical analysis and test the feasibility and suitability of the proposed ultra-gain converter, the experimental results were observed by a 500 W fabricated prototype. [ABSTRACT FROM AUTHOR]

Abstract Background In China, traditional energy planning is subordinate to city planning, with a primary purpose of meeting the energy demand in urban areas by planning and designing an energy system. However, most of the current energy planning in China pertains to a low-efficient system and proves to be unfriendly to the environment, which is no longer appropriate for the future development of modern industrial parks and cities. Methods A combined cooling, heating, gas and power energy network with distributed cogeneration units and renewable energy sources provides an effective solution to the related problems. In this paper, a new energy planning method for the Eco-City is proposed, which is based on a Distributed Energy Network (DEN) system. The designed method of a four-stage energy integration mode is implemented, which includes prediction of the energy demand, optimization of the energy utilization, matching of the energy supply and the recovery side. Based on the planning and designing of the DEN, the technical and economic analysis draw reasonable results. Results As implemented in the practical case of the Sino-German Eco-Park, the procedure of the proposed energy planning method is comprehensively explained. Finally, by reviewing the planning, design and operation, the current state and problems of the Sino-German Eco-Park are described. Conclusions A new energy design for Eco-Parks is developed as a result of a thorough analysis of the planning process, which integrates city planning and energy planning together and provides energy solutions for high-level cities and industrial parks. The four-stage integrated energy model provides a guideline for the entire process and its detailed steps. Relevant experiences and lessons from the energy planning of the Sino-German Eco-Park are of significance for supporting similar projects in the future.

In this study, for optimal power dispatch of a grid-connected micro-grid, a new stochastic model has been built up to minimize the operating cost of micro-grid that equipped with plug-in hybrid electric vehicles, renewable energy sources, and storage devices. Impact of electric vehicles on power dispatch is studied by considering its uncertainty charging characteristics. Monte–Carlo simulation is employed for uncertainty modeling. For this work, three different charging strategies are followed up, namely, uncontrolled, controlled, and smart charging strategy to observe the impact of electric vehicles on micro-grid. So, here an endeavor has been made using a potent and robust technique i.e. improved whale optimization algorithm for obtaining optimal power dispatch. The suggested method's appropriateness and effectiveness are evaluated by modeling a grid-connected micro-grid. The outcomes of this technique improve the MG's performances in terms of best solution and economic operation. On different case studies, the outcomes are compared with other methods without and with charging strategy of electric vehicles. It is seen that operating cost obtained without electric vehicles is nearly 300$ and with charging strategy, i.e. uncontrolled, controlled, and smart of electric vehicles are 664$, 390$, and 327$, respectively. So, inclusion of vehicles on MG increases operating cost but its impact on micro-grid significantly reduces the operating cost for smart charging strategy in comparison with other charging strategy. [ABSTRACT FROM AUTHOR]

The complication of economic load dispatch (ELD) of an electrical power system can be considered as an optimization problem with constraints to minimize the cost and emission concurrently. The optimization problems with various objectives gain significant momentum and priority with the advancements in renewable energy sources (RESs). Many techniques have been put forward to dispense this issue, but it still remains a challenging one. The existing optimization approaches are accompanied by a slow gathering rate with inferior computational complexity. To augment the ELD performance with renewable resources, in this research, the hybrid wind‐driven water wave optimization (WDWWO) strategy is presented. The effectiveness of the proposed method is validated by the consideration of the IEEE 30‐bus system. The proposed hybrid method is capable of solving convex and non‐convex economic and emission dispatch issues. From the simulation results, it can be seen that, in the case of both PV and wind, the cost analysis of the developed WDWWO model effectuate 21% reduction when compared with wind‐driven optimization (WDO), 23% reduction when compared with water wave optimization (WWO), 40% reduction when compared with modified shuffle leaf algorithm (MSFLA), and 42% reduction when compared with constrained multi‐objective population external optimization technique (COMPEO) under 200 MW. [ABSTRACT FROM AUTHOR]

With advancements in renewable energy technologies, consumers are becoming prosumers, and renewable energy resources are being used in distributed networks. In an isolated distributed system, peer-to-peer (P2P) energy trading is one of the most promising energy management solutions. In this paper, we propose a P2P energy trading method for micro-grids using open resources and technology. The proposed setup comprises an Internet of Things (IoT) server to transfer energy amongst the peers without human intervention, and an Ethereum based private blockchain is suggested for money transfer in the form of cryptocurrency. The IoT server enables the peers to control and monitor self-produced energy. Arduino UNO, ACS 712 halleffect current sensor, and a relay are the main components used in the hardware setup. The current sensor data is sent in realtime to Arduino for onward communication to the IoT server. A user-friendly interface has been developed on the server to perform various energy trading tasks. Peers have the choice to access the server remotely to perform energy trading tasks. The energy trading events can be shared amongst peers through e-mail notifications. For financial transactions, we utilized Ganache graphical user interface (GUI) a private Ethereum blockchain eliminating the need for financial institutions. The proposed peerto-peer energy trading model has been successfully tested for energy trading between two peers. This paper provides details of the proposed hardware and software setup and explains how lowcost P2P energy trading can be achieved. [ABSTRACT FROM AUTHOR]

It is now more then a choice to use small-distributed generators (DGs) based on renewable energy resources (RES) due to their benefits in minimizing environmental problems and simplifying power system planning and operation. The integration of DGs in the main grid or in forming a standalone microgrid (MG) is increasing day by day and became as an alternative solution to large conventional central power stations and as a key for the electrification of rural areas. This paper presents an overall comparison of controlling the MG system combining between different DGs in islanded mode. Energy management system (EMS) applied is mainly an essential task to maintain an ideal flow of energy in between generation and consumption, many are strategies to do so, the artificial intelligence (AI) (highlighted) is one of the most advantages methods to help sizing, optimizing, and power energy managing. To solve this problem in this paper we present a Fuzzy Logic Control of isolated MG in comparison with a classic PI control system to see the different influences in maintaining stability in voltage and frequency output especially in the standalone application. The considered HRES combines a wind turbine (WT) and photovoltaic (PV) and an energy storage system (ESS). Simulation results obtained from MATLAB/Simulink environment demonstrates the effectiveness of the proposed intelligent artificial algorithm.

This research work proposes a modified sine cosine algorithm (mSCA) to optimize the parameters of a multistage fractional‐order PD‐PI (MSFOPD‐PI) controller for load frequency control of an isolated AC microgrid. Microgrid considered here is a cluster of renewable energy sources like photovoltaic (PV), wind turbine generators (WTG) which diverges from their optimal operating point due to change in its environment. Initially, the performance of proposed mSCA algorithms is compared with the original sine cosine algorithm (CSA) well as other techniques like Crow search algorithm (CSA), Artificial bee colony (ABC), Cuckoo Search (CS), Gravitational search algorithm (GSA), Dragonfly (DA) and Genetic algorithm (GA) techniques using benchmark test functions reported in the literature. In the next stage, proposed mSCA is applied to optimize MS FOPD‐PI controller parameters under different fluctuating scenarios of source and load. To demonstrate the supremacy of the proposed MS FOPD‐PI controller the performances are compared with conventional PID, PI and I controllers. A sensitivity analysis is made by varying the system parameters to justify the controller's robustness. [ABSTRACT FROM AUTHOR]

This paper introduces a new meta-heuristic algorithm called Thieves and Police Algorithm (TPA), for solving probabilistic optimization problems. The proposed algorithm is designed in five different phases each following a certain objective. In this algorithm, conventional matters between the thieves' team and the police are inspired as a social phenomenon. Motivation of thieves for stealing more properties is utilized in this algorithm to seek the optimum solution, and police track that result in fear and no risk taking of the thieves to enter areas under police surveillance is inspired to omit areas with weaker solutions. Algorithm optimization procedure ends with apprehending the leader thief by police as the optimum solution. The performance of the TPA is evaluated in terms of local optima avoidance, exploration, exploitation, effectiveness, and convergence properties using a set of classical and modern test functions. Comparing the outcomes of other meta-heuristic optimization algorithms, the successful performance of the TPA in solving different optimization problems is confirmed. Moreover, the efficiency of the TPA in terms of solving problems with probabilistic nature is assessed by solving an optimization problem in power engineering area, known as Probabilistic Power Flow (PPF), in a Micro-Grid with renewable energy resources. Based on the outcomes, the proficiency of the proposed algorithm is confirmed by solving the PPF problem and producing timely acceptable results. Hence, the TPA algorithm is suggested to solve complicated problems, with probabilistic nature, in the optimization field and other optimization problems. [ABSTRACT FROM AUTHOR]

Mountain huts, as special, stand-alone, micro-grid systems, are not connected to a power grid and represent a burden on the environment. The micro-grid has to be flexible to cover daily and seasonal fluctuations. Heat and electricity are usually generated with fossil fuels due to the simple on-off operation. By introducing renewable energy sources (RESs), the generation of energy could be more sustainable, but the generation and consumption must be balanced. The paper describes the integration of a hydrogen-storage system (HSS) and a battery-storage system (BattS) in a mountain hut. The HSS involves a proton-exchange-membrane water electrolyser (PEMWE), a hydrogen storage tank (H 2 tank), a PEM fuel cell (PEMFC) and a BattS consisting of lead-acid batteries. Eight micro-grid configurations were modelled using HOMER and evaluated from the technical, environmental and economic points of view. A life-cycle assessment analysis was made from the cradle to the gate. The micro-grid configurations with the HSS achieve, on average, a more than 70% decrease in the environmental impacts in comparison to the state of play at the beginning, but require a larger investment. Comparing the HSS with the BattS as a seasonal energy storage, the hydrogen-based technology had advantages for all of the assessed criteria. • Eight configurations of mountain-hut stand-alone energy system are studied. • A novel approach using technical, economic and environmental criteria is introduced. • Hydrogen-storage combined with renewable energy sources is the optimal solution. • For seasonal energy storage, hydrogen is more favourable than a battery-storage. [ABSTRACT FROM AUTHOR]

Combined cooling, heating, and power system has been widely applied in the micro-grid system. However, the micro-grid system reliability and safety may be adversely affected when renewable energy resources are connected to the grid. This has made the power distribution system more challenging to manage for grid operators. The proposed energy optimization management model aims to maintain a stable and reasonable power grid distribution due to biogas power, photovoltaic power, and micro gas turbine involvement to realize better utilization. For this reason, the improved chameleon swarm algorithm is developed to solve the optimal operation cost of the system by integrating different improvement strategies. Therefore, the operation strategy can achieve both economic and environmental costs more effectively. It is verified by the objective function constructed by the economic cost, environmental cost, and renewable energy utilization. Based on the specific arithmetic analysis, the use of the strategy proposed in this study can reduce the economic cost by 1.67% in summer, 6.89% in winter, and 3.42% in transition season compared to the traditional strategy, which confirms the operational cost-effectiveness of the combined cooling, heating, and power micro-grid system in different scenarios. [ABSTRACT FROM AUTHOR]

Reliable electrical distribution system is the primary requirement of smart grid. Further, with the integration of intermittent renewable energy sources (RESs), reliability assessment is very vital. Various deterministic and probabilistic methods are utilized to assess the reliability of distribution system. This review study is about distribution system reliability assessment (DSRA) with and without renewable energy generation technologies such as micro grid, distributed generation, solar and wind. For that purpose, DSRA methods such as Monte Carlo simulation (MCS) and other DSRA methods are discussed. The distribution system reliability is considered by using the renewable energy generation techniques. The stochastic features of the parameters in the designing process defined the type of MCS simulation technique. These techniques are utilized to provide reliability assessment of compact system due to huge computational time associated with them. It can be restricted by restricting number of lumped equipments for a given renewable energy source. Further, numerous states can also be used to describe the arbitrariness in the renewable energy generation, because of the stochastic behavior of the resources and the mechanical degradation of the system.

Optimization of the stand‐alone micro‐grid is needed to minimize energy generation cost and environmental impacts. Protect the environment and saving cost can be obtained by choosing renewable energy sources as the power generation, such as photovoltaic (PV) and wind turbine. In this paper, authors investigate the hybridization utility and size optimization of a stand‐alone micro‐grid using the genetic algorithm. The considered micro‐grid consists on PV generator, wind turbines and storage batteries. This study is based on a bi‐objective optimization process. The life cycle cost (LCC) and the loss of power supply probability are respectively the economic criterion and the system reliability criterion under constraints. The energy modeling of the system's components enables energy flows simulation and further objective functions evaluation. The meteorological data of the location situated in Tunisia are considered. In order to study the resources randomness impact on systems optimal design, the data relating to December and August are used as references respectively to winter and summer. According to hybridization study, the hybrid PV/wind/battery system is the most appropriate system configuration for the studied location for both seasons compared to PV/battery or wind/battery. Results showed the significant influence of the battery bank design on the systems reliability and LCC. [ABSTRACT FROM AUTHOR]

Background: In China, traditional energy planning is subordinate to city planning, with a primary purpose of meeting the energy demand in urban areas by planning and designing an energy system. However, most of the current energy planning in China pertains to a low-efficient system and proves to be unfriendly to the environment, which is no longer appropriate for the future development of modern industrial parks and cities. Methods: A combined cooling, heating, gas and power energy network with distributed cogeneration units and renewable energy sources provides an effective solution to the related problems. In this paper, a new energy planning method for the Eco-City is proposed, which is based on a Distributed Energy Network (DEN) system. The designed method of a four-stage energy integration mode is implemented, which includes prediction of the energy demand, optimization of the energy utilization, matching of the energy supply and the recovery side. Based on the planning and designing of the DEN, the technical and economic analysis draw reasonable results. Results: As implemented in the practical case of the Sino-German Eco-Park, the procedure of the proposed energy planning method is comprehensively explained. Finally, by reviewing the planning, design and operation, the current state and problems of the Sino-German Eco-Park are described. Conclusions: A new energy design for Eco-Parks is developed as a result of a thorough analysis of the planning process, which integrates city planning and energy planning together and provides energy solutions for high-level cities and industrial parks. The four-stage integrated energy model provides a guideline for the entire process and its detailed steps. Relevant experiences and lessons from the energy planning of the Sino-German Eco-Park are of significance for supporting similar projects in the future. [ABSTRACT FROM AUTHOR]

Our objective through this work is to have a cogeneration system integrated into a hybrid production system, coupled to the public grid and equipped with an ecological production system (photovoltaic) with a storage system (accumulator), and to establish rules and tools for optimizing energy management as well as the dimensioning of an energy system. The optimizations will be carried out on the basis of consumption and production data. The optimization criterion will be economic with a view to minimizing the total cost of the system to meet the energy demand of a typical profile during a basic test period. However, "optimal" sizing requires coordination with effective energy management to identify the optimum power references, the minimum and maximum powers provided by each source and to estimate the depth of discharge of the storage element. The parts presented concern the dimensioning and management of energy transfers on the basis of consumption data and tariffs are: - Establishing reliable and adapted electrical models of the subsystems. In order to be able to determine, at each moment, for a given consumption and a given deposit, the power that the production systems can provide. These models must be sufficiently accurate to account for energy transfers and fast enough to allow calculations over long periods of time. - Establish economic models of the different modules of the system. Climate deposits, consumption profiles, fuel consumption, energy efficiency of the systems. - Finally, carry out economic and energy optimization studies of the energy system using microgrid management algorithms based on different configurations of the system. [ABSTRACT FROM AUTHOR]

With a growing demand for more energy from subscribers, a traditional electric grid is unable to meet new challenges, in the remote areas remains the extension of the conventional electric network very hard to do make prohibitively expensive. Therefore, a new advanced generation of traditional electrical is inevitable and indispensable to move toward an effective, economical, green, clean and self-correcting power system. The most well-known term used to define this next generation power system is micro grid (MG) based on renewable energy sources (RES). Since, the energy produced by RES are not constant at all times, a wide range of energy control techniques must be involved to provide a reliable power to consumers. To solve this problem in this paper we present a fuzzy logic control of isolated hybrid systems (HRES) including renewable energy in micro-grids to maintain a stability in voltage and frequency output especially in the standalone application. The considered HRES combine a wind turbine (WT) and photovoltaic (PV) panels as primary energy sources and an energy storage system (ESS) based on battery as a backup solution. Simulation results obtained from MATLAB/Simulink environment demonstrate the effectiveness of the proposed algorithm in decreasing the electricity bill of customer. [ABSTRACT FROM AUTHOR]

From the last decade, there is an increase in the demand of electricity, this will causing depletion in the fossil fuels which results increase in cost. So the focus is shifted to use of renewable energy sources along with the only utility grid but it is not sufficient to supply the power different loads. To overcome these problems, micro-grid (MG) is introduced and it is powered by renewable distributed generation (DG) systems, such as, micro turbines, fuel cells, PV and wind generation due to the limited fossil fuel. Out of the above sources, solar energy provides extraordinary benefits including environmental friendly, surplus availability and low installation cost due to the advanced technology and mass production. The solar grid connected micro inverters gain lot of intention in past few years due to its simple construction, reliability and endurability. Moreover, the grid connected micro inverter has high reliability and it can operate in abnormal conditions also like variations in voltage and current. The micro-inverter has attracted recent market success due to unique features such as lower installation cost, improved energy harvesting, and improved system efficiency. This article gives detailed review on different topologies for grid connected solar PV micro-inverter and suggests the reliable, suitable and efficient topology for micro-inverter. Article History: Received Dec 16th 2017; Received in revised form May 14th 2018; Accepted June 1st 2018; Available online How to Cite This Article: Premkumar, M., Karthick, K and Sowmya, R. (2018) A Review on Solar PV Based Grid Connected Microinverter Control Schemes and Topologies. Int. Journal of Renewable Energy Development, 7(2), 171-182. https://doi.org/10.14710/ijred.7.2.171-182

Abstract Background Micro-grid (MG) can be described as a group of controllable loads and distributed energy resources that can be connected and disconnected from the main grid and utilized in grid-connected or islanded modes considering certain electrical constraints. Methods The objective of this article are as follows: (1) predict the uncertainties through the hybrid method of WT-ANN-ICA and (2) determine the optimal generation strategy of a MG containing wind farms (WFs), photovoltaic (PV), fuel cell (FC), combined heat and power (CHP) units, tidal steam turbine (TST), and also boiler and energy storage devices (ESDs). The uncertainties include wind speed, tidal steam speed, photovoltaic power generation (PVPG), market price, power, and thermal load demand. For modeling uncertainties, an effort has been made to predict uncertainties through the hybrid method of wavelet transform (WT) in order to reduce fluctuations in the historical input data. An improved artificial neural network (ANN) based on the nonlinear structure is applied for better training and learning. Furthermore, the imperialist competitive algorithm (ICA) is applied to find the best weights and biases for minimizing the mean square error of predictions. Result The scenario-based stochastic optimization problem is proposed to determine the optimal points for the energy resources generation and to maximize the expected profit considering demand response (DR) programs and uncertainties. Conclusions In this study, three cases are assessed to confirm the performance of the proposed method. In the first case study programming, MG is isolated from grid. In the second case study, which is grid-connected mode, the WT-ANN-ICA and WT-ANN uncertainty prediction methods are compared. In the third case, which is grid-connected mode, the effect of DR programs on the expected profit of energy resources is assessed.

Mountain huts are stand-alone micro-grid systems that are not connected to a power grid. However, they impact the environment by generating electricity and through day-to-day operations. The installed generator needs to be flexible to cover fluctuations in the energy demand. Replacing fossil fuels with renewable energy sources presents a challenge when it comes to balancing electricity generation and consumption. This paper presents an integration-and-optimization process for renewable energy sources in a mountain hut’s electricity generation system combined with a lifecycle assessment. A custom computational model was developed, validated with experimental data and integrated into a TRNSYS model. Five different electricity generation topologies were modelled to find the best configuration that matches the dynamics and meets the cumulative electricity demand. A lifecycle assessment methodology was used to evaluate the environmental impacts of all the topologies for one typical operating year. The carbon footprint could be reduced by 34% in the case of the actually implemented system upgrade, and by up to 47% in the case of 100% renewable electricity generation. An investment cost analysis shows that improving the battery charging strategy has a minor effect on the payback time, but it can significantly reduce the environmental impacts.

Micro-grids consist of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. Micro-grids are small-scale networks at low voltage levels that are use to provide thermal and electrical loads of small locations where there is no access to the main electrical grid. Given the environmental and economic issues for these areas, micro-grids can be a good solution for energy production. In this paper, determining the size and location of optimal electrical energy storage systems is presented. In other side, a new method based on the cost benefit analysis for optimal sizing of an energy storage system in a microgrid (MG) is proposed. The uncertainties associated with renewable energy sources and the occurrence of defects in the grid connection network and the effect of the contribution of load responses in a micro-grid are taken into account. The combined system consists of wind turbines and fuel cells. Basically, wind power is not definitively available. The new proposed method is based on two-stage randomization design (TSRD) for modeling the effect of wind power uncertainty so that the predicted wind energy error is considered as the main random parameter in the model. A standard probability distribution function is used to represent the error variations. Given the continuity of the mentioned function, the probability error function is extracted using the new discrete method and a certain number of scenarios with a certain probability. Finally, the problem has been transformed into an optimization problem, and a gray wolf optimization (GWO) algorithm has been used to solve it. In the proposed developed model based on local and global search, the algorithm tries to reach the final result in the shortest possible time and with the most precision. The results of the simulation show the efficiency of the proposed method in solving the micro-grid problem. • Providing a model for determining the optimum location and size of the micro-grid storage system. • Providing a probability of wind energy uncertainty and photovoltaic system through prediction error modeling. • Considering the possibility of a downstream grid failure as a random phenomenon. • Providing an extended gray wolf optimization algorithm. • Reviewing and analyzing the proposed model in different scenarios. [ABSTRACT FROM AUTHOR]

In this paper, the efficient salp swarm algorithm (ESSA) is proposed to solve the energy management (EM) with emission problem of renewable micro-grid (MG) including storage devices. Because of the uncertainties in the renewable energy sources (RESs), load demand and market prices, the probabilistic approach based on (2m + 1) point estimate method and ESSA is employed to solve the probabilistic EM problem. The proposed ESSA can be derived by introducing two modifications on the conventional salp swarm algorithm (SSA) to improve the balance between exploration and exploitation, speed up the convergence and avoiding the stuck in local optima of the SSA. The ESSA is employed to solve the deterministic and probabilistic EM with emission problem. Where the multi-objective optimization problem of cost and emission functions is transferred into a single objective function to minimize the total operating cost of the MG. The proposed ESSA is evaluated using a typical grid-connected MG with energy storage devices and compared with other methods. The results verify the superiority of the ESSA to solve the EM problem of the MG over other methods. • The probabilistic approach based on ESSA and point estimate method is proposed. • The proposed method is used to solve the deterministic and probabilistic EM of MG. • The EM problem considers minimization of cost and emission as a single problem. • The proposed method has been evaluated using a typical MG with different scenarios. • Results of all cases show the superiority of the proposed method over other methods. [ABSTRACT FROM AUTHOR]

Hydrogen has been a promising energy carrier to meet the world's energy needs as well as reduce pollutant emissions. Although many countries have policies and programs to expand hydrogen production, the potential for hydrogen production in different regions of Qatar has not yet been evaluated. Therefore, this paper, for the first time, evaluates the possibility of an average annual cogeneration of 14 kWh of electricity and 85 kg/day of hydrogen by a home-scale solar-wind system connected to the grid in Qatar. NASA's 20-year average of meteorological data, the electricity tariff and gasoline price in 2018, along with annual real interest rate, were used as inputs to HOMER software. The techno-econo-enviro analysis was done over a one-year period hour by hour. From the results, it was found that the lowest prices of hydrogen and electricity generated, with $ 2.092/kg and $ 11.495/kWh, were related to Grid and PV-Wind-Grid scenarios, respectively. Also, results indicated that Ar-Ruways station and PV-Wind-Grid scenario were the most environmentally suitable options that resulted in a CO 2 emission rate of 1434 kg annually. To select just one station among five areas, a fuzzy method was deployed as a prioritization technique. Its results suggested that Doha Intl Airport site is the most suitable one for constructing solar-wind hybrid energy generation system. • The lowest electricity and hydrogen price are 11.495 $/kWh and 2.092 $/kg, respectively. • The best eco-friendly station and scenario are Ar-Ruways and Wind-PV-Grid, respectively. • Deploying FTOPSIS, implied Doha Intl Airport site was the best option. [ABSTRACT FROM AUTHOR]

Most of the Indian rural area is electrified now, but the gap between supply and demand is much greater and increasing due to rapid industrial growth and increase in rural as well as urban living standards. That cause the extra burden on power grid. In order to reduce the burden on the national grid, India is moving fast towards distributed generation, and various studies show that renewable-based micro-grid is a preeminent alternative for decentralized energy generation. Most of the Indian population live in rural areas with access to wide varieties of alternative energy sources (wind, solar, hydro, biomass, etc.). Therefore, to reduce the dependency on heavily loaded power grid, a hybrid renewable energy system is required by utilizing the locally available sources, which incorporates more than one source of energy by combining the benefits and strength of one source to complement each other. This paper proposed a PV-Biomass-Grid-based optimal micro-grid system, based on locally available resources in Kashmir region of Jammu and Kashmir, India. In the proposed micro-grid, load is served on the basis of priorities to employ minimum burden on the grid and increase the use of renewable resources. The overall goal of the study is to minimize the cost of energy and maximize the energy output from hybrid renewable energy system. Extra electricity generated beyond the load demand would be supplied to grid. In this way, system energy cost is reduced with improvement in electrical system and reducing the environmental impact. [ABSTRACT FROM AUTHOR]

Nowadays, renewable energy sources such as photovoltaic and active generators are utilized frequently in modern power systems especially micro-grid. By providing electrical energy using micro-grid systems, the reliability and quality of the system can be improved. A micro-grid connected to the network depends on the main grid and consequently assessing the power quality during independent functioning (islanded mode) is important. The aim of the present work is to perform active, reactive power distribution strategies (control) for active generators used in smart grids. The proposed control method is the sliding mode which controls the active as well as reactive power and frequency. Here, based on sliding mode control method, droop control is developed for active generator. This controller is simulated for active generator by MATLAB/SIMULINK and simulation results are given. Based on the obtained simulation results, efficiency of sliding mode in regulating active and reactive power and controlling domain voltage and frequency are shown. [ABSTRACT FROM AUTHOR]

The impact of integrating hybrid (wind and solar) renewable energy sources with energy storage devices in Micro-grid (MG) operations under the deregulated electricity market is becoming a burning issue now a days. Due to the intermittent nature of renewable, accurate load forecasting in the day-ahead electricity market is challenging. As a result, it is very difficult for retailers to balance the supply-demand in the real-time electricity market. This paper comprehensively reviews (i) the optimization approaches to determine the best results for different proposed problem-formulation techniques for precise load forecasting and (ii) profit maximizations for retailers and energy users to decrease the electricity bill for end-users and other utilities. The results obtained from the discussed problem formulation techniques have also been analyzed. Finally, based on the review, potential obstacles in this topic have been identified, and future research directions in this context have been pointed out. [ABSTRACT FROM AUTHOR]

Featured Application: Hybrid micro-grids for residential applications with connections to renewable energy sources, electric vehicle recharging stations and the AC energy grid. This paper analyzes trends in renewable-energy-sources (RES), power converters, and control strategies, as well as battery energy storage and the relevant issues in battery charging and monitoring, with reference to a new and improved energy grid. An alternative micro-grid architecture that overcomes the lack of flexibility of the classic energy grid is then described. By mixing DC and AC sources, the hybrid micro-grid proposes an alternative architecture where the use of bi-directional electric vehicle chargers creates a micro-grid that directly interconnects all the partner nodes with bi-directional energy flows. The micro-grid nodes are the main grid, the RES and the energy storage systems, both, on-board the vehicle and inside the micro-grid structure. This model is further sustained by the new products emerging in the market, since new solar inverters are appearing, where a local energy storage for the RES is available. Therefore, the power flow from/towards the RES becomes bi-directional with improved flexibility and efficiency. [ABSTRACT FROM AUTHOR]