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Demand forecasting produces valuable information for optimal supply chain management. The basic metals industry is the most energy-intensive industries in the electricity supply chain. There are some differences between this chain and other supply chains including the impossibility of large-scale energy storage, reservation constraints, high costs, limitations on electricity transmission lines capacity, real-time response to high-priority strategic demand, and a variety of energy rates at different hours and seasons. A coupled demand forecasting approach is presented in this paper to forecast the demand time series of the metal industries microgrid with minimum available input data (only demand time series). The proposed method consists of wavelet decomposition in the first step. The training subsets and the validation subsets are used in the training and fine-tuning of the LSTM model using the ELATLBO method. The ESC dataset used in this study for electrical demand forecasting includes 24-h daily over 40 months from 21 March 2017, to 21 June 2020. The obtained results have been compared with the results of Support Vector Machine (SVM), Decision Tree, Boosted Tree, and Random Forest forecasting models optimized using the Bayesian Optimization (BO) method. The results show that performance of the proposed method is well in demand forecasting of the metal industries.

Self-healing grids are one of the most developing concepts applied in electrical engineering. Each restoration strategy requires advanced algorithms responsible for the creation of local power systems. Multi-agent automation solutions dedicated for smart grids are mostly based on Prim’s algorithm. Graph theory in that field also leaves many problems unsolved. This paper is focused on a variation of Prim’s algorithm utility for a multi-sourced power system topology. The logic described in the paper is a novel concept combined with a proposal of a multi-parametrized weight calculation formula representing transmission features of energy delivered to loads present in a considered grid. The weight is expressed as the combination of three elements: real power, reactive power, and real power losses. The proposal of a novel algorithm was verified in a simulation model of a power system. The new restoration logic was compared with the proposal of the strategy presented in other recently published articles. The novel concept of restoration strategy dedicated to multi-sourced power systems was verified positively by simulations. The proposed solution proved its usefulness and applicability.

Pooling different renewable energy sources (hydrogen, solar, wind, geothermal, etc.) enables developing a standalone energy micro-grid. The energy flows from these various sources are neither constant nor equivalent. Therefore, control and balancing mechanisms should be established for optimal energy utilization through an intelligent system based on interconnected microcontrollers networked with sensors. Our contribution addresses this issue by proposing an original architecture of an intelligent and distributed control system based on a sensor network and a strategy to share the electric power through the micro-grid. In our work we consider a micro-grid powered by sources of wind turbine, pv panels and battery which energy flows are controlled and balanced through our system depending on power demand of the loads. Alternating Current (AC) bus and Direct Current (DC) bus are tied together by an inverter. A set of microcontroller-sensor-actuators (which we named S.A.D for Sensor/Actuator Device) are deployed at strategic points in the micro-grid providing constantly data from power generated and consumed, equipment health and status. A control algorithm developed in relation to a network control strategy is implemented by combining the performance different microcontroller boards. Relying on existing literature works, a review of solution approaches to the challenging problem, of the power flows balancing between the different energy sources and storage batteries embedding appropriate IoT technologies and exploiting energy big-data platforms, is presented

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]

This paper presents an efficient data-driven building electricity management system that integrates a battery energy storage (BES) and photovoltaic panels to support decision-making capabilities. In this micro-grid (MG) system, solar panels and power grid supply the electricity to the building and the BES acts as a buffer to alleviate the uncertain effects of solar energy generation and the demands of the building. In this study, we formulate the problem as a Markov decision process and model the uncertainties in the MG system, using martingale model of forecast evolution method. To control the system, lookahead policies with deterministic/stochastic forecasts are implemented. In addition, wait-and-see, greedy and updated greedy policies are used to benchmark the performance of lookahead policies. Furthermore, by varying the charging/discharging rate, we obtain the different battery size E s and transmission line power capacity (P max) accordingly, and then we investigate how the different E s and P max affect the performance of control policies. The numerical experiments demonstrate that the lookahead policy with stochastic forecasts performs better than the lookahead policy with deterministic forecasts when the E s and P max are large enough, and the lookahead policies outperform the greedy and updated policies in all case studies. [ABSTRACT FROM AUTHOR]

This article presents a three-port dc/dc converter along with a new control system to integrate solar panels and energy storage in a dc nanogrid. The proposed control system utilizes the duty cycle as well as the switching frequency to control the power flow between the three ports. The proposed converter offers a low number of components, low cost, and high reliability. Moreover, it is capable of providing bidirectional power flow for the energy storage system and the dc grid while having soft-switching for power semiconductors. The converter demonstrates high design flexibility in accommodating different applications. Theoretical analysis and experimental results verify the feasibility and demonstrate the superior performance of the proposed converter. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management system for an effective operation of micro-grids. In recent years, the penetration of the distributed energy resources such as photovoltaic and wind turbine generators into the electrical networks has been increasing. These resources generate perturbations and instability in the electrical grid. Battery energy storage system is the most used recently on micro-grids in order to manage the integration of intermittent of energy resources and help the control system to reduce the undesirable effects like power fluctuations because of perturbations (fault, short-circuit,...etc). However, for control and management purposes, a power distribution network can be modeled as a discrete or hybrid (discrete events and continuous variables) event system. This distributed topology will enhance the resilience of the power grid, facilitate the integration of distributed energy resources, especially renewable sources, accommodate energy storage units, and enhance the quality of delivered power. The main objective of this paper is to highlight a methodology for surveillance and performance analysis of a micro-grid during a perturbation based on discrete event systems. This systematic control method is effective in managing many operations in smart grids. As result, the formulation of power network control problems in discrete event system is useful for energy management, control, modeling, and monitoring with diagnosis applications. [ABSTRACT FROM AUTHOR]

Palestine has a large number of remote small communities with no electricity services and the probability of connecting them with local grid in the near future is very poor, because of political and financial issues. This paper will illustrate the techno- economic impact of electrification small communities using micro-grid photovoltaic systems, and also the CO2 emission that will be reduced in comparative of using diesel generator which pollutes the environment. The implementation of two micro-grid PV-systems for electrification of two communities in Palestine will cover the electricity needs of households and street lighting, and can replace candles, kerosene and traditional unsustainable biomass and diesel generators. Beside economic and social benefits, micro-grid PV systems also have positive impacts on people’s health and on the environment due to the reduction of smoke and toxic waste. [ABSTRACT FROM AUTHOR]

The design and control of an intelligent integrated standalone micro-grid (I-ISMG) have been proposed in this study. The ISMG system consists of solar photovoltaic (SPV), wind turbine generator (WTG), diesel engine generator (DEG) as distributed power generation (PG), and battery and flywheel as energy storage systems (ESSs). An improved incremental conductance (I-InC) maximum power point (MPP) tracking (MPPT) scheme, and a fuzzy wind power generation model (FWPGM) are utilized to obtain the power from solar, and wind energy systems respectively. The key contribution of this work is to control the power flow for synchronous micro-grid (MG) operation, which in turn resolves the problem of load frequency control (LFC). In this control strategy, an intelligent, i.e. fuzzy logic-based adaptive control scheme is proposed for the coordinated power flow among the generation, demand, and storage system. To minimize the frequency deviation (Δ f) and control of PG from WTG and DEG, frequency support (FS) fuzzy logic-based droop characteristic is employed. For the droop control in WTG and DEG, fuzzy logic-based proportional–integral–derivative (F-PID), and self-tuned-fuzzy PID (STF-PID) control schemes are utilized respectively. Apart from droop controls, a fuzzy observer (FO) is designed to manage power flow to/from the storage systems. Further, the proposed control scheme has been benchmarked using single area power system (SAPS) and modified New England IEEE 39 bus system. [Display omitted] • Developing the accurate model of ISMG system including SPV, WTG, DEG and energy storage systems. • Extraction of power from solar and wind energy using I-InC MPPT technique and FWPGM respectively. • To design frequency support FD i.e., F-PID and STF-PID droop controls for WTG and DEG respectively and compare with CDs. • Designing of fuzzy observer to enhance the storage and supply capability of energy storage systems. [ABSTRACT FROM AUTHOR]

In recent years, the integration of renewable generation into micro-grid has been growing. Therefore, it is essential to optimize the power generation from multiple sources with minimal cost. This paper presents a Memory-Based Gravitational Search Algorithm (MBGSA) for solving the economic load dispatch in a micro-grid. The problem with current metaheuristic optimization techniques and the conventional gravitational search algorithm (GSA) are largely associated with slow gathering rate, less memory to save the best agent position of the optimal solution and poor performance in solving the complex optimization problems. The MBGSA is based on the concept of saving the best solution of the agent from the last iteration to calculate the new agent based on Newton's laws of gravitation. In this work, the MBGSA has been utilized to optimize power generation from multiple generation sources such as Photovoltaic (PV) systems, combined heat power (CHP) systems, and diesel generators. The results have been compared to classic methods such as Quadratic Programming (QP) and other metaheuristics techniques such as the GSA, Artificial Bee Colony (ABC), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results illustrate that the proposed method has higher performance in solving the optimal power generation problem compared to other methods. [ABSTRACT FROM AUTHOR]

• Reactive power control of micro-grids during asymmetrical voltage sags to comply with modern grid code requirements. • Sharing requested reactive power among the grid connected sources in the secondary layer. • Restoring the PCC voltage by independently controlling each phase of the grid connected sources in the primary layer. • Implementing a nonlinear robust fractional-order sliding mode controller in the primary control layer. • Keeping the power quality indexes such as total harmonic distortion (THD) and voltage imbalance factor in acceptable range during LVRT conditions. Application of micro-grids (MGs) as a solution for future energy systems are significantly increasing in recent years. On the other hand, more stringent requirements are added to grid codes (GCs) of low-voltage distribution networks. Accordingly, this paper proposes a low-voltage ride-through (LVRT) control scheme for four-wire multi-source MGs. The novel strategy is composed of two layers for controlling each phase of the grid-connected sources independently. The primary layer contains a reverse-droop function for each phase and fractional-order sliding-mode-control (FOSMC). The secondary layer determines the total requested reactive power for each phase during symmetrical and asymmetrical voltage drops. Then, the mentioned power is shared among the phases of each inverter based on the reactive power-sharing strategy and GC requirements. Based on the proposed grid-following controller, the voltage of faulty phases is compensated by reactive power injection. In addition, power quality indexes are kept in acceptable ranges during abnormalities, and the FOSMC performs better than the conventional methods. The effectiveness of the proposed scheme is verified through offline simulations in MATLAB/Simulink as well as validation by real-time results. [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]

Purpose. Despite their economic and environmental benefits, distributed products in power systems have caused problems in power systems. One of the most important issues in this regard is voltage fluctuations and frequencies in Micro-grids, which depends on several factors, such as variable consumption load and errors in power systems. One of the main challenges associated with the use of Micro-grids is power management among distributed generation sources. Power management plays a pivotal role in numerous Micro-grids and may ensure the stable and improved performance of Micro-grids in the permanent status of the system. The present study aimed to examine the power control in Micro-grids by proposing an adaptive control method along with the PID controller for power management and coordination in Micro-grids. This coordination system operates between production sources and controlling the voltage and frequency levels against the possible disturbances occurring anywhere in the system loop. The results of the simulation of the proposed algorithm in MATLAB software environment exhibited a high success rate (i.e., proper response to the fluctuations in the Micro-grid) and extremely low error rate (i.e., proper reactive power in the grid).

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.

The smart energy management system as a powerful tool is implemented to manage both demands and generation units. The energy management problem in a Microgrid is usually formulated as a nonlinear optimization problem. According to nonlinear and discreet nature of the problem, solving it by a centralized method requires high computational capabilities. In this paper, two distributed energy management system called Alternating Direction Method of multiplier Predictor (ADMM) and Corrector Proximal Multiplier (PCPM) have been investigated in order to jointly schedule the central controller as well as local controllers. The algorithms consider optimal power flow equations within the distributed energy management problem. The proposed distributed algorithms have been investigated on a typical MG and the efficiency of the algorithm has been evidenced through case studies. Simulation results show that the proposed method decreases the operational cost of MG. Also, the results evidenced that the ADMM has been converged faster and provided a lower operation cost if compared to the PCPM.

Under the background of energy internet, the scale of renewable energy interconnection is increasing day by day. Due to the strong randomness of renewable energy generation power, the so-called "bilateral stochastic problems" appears in the operation of the power system, which affects the safety and stability. How to manage all kinds of renewable energy economically and effectively is one of the key technologies to realize the energy internet. In order to realize the overall optimization of microgrid operation, a multi-objective optimization model was established for microgrid including various distributed generators (DG) with the lowest comprehensive power generation cost and the best environmental benefits as the optimization objectives. The genetic algorithm is used to optimize the energy management. The simulation results of a microgrid with multiple DGs running on the grid show the effectiveness of the optimization model and algorithm.

This paper investigates the possibility of building the energy Internet via a packetized management of non-industrial loads. The proposed solution is based on the cyber-physical implementation of energy packets, where flexible loads send user requests to an energy server. Based on the existing literature, we explain how and why this approach could scale up to interconnected micro-grids, also pointing out the challenges involved in relation to the physical deployment of the electricity network. We then assess how machine-type wireless communications, as part of 5G and beyond systems, will achieve the low latency and ultra-reliability needed by the micro-grid protection while providing the massive coverage needed by the packetized management. This more distributed grid organization also requires localized governance models. We cite few existing examples as local markets, energy communities, and micro-operator that support such novel arrangements. We conclude this paper by providing an overview of ongoing activities that support the proposed vision and the possible ways to move forward.

With the increasing capacity of new energy in the power system, new energy cannot provide support for the system frequency directly. This characteristic of new energy affects the frequency stability of the power system. Therefore the control strategy of a virtual synchronous generator (VSG) is proposed to improve the frequency stability of the system. An adaptive virtual inertia control strategy based on an improved bang-bang control strategy for a micro-grid is presented. On one hand, it can make full use of the variability of virtual inertia to reduce dynamic frequency deviation. On the other hand, the steady-state interval of frequency and the steady-state inertia are set to improve the system frequency stability. Then the stability analysis of the value range of the virtual inertia is performed by the small signal model of the VSG for the micro-grid. Meanwhile, the ranges of virtual inertia and steady-state inertia are determined. Finally, Matlab/Simulink is applied to accomplish simulation experiments to compare various virtual inertia control strategies. The results indicate the effectiveness of the proposed strategy.

Distributed Energy Resources (DERs) are being integrated into the power market by customers rather than large scale energy suppliers, thereby slowly transforming the centralized, unidirectional market to a decentralized, bidirectional market and transitioning customers into prosumers. Various system architectures are used in the real field to coordinate the energy distribution in the micro/ mini-grids integrated with DERs, all of which have their strengths, weaknesses and challenges. Peer-to-peer (P2P) is an emerging architecture in the field of electrical energy trading and Distributed Generation (DG) management that can be applied in local energy markets. This paper focuses on P2P energy trading, with an in-depth discussion on its various operating algorithms, their principles, characteristics, features and scope through state of art review on P2P. Furthermore, the energy system of Nepal is used as a case study in this paper, and the micro/mini-grids of Nepal and their associated challenges, constraints and opportunities for improvement are discussed. Finally, an energy trading model is proposed to address the problems occurring in the specific case of Nepalese energy market.

Aiming at the cooperative optimization problem of economy and environmental protection of the traditional microgrid, including micro gas turbine and diesel engine, carbon capture and storage, and a power to gas system which can consume wind and light and deal with carbon dioxide, is introduced, and three optimization scheduling models of the microgrid based on improved BBO algorithm are proposed. Firstly, a micro-grid with a power to gas system is constructed, and an optimal scheduling model is built which takes into account the system operation cost, environmental governance cost and comprehensive economic benefit. Secondly, the ecological expansion operation is introduced, an improved BBO algorithm is explored by improving the mobility model, and its convergence is derived in detail. Finally, the microgrid system energy optimization scheduling is realized based on the improved BBO algorithm. Compared with the scheduling model that only considers the operation cost or pollution gas control cost, the total cost of the comprehensive economic benefit scheduling model is reduced by 15.5% and 5.5%, respectively, which reflects the reasonableness of the scheduling model and the effectiveness of the improved algorithm.

Bridging the gap between simulation and reality for successful micro-grid (MG) implementation requires accurate mathematical modelling of the underlying energy infrastructure and extensive optimisation of the design space defined by all possible combinations of the size of the equipment. While exact mathematical optimisation approaches to the MG capacity planning are highly computationally efficient, they often fail to preserve the associated problem nonlinearities and non-convexities. This translates into the fact that the available MG sizing tools potentially return a sub-optimal (inferior) MG design. This brings to light the importance of nature-inspired, swarm-based meta-heuristic optimisation algorithms that are able to effectively handle the nonlinear and non-convex nature of the MG design optimisation problem – and better approximate the globally optimum solution – though at the expense of increased computational complexity. Accordingly, this paper introduces a robust MG capacity planning optimisation framework based on a state-of-the-art meta-heuristic, namely the Lévy-flight moth-flame optimisation algorithm (MFOA). An intelligent linear programming-based day-ahead energy scheduling design is, additionally, integrated into the proposed model. A case study is presented for a real grid-tied community MG in rural New Zealand. A comparison of the modelling results with those of the most popular tool in the literature and industry, HOMER Pro, verifies the superiority of the proposed meta-heuristic-based MG sizing model. Additionally, the efficiency of the Lévy-flight MFOA is compared to nine well-established meta-heuristics in the MG capacity planning literature. The comparative analyses have revealed the statistically significant outperformance of the Lévy-flight MFOA to the examined meta-heuristics. Notably, its superiority to the original MFOA, the hybrid genetic algorithm-particle swarm optimisation, and the ant colony optimiser, by at least ~6.5%, ~8.4%, and ~12.8%, is demonstrated. Moreover, comprehensive capital budgeting analyses have confirmed the financial viability of the test-case system optimised by the proposed model.

When the micro-grid is connected to the grid, the influence of the load and the micropower supply’s operating characteristics may cause the power exchange between the micro-grid and the large grid to cause unnecessary losses. The objective function of power supply satisfaction of large power grids. The article takes the cogeneration micro-grid as the research object. It proposes establishing an objective function including the satisfaction of micro-grid decision-makers, the satisfaction of social benefits, and the satisfaction of large-scale power supply using the analytic hierarchy process. After that, the article uses the particle swarm optimization algorithm combined with the typical dispatching strategy of the micro-grid to solve the aforementioned model with MATLAB as the primary tool, and obtains the 24 hour output optimization results of the micro-grid, and analyzes the results in detail. The calculation examples show that the model can achieve economic optimization based on satisfying the satisfaction of different groups, which verifies the scientificity and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Summary: In the islanded micro‐grids the power cannot be exchanged by the upstream network and the system inertia level is low. Therefore, the network sensitivity to disturbances and load changes is more than the connected mode. In such situation, the system states can behave with more degree of freedom and have less dependency to total system dynamic. The variety of equipment with different dynamics and the load level uncertainty leads to continuous changes in the islanded micro‐grid operating point. These changes are more considerable in the islanded micro‐grid due to lack of inertia and limited stability margins. Therefore, the determination of appropriate model order for system in such way that the reduced order model keeps the essential dynamic behavior and neglects the less effective modes, is critical. Therefore, in this paper a three‐step method based on multi‐time scale theory and singular perturbation method is proposed for optimum model order reduction in islanded micro‐grid. The proposed method is applied to a typical micro‐grid with different types of DGs and loads. The load level uncertainty is modeled by probability density function (PDF) and reduced order model of system is validated in different operating scenarios by sensitivity analysis, participation factor investigation and time domain simulation. [ABSTRACT FROM AUTHOR]

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]

Owing to a lack of systematic mathematical models to represent the parameters related to micro‐source disconnection, the stability of interaction between a grid and a micro‐grid with series micro‐source inverters (SMSI‐MG) is difficult to analyze. In this study, the average ratio and the degree of imbalance of three‐phase micro‐source disconnection, denoted respectively by δ and ϵ, are defined, and the positive‐ and negative‐sequence impedances of the SMSI‐MG containing the two parameters are established using a combination of the harmonic linearization and the symmetrical component methods. Moreover, rules of the behavior of the SMSI‐MG after micro‐source disconnection as well as its mechanism of interaction with the grid are revealed by the impedance models. The results of analysis show that high‐frequency harmonic resonance was generated around the carrier frequency when δ ≠ 0, and both high‐ and low‐frequency harmonic resonances were generated when both δ and ϵ increased in the interactive system. Furthermore, three SMSI‐MGs containing five, 10, and 20 micro‐sources in a single phase were simulated, and the results verified the correctness, feasibility, and advantages of the simplified analysis of the impedance models. These insights can be used to apply the corresponding control strategies in a timely manner according to the given conditions of micro‐source disconnection to reduce the harmonic resonance and increase the stability of the SMSI‐MG and the grid. The results of simulations verified the effectiveness of such a control strategy. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In traditional distribution networks, due to the radial structure and subscriber power supply, in the event of errors in the main feeders, downstream subscribers experienced a long blackout, which reduced the reliability level of the network. With the expansion of the use of scattered generations, storage resources and the use of load response methods, load retrieval is provided in the downstream area. In this paper, a method is proposed for locating sectionners and distributed generations in distribution networks with the aim of creating flexible micro-grids. By creating an island, disassembling and restoring loads of more importance in the distribution network, on the one hand, it reduces the amount of fines paid by the distribution company to Subscribers and economic interests of the distribution company, and, on the other hand, by improving reliability, provides customer satisfaction. [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]

This paper presents an Enhanced PSO (EPSO) method to address the weaknesses associated with the traditional PSO such as high sensitivity to the initial conditions, fast convergence, decline of solutions' variety, and trapping in the local optimum. For this, several strategies are suggested including segmentation of search space, modification of solution's updating rule, accepting poor solutions using an intelligent probabilistic function, searching in the regions with poor solutions, gradually removing the regions with poor solutions, and focusing the solutions on the local search after removing all regions with poor solutions. The performance of the EPSO was investigated using the 30 CEC 2014 test functions, 30 CEC 2017 test functions, 10 standard optimization algorithms, and a challenging optimization problem called Probabilistic Load Flow (PLF) in a distribution network. The results of the Wilcoxon signed-rank test on the 2014 and 2017 test functions revealed the superiority of the EPSO over nearly 80% of cases compared to the other algorithms. The obtained results of solving 10 benchmark functions by the proposed EPSO and the other six improved PSO algorithms indicated advantages of the proposed EPSO compared to the other algorithms in finding the optimal value. Meanwhile, the proposed EPSO took the average time of 0.66 s to solve the 10 test functions; it was the shortest time compared to other improved PSO algorithms. According to the results of implementing the EPSO algorithm and other algorithms with random agents for 61 times, in more than 65% of the test functions, the proposed EPSO could find the global optimal solution in a shorter time than the other algorithms. The results of solving the probabilistic load flow problem indicated 89% similarity of the results of the proposed EPSO to those of the most accurate method i.e. Monte Carlo Simulation (MCS). Comparison of the obtained results with the other algorithms as well as outcomes of several improved versions of the PSO indicated the competitive proficiency of the proposed EPSO in various optimization circumstances. [ABSTRACT FROM AUTHOR]

To solve the problems of heavy communication load and economic distribution of active power in distributed control in the traditional micro-grid system, an event-based active power control strategy is proposed. Based on the traditional consistent algorithm, each distributed power source adoptes the event triggering method, which effectively reduces the communication burden. Each distributed power source communicates with its neighbors through the event trigger controller and exchanges active power information with each other. The optimal active power value is obtained through the event trigger control algorithm, and the economic distribution of power is realized under the minimum cost. Finally, a micro-grid system model is established by MATLAB/Simulink platform, and the simulation experiment verifies the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

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Background: The smart grid systems with advanced cyber technologies are vulnerable to the false data injection attacks. The attacks will bypass the online state estimation monitoring, steal energy by a false dispatch in the electricity markets, and gain financial benefits. In this paper, the FDI attack case in the electricity markets of a smart grid power system is considered with micro‐grids connected. The estimated transmitted power is modified, leading to the change of electricity prices which will bring the benefit to the attacker. Further, influence on the optimization output of energy management for a Micro‐grid has been discussed. By trading online electricity with the main grid, the micro‐grids are able to maintain power balance and reduce generation cost of the whole smart grid system power system. Then different optimal generation outputs of Micro‐grid and total generation cost of the power system are also shown and discussed. Aims: The focus of this paper is to investigate the FDI attack with limited information of the system topology and dynamics, and to discuss the economic impact of such limited attacks, where the estimated transmitted power is modified and the electricity prices are correspondingly changed for benefits. Different optimal generation outputs of Micro‐grid and total generation cost of the power system are also shown and discussed. Materials & Methods: MG‐based electricity markets model of a SG power system is considered for FDI attacks. Strategy with partial information is proposed from the attack's view.In real‐time market, optimization output changes of energy management for a MG when considering the electricity trading are discussed.The quick time detection method is developed with the feature extraction technique for an accurate real‐time estimation of the fault detection, when the operator is with partial information of the system topology.Digital signal processing approaches of the discrete wavelet transform (DWT) are adopted to better extract the time‐frequency characteristics of the input transient signals and improve the detection. Results: By considering of the distributed characteristics of MGs and the optimal operation requirements of system, we transformed the problem into a standard stochastic convex program, then solved the problem by the scenario generation method. We used a modified‐IEEE standard 14‐bus system and an IEEE standard 118‐bus system to show the effect of false data injection attacks in electricity prices and the influence of the optimization output of energy management for a MG. We employed modified‐online CUSUM algorithm to establish the quickest detection of the attacks. Numerical simulation results have shown that the proposed scheme is robust to the time‐varying dynamic system, and is efficient in terms of detection accuracy for the random and structured attacks. Discussion Modified‐online CUSUM algorithm is proposed with wavelet transform and feature extraction method for the quickest detection of the attacks. Numerical simulation results have shown that the proposed scheme is robust to the time‐varying dynamic system, and is efficient in terms of detection accuracy for the random and structured attacks. The proposed detectors are also proved to have quick and reliable responses to small attack magnitudes, which are difficult to be detected by traditional CUSUM algorithm. Further, the detection method is employed on IEEE standard 118‐bus system to be compared. The simulation results have shown that the proposed model is also adapted to complex system. Conclusion: In this paper, the impact of false data injection attacks on real‐time electricity markets of the SG power system with MGs connected is investigated. Attack strategies are designed of worst‐case robustness about grid dynamics when the attacker has partial information about the grid, where the attacker can still manipulate nodal prices of the real‐time markets and maximize the profit under different degrees of uncertainties. The optimal generation outputs of MGs are discussed for the ability of maintaining power balance of the whole SG power system by trading online electricity. Besides, the detection problem with partial information of the grid topology is also investigated from grid operator's view, and a new modified detection method is proposed to defend FDI attack when considering the existence of the unknown. Modified‐online CUSUM algorithm is proposed with wavelet transform and feature extraction method for the quickest detection of the attacks. Numerical simulation results have shown that the proposed scheme is robust to the time‐varying dynamic system, and is efficient in terms of detection accuracy for the random and structured attacks. [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]

Modern distribution system including Distributed Generation (DG) requires reliable and fast islanding detection algorithms in order to determine the grid status. In this paper, a new multi-model classification-based method is proposed, in order to detect islanding condition for photovoltaic units. Decision tree is chosen as the classification algorithm to classify input feature vectors. The final result is based on voting among three decision tree algorithms. First order derivatives of electrical parameters are employed to construct feature vectors. To cover intermittent nature of renewable sources, different generating states for PV unit are assumed. Probable events are simulated under different system operating states to generate classification data set. The proposed method is tested on typical distribution system including the PV unit, different loads, and synchronous generator. This study showed that this method succeeds in highly fast islanding detection. This quick response can be used in micro-grid application as well as anti-islanding strategy. The results revealed that the proposed voting-base algorithm could classify instances with very high accuracy which leads to reliable operation of distributed generation units. [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]

Even though solar power generation has become an emerging trend in the world, its penetration into the utility grid as a distributed generation source is not a satisfactory measure due to the inherent issues related to solar photovoltaic systems (SPVSs). In addressing these issues, microgrids have been identified as suitable integrating platforms for distributed, clean energy resources such as SPV. Different SPV and microgrid architectures are available for different applications depending on the resource availability and controllability. Reconfigurability is a concept that makes a system adaptable to two or more different environments by effectively utilizing the available resources. The review explains the applications of reconfigurable approaches on solar PV systems such as reconfigurable PV arrays, power conditioning unit (DC/DC converter, DC/AC inverter), microgrid controller and topology of distribution network with relevant studies. An analysis is also presented considering the unique features of reconfigurable systems in comparison to the static systems.

On account of the variable topology and multi-terminal power supply in micro-grid, the fault diagnosis faces more and more challenges. Traditional fault location criteria are unsuitable and fault diagnosis modelling is complex or poor versatility. Further on, the fault reasoning operation is time-consuming. A high transplantable fault diagnosis model aiming at the fault features in micro-grid is established in this paper, and a simple inference algorithm with good error-detecting capability is proposed. Firstly, the fault location criterion based on current magnitude, current phase and Distributed Generation’s current direction information is proposed, and the fault transient component is adopted as a supplementary criterion. Secondly, a hierarchical Petri net model utilizing the electrical information, relays’ and circuit breakers’ state information is accomplished. The model consists of fault location layer and fault clearance layer. In order to increase the portability of the model, the collective processing for the breakers is implemented. Moreover, ‘bidirectional arrowhead arc’ is introduced to reduce the number of places to optimize the Petri net model well. An improved redundant coding Petri net reasoning algorithm is proposed based on the fault clearance layer of the Petri net model. Finally, the validity of the method is verified through case analysis and comparison.

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

The increasing concern of power systems toward distributed generation enables modern power grids and energy management systems to focus their concentration to derive an optimal operational planning with regard to energy costs minimization of Micro-Grid and better utilization of Renewable Energy Sources in the presence of Battery Energy Storage. This paper presents Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (SIMBO-Q) to minimize total operation cost of Micro-Grid considering optimal size of Battery Energy Storage. SIMBO-Q performs the optimization through quarantine and treatment loop based on probability. However SIMBO-Q algorithm takes large number of iterations to reach to the optimum solution if the system has large number of variables. To overcome this limitation and to improve computational efficiency, quasi-opposition based learning concept is introduced in basic SIMBO-Q algorithm. The proposed algorithm is tested on a typical Micro-Grid and simulation results establish that the proposed approach outperforms several existing optimization techniques.

To enhance the stability of a micro-grid under fault conditions, this paper proposes the coordination control of a superconducting magnetic energy storage (SMES), an active superconducting fault current limiter (SFCL), and distributed generation units via wireless network communications. This coordination control can smoothly separate the micro-grid from the main network in the case of severe or permanent faults, and assist the micro-grid to achieve the fault ride-through (FRT) operation if the fault is minor or temporary. Details on the modeling, control strategy, and network architecture are presented. Moreover, the simulation analysis of a 10-kV class micro-grid including the SMES, SFCL, and photovoltaic generation units is implemented in MATLAB. Concerning the performance evaluation of the coordination control not only the severe and minor faults but also different communication delays are taken into account. The results confirm the effectiveness of the proposed coordination control.