The significant benefits associated with microgrids have led to vast efforts to expand their penetration in electric power systems. Although their deployment is rapidly growing, there are still many challenges to efficiently design, control, and operate microgrids when connected to the grid, and also when in islanded mode, where extensive research activities are underway to tackle these issues. It is necessary to have an across-the-board view of the microgrid integration in power systems. This paper presents a review of issues concerning microgrids and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, microgrid operation and control, microgrid clusters, and protection and communications issues.

State of the Art in Research on Microgrids: A Review

Hybrid energy systems are being utilized for supplying electrical energy in urban, rural and remote areas to overcome the intermittence of solar and wind resources. A hybrid renewable energy system incorporates two or more electricity generation options based on renewable energy or fossil fuel unit. The techno-economic analysis of the hybrid system is essential for the efficient utilization of renewable energy resources. Due to multiple generation systems, hybrid system analysis, is quite complex and requires to be analyzed thoroughly. This requires software tools for the design, analysis, optimization, and economic viability of the systems. In this paper, 19 softwares with their main features and current status are presented. The softwares studied are HOMER, Hybrid2, RETScreen, iHOGA, INSEL, TRNSYS, iGRHYSO, HYBRIDS, RAPSIM, SOMES, SOLSTOR, HySim, HybSim, IPSYS, HySys, Dymola/Modelica, ARES, SOLSIM, and HYBRID DESIGNER. The research work related to hybrid systems carried out using these softwares at different locations worldwide is also reviewed. The main objective of the paper is to provide the current status of these softwares to provide basic insight for a researcher to identify and utilize suitable tool for research and development studies of hybrid systems. The capabilities of different softwares are also highlighted. The limitations, availability and areas of further research have also been identified.

Review of software tools for hybrid renewable energy systems

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

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Online Short-term Solar Power Forecasting

This paper performs an extensive review on control schemes and architectures applied to dc microgrids (MGs). It covers multilayer hierarchical control schemes, coordinated control strategies, plug-and-play operations, stability and active damping aspects, as well as nonlinear control algorithms. Islanding detection, protection, and MG clusters control are also briefly summarized. All the mentioned issues are discussed with the goal of providing control design guidelines for dc MGs. The future research challenges, from the authors’ point of view, are also provided in the final concluding part.

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Review on Control of DC Microgrids and Multiple Microgrid Clusters

Variability and intermittency are some of the main features that characterize renewable energy sources. Intermittency usually includes both predictable and unpredictable variations. The many drawbacks of intermittency of renewable sources can be overcome by considering some special design considerations. Integrating more than one renewable energy source and including backup sources and storage systems are among the few measures to overcome these drawbacks. These additional design considerations usually increase the overall cost of the renewable system. Furthermore, the presence of more than one energy supply/storage system requires the control of energy flow among the various sources. Therefore, optimizing the size of the components and adopting an energy management strategy (EMS) are essential to decreasing the cost of the system and limiting its negative effects. The energy management strategy is commonly integrated with optimization to ensure the continuity of load supply and to decrease the cost of energy production. Therefore, energy management is a term that collects all the systematic procedures to control and minimize the quantity and the cost of energy used to provide a certain application with its requirements. The energy management strategy usually depends on the type of energy system and its components. Various approaches and techniques have been used to develop a successful energy management strategy. In this paper, a comprehensive review of the approaches proposed and used by authors of many papers is conducted. These approaches include both the standalone hybrid renewable energy systems and the grid-connected hybrid renewable systems. More attention is focused on popularly used techniques to address the features of each system. The selected papers in this review cover the various configurations of the hybrid renewable energy systems for electric power generation only.

https://scholar.ptuk.edu.ps/bitstream/123456789/89/1/2.pdf

Energy management strategies in hybrid renewable energy systems: A review

Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review

Based on power line carrier communication technology, a decentralized multi-agent system (DMAS)-based frequency control strategy is proposed and investigated in this study on an autonomous microgrid with communication constraints, where each agent can only communicate with its neighboring agents. Using the optimized average consensus algorithm, the global information (i.e., total active power deficiency of the microgrid) can be accurately shared in a decentralized way. Depending on the discovered global information, the cooperative frequency control strategy, which involves primary and secondary frequency control and multi-stage load shedding, is executed to achieve a cooperative frequency recovery. Simulation results indicate that the proposed frequency control approach can guarantee the consensus and coordination of the distributed agents and maintain the frequency stability of islanded microgrids even in emergency conditions.

Decentralized Multi-Agent System-Based Cooperative Frequency Control for Autonomous Microgrids With Communication Constraints

This paper proposes a distributed adaptive robust voltage/var control (DAR-VVC) method in active distribution networks to minimize power loss while keeping operating constraints under uncertainties. The DAR-VVC aims to coordinate on-load tap changers, capacitor banks and PV inverters in multiple operation stages through a distributed algorithm. To improve efficiency of the distributed algorithm, an affinity propagation clustering algorithm is employed to divide the distribution network by aggregating “the close nodes” together and setting “the far nodes” apart, leading to the network partition where the information exchange between adjacent sub-networks is reduced. Moreover, the virtual load which describes load characteristics of the sub-networks is applied to enhance the boundary conditions. To fully deal with the uncertainties, the proposed DAR-VVC is formulated in a robust optimization model which considers the worst case to guarantee solution robustness against uncertainty realization. Besides, this paper develops an alternating optimization procedure integrating a column-and-constraint generation algorithm and an alternating direction method of multipliers to solve the DAR-VVC problem. The proposed approach is tested on IEEE 33 and IEEE 123 bus distribution test system and numerical simulations verify high efficiency and full solution robustness of the DAR-VVC.

Distributed Adaptive Robust Voltage/VAR Control With Network Partition in Active Distribution Networks

Frequency stability of islanded smart distribution networks with peer-to-peer (P2P) controlled distributed generators (DGs) has attracted special attention recently. Using power line communication (PLC) technology, a multi-agent system (MAS)-based, decentralized, under-frequency load shedding (UFLS) scheme is investigated in this study for smart distribution networks with the communication constraint that each agent can only communicate with its neighboring agents. The proposed scheme uses the two-layer nearest neighbor consensus algorithm (NNCA) to overcome the drawback of the leader–follower consensus algorithm and guarantee the implementation of a decentralized UFLS. Based on the global information (i.e., the magnitude of the total active power imbalance) discovered in the first layer of the NNCA, the multi-stage UFLS can be executed by the second layer of the NNCA. Simulation results demonstrate that the proposed scheme can effectively implement the decentralized UFLS while maintaining the frequency stability of an islanded smart distribution network.

Adaptive Decentralized Under-Frequency Load Shedding for Islanded Smart Distribution Networks

This paper proposes an image-based algorithm for detecting and cleaning the wind turbine abnormal data based on wind power curve (WPC) images. The abnormal data are categorized into three types, negative points, scattered points, and stacked points. The proposed algorithm includes three steps, data pre-cleaning, normal data extraction, and data marking. The negative abnormal points, whose wind speed is greater than cut-in speed and power is below zero, are first filtered in the data pre-cleaning step. The scatter figure of the rest wind power data forms the WPC image and corresponding binary image. In the normal data extraction step, the principle part of the WPC binary image, representing the normal data, is extracted by the mathematical morphology operation (MMO). The optimal parameter setting of MMO is determined by minimizing the dissimilarity between the extracted principle part and the reference WPC image based on Hu moments. In the data mark step, the pixel points of scattered and stacked abnormal data are successively identified. The mapping relationship between the wind power points and image pixel points is built to mark the wind turbine normal and abnormal data. The proposed image-based algorithm is compared with k -means, local outlier factor, combined algorithm based on change point grouping algorithm and quartile algorithm (CA). Numerous experiments based on 33 wind turbines from two wind farms are conducted to validate the effectiveness, efficiency, and universality of the proposed method.

Zaijun Wu

Papers

Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review

Decentralized Multi-Agent System-Based Cooperative Frequency Control for Autonomous Microgrids With Communication Constraints

Distributed Adaptive Robust Voltage/VAR Control With Network Partition in Active Distribution Networks

Adaptive Decentralized Under-Frequency Load Shedding for Islanded Smart Distribution Networks

Image-Based Abnormal Data Detection and Cleaning Algorithm via Wind Power Curve