Showing papers on "Microgrid published in 2022"

Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-Based Multi-Period Forecasting

Abstract: In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement learning-based multi-period forecasting of renewable power generations and loads. Firstly, a prioritized experience replay automated reinforcement learning (PER-AutoRL) is designed to simplify the deployment of deep reinforcement learning (DRL)-based forecasting model in a customized manner, the single-step multi-period forecasting method based on PER-AutoRL is proposed for the first time to address the error accumulation issue suffered by existing multi-step forecasting methods, then the prediction values obtained by the proposed forecasting method are revised via the error distribution to improve the prediction accuracy; secondly, a scheduling model considering demand response is constructed to minimize the total microgrid operating costs, where the revised forecasting values are used as the dispatch basis, and a spinning reserve chance constraint is set according to the error distribution; finally, by transforming the original scheduling model into a readily solvable mixed integer linear programming via the sequence operation theory (SOT), the transformed model is solved by using CPLEX solver. The simulation results show that compared with the traditional scheduling model without forecasting, this approach manages to significantly reduce the system operating costs by improving the prediction accuracy.

Virtual synchronous generator of PV generation without energy storage for frequency support in autonomous microgrid

Abstract: In autonomous microgrids frequency regulation (FR) is a critical issue, especially with a high level of penetration of the photovoltaic (PV) generation. In this study, a novel virtual synchronous generator (VSG) control for PV generation was introduced to provide frequency support without energy storage. PV generation reserve a part of the active power in accordance with the pre-defined power versus voltage curve. Based on the similarities of the synchronous generator power-angle characteristic curve and the PV array characteristic curve, PV voltage Vpv can be analogized to the power angle δ. An emulated governor (droop control) and the swing equation control is designed and applied to the DC-DC converter. PV voltage deviation is subsequently generated and the pre-defined power versus voltage curve is modified to provide the primary frequency and inertia support. A simulation model of an autonomous microgrid with PV, storage, and diesel generator was built. The feasibility and effectiveness of the proposed VSG strategy are examined under different operating conditions.

Demand side management in microgrid: A critical review of key issues and recent trends

Abstract: In a deregulated power system, Demand Side Management (DSM) plays a vital role in handling the uncertain renewable power generation and load. The flat load-profile can be obtained using the Demand Response (DR) techniques with the storage elements and proper switching. The increasing penetration of Renewable Energy Sources (RES) and Electric Vehicles (EVs) supports the DR measures which facilitate both the utility and the consumer. The objective of DSM is to minimize the peak demand, electricity cost and emission rate by the effective utilization of storage with RES. This review article mainly focuses on the layers of microgrid, different techniques involved in DSM, mathematical models of DSM, latest optimization techniques and application of storage devices such as battery energy storage system and EVs in DSM. The state of the art of this article lies on the critical analysis related to datascience, advanced metering infrastructure and blockchain technologies which are the uniqueness of this article. The key issues and approaches are examined critically with the existing works to show how DSM implementation can be effectively done in the microgrids to reduce the electricity cost. This article helps the researchers to identify the research gap by gaining knowledge on the implementation of DSM in the microgrid and the factors affecting the DSM implementation. Few recommendations are discussed to provide future directions for researchers who started working in the DSM implementation.

Optimal scheduling of distributed energy resources in microgrid systems based on electricity market pricing strategies by a novel hybrid optimization technique

Abstract: The upsurge in microgrid demand is an important aspect of imparting energy in future primarily because of the involvement of renewable energy sources, which alleviates the emission of toxic gases from fossil fuelled generators. The grid-connected mode of microgrid operation is the most economical and definitive mode of service wherein the grid is actively involved in the buying and selling of power prompting diminished generation cost of microgrid system. These cases, pertaining to two different low voltage microgrid systems, are applied consecutively for obtaining the generation cost of the systems and thus devise the cheapest strategy among them. The Grey Wolf Optimizer (GWO) is improvised by incorporating strategies from population-based Sine Cosine Algorithm (SCA) along with position updating methods of crows from Crow Search Algorithm (CSA) to form a hybrid modified Grey Wolf Optimizer Sine Cosine Algorithm Crow Search Algorithm (GWOSCACSA) algorithm. The implementation of the proposed technique produces a comprehensive generator cost reduction of the microgrid system. It was evident from the results that generation cost was minimum when Time of Usage (TOU) based market pricing strategy was considered. Further, it was also established that dynamic grid participation was reduced 47% in the system generation cost for the same scenario compared to the case when the grid was operating passively. The statistical analysis endorses the improvements of GWOSCACSA over other algorithms presented in the state-of-art-literature.

Review of energy storage system technologies integration to microgrid: Types, control strategies, issues, and future prospects

Abstract: Microgrids (MGs) have emerged as a viable solution for consumers consisting of Distributed Energy Resources (DERs) and local loads within a smaller zone that can operate either in an autonomous or grid tide mode. The DERs usually utilize Renewable Energy Resources (RERs), which have the advantages of meeting enhanced power demand, mitigating the pollutants of the environment, natural source of energy, needs minimal maintenance and cheap. Although MG integration provides several benefits, it faces many challenges and issues in its control and management, which can be effectively dealt with incorporating Energy Storage System (ESS) technologies into MGs. The addition of ESS to MGs has acquired increased attention as ESS can store energy during off-peak hours and deliver when required during peak hours. However, despite so many benefits, the ESS faces numerous issues in its integration, such as control, protection, state of charge (SoC), state of discharge (SoD), safety, life span, capacity, reliability and cost. So, to enhance the application of ESS in MG, the above issues need to be dealt with seriously. This research paper highlights the integration of ESS for MG application with a comprehensive review of issues, control methods, challenges, solutions, application, and overall management prospects. Further, the future trends and real time applications are also elucidated, which remarkably contributes to developing a cost effective and robust ESS architecture having a longer life span for renewable MGs application. Thus, an overview of this survey article's projected insights contributes to developing a techno-economic and effective integration of ESS with an extended life cycle for green MG employment.

Enriching the stability of solar/wind DC microgrids using battery and superconducting magnetic energy storage based fuzzy logic control

Abstract: Utilizing robustly-controlled energy storage technologies performs a substantial role in improving the stability of standalone microgrids in terms of voltages and powers. The majority of investigations focused less on integrating energy storage systems (especially superconducting magnetic energy storage 'SMES') within DC-bus microgrids. Besides, implementing fuzzy logic control (FLC) for both batteries and SMES within the DC-bus microgrids to enrich their stability and power quality under extreme climatic and loading variations has been seldomly addressed. Consequently, this paper introduces a comparative analysis of the performance of a hybrid renewable PV/wind DC-bus microgrid that separately implements fuzzy-controlled battery and SMES systems to enhance the microgrid stability and power quality. The proposed FLC approaches supervise energy interchange inside the system, mitigate the DC-bus voltage fluctuations, and smooth out the load power during the different instabilities. The system is examined under distinct normal and extreme climatic fluctuations such as wind gusts and rapid shadow and under sudden balanced and unbalanced loading events. The proposed FLC approaches are established based on quantifying the DC-bus voltage variation and measuring the actual battery and SMES currents which can be employed directly for the control action; hence, reducing both calculations/calibrations and complexity of the control system. Besides, they offer very quick charging/discharging actions for both battery and SMES systems to mitigate unexpected and rapid variations efficiently. For the load side, the study proposes a variable modulation index control based-sinusoidal pulse width modulation for controlling the prime inverter to preserve the load voltage and frequency constant during both balanced and unbalanced loading and extreme climatic disturbances. The obtained findings confirmed the efficacy of the proposed approaches in enriching the microgrid stability. Besides, they unveiled the magnificent performance of SMES over batteries regarding the response time, peak over- and undershoot, load voltage profile, and load power smoothness.

Electric vehicles integration and vehicle-to-grid operation in active distribution grids: A comprehensive review on power architectures, grid connection standards and typical applications

Abstract: Electric vehicles (EVs) are believed as efficient solutions to reduce carbon emissions and fossil fuel reliance in transportation sectors. Yet, the ever-increasing penetration of EVs also poses great challenges for distribution grid planning and operation. As a research hotspot, the vehicle-to-grid (V2G) technology could not only relieve the adverse effects of large-scale uncoordinated EV charging but also offer varied auxiliary services for the utility grid via the proper charging/discharging schedule. In this paper, a comprehensive review of distribution grid architectures, grid connection infrastructures and standards, and typical applications is conducted from the perspective of EV-grid integration and V2G operation for the first time. The nanogrid, microgrid, and cluster architectures that are critical components in active distribution grids are all involved in the discussion. Considering EV's triple roles played in grid interaction, the corresponding infrastructure and grid-connection standards, as well as bidirectional/unidirectional charger topologies, are overviewed. Then, four-type nanogrid and microgrid architectures are reviewed and fully evaluated in six aspects, viz., charging demand compatibility, power quality severity, V2G availability, architecture scalability, local control complexity, and technology maturity. The assessment result reveals that the hybrid AC/DC-coupled architecture based on advanced interlinking converters has flexible power flow control and power quality compensation capability, thus facilitating the EV-grid integration and V2G operation effectively. The assessment for three-type cluster architectures indicates that the hybrid parallel-series microgrid cluster has balanced performance in complexity, cost, redundancy, and reliability. Finally, the typical applications of the nanogrid, microgrid, and cluster with EVs integration are exemplified and discussed.

Power‐frequency oscillation suppression algorithm for AC microgrid with multiple virtual synchronous generators based on fuzzy inference system

Abstract: In the microgrid, virtual synchronous generator technology can significantly enhance the anti-interference characteristics of the system frequency and bus voltage, as well as solve the problems of insufficient damping and low inertia. However, the system frequency and active power oscillation caused by power fluctuations and grid faults threaten the stable operation of the grid seriously. Therefore, for an alternating current (AC) microgrid multi-virtual synchronous generator (VSG) parallel system, an improved virtual synchronous generator control algorithm based on a fuzzy inference system is proposed, which adjusts the values of virtual inertia and damping coefficient dynamically through fuzzy logic rules to realize the coordinated control of the two. The enhanced VSG algorithm described in this research has a substantial influence on power-frequency oscillation suppression, decreases active power and frequency overshoot, shortens the adjustment time, and improves system frequency stability active power, according to simulation and experimental findings.

Reliability aspects in microgrid design and planning: Status and power electronics-induced challenges

Abstract: Microgrids are highlighted as the technology which can help in providing sustainable and efficient electrical energy solutions. They employ distributed energy resources to efficiently supply local load and increase the reliability of the local network. Design and planning are of a pivotal importance in yielding all of the advantages this concept can provide. Reliability-oriented design is of a special interest for microgrids utilizing a large share of the renewable energy-based, power electronics-interfaced distributed energy resources. A state-of-the-art overview included in this paper has shown that the main reliability-oriented microgrid design improvements are done in the field of distributed energy resources sizing and scheduling combined with the relevant forecasting and optimization methods. It is, further on, concluded that the standard power system reliability assessment within the design often excludes the wear-out failure of power electronics. However, previous field experience has shown that the power electronics is prone to wear-out failure and can have adverse impact on the reliability of the power electronics-dominated system. Therefore, it is necessary to adjust the current reliability methods to enable accurate investigation of power electronics reliability and its impact on system design. To do so, the main characteristics of the wear-out modelling concepts together with the recent publications bridging the power electronics and power system reliability are discussed in detail. Finally, the main findings included in this overview paper can serve as basis for development of the new procedures for reliability-oriented design and planning of future, power electronics-dominated microgrids. • Microgrid design and planning is important in assuring high reliability. • Overview of practices helps indicating reliability critical parts of design. • Microgrids will be dominated by power electronics interfaced distributed resources. • Excluding power electronics reliability can impact finding optimum design solution. • New design methods incorporating power electronic reliability need to be developed.

Distributed Resilient Secondary Control for DC Microgrids Against Heterogeneous Communication Delays and DoS Attacks

Abstract: In this article, a cooperative resilient control method for dc microgrid (MG) is proposed to dispel the adverse influences of both communication delays and denial-of-service (DoS) attacks. To avoid that the sampling period is captured by intelligent attackers, a new time-varying sampling period, and an improved communication mechanism are first introduced under the sampling control framework. Based on the designed sampling period and communication mechanism, a resilient secondary controller is designed. It is theoretically shown that the developed method can achieve the goals of bus voltage restoration and current sharing even in the presence of both DoS attacks and heterogeneous communication delays. Finally, a dc MG test system is built in a controller-hardware-in-the-loop testing platform to illustrate and verify the effectiveness of our developed method against both communication delays and DoS attacks.

Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm

Abstract: Electricity can be provided to small-scale communities like commercial areas and villages through microgrid, one of the small-scale, advanced, and independent electricity systems out of the grid. Microgrid is an appropriate choice for specific purposes reducing emission and generation cost and increasing efficiency, reliability, and the utilization of renewable energy sources. The main objective of this paper is to elucidate the combined economic emission dispatch CEED problem in the microgrid to attain optimal generation cost. A combined cost optimization approach is examined to minimize operational cost and emission levels while satisfying the load demand of the microgrid. With this background, the authors proposed a novel improved mayfly algorithm incorporating Levy flight to resolve the combined economic emission dispatch problem encountered in microgrids. The islanded mode microgrid test system considered in this study comprises thermal power, solar-powered, and wind power generating units. The simulation results were considered for 24 hours with varying power demands. The minimization of total cost and emission is attained for four different scenarios. Optimization results obtained for all scenarios using IMA give a comparatively better reduction in system cost than MA and other optimization algorithms considered revealing the efficacy of IMA taken for comparison with the same data. The proposed IMA algorithm can solve the CEED problem in a grid-connected microgrid.

Distributed Event-Triggered Control for Reactive, Unbalanced, and Harmonic Power Sharing in Islanded AC Microgrids

Abstract: For several reasons, particularly due to the mismatch in the feeder impedance, accurate power sharing in islanded microgrids is a challenging task. To get around this problem, a distributed event-triggered power sharing control strategy is proposed in this article. The suggested technique adaptively regulates the virtual impedances at both fundamental positive/negative sequence and harmonic frequencies and, therefore, accurately share the reactive, unbalanced, and harmonics powers among distributed generation units. The proposed method requires no information of feeder impedance and involves exchanging information among units at only event-triggered times, which reduces the communication burden without affecting the system performance. The stability and interevent interval are analyzed in this article. Finally, experimental results are presented to validate the effectiveness of the proposed scheme.

Multi-Stage Real-Time Operation of a Multi-Energy Microgrid With Electrical and Thermal Energy Storage Assets: A Data-Driven MPC-ADP Approach

Abstract: This paper studies the multi-stage real-time stochastic operation of grid-tied multi-energy microgrids (MEMGs) via the hybrid model predictive control (MPC) and approximate dynamic programming (ADP) approach. In the MEMG, practical power and thermal network constraints, heterogeneous energy storage devices, and distributed generations are involved. Given the relatively large thermal inertia and slow thermal energy fluctuation, only uncertainties of renewable energy sources and active/reactive power loads are considered. Then, historical data are adopted as training scenarios for the MPC-ADP method to acquire empirical knowledge for dealing with all the diverse uncertainties. Further, piecewise linear functions are used to approximate value functions with respect to the operation status of energy storage assets, which enables sequentially solving the Bellman’s equation forward through time to minimize MEMG operation cost. Finally, numerical case studies are conducted to illustrate the effectiveness and superiority of the proposed MPC-ADP approach. Simulation results indicate that with sufficient information embedded, the MPC-ADP approach could obtain good-enough real-time operation solutions with the successively updated forecast. Further, it outperforms alternative real-time operation benchmarks in terms of optimality and convergence for various application scenarios.

Current-Source Solid-State DC Transformer Integrating LVDC Microgrid, Energy Storage, and Renewable Energy Into MVDC Grid

Abstract: Solid-state dc transformer to integrate low-voltage dc (LVdc) microgrid, wind turbine (WT) generator, photovoltaic (PV), and energy storage (ES) into medium-voltage (MV) direct-current (MVdc) distribution grids is attractive. This article proposes current-source dc solid-state transformer (SST) for MVdc collection system in WT, PV, and ES farms or as an interface between the MVdc grid and the LVdc microgrid. Compared to conventional current-source converter (CSC) based SSTs, a switch reduction scheme on reverse-blocking device bridges is proposed to reduce device count and the number of devices on the dc-link current path. Importantly, the proposed switch reduction scheme is generic and can be applied to the dc ports of dc–ac, ac–dc, or dc–dc hard-switching or soft-switching CSC-based SSTs. Based on this scheme, the proposed current-source dc SSTs are derived, which have reduced electrolytic-capacitor-less dc-link. The proposed dc SSTs also achieve single-stage isolated dc–dc or dc–ac conversion, full-range zero-voltage switching (ZVS) for main switches, zero-current switching (ZCS) for resonant switches, and controlled $ dv/dt $ . The proposed dc SSTs, operating principles, predictive control method, the ZVS, and the controlled $ dv/dt $ under voltage buck-boost ranges are verified with MV simulations and an experimental prototype based on SiC mosfets , diodes, and a nanocrystalline transformer.

Data-Driven Dynamical Control for Bottom-up Energy Internet System

Abstract: With the increasing concern on climate change and global warming, the reduction of carbon emission becomes an important topic in many aspects of human society. The development of energy Internet (EI) makes it possible to achieve better utilization of distributed renewable energy sources with the power sharing functionality introduced by energy routers (ERs). In this paper, a bottom-up EI architecture is designed, and a novel data-driven dynamical control strategy is proposed. Intelligent controllers augmented by deep reinforcement learning (DRL) techniques are adopted for the operation of each microgrid independently in the bottom layer. Moreover, the concept of curriculum learning (CL) is integrated into DRL to improve the sample efficiency and accelerate the training process. Based on the power exchange plan determined in the bottom layer, considering the stochastic nature of electricity price in the future power market, the optimal power dispatching scheme in the upper layer is decided via model predictive control. The simulation has shown that, under the bottom-up architecture, compared with the conventional methods such as proportional integral and optimal power flow, the proposed method reduces overall generation cost by 7.1% and 37%, respectively. Meanwhile, the introduced CL-based training strategy can significantly speed up the convergence during the training of DRL. Last but not least, our method increases the profit of energy trading between ERs and the main grid.

Distributed Robust Model Predictive Control-Based Energy Management Strategy for Islanded Multi-Microgrids Considering Uncertainty

Abstract: A microgrid is considered to be a smart power system that can integrate local renewable energy effectively. However, the intermittent nature of renewable energy causes operating pressure and additional expense in maintaining the stable operation by the energy management system in a microgrid. The structure of multi-microgrids provides the possibility to construct flexible and various energy trading framework. In this paper, in order to reduce the adverse effects of uncertain renewable energy output, a distributed robust model predictive control (DRMPC)-based energy management strategy is proposed for islanded multi-microgrids. This strategy balances the robustness and economy of single-microgrid system operation by combining the advantages of robust optimization and model predictive control, while coping with the uncertainty of renewable energy sources. Furthermore, a dynamic energy trading market is formed among microgrids, which can enhance the overall economy of the multi-microgrids system. Simulation results verify the feasibility of the proposed DRMPC strategy.