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Towards energy-efficient and cost-effective DC nanaogrid: A novel pseudo hierarchical architecture incorporating V2G technology for both autonomous coordination and regulated power dispatching
Abstract
Most recently, DC nanogrid incorporating effective energy management has attracted widespread attention. Due to its favorability to integrate renewable energy sources and emerging power electronic loads, such as photovoltaics and electric vehicles (EVs), DC nanogrid is believed to be able to improve the energy utilization efficiency and mitigate the carbon footprint in the coming decades. Towards a compact, cost-effective, and easy-to-build energy management scheme for nanogrid, this paper presents a pseudo hierarchical management architecture built upon the smart charging point. The proposed architecture incorporates the upper-level central controller with the local power role creatively and comprises two timescale management levels with corresponding operation strategies. In the short-timescale local management level, a state-triggered droop strategy based on the decentralized control mechanism is introduced to realize the autonomous power coordination without extensive communication links. The autonomous vehicle-to-grid (V2G) operation is also implemented with providing real-time power balance capability to unpredicted and short-timescale load variation in peak periods. In the power dispatching level, a multi-mode power dispatching strategy involving six operation modes is introduced to realize the efficient power scheduling for the nanogrid. The effectiveness of the proposed architecture and operation strategy is verified in the detailed simulation model and hardware-in-loop experiment platform. The results show that the real-time, autonomous, and stabilized power coordination in nanogrid could be realized along with a self-regulated V2G operation. Additionally, the peak-shaving and valley-filling of load curve, the satisfaction of EV charging demand, and an improved operation economy are achieved under the proposed architecture and operation strategy.
... The latter concentrates on the grid integration challenges of EVs within the carbon market framework, proposing a method grounded in carbon-inclusive management strategies, which improve the economic viability of low-carbon transitions through the optimization of EV charging and discharging schedules. In [13,14], the authors proposed an innovative infrastructure for V2G technology. Reference [13] introduces a distributed edge computing framework that employs Long Short-Term Memory (LSTM) networks to forecast electricity data, enhancing the prediction accuracy and robustness by integrating attention mechanisms and data clustering. ...
... Reference [13] introduces a distributed edge computing framework that employs Long Short-Term Memory (LSTM) networks to forecast electricity data, enhancing the prediction accuracy and robustness by integrating attention mechanisms and data clustering. Reference [14] proposed a pseudo-hierarchical management architecture for direct current microgrids predicated on intelligent charging points, which facilitates autonomous power coordination and real-time V2G operations via decentralized control mechanisms, effectively mitigating short-term load fluctuations. In [15], the authors proposed an evaluation metric tailored for the forecasting value in the context of V2G scheduling, which is used to assess the impact of forecasting performance on the degradation rate of scheduling performance, providing valuable insights into potential directions for improvement in future load forecasting technology. ...
With the increasing penetration of electric vehicles (EVs), the spatial–temporal coupling between the transportation network (TN) and the power distribution network (PDN) has intensified greatly. Large-scale uncoordinated charging of EVs significantly impacts both the PDN and TN. In this paper, an optimal scheduling strategy for EV charging and discharging in a coupled power–transportation network (CPTN) with Vehicle-to-Grid (V2G) scheduling and dynamic pricing is proposed. The strategy considers the influence of dynamic transportation road network (DTRN) information on EV driving patterns, as well as the unique vehicle characteristics and mobile energy storage capabilities of EVs. Firstly, a DTRN model is established. Subsequently, the dynamic Dijkstra algorithm is utilized to accurately simulate the EV driving paths and predict the spatial–temporal distribution of the EV charging load. Secondly, optimal scheduling for EV charging and discharging within the CPTN is performed, guided by a V2G model coupled with a multi-time dynamic electricity price (MTDEP) strategy to optimize the grid load curve while accommodating the charging requirements of EVs. Finally, the effectiveness and superiority of the proposed optimization scheduling model are validated by the IEEE 33-node PDN test system.
... The integration of EVs and renewable energy sources constitutes an important measure in responding to the Energy Internet Strategy [9,10]. However, with the increasing number of EVs, the largescale charging of EVs may lead to issues such as decreased power quality in the distribution network and significantly increased peak-valley difference [11,12] due to the uncertainty of user demand and behaviors. To ensure the safe and stable operation of the system, it is necessary to develop optimal dispatching strategies for PV-storage charging stations. ...
... Mathematics 2024, 12, 3410 (11) where v ch,max t and v ch,min t are the upper and lower limits of the EV charging price of the microgrid at time t, respectively. v disc,max t and v disc,min t are the upper and lower limits of the EV discharge electricity price of the microgrid at time t. ...
Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the “curse of dimensionality” and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services.
... For the purpose of minimising branch total losses and various other losses, all the aforementioned units were developed using a MILP framework. Yu, H., et al. [11] discussed to reduce electricity costs and flatten power load profiles, an integrated smart home using EV and PV technology uses a two-stage energy management strategy. This study provides a smart home energy management method that incorporates an electric car with or without solar power generation. ...
... The "Red Deer" moniker refers to the RD array used in the RD optimization process. Assume Z is the workable option for the relevant results area in the N VAR size optimization process conveyed in Equation (11). ...
Microgrids are seen as the future of reliable, sustainable and green energy source for myriad applications. The increasing dependence on microgrid also adds challenges on reliable management of power supply to vividly variant consumers, the major chunk being households coupled with an unprecedented rise in the demand for EV charging. This study aims at presenting a deep Long Short-Term Memory with Deep Brief Network model to reliably predict the grouped energy load and solar energy outcome in a community microgrid. A cutting-edge hybrid metaheuristic algorithm will be taken into consideration for optimizing the load dispatch of community microgrids that are connected to the grid. Three different scheduling scenarios are evaluated to establish an ideal dispatching design for a grid-linked community microgrid with solar elements and energy storage systems feeding electricity loads and charging electric vehicles. The prediction outcomes are integrated into the model to accommodate the uncertainties associated with solar energy outcome and residential energy load and EV charging to achieve a supply-demand equilibrium. The objective of the proposed model is to obtain an energy-efficient system capable of balancing the load and power of microgrid system which remains unperturbed by the aforesaid oscillations.
... The V2B nanogrid is compact and cost-effective and does not require an external central controller and extensive communication links [49]. There are two architectures involved: AC and DC systems. ...
This paper presents an overview of the status and prospects of fuel cell electric vehicles (FC-EVs) for grid integration. In recent years, renewable energy has been explored on every front to extend the use of fossil fuels. Advanced technologies involving wind and solar energy, electric vehicles, and vehicle-to-everything (V2X) are becoming more popular for grid support. With recent developments in solid oxide fuel cell electric vehicles (SOFC-EVs), a more flexible fuel option than traditional proton-exchange membrane fuel cell electric vehicles (PEMFC-EVs), the potential for vehicle-to-grid (V2G)’s implementation is promising. Specifically, SOFC-EVs can utilize renewable biofuels or natural gas and, thus, they are not limited to pure hydrogen fuel only. This opens the opportunity for V2G’s implementation by using biofuels or readily piped natural gas at home or at charging stations. This review paper will discuss current V2G technologies and, importantly, compare battery electric vehicles (BEVs) to SOFC-EVs for V2G’s implementation and their impacts.
... Various control topologies exist for NGs against demand side management (DSM) and supply side management (SSM) in the literature [41]. Key considerations include ensuring load feeding without overload, DSM to counter unit price fluctuations [44], financial savings through peak load management [45], predictive control for optimal management [46], and voltage and frequency control via droop control mechanisms [47]. ...
... When compared to internal combustion engine vehicles (ICEVs), EVs have many advantages, including less noise, zero greenhouse gas emissions, and lower maintenance cost [4]. With legislation support and technical advancement, the EV market is poised for sustained expansion over the coming decades [5]. Even with the Covid-19 pandemic and related problems of the EV supply chain, EV sales hit another record high in 2021. ...
The transition of the automotive sector to electric vehicles (EVs) necessitates research on charging demand forecasting for optimal station placement and capacity planning. In the literature, extensive studies have been conducted on model-based and probabilistic EV charging demand forecasting schemes. The studies provide a solid research foundation but result in complicated models with limited scalability. Meanwhile, emerging machine learning techniques bring promising prospects, yet exhibit suboptimal performance with insufficient data. Additionally, existing studies often overlook several critical areas such as overcoming data scarcity, security and privacy concerns, managing the inherent stochasticity of demand data, selecting forecasting methods for a specific feature, and developing standardized performance metrics. Considering the impact of the research topic, EV charging demand forecasting demands careful study. In this paper, we present a comprehensive survey of EV charging demand forecasting, focusing on both probabilistic and learning algorithms. First, we introduce the general procedure of EV charging demand forecasting, encompassing data sources, data pre-processing, and the key EV features. We then provide a taxonomy of existing EV charging demand forecasting techniques, followed by a critical analysis and comparative study of state-of-the-art research. Finally, we discuss open issues, which offer useful insights and future direction for various stakeholders.
... Various control topologies exist for NGs against demand side management (DSM) and supply side management (SSM) in the literature [21]. Key considerations include ensuring load feeding without overload, DSM to counter unit price fluctuations [24], financial savings through peak load management [25], predictive control for optimal management [26], and voltage and frequency control via droop control mechanisms [27]. ...
The ongoing evolution of the Metaverse, digital twin
(DT), Artificial Intelligence (AI), and Blockchain technologies is
fundamentally transforming the utilization of sustainable energy
resources (SERs) within smart grids (SGs), ushering in the era of smart grid 3.0 (SG 3.0). This paradigm shift presents unprecedented opportunities to establish robust and sustainable energy trading frameworks between consumers and utilities within the SG 3.0 environment. The integration of DT, AI, and Blockchain technologies in the context of the Metaverse’s
smart grids, facilitated by optimized communication protocols,
represents a transformative approach. AI models play a pivotal
role in predictive energy trading analysis, while DT assumes a
crucial role in effectively managing diverse SERs within SGs. Simultaneously, Blockchain technology holds the potential to create a trusted and decentralized environment, laying the foundation
for an energy trading system within the SG 3.0 universe of the
Metaverse. This visionary convergence of DT and Blockchain
sets the stage for a futuristic paradigm in SGs, establishing a
robust energy trading and management foundation. This paper
embarks on exploring uncharted research paths and unlocking
new dimensions by surveying and delving into the convergence
of the Metaverse, Blockchain, AI, and DT. Its primary objective
is to drive the digitization of SERs within SGs, to revolutionize
energy systems. The envisioned SG 3.0 represents a significant
leap by amalgamating cutting-edge technologies in sustainable
energy, paving the way for revolutionary advancements in SGs’
digitization, which aligns with and contributes to achieving the
sustainable development goals outlined by the United Nations.
... In the literature, three different approaches are used to control the charging and discharging of EVs on the power grid. These include centralized, decentralized, or hierarchical control (Yu et al., 2022). Each of these systems has advantages and disadvantages that are influenced by their distinct coordinating methods. ...
Smart grids (SG) constitute a revolutionary concept within the energy sector, enabling the establishment of a bidirectional communication infrastructure. This infrastructure significantly improves control, efficiency, and overall service quality in power systems. The study provides an in-depth survey on the classification of EVs, including both plug-in and non-plug-in EVs, and the integration process of V2G, including bidirectional power flow analysis. Moreover, various control strategies for EV integration are explored, ranging from centralized and decentralized to hierarchical control structures. Further, the research thoroughly examines the potential benefits of EV integration and addresses associated challenges, such as battery degradation, infrastructure requirements, cybersecurity and communication issues, grid congestion, and consumer behavior. The study goes beyond theoretical exploration and offers a comprehensive simulation analysis. This analysis leverages the storage capabilities of EVs to provide grid support services. A real-time dynamic dispatch strategy is formulated to integrate EVs into the automatic generation control of multi-energy systems. The findings demonstrate that EVs can effectively mitigate forecasting errors in a power network heavily reliant on wind energy sources. Consequently, the storage capabilities of EVs contribute to enhancing grid flexibility in managing the intermittency of renewable energy resources.
... Although investment costs are not extensively discussed, there are indications that the proposed EMS application can lead to monetary and energy savings. In [9], a DC nanogrid is operated via a bilevel EMS, having as the primary control strategy the droop characteristic of BESS and V2G (vehicle-to-grid), if available, absorbing the intermittency of renewables, while power economic dispatch is pursued in longer timescales. The methodology is validated through simulations in the MATLAB Simulink platform, which is also employed to design feasible nanogrids in [10]. ...
Microgrids have emerged as a popular solution for electric energy distribution due to their reliability, sustainability, and growing accessibility. However, their implementation can be challenging, particularly due to regulatory and market issues. Building smaller-scale microgrids, also known as nanogrids, can present additional challenges, such as high investment costs that need to be justified by local demands. To address these challenges, this work proposes an economic feasibility assessment model that is applied to a real nanogrid under construction in the Brazilian electrical system, with electric vehicle charging stations as its main load. The model, which takes into account uncertainties, evaluates the economic viability of constructing a nanogrid using economic indicators estimated by the Monte Carlo simulation method, with the system operation represented by the OpenDSS software. The model also considers aspects of energy transactions within the net-metering paradigm, with energy compensation between the nanogrid and the main distribution network, and investigates how incentives can impact the viability of these microgrids.
... Furthermore, the co-simulation between transmission and distribution networks in RT platforms with PHIL is useful to analyse critical operating conditions interacting with real hardware. However, these test environments require additional considerations for handling the non-ideal conditions, like noise and harmonic distortion, present in actual signals, which imposes additional challenges compared to offline simulations [14,15]. In this context, real-time cosimulation setups with real interaction of distributed energy resources (DERs) are imperative to better understand the short-time dynamics of modern power systems [7]. ...
This paper proposes a real-time co-simulated framework to experimentally validate the dynamic performance of network-level controllers in power systems. The experiment setup includes the coordinated emulation of a transmission network linked to a distribution feeder and real distributed energy sources, working in a multi-platform and multi-manufacturer environment. The operation of an optimal hierarchical controller for voltage and frequency support of the transmission network, which exploits the power injection from battery energy storage systems (BESS), is investigated to demonstrate the feasibility, accuracy and effectiveness of the proposed setup based on a co-simulation environment. The results of different study cases implemented in the laboratory are presented, where a successful interconnection between real-time emulators and real hardware from different manufacturers was realised. The fast and timely response of the controller to disturbances caused by sudden load changes, three-phase faults and renewable generation losses is experimentally validated. Finally, the robustness of the developed test bench against noise and harmonic distortion of real signals is also demonstrated.
... One possible solution to carbon emissions, the fossil fuel crisis and climate change concerns the adoption of EVs [8]; these vehicles, compared to traditional Internal Combustion Engine Vehicles (ICEVs), feature zero carbon emissions, high operating efficiency, lower maintenance costs and reduced noise [9]. Thanks in part to policy strategies and incentives and technological advances, the electric vehicle industry is expected to experience growth in the coming years [10]. In addition to the spread of EVs, there is also an increase related to the installation of charging infrastructure, which can be slow, fast, or ultrafast charging also depending on the specific needs of the user [11]. ...
In this paper, a quantitative model is implemented with the main goal of building a decision support tool to assess the feasibility of implementing a Vehicle-To-Grid (V2G) service by a company operating a fleet of electric buses. The proposed model can calculate the energy that a vehicle within a depot can deliver back to the grid during periods of peak energy demand, based on the operational schedule that must be guaranteed (number of buses in service). After a presentation of the main features of V2G and the main benefits this technology can bring to the transportation company, the model structure, and related algorithms, as well as input and output data, are presented and discussed. To verify the effectiveness and validity of the proposed model, a case study related to the company that manages public transportation in the city of Milan, Italy, is described. In particular, 2 depots were analyzed considering the energy load during peak hours and the energy that could be injected into the grid considering the vehicles parked in the depot. From a quantitative point of view, V2G could feed about 7 MW to 10 MW into the grid, depending on the day of the week and time of day. Considering an average connection of 3 kW for a household, between 2,300 and 3,300 households could be served. In addition, an economic evaluation was performed considering energy trading: monthly, total revenues are 45,922 € and total costs are 42,848 €; the economic benefit can be estimated at about 3,074 €.
... Although highly accurate, they did not consider the impacts of variable coolant flow rate. Yu et al. [3], demonstrated the usefulness of reduced order modeling of lithiumion batteries. They used the developed ROM to calculate the cell de-rate which is an essential function of the battery management system or BMS. ...
The safety, performance, and operational life of power dense Lithium-ion batteries used in Hybrid and Electric Vehicles are dependent on the operating temperature. Modeling and simulation are essential tools used to accelerate the design process of optimal thermal management systems. However, high-fidelity 3D computational fluid dynamics (CFD) simulation of such systems is often difficult and computationally expensive. In this paper, we demonstrate a multi-part coupled system model for simulating the heating/cooling system of the traction battery at a module level. We have reduced computational time by employing reduced-order modeling (ROM) framework on separate 3D CFD models of the battery module and the cooling plate. The order of the thermal ROM has also been varied to study the trade-off between accuracy, fidelity, and complexity. The ROMs are bidirectionally coupled to an empirical battery model built from in-house test data. This makes sure that we capture the interdependence of different parameters and behaviors and closely resemble the real system behavior. The complete system model is built up in a commercial system simulation platform and has the capability of simulating the system response to drive-cycles, charge profiles or any system dependent test conditions, under the influence of various heating/cooling control algorithms.
In the context of user-side demand response, flexible resources in buildings such as air conditioners and electric vehicles are characterized by small individual capacities, large aggregate scales, and geographically dispersed distributions, necessitating integration by intelligence buildings (IRs). However, the optimization scheduling of IR clusters often involves detailed energy consumption data, posing privacy issues such as revealing household routines. The traditional aggregator-IRs bi-level architecture typically employs centralized or game-theoretic strategies for optimization scheduling, which struggle to balance efficiency and privacy simultaneously. To address this issue, this paper proposes a bi-level optimization scheduling strategy that balances efficiency and privacy. First, deep reinforcement learning models are established for both the aggregator and the IRs to address efficiency. Then, the trained demand response models of the IRs are encapsulated into strategy black boxes and uploaded to the aggregator’s deep reinforcement learning model. Throughout this process, the aggregator remains unaware of the user-side data, thus protecting user privacy. Additionally, considering that training IR strategy black box models is a parallel and similar process, this paper introduces the paradigm of federated learning to reduce learning costs and improve training efficiency on the IRs side. Furthermore, an adaptive clustering federated deep reinforcement learning method is proposed to address the heterogeneity of the IRs. Finally, case studies demonstrate the feasibility and effectiveness of the proposed method.
Due to the advantages of effectively integrating diversified loads and renewable sources, hybrid DC/AC microgrids have attracted the common attention of industry and academia. Meanwhile, with the increasing number of power electronic devices connected to the system, the complexity of the system increases, which puts forward higher requirements for galvanic isolation and intelligent control methods. This study proposes a unified decentralized framework for isolated interlinking converters (IICs) in hybrid DC/AC microgrids, which include topology and a control strategy to solve the aforementioned problems. Firstly, in terms of topology, a dual active bridge (DAB) converter is adopted to realize galvanic isolation and DC voltage conversion, which can facilitate the interconnection of DC microgrids or be combined with VSC for the interconnection of DC and AC microgrids. Secondly, in terms of the control strategy, a bidirectional voltage–supporting control strategy is proposed for IICs, in which the frequency and voltage are adopted as the indications to balance the power and strengthen the inertia interaction of microgrids. Furthermore, with the proposed control strategy, the voltage sources of different microgrids can share the power fluctuation, cooperatively. The stability of the proposed strategy is analyzed, and the effectiveness is verified by several HIL experiments.
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.
This paper introduced a novel hybrid AC/DC-coupled charging station architecture and corresponding hierarchical control strategy. The core element of the architecture is an integrated interlinking converter (ILC) named the energy routing unit (ERU). The ERU features multiple conversion stages, dedicated plug-and-play interfaces, and varied operation functionalities. The dual inputs of utility sources enhance the reliability of the power supply. The contained scalable charging module facilitates the DC fast charging via DC grids directly, and the traditional AC slow charging is also accommodated in this architecture. Regarding the architecture, a comprehensive hierarchical control strategy containing the local-level short-timescale transient control and upper-level long-timescale optimal power scheduling is proposed. The multi-objective optimization concerning the optimal economical operation and minimum load variance is incorporated into the upper-level power flow control. The detailed simulation cases demonstrate that the stable operation of the proposed architecture could be realized, and the desirable economical operation and low peak-to-valley load variance are to be implemented simultaneously. The enabled vehicle-to-vehicle (V2V) power coordination achieves mutual power support between AC and DC subgrids and increases the local consumption of renewable energy sources effectively.
DC networks for residential consumers have gained attraction in recent years, primarily due to building-integrated photovoltaics and increasing electronic loads coupled with the decreasing prices of DC appliances. Unlike AC (110V, 60Hz, or 220 V, 50Hz), the existing standards for DC distribution are disjoint, ranging from 48V to 380V with distribution voltage selection affecting the system efficiency. In this work, we formulate a framework to analyze the impact of various voltages on residential system losses incorporating both distribution losses and power electronic conversion losses. Subsequently, we evaluate the system efficiency for a typical DC home at 48 V, 220 V, and 380 V DC and compare it with the 220 V AC using the developed analytical framework as well as through simulation. Results show that for a medium scale solar integrated house, the DC system at 220 V and 380 V is 4% and 10% more efficient than the AC 220 V system, respectively. Further, for 48 V DC, the system efficiency is higher than 380 V DC for wire size AWG-6 and beyond. While the efficiency depends on several factors such as conductor size, voltage selection, loads connected, and the solar capacity, the framework presented is the key in the quantification of losses and selection of suitable system components for DC home.
This paper presents an overview of DC voltage levels used for DC nanogrids in residential remote area. The DC bus is the common link between distributed sources and loads. As there are not yet requirements and standards for the DC microgrids, the DC voltage level was often made from previous experiences and best practices. The most common values are 12V, 24V, 48V, and 100V. In this context, this work targets to compare the consequences of these different choices on the DC nanogrid from one hand, and to establish a reasonable roadmap to help the appropriate choice of the DC bus from the other hand. This choice should allow reliability, safety, energy efficiency and cost saving. Hence, several criteria have to be considered, such as line thermal limits, voltage drop and power losses through the cables connecting local loads to DC bus. Finally, the proposed methodology was adopted for a small scale laboratory DC home nanogrid and the final choice of the DC voltage was a single bus of 48V.
This paper presents a review of control strategies, stability analysis, and stabilization techniques for dc microgrids (MGs). Overall control is systematically classified into local and coordinated control levels according to respective functionalities in each level. As opposed to local control, which relies only on local measurements, some line of communication between units needs to be made available in order to achieve the coordinated control. Depending on the communication method, three basic coordinated control strategies can be distinguished, i. e., decentralized, centralized, and distributed control. Decentralized control can be regarded as an extension of the local control since it is also based exclusively on local measurements. In contrast, centralized and distributed control strategies rely on digital communication technologies. A number of approaches using these three coordinated control strategies to achieve various control objectives are reviewed in this paper. Moreover, properties of dc MG dynamics and stability are discussed. This paper illustrates that tightly regulated point-of-load converters tend to reduce the stability margins of the system since they introduce negative impedances, which can potentially oscillate with lightly damped power supply input filters. It is also demonstrated that how the stability of the whole system is defined by the relationship of the source and load impedances, referred to as theminor loop gain. Several prominent specifications for the minor loop gain are reviewed. Finally, a number of active stabilization techniques are presented.
Multiple-voltage-region control, in which the bus-voltage range is divided into several regions, is usually implemented for dc microgrid operation in distributed manner. Voltage/power droop relationships are imposed for active power sharing among slack terminals. Conventionally, threshold voltages for voltage region partition are determined with fixed percentage of variation around the nominal value, which may result in unevenness of droop coefficients in different regions. If system droop coefficient is too high, significant bus-voltage step change due to load variation will occur. On the other hand, significant power sharing error among slack terminals will be induced if the droop coefficient is too low. In this paper, a compromised solution with power-capacity-based bus-voltage region partition is proposed to equalize the droop coefficients in different regions. However, the droop coefficients are determined based on the rated power capacity of system units. Bus-voltage discontinuity appears when the power capacity reduces in actual implementation. To eliminate the voltage discontinuity, online droop coefficient tuning according to the real-time power capacity is implemented. Algorithms for local power capacity estimation of solar photovoltaic (PV) and battery energy storage have been proposed. A lab-scale dc microgrid has been developed for verification of the proposed methods.
Optimization Problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed “Many-Objective Optimization Problems (MaOPs)”. While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a Multi-Objective Evolutionary Algorithm (MOEA) and its MaOP counterpart.
Time of use (TOU) tariffs are commonplace in developed economies around the world. These tariffs incentivise customers to lower peak loads in order to reduce their electricity bills. The objective for the power utility is to reduce peak loads and/or shift load from peak to off-peak periods. This paper will discuss the effective application of TOU tariffs for industrial, residential and commercial power users.
This paper proposes an energy management strategy for a residential microgrid comprising photovoltaic (PV) panels, a small wind turbine and solar thermal collectors. The microgrid can control the power exchanged with the grid thanks to a battery and a controllable electric water heater, which provide two degrees of freedom to the control strategy. As input data, the proposed control strategy uses the battery state of charge (SOC), the temperature of the hot water tank, the power of each microgrid element as well as the demand and renewable generation forecasts. By using forecasted data and by controlling the electric water heater, the strategy is able to achieve a better grid power profile while using a smaller battery than previous works, hence reducing the overall cost of the system. The strategy is tested by means of simulation with real data for one year and it is also experimentally validated in the microgrid built at the Renewable Energy Laboratory at the UPNA.
With the progress of renewable energy technologies, distributed energy system (DES) has become attractive due to its flexibility and interaction with power systems. Battery energy storage system (BESS) is an indispensable part of DESs, the control strategies of which have a great influence on system performance. In this paper, we present a novel adaptive droop control (ADC) for energy storage batteries. The state and model parameters of energy storage batteries are estimated simultaneously online by dual extended Kalman filter (DEKF) algorithm. Then the droop controller is designed based on the battery parameters by the “virtual battery” algorithm, benefit from which power can be distributed among battery packs adaptively for their power characteristics, meanwhile SOC balancing can be achieved automatically. The proposed control strategy is validated in MATLAB/Simulink environment with a direct-current (DC) microgrid model where battery packs are connected to the DC bus through dual active bridge (DAB) converters. Compared with conventional droop controls, the proposed ADC method considers the battery characteristics and battery aging, and it is more suitable for DES with multiple sets of batteries.
In order to accommodate additional plug‐in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle‐to‐grid (V2G) technology has been regarded as a cost‐effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users’ privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub‐problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak‐shaving and valley‐filling scheduling algorithm is built up. The proposed algorithm can be conducted in real‐time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak‐shaving and valley‐filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak‐shaving and valley‐filling strategy is near‐linear, which verifies the proposed scheme can be carried out very efficiently.
Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system’s operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.
Plug-in electric vehicles (PEVs) and renewable energy sources (RESs) can relief the stress on air pollution. Particularly, using RES for PEV energy requirement can integrate more RESs on the grid. In this paper, a vehicle-to-grid (V2G) scheme concerning on RES and edge computing, i.e. the internet of smart charging points with photovoltaics integration, is presented. Within the architecture of the scheme, each charging point equips computing and storage units, so as to store PEV sensitive information locally and conduct “burn after scheduling”. Besides, this architecture can transform the traditional large-scale V2G problem into several sub-problems, which are small enough to optimize. Based on the architecture of the scheme, an associated high-efficiency algorithm is designed. Six typical scenarios of PEV charging are elaborated and two indexes are presented to facilitate 1) the self-consumption of photovoltaics energy by PEV charging and 2) the peak-shaving and valley-filling of net load. Additionally, voltage regulation and real-time control are applied to ensure the security of the distribution grid and mitigate the uncertain conditions. Finally, compared with uncoordinated charging, the short-time scale simulation realizes the peak-shaving and valley-filling by 17.54% and 12.42%, respectively; and the amount of self-consumption of photovoltaics energy increases by 258.74%. Furthermore, the long-time scale simulations also present a satisfying performance for the grid energy saving and the load factor. Particularly, the proposed scheme offers high computational efficiency compared with different architecture and algorithm, and the execution time for scheduling one PEV at one-time interval shows a microsecond basis.
This paper presents a hierarchical two-layer home energy management system to reduce daily household energy costs and maximize photovoltaic self-consumption. The upper layer comprises a model predictive controller which optimizes household energy usage using a mixed-integer linear programming optimization; the lower layer comprises a rule-based real-time controller, to determine the optimal power settings of the home battery storage system. The optimization process also includes load shifting and battery degradation costs. The upper layer determines the operating schedule for shiftable domestic appliances and the profile for energy storage for the next 24 h. This profile is then passed to the lower energy management layer, which compensates for the effects of forecast uncertainties and sample time resolution. The effectiveness of the proposed home energy management system is demonstrated by comparing its performance with a single-layer management system. For the same battery size, using the hierarchical two-layer home energy management system can achieve annual household energy payment reduction of 27.8% and photovoltaic self-consumption of 91.1% compared to using a single layer home energy management system. The results show the capability of the hierarchical home energy management system to reduce household utility bills and maximize photovoltaic self-consumption. Experimental studies on a laboratory-based house emulation rig demonstrate the feasibility of the proposed home energy management system.
The energy utilization optimization strategies in a smart house without and with vehicle to home (V2H) and/or home distributed photovoltaic (HDPV) in Shanghai are investigated in detail for the efficient household energy utilization and the reduction of net electricity expenditure. Such influences as EV travel distances, weather conditions and different PV subsidies are also taken into account. The results show that transferring valley electricity and PV by V2H can not only improve the utilization rate of valley electricity and PV, but also obtain considerable economic benefits. Transferring PV by V2H can get more revenues than transferring valley electricity by V2H. The energy arbitrage of V2H decreases with the increase of the EV travel distance. The HDPV-V2H mode in the case studied can completely cover the electricity demand of the household load in sunny and cloudy days without additional grid electricity while the combination of PV with transferred valley electricity by V2H is enough to support the household load demand in rainy days. The positive return of HDPV still can’t do without the support from government’s subsidy in Shanghai in the coming time. However, the HDPV-V2H mode can improve the benefit of HDPV. Meanwhile, there are a lot of EVs in Shanghai, charging with green power in priority. The HDPV-V2H mode can promote the synergetic development of HDPV and EVs in Shanghai.
With the ever-increasing of population and economy worldwide, buildings have become major energy consumers and greenhouse gas (GHG) emitters. The hybrid AC/DC microgrid is a promising alternative for existing power distribution systems to achieve the goal of nearly/net zero energy buildings (nZEBs). However, the increasing demand for compact structure, seamless integration of distributed generators (DGs) and loads, as well as more control flexibility of hybrid microgrids cannot be adequately satisfied by conventional grid architectures. In view of this, an integrated and reconfigurable hybrid AC/DC microgrid architecture with its hierarchical control strategy is proposed in this paper. Firstly, a novel interlinking converter named smart interlinking unit (SIU) is presented, which can provide multiple AC/DC interfaces and diverse operation modes with various control functionalities. Secondly, the SIU-based hybrid microgrid architecture and its hierarchical control structure are established. The dedicated interfaces and cluster controllers for electric vehicles (EVs) facilitate the implementation of centralized vehicle-to-grid (V2G) service. Thirdly, a hierarchical control strategy of SIU, which involves local control in primary control level and power flow control in secondary control level, is introduced to realize coordinated operation of microgrid. The model of the proposed hybrid microgrid architecture is built, and the simulation results demonstrate that the microgrid architecture and hierarchical control strategy can achieve a reliable and coordinated system operation under various kinds of scenarios. Additionally, the mutual power support between AC and DC sub-grids is realized with increased utilization and local consumption of renewable energy resources (RESs).
In a hydrogen-based DC microgrid (MG), the integration of hydrogen subsystem increases the system complexity and flexibility. Effective power split, bus voltage stability and reliable operation become significant control issues of hydrogen-based DC MG. This paper proposes a simple and effective decentralized energy management strategy based on a mode-triggered droop scheme for an islanded PV/hydrogen/battery DC MG. In a decentralized manner, the proposed decentralized energy management strategy is implemented through two control steps: mode divisions and droop control. For practical implementation purposes of the proposed energy management strategy, the mode divisions scheme autonomously divides this DC MG into eight operating modes based on the system local information; the adaptive droop control method controls the distributed generation units based on their droop relationships under different operating modes. In addition, an islanded DC MG hardware-in-the-loop (HIL) RT-LAB simulation platform and a lab-scaled experimental platform are built to validate the effectiveness of the proposed decentralized strategy. The simulation and experiment results show that the proposed energy management strategy has an efficient power distribution ability without communication link under various operating modes.
With the modernization of the smart grid, Plug-in Electric Vehicles (PEVs) have attracted attention thanks to the effective energy support through the bi-directional power flow exchanging. In particular, vehicle-to-home technology has drawn a significant interest in PEVs' parked at smart home to enhance the power consumption profile. This paper proposes a collaborative energy management among PEVs, smart homes and neighbors' interaction. For that, a new supervision strategy based on PEVs power scheduling for smoothing the residential power load profile is developed. The objective of this study is to improve the power demand profile by controlling the PEV power charging/discharging amount to fill the valley of the power consumption curve or by providing power to home especially during peak periods to shave peak. The home energy management for achieving a flattened power load profile is divided into two parts: a local control according to the base demand profile of the considering home, the availability of their PEVs, their arrival and departure times and their initial state of charge (SOC) values. A global control according to the power demand of the specific home, the total power demand of neighbors and the availability of PEVs’ neighbors (arrival and departure times, initial energy of the battery). The simulation results of the power load profile of such smart homes highlights the interaction between PEVs, smart home and their neighbors in order to flatten the power demand curve to the greatest extent possible.
This paper explores the technical and economic characteristics of an accelerated energy transition to 2050, using new datasets for renewable energy. The analysis indicates that energy efficiency and renewable energy technologies are the core elements of that transition, and their synergies are likewise important. Favourable economics,
ubiquitous resources, scalable technology, and significant socio-economic benefits underpin such a transition. Renewable energy can supply two-thirds of the total global energy demand, and contribute to the bulk of the greenhouse gas emissions reduction that is needed between now and 2050 for limiting average global surface temperature increase below 2 °C. Enabling policy and regulatory frameworks will need to be adjusted to mobilise the six-fold acceleration of renewables growth that is needed, with the highest growth estimated for wind and solar PV technologies, complemented by a high level of energy efficiency. Still, to ensure the eventual elimination of carbon dioxide emissions will require new technology and innovation, notably for the transport and manufacturing sectors, which remain largely ignored in the international debate. More attention is needed
for emerging infrastructure issues such as charging infrastructure and other sector coupling implications.
https://doi.org/10.1016/j.esr.2019.01.006
-The impedance-based stability analysis of single-phase voltage source converters (VSCs) emphasizes the precise impedance modeling. Considering the complexity of dq-frame impedance modeling and measurement, this paper proposes a mirror-frequency impedance modeling and measurement approach of single-phase VSCs in the stationary frame based on harmonic linearization. First, the mirror-frequency definition is put forward and the dynamics of the second-order generalized phase-locked loop considering the mirror-frequency effects is validated by simulations. Next, the impedance model considering the mirror-frequency effects and dc-link voltage control loops is compared with the conventional one-dimension impedance model, and a mirror-frequency impedance measurement method is presented, which are verified by experiments on the hardware in the loop platform. The results indicate that the mirror-frequency impedance model considering the dc-link voltage control are more accurate and better captures the cross-coupling dynamics of VSCs. Finally, the different bandwidths of dc-link voltage control loops and different capacitances are designed to study the impacts of dc-link voltage control loops and ripples on impedance. The experiments and impedance-based analysis illustrate that the wider bandwidth of dc-link voltage control loops or the less capacitance with high dc-link voltage ripple will expand the frequency range of negative impedance in the low frequency. Index Terms-Impedance model; single-phase source voltage converter; mirror frequency; dc-link voltage control; dc voltage ripple; bandwidth;
The supervision of energy consumption applied to a smart home, seems to be one of the main challenges for the future smart grid. The purpose of this study was to investigate a double layer supervision strategy in order to ensure a flattened power load curve in a residential application. This control is mainly divided into two levels. The first consists of a demand response algorithm, which plays an important role in scheduling the operation of home appliances by moving the shiftable appliances from peak hours, when electricity prices are high, to off-peak hours when prices are low and thus contributes to improving the daily load profile. The first strategy was coupled with the emergence of Plug-in Electric Vehicle (PEV), which is a new electrical load that must be considered in a smart home. The second consists of a PEV power management, which aims to ensure the bi-directional power flow from the smart home to PEVs (H2V) and from the Vehicle to Home (V2H). This procedure results in monitoring the power of each PEV connected to the home to determine its power reference and thereby, enhance again the power load curve. Simulation results highlight the performance of the two-layer home energy supervision strategy to achieve the smoothness for the daily power demand curve.
Energy router is one of the key elements for power electronic based dc microgrid cluster system. Traditional AC/DC converter and Solid-State Transformer (SST) can act as an energy router, but their functions and interfaces are restricted. In this paper, a novel modular-based energy router(MBER) for DC microgrid cluster has been proposed to extend the functions of energy router. Each module of MBER is composed of an AC/DC converter and an isolated dual active bridge (IDAB) converter with high frequency transformers. The power multi-directional exchange mechanism between AC grid and DC microgrid cluster are shown. Then, the operation mechanism and the operation modes of MBER are analyzed. Considering the operation range of MBER is limited by the operation modes and the DC voltage of each module, a dc voltage adjustment strategy and control method have been proposed to expansion the operation range of MBER. Finally, simulation and experimental results are presented to validate the proposed topology and control methods.
The integration of distributed generators (DGs) exacerbates the feeder power flow fluctuation and load unbalanced condition in active distribution networks (ADNs). The unbalanced feeder load causes inefficient use of network assets and network congestion during system operation. The flexible interconnection based on the multi-terminal soft open point (SOP) significantly benefits the operation of ADNs. The multi-terminal SOP, which is a controllable power electronic device installed to replace the normally open point, provides accurate active and reactive power flow control to enable the flexible connection of feeders. An enhanced SOCP-based method for feeder load balancing using the multi-terminal SOP is proposed in this paper. By regulating the operation of the multi-terminal SOP, the proposed method can mitigate the unbalanced condition of feeder load and simultaneously reduce the power losses of ADNs. Then, the original non-convex model is converted into a second-order cone programming (SOCP) model using convex relaxation. To tighten the SOCP relaxation and improve the computation efficiency, an enhanced SOCP-based approach is developed to solve the proposed model. Finally, case studies are performed on the modified IEEE 33-node system to verify the effectiveness and efficiency of the proposed method.
For microgrids with high penetration of renewable energy sources (RESs) and electric vehicles (EVs), the stochastic charging/discharging of the EVs would result in a large impact on the secure and stable operation of the microgrids. Therefore, the coordinated control between EVs and RESs becomes an important challenge for keeping the microgrid stable. In this paper, a coordinated sectional droop charging control (CSDCC) strategy is proposed for an EV aggregator that participates in the frequency regulation of the microgrids with high penetration RESs. All EVs are controlled as grid-friendly loads, and the CSDCC strategy operates as a virtual synchronous generator by only controlling the charging power of the EVs. Because the discharging of an EV is not required, the CSDCC strategy has no detrimental effects on the EV battery life. The inertia damping characteristic of a synchronous generator is modelled as a virtual inertia factor, which can eliminate charging power vibration, and can improve the system inertia. Finally, the validity of the proposed strategy in enhancing frequency regulation is verified by demonstrating a set of comparative cases.
Hybrid solar-battery power source is essential in the nexus of plug-in electric vehicle (PEV), renewables, and smart building. This paper devises an optimization framework for efficient energy management and components sizing of a single smart home with home battery, PEV, and potovoltatic (PV) arrays. We seek to maximize the home economy, while satisfying home power demand and PEV driving. Based on the structure and system models of the smart home nanogrid, a convex programming (CP) problem is formulated to rapidly and efficiently optimize both the control decision and parameters of the home battery energy storage system (BESS). Considering different time horizons of optimization, home BESS prices, types and control modes of PEVs, the parameters of home BESS and electric cost are systematically investigated. Based on the developed CP control law in home to vehicle (H2V) mode and vehicle to home (V2H) mode, the home with BESS does not buy electric energy from the grid during the electric price's peak periods.
The charging time-consuming and lifespan of lithium-ion batteries have always been the bottleneck for the tremendous application of electric vehicles. In this paper, cycle life tests are conducted to reveal the influence of different charging current rates and cut-off voltages on the aging mechanism of batteries. The long-term effects of charging current rates and cut-off voltages on capacity degradation and resistance increase are compared. The results show that there exists a critical charging current and a critical charging cut-off voltage. When the charging stress exceeds the critical value, battery degradation speed will be greatly accelerated. Furthermore, battery aging mechanisms at various charging currents and cut-off voltages are investigated using incremental capacity analysis. It is indicated that charging current and cut-off voltage should be reduced to retard battery degradation when the battery degrades to a certain extent. The time when the loss of electrode material accelerates is taken as the crisis to reduce charging current and the time when the loss of lithium inventory accelerates is taken as the crisis to reduce charging cut-off voltage. Moreover, an experiential model quantitatively describing the relationship between capacity degradation rate and charging stresses at different aging states is established.
Plug-in electric vehicles (PEVs) seem to be an interesting new electrical load for improving the reliability of smart grid. The purpose of this work is to investigate a supervision strategy based on regulated charging of PEVs in order to guarantee an optimized power management of the system and consequently a flatter power demand curve. The system mainly includes PEVs powered by a Lithium-ion battery ensuring the charging and discharging operations of these PEVs at home and a daily load power demanded by home appliances. The purpose of the considered strategy is to detect the connection status of each PEV and to establish the priority order between these PEVs with certain flexibility which results in managing the PEVs through seven operating modes. The response of the control algorithm enables to ensure the power flow exchange between the PEVs and the electrical grid, especially at rush hours, and to minimize load power variance aiming to achieve the smoothness for the power demand curve and to reduce the stress of the electrical grid. The simulation results are presented in order to illustrate the efficiency of this power control approach.
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The centralised power grid bears a heavy burden in a time when consumers expect an uninterrupted reliable power supply, a reduction in carbon emissions, increased efficiency within the national grid and power supplied to remote communities. As expectations increase, it becomes the task of power systems research and design to develop new structures to meet these demands. This has led to alternatives being sought for centralised power generation, which is prone to outages (due to long distance transmission), is a substantial contributor to global carbon emissions, has large transmission losses and is often not a practical solution when supplying remote communities. Distributed generation (DG) looks to remedy these inadequacies by producing power close to its point of consumption, often utilising carbon neutral, renewable energy (RE) sources (sun, wind). To maximise the efficient use of DG, control structures are used to balance the intermittent RE power production with consumer power consumption. One such structure is used to implement control of small scale DG, at a single house/small building level: the nanogrid. This paper explores the current nanogrid research, it collates the existing definitions and uses the knowledge to give a concise definition of a nanogrid. It then discusses the control topologies and techniques which enable the intelligent control of the nanogrid, before presenting the hardware platform used to ensure the efficient operation of a small scale DG system. The paper then considers the interconnection of multiple nanogrids forming a network (microgrid), facilitating the sharing of power between individual nanogrids. The future developments are then explored before the paper's conclusions are presented.
This paper presents a three-layered coordinated control to incorporate three-phase (3P) alternating current (AC) and direct current (DC) type electric vehicle energy storage systems (EV–ESSs) for improved hybrid AC/DC microgrid operations. The first layer of the algorithm ensures DC subgrid management by regulating the DC bus voltage and DC side power management. The second and third layer manages AC subgrid by regulating the AC bus voltage and the frequency by managing reactive and active power respectively. The multi-layered coordination is embedded into the microgrid central controller (MGCC) which controls the interlinking controller in between AC and DC microgrid and the interfacing controllers of the participating electric vehicles (EVs) and distributed generation (DG) units. The whole system is designed in MATLAB/SIMULINK environment resembling the under construction microgrid at Griffith University, Australia. Extensive case studies are performed using real life irradiation data and commercial loads of the campus buildings. Impacts of homogeneous and heterogeneous single-phase EV charging are investigated to observe both balanced and unbalanced scenarios. Synchronization during the transition from the islanded to grid-tied mode is tested considering a contingency situation. From the comparative simulation results it is evident that the proposed controller exhibits effective, reliable and robust performance for all the cases.
As the world's population becomes more reliant on power, the stability and reliability of power systems also needs to increase. One avenue of research is distributed generation (DG) which decreases the need for long distance distribution. As the generation is close to its point of use, the system becomes better equipped to endure extreme weather events and natural disasters. However, DG is typically renewable energy, which presents its own difficulties, such as, intermittency. This paper proposes a nanogrid control system which will increase the efficient use of DG renewable energy sources. This nanogrid controller implements local source/load control, demand side management, a hierarchical load algorithm and interfaces with a national grid. Simulations show that by implementing the demand side management, the amount of power purchased from the grid can be decreased by up to 23%.
The hierarchical control structure of a microgrid can be described as having four levels responsible for processing, sensing and adjusting, monitoring and supervising, and maintenance and optimization. The responsibility of the hierarchical control level is to provide control over the production of power from renewable sources. This paper comprehensively investigates the principles of hierarchical control in microgrids from a technical point of view. In the first step, this article covers the control of the power generation using two popular renewable energy sources, namely wind turbines and photovoltaics. The synchronization and power flow between the microgrid and the main network is then investigated. Finally, some research questions are presented to improve the performance of the hierarchical control, especially in the secondary decentralized control and energy storage systems.
In this paper, a control strategy is proposed to achieve decentralized power management of a PV/battery hybrid unit in a droop controlled islanded microgrid. In contrast to the common approach of controlling the PV unit as a current source, in the proposed strategy, the PV unit is controlled as a voltage source that follows a multi-segment adaptive power/frequency characteristic curve. The proposed power/frequency characteristics, of the hybrid unit and of the whole microgrid, adapt autonomously to the microgrid operating conditions so that the hybrid unit may supply the maximum PV power, match the load, and/or charge the battery, while maintaining the power balance in the microgrid and respecting the battery state of charge (SOC) limits. These features are achieved without relying on a central management system and communications, as most of the existing algorithms do. The control strategy is implemented using multi-loop controllers, which provide smooth and autonomous transitions between the operating scenarios. Small-signal stability of the proposed control loops is investigated and the system performance is experimentally validated on a 3.5 KVA microgrid.
An effective method to estimate the state of health (SOH) of lithium ion batteries is illustrated in this work. This method uses an adaptive transformation of charging curves at different stages of life to quantify the extent of capacity fade and derive a time-based parameter to enable an accurate SOH estimation. This approach is easy for practical implementation and universal to chemistry or cell geometry, with minimal demand of learning. With a typical constant current-constant voltage (CC-CV) charging method for a lithium ion battery, this approach uses an equivalent circuit model to characterize the CC portion of the charging curve and derive a transformation function and a time-based parameter to estimate SOH at any stage of life via a nonlinear least squares method to identify model parameters. The SOH estimation errors (discrepancy between estimated and experimental values, denoted as Delta SOH) are under 2% before the end of life in cases shown at 25 degrees C and 60 degrees C and a range of typical discharging rates up to 3C. With different sizes and chemistries, the Delta SOHs are all less than 3%.
The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.
In the smart grid, the large scale wind power penetration tends to expand vastly. Nevertheless, due to the intermittent power generation from wind, this may cause a problem of large frequency fluctuation when the load-frequency control (LFC) capacity is not enough to compensate the unbalance of generation and load demand. Also, in the future transport sector, the plug-in hybrid electric vehicle (PHEV) is widely expected for driving in the customer side. Generally, the power of PHEV is charged by plugging into the home outlets as the dispersed battery energy storages. Therefore, the vehicle-to-grid (V2G) power control can be applied to compensate for the inadequate LFC capacity. This paper focuses on the new coordinated V2G control and conventional frequency controller for robust LFC in the smart grid with large wind farms. The battery state-of-charge (SOC) is controlled by the optimized SOC deviation control. The structure of frequency controller is a proportional integral (PI) with a single input. To enhance the robust performance and robust stability against the system uncertainties, the PI controller parameters and the SOC deviation are optimized simultaneously by the particle swarm optimization based on the fixed structure mixed H2/H∞ control. Simulation results show the superior robustness and control effect of the proposed coordinated controllers over the compared controllers.
Although it has long been argued that electronic power converters can help improve system controllability, reliability, size, and efficiency, their penetration in power systems is still quite low. The often-cited barriers of higher cost and lower reliability of the power converters are quite high if power electronics is used as direct, one-to-one, replacement for the existing electromechanical equipment. However, if the whole power distribution system were designed as a system of controllable converters, the overall system cost and reliability could actually improve, as is currently the case at low power levels within computer and telecom equipment. Starting from the example of a computer power system, the paper contemplates possible future ac and dc electronic power distribution system architectures, especially in the presence of renewable energy sources. The proposed nanogrid-microgrid-...-grid structure achieves hierarchical dynamic decoupling of generation, distribution, and consumption by using bidirectional converters as energy control centers. This is illustrated by the description and simulation of static and dynamic operation of a dc nanogrid in a hypothetical future sustainable home. Several ideas for modeling, analysis, and system-level design of such systems, including power flow control, protection, stability, and subsystem interactions, are presented.
Evolving our electricity systems from the bottom up. Darnell Green Power
- Nordman
Current-fed switched inverter based hybrid topology for DC nanogrid application. IECON 2013-39th Annual Conference of the IEEE Industrial Electronics Society: IEEE
- S S Nag
- R Adda
- O Ray
- S K Mishra