Steady-State Analysis and Optimal Power Routing of Standalone Unbalanced Hybrid AC/DC Microgrids

The concept of ac microgrids was introduced to integrate distributed generators (DGs) and loads within one entity that can operate autonomously or connected to a utility grid. Furthermore, dc microgrids have received increasing attention as a potential solution to deliver power from DGs to modern dc loads with reduced conversion stages. Moreover, hybrid ac/dc microgrids have been introduced as a paradigm combining the benefits of the two types of microgrids by interconnecting them through interlinking converters (ICs). Steady-state analysis is essential for planning and operation studies of electrical power systems. However, conventional analysis approaches cannot be applied to hybrid ac/dc microgrids due to their distinctive features, such as droop characteristics, lack of a slack bus, and coupling between the ac and dc variables. Additionally, the unbalanced nature of ac microgrids adds to the complexity of modeling and analysis in such networks. Therefore, this thesis is focused on developing steady-state modeling and analysis framework for standalone unbalanced hybrid ac/dc microgrids. First, a steady-state analysis tool for unbalanced hybrid ac/dc microgrids is developed. The ac subgrid's components are modeled in phase coordinates. Furthermore, the dc subgrid's components are modeled and the coupling between the ac and dc variables is formulated. The models of the various system elements are incorporated into a unified power flow formulation, which is solved using a Newton-Trust Region (NTR) method. The developed power flow algorithm is verified through comparisons with time-domain simulations of test microgrids. The analysis tool is used to analyze a larger hybrid ac/dc microgrid through case studies. The case studies shed light on some challenges of these microgrids, namely, imposed limitations on microgrid loadability due to unbalanced ac subgrid's loading, effect of IC settings on microgrid operation, and trade-off between proportional loading of the ac and dc subgrids and proportional power-transfer sharing among ICs. Second, based on the identified microgrid loadability limitation of unbalanced microgrids, a novel adaptive power routing (APR) scheme is proposed to maximize the microgrid loadability. The proposed scheme allows independent control of active and reactive powers flowing through IC phases, so that power can be routed among the ac subgrid's phases. The DPR scheme is integrated into an optimal power flow (OPF) formulation with the objective of minimizing load shedding. A supervisory controller is proposed to solve the OPF problem by adjusting the DG and IC settings. Several case studies are conducted to show the ineffectiveness of conventional supervisory controllers in resolving the loadability issue, and to verify the success of the proposed controller in solving the problem. Third, a power flow approach based on sequence component analysis of the ac microgrid's elements is adopted for faster convergence and improved modeling accuracy as compared to conventional approaches in phase coordinates. This approach breaks down the system model into positive-, negative-, and zero-sequence subsystems that can be solved in parallel for enhanced performance. The positive-sequence power flow is solved using a Newton-Raphson (NR) method, while the negative- and zero-sequence voltages are obtained by solving linear complex equations. The approach is verified through comparisons with time-domain simulations. In addition, the algorithm is utilized to investigate the operation of droop-controlled DGs in larger-scale isochronous unbalanced ac microgrids, and to examine its limit-enforcement abilities at the same time. The algorithm demonstrates significant improvements in terms of accuracy and convergence time when compared against the conventional NTR-based approach in phase coordinates. Finally, the power flow approach developed in the third part is extended to include the IC's and dc subgrid's models so that it can be applied to hybrid ac/dc microgrids. A power flow algorithm is proposed to solve the ac and dc power flows independently in a sequential manner, while maintaining the correlation between the two. The algorithm is verified through comparisons with time-domain models of test hybrid microgrids. Case studies are introduced to test the algorithm's effectiveness in enforcing the DG and IC limits in the power flow solution under various conditions. The algorithm also shows enhanced accuracy and solution speed with respect to the tool developed in the first stage

Optimal integration of wind energy with a renewable based microgrid for industrial applications.

Wind energy in urban environments is a rapidly developing technology influenced by the terrain specifications, local wind characteristics and urban environments such as buildings architecture. The urban terrain is more complex than for open spaces and has a critical influence on wind flow at the studied site. This approach proposes an integration of the surrounding buildings in the studied site and then simulating the wind flow, considering both simple and advanced turbulence models to quantify and simulate the wind flow fields in an urban environment and evaluate the potential wind energy. These simulations are conducted with an accessible computational fluid dynamic tool (Windsim) implementing available commercial wind turbines and performed on a case study at Agder county in the southern part of Norway for an industrial facility specialized in food production. Several simulations were considered and repeated to achieve a convergence after adding the buildings to the domain, which mainly simulates the wind flow patterns, power density, and annual energy production. These simulations will be compared with previous results, which adapted different manipulation techniques applied on the same site where the elevation and roughness data were manipulated to mimic the actual conditions in the studied urban site. The current approach (adding the buildings) showed a reduction in the average wind speed and annual energy production for certain levels with increased turbulence intensity surrounding the buildings. Moreover, a feasibility study is conducted to analyze the techno-economic of the facility's hybrid system, including the planned installation of a wind energy system using commercial software (HOMER). The simulation results indicated that HOMER is conservative in estimating the annual energy production of both wind and solar power systems. Nevertheless, the analysis showed that integrating a wind turbine of 600 kW would significantly reduce the dependence on the grid and transform the facility into a prosumer with more than 1.6 GWh traded with the grid annually. However, the proposed system's net present cost would be 1.43 M USD based on installation, maintenance, and trading with the grid, without including self-consumption, which counts for approximately 1.5 GWh annually. Moreover, the proposed system has a low levelized cost of energy of 0.039$ per kWh, which is slightly above the levelized cost of wind energy but 2 to 4 times less than the installed solar panels

Ancillary Services in Hybrid AC/DC Low Voltage Distribution Networks

In the last decade, distribution systems are experiencing a drastic transformation with the advent of new technologies. In fact, distribution networks are no longer passive systems, considering the current integration rates of new agents such as distributed generation, electrical vehicles and energy storage, which are greatly influencing the way these systems are operated. In addition, the intrinsic DC nature of these components, interfaced to the AC system through power electronics converters, is unlocking the possibility for new distribution topologies based on AC/DC networks. This paper analyzes the evolution of AC distribution systems, the advantages of AC/DC hybrid arrangements and the active role that the new distributed agents may play in the upcoming decarbonized paradigm by providing different ancillary services.Ministerio de Economía y Competitividad ENE2017-84813-RUnión Europea (Programa Horizonte 2020) 76409

Hierarchical-power-flow-based energy management for alternative/direct current hybrid microgrids

Modern microgrids are systems comprising both Alternative Current (AC) and Direct Current (DC) subgrids, integrated with Distributed Generations (DGs), storage systems, and Electric Vehicles (EVs) parking facilities. Achieving stable and reliable load flow control amidst varying load, generation, and charging/discharging strategies requires a hierarchical control scheme. This paper proposes an hourly power flow (PF) analysis within an Energy Management System (EMS) for AC/DC Hybrid Microgrids interconnected via an Interlinking Converter (IC) in both grid-connected and islanded modes. The framework operates within a two-level hierarchically controlled platform. Tertiary control at the top level optimizes DGs' reference power for generation and consumption, minimizing power purchase costs and load shedding in grid-connected and islanded modes, respectively. DG converters employ current control mode to share their power references as the primary controller. While no secondary controller is adopted in this scheme, the Battery Energy Storage System (BESS) in islanded mode utilizes P/Q droop control to maintain voltage and frequency in the AC subsystem. Power sharing between AC and DC subgrids through IC is determined by the difference between AC grid frequency and DC link voltage. Integration of controlled converters’ buses into PF equations enables solving the unified system using the traditional Newton-Raphson (NR) method. A segment of a real distribution grid planned for installation in Italy under the HYPERRIDE project serves as a case study. Comparison with MATLAB/Simulink results confirms the effectiveness, precision, and convergence speed of the proposed model and control schemes, demonstrating efficient load distribution and voltage/frequency restoration in islanded mode

Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm

Microgrids (MGs) are capable of playing an important role in the future of intelligent energy systems. This can be achieved by allowing the effective and seamless integration of distributed energy resources (DERs) loads, besides energy-storage systems (ESS) in the local area, so they are gaining attraction worldwide. In this regard, a DC MG is an economical, flexible, and dependable solution requiring a trustworthy control structure such as a hierarchical control strategy to be appropriately coordinated and used to electrify remote areas. Two control layers are involved in the hierarchy control strategy, including local- and global-control levels. However, this research focuses mainly on the issues of DC MG’s local control layer under various load interruptions and power-production fluctuations, including inaccurate power-sharing among sources and unregulated DC-bus voltage of the microgrid, along with a high ripple of battery current. Therefore, this work suggests developing local control levels for the DC MG based on the hybrid particle swarm optimization/grey wolf optimizer (HPSO–GWO) algorithm to address these problems. The key results of the simulation studies reveal that the proposed control scheme has achieved significant improvement in terms of voltage adjustment and power distribution between photovoltaic (PV) and battery technologies accompanied by a supercapacitor, in comparison to the existing control scheme. Moreover, the settling time and overshoot/undershoot are minimized despite the tremendous load and generation variations, which proves the proposed method’s efficiency

Priority-driven self-optimizing power control scheme for interlinking converters of hybrid AC/DC microgrid clusters in decentralized manner

Hybrid AC/DC microgrid clusters are key building blocks of smart grid to support sustainable and resilient urban power systems. In microgrid clusters, the subgrid load-priorities and power quality requirements for different areas vary significantly. To realize optimal power exchanges among microgrid clusters, this paper proposes a decentralized self-optimizing power control scheme for interlinking converters (ILC) of hybrid microgrid clusters. A priority-driven optimal power exchange model of ILCs is built considering the priorities and capacities in subgrids. The optimization objective is to minimize the total DC-voltage/AC-frequency state deviations of subgrids. To realize the decentralized power flow control, an optimal-oriented quasi-droop control strategy of ILCs is introduced to not only achieve a flexible self-optimizing power flow management, but also provide an ancillary function of voltage support. Consequently, as each of ILCs only monitors the local AC-side frequency and DC-side voltage signals, the whole optimal power control of the wide-area microgrid clusters is achieved in a decentralized manner without any communication link. Thus, the proposed control algorithm has the features of decreased cost, increased scalability, reduced geographic restrictions and high resilience in terms of communication faults. Finally, the proposed method is validated by case studies with four interconnected microgrids through hardware-in-loop tests

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency