Model Predictive Control for Microgrids: From power electronic converters to energy management
2: Department of Energy Technology, Aalborg University, Aalborg, Denmark
3: School of Engineering and Information Technologies, Federation University Australia, Australia
Microgrids have emerged as a promising solution for accommodating the integration of renewable energy resources. But the intermittency of renewable generation is posing challenges such as voltage/frequency fluctuations, and grid stability issues in grid-connected modes. Model predictive control (MPC) is a method for controlling a process while satisfying a set of constraints. It has been in use for chemical plants and in oil refineries since the 1980s, but in recent years has been deployed for power systems and electronics as well. This concise work for researchers, engineers and graduate students focuses on the use of MPC for distributed renewable power generation in microgrids. Fluctuating outputs from renewable energy sources and variable load demands are covered, as are control design concepts. The authors provide examples and case studies to validate the theory with both simulation and experimental results and review the shortcomings and future developments. Chapters treat power electronic converters and control; modelling and hierarchical control of microgrids; use of MPC for PV and wind power; voltage support; parallel PV-ESS microgrids; secondary restoration capability; and tertiary power flow optimization.
Inspec keywords: power grids; renewable energy sources; power control; power electronics; DC-DC power convertors; predictive control; invertors; photovoltaic power systems; distribution networks; distributed power generation
Other keywords: power electronics converter; microgrids; power control; invertors; power grids; distribution networks; distributed power generation; photovoltaic power systems; model predictive control; energy management; DC-DC power convertors; renewable energy sources
Subjects: Solar power stations and photovoltaic power systems; General electrical engineering topics; Power convertors and power supplies to apparatus; Optimal control; Control of electric power systems; Power electronics, supply and supervisory circuits; Distribution networks; Distributed power generation; Power and energy control; General and management topics
- Book DOI: 10.1049/PBPO199E
- Chapter DOI: 10.1049/PBPO199E
- ISBN: 9781839533976
- e-ISBN: 9781839533983
- Page count: 285
- Format: PDF
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Front Matter
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1 Introduction
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This paper is about the fast depletion of traditional fossil fuels and the urge for the reduction of green-house gas emission are the key factors driving the growing use of distributed generation (DG) units, including both renewable and nonrenewable energy sources such as solar photovoltaics (PVs) fuel cells, wind turbines, wave generators, and gas- or steam- powered combined heat and power (CHP) stations. Meanwhile, traditional power grid is getting more and more stressed because of the ongoing increase in power demand, limited power delivery capability of the power network, and the costly reinforcement of the existing transmission- distribution lines. In this context, the existing electrical power network is going through a major transformation from traditional centralized architecture to a decentralized and distributed form. The basic units of the decentralized and distributed power grid are the aforementioned DGs. Such DGs, consisting of renewable energy sources (RESs) and energy storage systems (ESSs), provide electricity as individual power supplies for electric appliances. Among them, solar PV systems and wind turbines are the most popular DG types. In such an energy system, the major portion of the electrical power generated by a DG is consumed locally, and the surplus will be exported to the grid. If the load demand is larger than local generation, more power can be drawn from the grid. Besides, a DG can operate in islanded mode in which electrical power can continue to be supplied to local loads, similar to uninterruptible power supply devices. Despite the benefits provided by DGs, there are technical issues in actual applications on the degree to which DGs can be interconnected. The direct connection of DGs causes profound impacts on the traditional power distribution network, such as a decrease in reliability, stability, and power quality.
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2 Power electronic converters and control
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Power electronic converters, as the electronic interface between the local distribution network and the distributed generations (DGs), play vital roles in reliable power supply and high power quality. In DG interfacing, either connected to the grid or a local load, the key components during the power conversion process are the power electronic converters, which provide flexible interfaces between the energy sources and the end users. Particularly in microgrids (MGs)
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3 Distributed renewable power generation
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This paper is about a microgrid (MG), the basic power sources are distributed generation (DG) unit. Particularly, two most popular distributed energy resources, namely, wind generators and solar photovoltaic (PV) system. The rapid depletion of conventional energy sources such as coal, increased electricity demand, and ever-tightening government regulations on the reduction of green-house gas emissions, together with the inefficiency of traditional electricity grid, causes major transformation in electricity generation, distribution, and consumption patterns all over the world. In the last decade, much attention has been paid to DGs including both non-renewable and renewable sources, such as solar PV, wind tur-bine generators, panels, fuel cells, gas microturbine generators, and gas- or steam-powered combined heat and power stations. To some extent, these DG sources can reduce the burden of the existing power grid, and they can offer competitive generation options. DG is an electrical power generation unit that can be connected to the local distribution network or just supply power to local electric appliances in a manner similar to an uninterruptible power supply (UPS) system.
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4 Modeling and hierarchical control of microgrids
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In this chapter, mathematical models of distributed generations (DGs) and the entire microgrid (MG) are presented. These mathematical models are the key elements in designing control schemes for MGs. After that, the hierarchical control framework, i.e., primary control, secondary control, and tertiary control layers are discussed, and the corresponding control objectives are presented.
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5 MPC of PV-wind-storage microgrids
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In this research, a complete control strategy covering local-level power converter control and system-level energy management for a hybrid AC-DC MG with PV-wind-battery sources is developed. The main contribution of this work can be high-lighted as follows. At the local level, the grid-interfaced power electronic converters are coordinated to cater to different operation modes and requirements. A model MPPVC method, which actually consists of a MPPC for grid-connected operation and a MPVC for islanded operation, is developed for the bidirectional converter interlinking the DC subgrid and AC subgrid, aiming to supply high-quality voltages at critical buses and to achieve fast and smooth grid synchronization and connection.
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6 MPC of PV-ESS MGs with voltage support
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This chapter investigates the impacts of microgrids (MGs) with high penetration of renewable energies on the distribution network. A model predictive power control (MPPC) scheme is presented to control and coordinate the DC-DC converter and inverter for grid-connected photovoltaic (PV) systems with energy storage system (ESS). By regulating the DC-bus voltage and controlling the active and reactive power flows, MPPC can support the power grid to maintain stable voltage and frequency and improve the power factor.
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7 MPC of parallel PV-ESS microgrids
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In this chapter, a control method consisting of a model predictive current control (MPCC) algorithm, a model predictive power control (MPPC) scheme, and a model predictive voltage control (MPVC) strategy is presented for microgrids (MGs) with parallel photovotaic energy storage systems (PV-ESS). The photovoltaic (PV) boost converter is controlled by the MPPC for maximum power point tracking (MPPT) operation. By controlling the bidirectional buck-boost converters of the battery energy storage systems (BESSs) based on the MPPC algorithm, the fluctuating output from the renewable energy sources (RESs) can be smoothed, while stable DC-bus voltages can be maintained as the inverter inputs. Then, the parallel inverters are controlled using a combination of the MPVC scheme and the droop method to ensure stable AC voltage output and proper power sharing.
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8 MPC of MGs with secondary restoration capability
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In Chapter 7, a model predictive control (MPC) scheme has been incorporated with a droop method to control parallel photovoltaics-energy storage system - microgrids (MGs). Yet, there is still one problem that needs to be addressed, i.e., the voltage and frequency deviations. In this chapter, the drawbacks in the primary control layer are discussed. Then, secondary control is studied to restore the frequency and voltage. Specifically, a model predictive voltage control (MPVC) scheme taking into account the voltage changing trend is then developed to control the distributed inverters to improve the output voltage quality. A washout filter-based secondary control scheme with the plug-and-play capability is adopted to achieve proper load-sharing among parallel inverters and mitigate the voltage deviation.
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9 MPC of MGs with tertiary power flow optimization
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In this chapter, the tertiary control strategy is discussed for economic dispatch and power flow optimization. The model predictive control (MPC)-based algorithm is used to determine the scheduling of power exchanges between the microgrid and the main grid. By considering the power prices, power generation and load forecasts, it enables a supply-demand balance in an economic way.
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Back Matter
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