Coordination of Distributed Energy Resources in Microgrids: Optimisation, control, and hardware-in-the-loop validation
2: Rolls-Royce-NTU Cooperate Lab, Nanyang Technological University, Singapore
3: School of Electrical Engineering and Telecommunications, The University of New South Wales, Australia
Coordination of Distributed Energy Resources in Microgrids provides a structured overview of research into techniques for managing microgrids with distributed energy resources (DERs). The DERs including distributed generators, energy storage systems, and flexible loads are posing both challenges and opportunities to microgrids' security, planning, operation, and control. Advanced operation and control techniques are needed to coordinate these components in the microgrids and maintain power quality, as well as keeping the system economically feasible. This book is for researchers and students in the area of smart grids, power engineering, and control engineering, as well as for advanced students, transmission network and grid operators. It focuses on cutting-edge techniques for secure, economic, and robust operation and control of microgrids. Effective coordination of DERs on both temporal and spatial scales are introduced in detail. Topics covered include comprehensive mathematical models of DERs and microgrids, sizing and siting of DERs under uncertainties, stochastic and robust optimisation for active and reactive power dispatch of DERs in microgrids, distributed coordinated control, and hardware-in-the-loop tests for validation of control algorithms.
Inspec keywords: energy storage; power generation control; power distribution control; power grids; distributed power generation; optimisation
Other keywords: distributed power generation; optimisation; distribution networks; power distribution control; hardware-in-the-loop; power generation dispatch; power generation economics; voltage control; microgrids; power grids; energy storage; power generation control
Subjects: General electrical engineering topics; Control of electric power systems; Distribution networks; Optimisation techniques; Power system control; Distributed power generation; General and management topics; Optimisation techniques
- Book DOI: 10.1049/PBPO188E
- Chapter DOI: 10.1049/PBPO188E
- ISBN: 9781839532689
- e-ISBN: 9781839532696
- Page count: 479
- Format: PDF
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Front Matter
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Part I: Distributed Energy Resources and Microgrids: Preliminaries
1 Distributed energy resources: introduction and classification
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An electric power system is a network of the generators that supply the power, the transmission and distribution grid that transports and delivers the power, and the loads which consume the power. Conventionally, the generators are of large capacities (up to hundreds of megawatts), centralised, and mainly fuelled by fossil energy resources such as coal and gas. In recent years, driven by the tremendous pressure of global climate changes and fossil-fuel crisis, modern power systems have been integrating high levels of renewable energy resources (RESs) at a fast-growing speed. Two types of RESs that stand out are solar photovoltaic (PV) and wind power. In addition to the centralised large-scale PV stations and wind power farms which are usually far from centres of the loads, smaller and modular-based RES units (typically less than10 MW) such as rooftop PV and small wind turbines that are owned by consumers or utilities have also been widely deployed in the distribution networks close to the loads. Besides traditional electrical-focused components, electrical-thermal coupled devices such as on-site tri-generation of cooling, heat, and power (CHP) which can enable district cooling/heating for a higher energy efficiency are also gaining increasing popularity at the distribution network level. In view of the highly stochastic and intermittent nature of the RES units, energy storage systems (ESSs) are considered to be a necessary component to overcome the potential issues brought by the intermittency.
2 Microgrids: introduction and research problem descriptions
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Compared with a traditional bulk power system, a microgrid is considerably different. Specifically, the microgrid system size is much smaller than that of a conventional large, interconnected power system. Besides, the microgrid networks are relatively short and have a higher resistance to reactance (R/X) ratio compared to the high-voltage power grids. This book focuses on three research areas of the microgrid: planning, operation, and control.
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Part II: Coordinated Planning of DERs in Micogrids: Optimal Sizing and Siting
3 Composite sensitivity factor-based method for DG planning
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Distributed generation (DG) can provide multiple benefits to distribution systems such as network power loss reduction and voltage stability enhancement. In this chapter, a novel composite sensitivity factor (CSF)-based method is introduced for optimal placement of network-owned DG units to simultaneously decrease losses and improve voltage stability in a distribution network. This method prioritises the buses which are more sensitive to power loss and voltage stability, and then it applies sensitivity factors to settle DG units iteratively. Besides, uncertainties of renewable DG outputs are fully considered with a discrete Monte Carlo simulation. Via tests on two distribution systems with different scenarios, this chapter presents high computing efficiency and unique satisfying solutions of the CSF-based method for DG planning.
4 Probability-weighted robust optimisation method for DG planning
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Distributed generation (DG) has been rapidly integrated into microgrids. However, uncertain power generation of intermittent DG such as wind turbines (WTs) brings challenges to DG planning problems. This chapter introduces a novel probability-weighted robust optimisation (PRO) method to allocate DG units including microturbines and WTs in microgrids, aiming to maximise the total profit over a long-term planning horizon. First, probability-weighted uncertainty sets are proposed to model uncertainties including wind power outputs and loads during a long-term planning horizon, which can fully cover the uncertainty spectrum with uncertainty probability distribution. Then, the PRO method optimises DG locating and sizing under the worst uncertainty cases considering their occurrence probabilities. Furthermore, a modified column-and-constraint generation algorithm is developed to solve the PRO problem. Simulation results show that the DG planning obtained by the proposed PRO method can achieve full operating robustness against any possible uncertainty realisation.
5 Multi-stage stochastic programming method for multi-energy DG planning
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The multi-energy microgrid (MEMG) comprises heterogeneous distributed generators (DGs) such as wind turbines (WTs), diesel generators (DEs), and combined cooling, heat, and power (CCHP) plants. Proper placement of these DGs is critical for the system energy efficiency and network reliability performance. This chapter proposes a two-stage coordinated method for optimally placing heterogeneous DGs in an MEMG project considering the uncertainties from renewable energy sources (RESs).
6 Stochastic planning of heterogeneous energy storage (HES) in residential MEMG
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The deployment of heterogeneous energy storage (HES), which mainly consists of the power and thermal storage (TS), is critical for improving the overall energy utilisation efficiency of the multi-energy systems. Thus, a risk-averse method for HES deployment in a residential multi-energy microgrid (MEMG) is proposed taking diverse uncertainties and multi-energy demand-side management (DSM) into account. Besides the size and location planning of the HES, its optimal investment phase is optimised by maximising the system equivalent daily profit (EDP) and minimising the risk at the same time. To deal with the diverse uncertainties from renewable energy sources (RESs), power loads, outdoor temperature, and hot water demands, a multi-stage adaptive stochastic optimisation (SO) approach is used. Through the linearisation approaches and scenario sampling, the original non-linear deployment model is transformed into a mixed-integer linear programming (MILP) one and tested on an IEEE 33-bus distribution network-based residential MEMG. The effectiveness of the proposed method is verified in numerous case studies. All simulation results indicate that our proposed method can effectively improve the system EDP and is more immune to heterogeneous uncertainty sources. Besides, our method can be practically applied for the emerging residential MEMGs, such as smart buildings and green homes, with long-term DSM contracts.
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Part III: Coordinated Operation of DERs in Microgrids: Energy Management and Voltage Regulation
7 Hourly coordination of energy storage and direct load control
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This chapter introduces a robust optimisation (RO) approach for optimal operation of microgrids. The uncertain power generation of renewable energy sources (RESs) is addressed by collaborative schedule of energy storage (ES) and direct load control (DLC) through a two-stage complementary framework: an hour-ahead charging/discharging of ES and a quarter-hour-ahead activation of DLC. The objective is to maximise the total operating profit of the microgrid considering operation and maintenance (O&M) costs as well as energy transaction costs. Assuming the renewable power generation randomly varies within a bounded uncertainty set, the operation problem is modelled to a two-stage robust optimisation (TSRO) model and solved by a column-and-constraint generation algorithm. Compared with conventional operation methods, the ES and DLC are coordinated in different timescales, and RES uncertainties are fully addressed during operation decision-making, ensuring robust solutions.
8 Daily coordination of microturbines and demand response
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Microgrid is an effective means to integrate distributed generation (DG) resources. However, uncertain renewable DG such as wind turbine (WT) and photovoltaic (PV) power outputs and uncertain loads can bring in tremendous difficulties for energy management in microgrids. In this chapter, a two-stage robust microgrid coordination strategy is proposed: a price-based demand response (PBDR) programme is scheduled a day ahead and microturbine power outputs are modified hourly. A two-stage robust optimisation (RO) model is applied to address the coordinated operation problem with guaranteed robustness against the uncertainties of renewable DG and loads. Simulation results show the PBDR programme and multiple DG units can coordinate effectively to accommodate the renewable and load uncertainties while maximising microgrid benefits.
9 Optimal dispatch of MEMGs
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This chapter proposes a system-wide coordinated optimal energy dispatch method for multi-energy microgrids in both the grid-connected and islanded modes. The studied microgrid consists of multiple energy carriers covering controllable generation (CG) units (fuel cell (FC), electric boiler (EB), combined cooling, heat and power (CCHP) plant, and electric chiller (EC)), uncontrollable generation units (wind turbine (WT) and photovoltaic cell (PV)), and energy storage (ES) devices (battery storage, heat storage tank (HST), and ice storage tank (IST)). The proposed energy dispatch method aims to minimise the microgrid net operation cost and enhance the dispatch flexibility of CCHP plant in supplying power, heat, and cooling in the day-ahead energy market. The problems are formulated as the mixed-integer linear programming (MILP) models for both grid-connected and islanded modes, which can be efficiently solved by commercial solvers. Comprehensive case studies are performed to examine the effectiveness of the proposed models and compared with traditional dispatch methods which separately supply electricity and heat/cooling energies. Simulation results have shown that the proposed method has much higher energy efficiency.
10 Temporally coordinated dispatch of MEMGs under diverse uncertainties
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This chapter proposes a temporally coordinated operation method for a multi-energy microgrid under diverse uncertainties from renewable energy sources (RESs), power loads, and electricity transaction prices between the microgrid and the utility grid. The method aims to coordinate multiple energies in different timescales considering distinct properties of thermal and power energy: the combined cooling heat, and power (CCHP) plants, power-to-thermal conversion units, and thermal storage tanks (TSTs) are dispatched hourly in the day-ahead operation stage; in the intraday online operation stage, the battery storage units (BSs) are dispatched to supplement the day-ahead operation decisions every 5 min after the uncertainties are realised. Based on constraints linearisation and uncertain scenario generation/reduction, the problem is converted to a deterministic two-stage mixed-integer linear programming (MILP) equivalent model which can be solved efficiently. Finally, the proposed method is verified on an IEEE 33-bus distribution network-based multi-energy microgrid. Compared with the existing methods, the simulation results indicate that the proposed method can better coordinate multiple energies with low operating cost and high robustness.
11 Robustly optimal dispatch of MEMGs with flexible loads
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A multi-energy microgrid (MEMG) can simultaneously supply electric and thermal energy to customers to improve overall energy utilisation efficiency. However, intermittency and uncertainty from renewable power generation such as wind turbines (WTs) and solar photovoltaics (PVs), as well as electric and temperature-dependent thermal loads, can significantly challenge and complicate the operation of an MEMG. To conquer the challenges, this chapter introduces the utilisation of price-based demand response (PBDR) and indoor temperature control to flexibilise the electric and thermal loads, respectively. Then, a two-stage coordinated operation method is proposed to optimally coordinate the combined cooling, heat and power (CCHP) plants, flexible electric and thermal loads, and thermal storage under multiple uncertainties. The mathematical problem is modelled as a two-stage robust optimisation (TSRO) model and solved by column-and-constraint generation algorithm. Simulation results verify high energy utilisation efficiency and operating robustness of the proposed method.
12 Multi-timescale coordinated voltage/var control optimisation
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This chapter presents a multi-timescale coordinated voltage/var control (VVC) optimisation approach for the grid-connected microgrid with high-level solar photovoltaic (PV) units. It aims to utilise multiple devices to counteract uncertain voltage fluctuation and deviation caused by stochastic solar PV power output and load demand. In the hourly timescale (first stage), mechanical devices including capacitor banks (CBs) and transformer tap changers are scheduled. In the 15-min timescale (second stage), PV inverters are dispatched to provide reactive power support to supplement the first-stage decision after stochastic PV power output and load variations are realised. The coordination problem is formulated as a two-stage stochastic programming model and converted to a deterministic mixed-integer quadratic programming (MIQP) model, and then can be efficiently solved by off-the-shelf solvers. Compared with existing methods, the proposed approach can achieve lower expected energy loss and can sustain a secure voltage level under random PV power output and load variation.
13 Three-stage robust inverter-based voltage/var control optimisation
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This chapter introduces a novel three-stage robust inverter-based voltage/var control (TRI-VVC) optimisation approach for high photovoltaic (PV)-penetrated microgrids. The approach aims to coordinate three different control stages from central to local VVC to reduce network energy loss and mitigate voltage deviations. In the first stage, capacitor banks (CBs) and an on-load tap changer (OLTC) are scheduled hourly in a rolling horizon. In the second stage, PV inverters are dispatched in a short time window. In the third stage, the inverters respond to real-time voltage violations through local droop controllers. A new PV inverter model is developed to support both the central var dispatch and the local var droop control. To address uncertain PV output and load demand, a robust optimisation (RO) model is proposed to optimise the first two stages while taking into account the droop voltage control support from the third stage. A linearised distribution power flow model with branch power loss is developed and applied in the RO. The simulation results show high efficiency and robustness of the proposed TRI-VVC optimisation method.
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Part IV: Coordinated real-time control of DERs: distributed controller design and hardware-in-the-loop tests
14 Power system frequency control by aggregated energy storage systems
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The widespread distributed energy storage systems (ESSs) can be aggregated to provide grid ancillary services. In this chapter, an improved load frequency control scheme with the energy storage aggregators (ESAs) and a power disturbance observer is proposed. The disturbance observer is designed to assist the original automatic generation control (AGC) scheme for generators and ESAs, which improves the system frequency response and recovery. In each ESA, a finite-time leader-follower consensus control is proposed to aggregate the small-scale ESSs via sparse communication networks. By using such an algorithm, the ESA can track the frequency control signal and the state-of-charge (SoC) among each ESS can be balanced in finite time. The external characteristics of the ESA will resemble that of one large-scale ESS. The whole frequency control scheme is validated under a series of scenarios including contingency and normal operation.
15 Power system frequency support by grid-interactive smart buildings
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Grid-interactive smart buildings (GISBs) with thermostatically controlled loads (TCLs) can be modelled as virtual energy storage systems (ESSs) with dissipation. They have large potentials for providing grid ancillary services such as frequency support. In this chapter, a distributed aggregation control method based on leader-follower sliding mode consensus control is designed for multiple GISBs in a residential community to provide primary frequency support. A leader control is designed to provide power and comfort/energy level references for the smart building aggregator based on the area frequency deviation. The references are tracked by each smart building by using the proposed aggregation control. The Lyapunov method is used to prove the stability of the proposed control method for GISBs. By adopting the proposed method, the aggregated smart buildings can provide good power tracking and energy recovery capability, which effectively improves the system frequency response. Fair and efficient power and comfort/energy level sharing can be achieved among all participating GISBs. The proposed control method is validated on a three-area power system frequency control model considering smart buildings.
16 Decentralised-distributed hybrid voltage control by inverter-based DERs
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Due to high-level penetration of renewable energy, voltage fluctuations and violations are becoming two main voltage quality issues in power distribution networks. In this chapter, a decentralised-distributed control strategy by using reactive power from inverter-based DERs is proposed for network voltage regulation. A decentralised voltage control is developed to mitigate voltage fluctuations by regulating the voltage ramp rate. The var capacity from the power inverters becomes smoothed as a by-product. A distributed voltage control is developed to fairly utilise the var capacity of power inverters to deal with the network voltage deviations. In addition, on-load tap changers (OLTCs) control can provide supplementary voltage regulation when there is a shortage of var capacity from power inverters. The effectiveness of the proposed voltage regulation method is validated by simulation on IEEE 33-bus distribution network with real-world data.
17 Two-level distributed voltage/var control by aggregated PV inverters
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The continuous increase of the photovoltaic (PV) penetration level in power distribution networks results in severe voltage limit violation problems. This chapter proposes a fully distributed two-level volt/var control (VVC) scheme which aggregates PV inverters for voltage regulation. In the lower-level VVC, the rooftop PV inverters are aggregated via a consensus algorithm and controlled by user-defined droop control in medium-voltage (MV) networks. The reactive power output of each PV aggregator can be adjusted in real time from its dispatched value depending on the bus voltage variations. In the upper-level VVC, the reactive power of PV aggregators is dispatched every 15 min to minimise the network power loss. The upper-level dispatch signals are set as base values in droop control for PV aggregators. This problem is formulated as second-order cone programming (SOCP) and solved by the alternating direction method of multipliers (ADMM). The effectiveness of the proposed method is demonstrated by simulation results in both short- and long-term scenarios.
18 Event-triggered control of DERs and controller hardware-in-the-loop validation
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Microgrids (MGs) are cyber-physical systems with highly coupled electrical and communication networks. The centralised secondary control of MGs based on periodical communications restricts system efficiency and resilience. In this chapter, a distributed event-triggered secondary control scheme in islanded MGs is proposed and its cyber-physical implementation is introduced. The proposed control scheme reduces communications in secondary control, depending on the system state change 'events' (e.g. load variations and communication failures). The secondary control objectives, i.e. frequency/voltage regulation and accurate real/reactive power sharing, are decoupled into two timescales. Only event-triggering conditions (ETCs) for power sharing control in a slower timescale are designed. The overall communication times are significantly reduced as neighbour controllers only exchange information when the ETCs are satisfied. The proposed control is evaluated by means of the controller hardware-in-the-loop (CHIL) test. The physical system of MGs is operated in the OPAL-RT and the cyber system is implemented in Raspberry Pis (R-Pis). The experimental results validate the proposed method.
19 Three-level coordinated voltage control of DERs and power hardware-in-the-loop validation
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The reactive power of the photovoltaic (PV) inverters has great potential for voltage regulation in distribution networks. In this chapter, we propose a new three-level coordinated control method for PV inverters to deal with network voltage fluctuation and violation issues. A ramp-rate control in Level I is developed to smooth the network voltage fluctuations. A droop control in Level II is developed to alleviate the network voltage deviations locally. When local compensation from Levels I and II is not enough to regulate the network voltages within the required limits, a dynamic average consensus control in Level III is developed to fairly share the reactive power requirement among all inverters. The proposed control method functions to smooth the voltage profiles, restrain the voltage rise/drop problem, and coordinate PV inverters in the system when the voltage problem cannot be handled by individual PV inverter. The system model under the proposed three-level coordinated control is derived and the stability analysis is provided. The proposed control method is validated by means of power hardware-in-the-loop (PHIL) experiment.
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Back Matter
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