Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids
Abstract
:1. Introduction
- Seamless change in transient state for VSI-MG interactions;
- Steady-state stability for voltage, frequency and power variables;
- Power flow control injected or absorbed by the interactions between VSI and MG;
- Generation of an optimal law of control under constraints;
- Robustness against model uncertainties and disturbances in the MG.
2. Inverted-Based Microgrids Definitions
2.1. Inverter Structure
2.2. Microgrid Structure and Control
- Control of output variables. The output voltage and current variables in the IBMG must accurately follow their setpoints. In the event of disturbances, the oscillations of these variables must be damped to prevent critical deviations that affect the performance of the loads;
- Power flow control and protections. Over the years, MG was incorporated as support schemes for electrical generation shared with the utility grid. This scenario shows systems not prepared to incorporate more electrical generation due to the load capacity of their wires. For this reason, there are algorithms that limit the passage of high line currents due to voltage variations in the utility grid. In addition, the IBMG controller must maintain a balance between the power supply and demand in the event of any sudden load change. The controller aims to stabilize the IBMG regardless of the disconnection of equipment, increase of RES units, or the activation of electrical protections;
- Cost-benefit. The efficient control of IBMG must generate energy and economic benefits for its consumers. These advantages are recognized frequently in isolated areas that purchase IBMG equipment, and the investment will pay off in a short time. Benefits will be seen in lower electricity payments and the appropriate use of natural resources;
- Transition between operating modes. The IBMG is an independent generation system that supports the conventional grid in power-sharing activities or can work independently in island mode. The transitions between these two states must be carried out maintaining a smooth change among the voltage and frequency variables, ensuring their transient stability;
- Controller tuning. Controllers’ performance is reduced by the variability of process behavior in situations of major changes in the generation system. This event causes the need to re-tune the controller if the background conditions change dramatically. Tuning works appropriately for certain performance ranges but does not respond to regulation conditions when DG leaves its linearity zone. Problems caused by the high incorporation of nonlinear loads, the low ability to reject disturbances, and communication delays in hierarchical MG control systems are also studied.
2.3. Hierarchical Control Structure
2.3.1. Primary Control
- Regulation on internal voltage and current loops is done from linear and non-linear controllers. The objective is to measure and monitor the current in inductors and capacitors at the output of a filter. In this manner, the controller must maintain a fast and stable response to these values;
- The generation of a virtual impedance to emulate a physical impedance connected to the output of the system;
- The regulation of external control loops for active and reactive power variables, these values regulate the output voltage at the primary level. At this stage, droop power control is used.
2.3.2. Secondary Control
2.3.3. Tertiary Control
2.4. Microgrid Management Policies
3. Multi-Objective Functions Approach on IBMG
- Energy-cost reduction;
- Reliability of the service;
- Minimizing power fluctuations;
- Reduction on peak loading;
- Mitigation on CO emissions;
- Multi-objective optimization;
- Tuning algorithms.
3.1. Energy-Cost Reduction
3.1.1. Fast Response
3.1.2. Ramps for Power Setpoints Tracking
3.1.3. Voltage and Frequency Regulation
3.1.4. Virtual Impedance
3.1.5. Virtual Inertia
3.2. Reliability of the Service
3.2.1. Uncertainties
3.2.2. Adaptive Schemes
3.3. Minimizing Power Fluctuations
Active and Reactive Power Sharing
3.4. Reduction on Peak Loading
3.4.1. Performance Index Minimization
3.4.2. State Estimations
3.4.3. Constraints
3.4.4. Harmonic Mitigation
3.5. Mitigation on CO2 Emissions
3.6. Multi-Objective Proposals And Optimization
3.7. Tuning Algorithms
4. Future Trends
4.1. Convergence of Solutions
4.2. Computational Cost Administration
4.3. Mathematical Model Accuracy
4.4. Communication Delays
4.5. Optimal Tuning/Self-Tuning
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
ANN | Artifitial neural network |
CG | Centralized generation |
CO | Carbon dioxide |
DER | Distributed energy resources |
DG | Distributed generation |
LQR | Linear quadratic regulator |
MC | Microsource controller |
MG | Microgrid |
MPC | Model predictive control |
PCC | Point of common couplig |
PID | Proportional-integral-derivative |
PSO | Particle swarm optimization |
SMC | Sliding mode controller |
VSI | Voltage source inverter |
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Country | Title | Year |
---|---|---|
USA | State-level Renewable Portfolio Standards (RPS) | Multiple years |
USA | IEEE 1547-2018 | 2018 |
Poland | Renewable Energy Law of Poland | 2015 |
Germany | 2014 Amendment of the Renewable Energy Sources Act -EEG- | 2014 |
China | Renewable electricity generation bonus | 2013 |
China | The Notice of further improvement of New Energy Demonstration implementation | 2013 |
Slovakia | Act on Energy and amendments to certain acts (No. 251/2012) | 2013 |
Spain | Royal Decree Law on urgent measures to guarantee financial stability in the electricity system | 2013 |
UK | Electricity Market Reform (EMR) | 2013 |
Italy | National Energy Strategy | 2013 |
China | China Energy White Paper 2012 | 2012 |
China | The Notice on New Energy Demonstration City and Industrial Park | 2012 |
Croatia | Energy Act 2012 | 2012 |
Denmark | Regulation on Net-metering for the Producers of Electricity for Own Needs | 2012 |
Luxembourg | Energy performance requirements for residential buildings 2012-2020 | 2012 |
Slovakia | Act on Regulatory Office for Network Industries (Act No. 250/2012) | 2012 |
Lithuania | Law on Energy from Renewable Sources | 2011 |
Spain | Regulation of small power plants connection to the electricity grid (Royal Decree 1699/2011) | 2011 |
Slovakia | National Renewable Energy Action Plan (NREAP) | 2010 |
Germany | Energy Concept | 2010 |
DC injection | Distributed energy resources (DER) will not inject more than 0.5% of DC current compared to full rated output. current at the reference point of applicability |
Voltage fluctuation | Rapid voltage changes: DER in PCC at medium voltage must not exceed 3% of nominal voltage and 3% over a period of one second. DER in PCC at low voltage must not exceed 5% of nominal voltage and 5% over a period of one second. |
Flicker: Short-term flicker severity evaluated in a time of 600 s = 0.35. Long-term flicker severity evaluated in a time of 2 h = 0.25. | |
Current distortion | Odd harmonics: 4% for 11, 2% for 11 17, 1.5% for 17 23, 0.6 for 23 35, 0.3% for 35 50. |
Even harmonics: 1% for h = 2, 2% for h = 4, 3% for h = 6. | |
Overvoltage contribution | Over one fundamental frequency period: DER must not exceed 138% of nominal frequency value for a duration of one fundamental frequency period. Applicable to the line-to-ground or line-to-line voltage systems. |
On cumulative instantaneous overvoltage: DER instantaneous and cumulative voltage must not exceed acceptable region defined on Std Section 7.4.2. |
Method | Advantages | Disadvantages | Sources |
---|---|---|---|
Pareto optimality | Incorporates scalarization methods and provide flexible framework for algorithm design | Require priori knowledge of the Pareto front in objective space, the number of weight vectors grow exponentially with the objective space size | [1,100,101] |
Global criterion | Simplicity and effectiveness because a Pareto ranking procedure is not required | The definition of desired goals requires extra computational effort; a solution might be non-dominated if the goals are chosen in a feasible domain and such conditions can limit their applicability | [2,102] |
Linear combination of weights | Simplicity in implementation, use and computational efficiency | Difficult to determine appropriate weight coefficients when scare information of the problem is available | [103] |
The -constraint method | Simple approach, it has been applied in many areas of engineering | High computational cost, encoding of objective functions limited for a few objectives | [104,105,106] |
Multi-objective genetic algorithm (MOGA) | Versatility to find several members of the Pareto optimal set in a single performance of the algorithm | High computational cost | [107,108] |
Non-dominated sorting genetic algorithm II (NSGA-II) | No extra diversity control is needed, elitism preserve pareto-optimal solutions | More members in the first non-dominated set lead place to other pareto-optimal solutions | [109,110,111] |
Multi-objective evolutionary algorithm (MOEA) | The performance index are integrated as environmental selection, guided search and continuous optimization of the entire population | Slow convergence, poor performance and unknown convergence behavior of each non-dominated solution in the the Pareto optimal set | [112,113,114] |
Control | Objective | Source |
---|---|---|
PID | Voltage and frequency control, grid stabilization, improvement of power quality | [117,118,119] |
LQR | Seamless transition, current control in island mode, frequency regulation, power sharing, mitigation of small signal instability | [69,86,89,110] |
MPC | Voltage and frequency control, virtual inertia, dynamic stabilization, power sharing, primary control | [39,73,90,120,121,122,123] |
Fuzzy | Voltage and frequency control, power AC/DC control, virtual inertia, online tuning, power-sharing, voltage, current control | [39,87,98,124,125] |
ANN | Faults detections, adaptive voltage and frequency control, islanding detection, prediction of load demand | [80,88,91,126,127,128] |
SMC | Power flow control, disturbances rejection, voltage and frequency control, robust control for unbalanced load | [26,82,92,129,130,131] |
/ | Robust and optimal control, voltage and frequency control, cascade scheme | [28,72,83] |
Method | V/f Control | Inertia Stability | Fast Transient Response | Power Flow Control | THD Reduction | Uncertainty Rejection | Tuning | Sources |
---|---|---|---|---|---|---|---|---|
ANN + droop control | ✓ | ✓ | ✓ | ✓ | — | ✓ | — | [74,132,133] |
MPC + ANN | ✓ | — | — | ✓ | — | ✓ | — | [80,134] |
MPC + VI | ✓ | ✓ | ✓ | ✓ | — | — | — | [39] |
PI + VI + droop control | ✓ | ✓ | ✓ | ✓ | — | — | — | [77] |
SMC + / | ✓ | — | ✓ | — | ✓ | ✓ | ✓ | [28] |
+ VSG | ✓ | ✓ | ✓ | ✓ | — | ✓ | ✓ | [79] |
LQR + / | ✓ | — | ✓ | — | ✓ | ✓ | — | [89,135] |
SMC + ANFIS | ✓ | — | ✓ | — | ✓ | ✓ | — | [67,136] |
SMC + ANN | ✓ | — | ✓ | ✓ | — | — | — | [36,137] |
PSO + droop control | ✓ | ✓ | — | ✓ | — | — | ✓ | [138] |
LQR + Kalman + GA | ✓ | — | ✓ | ✓ | — | ✓ | ✓ | [74] |
Droop control + Kharitonov | ✓ | — | — | ✓ | — | ✓ | ✓ | [115] |
Fuzzy PD + I + PSO | ✓ | — | — | ✓ | — | — | ✓ | [116] |
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Gonzales-Zurita, Ó.; Clairand, J.-M.; Peñalvo-López, E.; Escrivá-Escrivá, G. Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids. Energies 2020, 13, 3483. https://doi.org/10.3390/en13133483
Gonzales-Zurita Ó, Clairand J-M, Peñalvo-López E, Escrivá-Escrivá G. Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids. Energies. 2020; 13(13):3483. https://doi.org/10.3390/en13133483
Chicago/Turabian StyleGonzales-Zurita, Óscar, Jean-Michel Clairand, Elisa Peñalvo-López, and Guillermo Escrivá-Escrivá. 2020. "Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids" Energies 13, no. 13: 3483. https://doi.org/10.3390/en13133483