A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications
Abstract
:1. Introduction
2. DC Microgrid Architecture
2.1. Physical Architecture
2.2. Communication Technologies
2.3. Control Structure
Control Objectives and Scope
- The primary control is implemented on each local DG according to a decentralized control scheme. The objective is essentially to regulate the local voltages and currents. It also controls the breakers/switches (on/off and protection functions) and load control (curtailment functions).
- Primary control may cause voltage deviations, especially when the heavy loads are connected to or disconnected from the microgrid. The system might run abnormally or drop into under or over voltage protection. The objective herein is to develop a distributed secondary control scheme for voltage quality enhancement, such as voltage unbalance compensation. For regulating the DC bus voltage, droop control is recommended for the voltage control among microgrids. The proposed control scheme should be flexible and make the controller have the plug-and-play property.
- A tertiary control structure is developed in a way that the microgrid central controller considers the optimal power flow, economic dispatch, and optimal energy scheduling problems in the microgrid to formulate an EMS. The EMS uses inputs (weather forecast, load demand, SOC, energy prices, etc.) to carry out scheduling and optimization procedures. It determines the optimal set points for distributed generation (DG) and load operation in the microgrid.
3. DC Microgrid Energy Management System (EMS)
3.1. Energy Management Based on Classical Methods
3.1.1. Iterative Algorithms
3.1.2. Linear Programming Methods
3.1.3. Mixed-Integer Linear Programming (MILP)
3.1.4. Stochastic and Robust Programming Methods
3.1.5. Model Predictive Control Methods
3.2. Energy Management Based on Artificial Intelligence Methods
3.2.1. Fuzzy Logic Methods
3.2.2. Neural Network Methods
3.2.3. Evolutionary Computation
3.2.4. Multi-Agent System (MAS)
3.3. Existing Software Tools and Sardware Components for Microgrid EMS
3.3.1. Software Tools
3.3.2. Hardware Components
3.4. Energy Management Strategies Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Voltage Level | Application in Residential Domain | Remarks (Advantages +, Disadvantages −) |
---|---|---|
5 V | Universal serial bus (USB) connections. Small rechargeable batteries. | + Easy to connect with a device and distribute power. − Short application range. |
12 V | Low load applications for short distance. | + Safe and same level of DC appliances. − Designed for low power usage. |
24 V | High power applications for long distance | + Low level power distribution according to the standard. |
48 V | New automobiles charging infrastructure. Telecommunication instruments such as wireless phones, ethernet. | + Within the standard of IEEE for DC microgrid. Simple protection. |
380–400 V | For DC computer data centers and possibly commercial buildings as a residential application. | + Within the standard of Emerge Alliance of buildings. Easily compatible with main grid. More appropriate for DC microgrid residential applications. − Additional converter required. Protection is compulsory. |
Appliance | Power Range (W) | Rated Voltage (V) | |||
---|---|---|---|---|---|
5 | 12 | 24 | 48 | ||
USB Port | 2.5 | ▲ | |||
Led lamp | 3–40 | ▲ | ▲ | ▲ | |
Fan | 3–36 | ▲ | ▲ | ▲ | |
Air conditioner | 444–816 | ▲ | |||
Refrigerator | 40–150 | ▲ | ▲ | ▲ | |
Microwave | 660–1100 | ▲ | |||
Coffee maker | 900–1200 | ▲ | ▲ | ||
Computer | 21–80 | ▲ | ▲ | ||
Television | 65–120 | ▲ | ▲ | ▲ | |
Washing machine | 70–360 | ▲ | ▲ | ||
Iron | 130–150 | ▲ |
Technology | Data Rate | Coverage Range | Applications |
---|---|---|---|
Wired | |||
Broadband PLC | Up to 300 Mbps | Up to 1500 m | Smart grid, HAN |
Narrowband PLC | 10–500 Kbps | Up to 3 km | Smart grid, HAN |
Ethernet | Up to 100 Gbps | Up to 100 m | SCADA, backbone Communication |
Fiber optics | Up to 100 Gbps | Up to 100 km | SCADA, HAN |
Wireless | |||
GSM | Up to 14.4 kbps | 1–10 km | AMI, HAN, BAN, IAN |
GPRS | Up to 170 Kbps | 1–10 km | AMI, HAN, BAN, IAN |
WiMAX | Up to 75 Mbps | Up to 50 km | AMI, Mobile workforce management |
Z-wave | 40–250 Kbps | 30 m point-point, Unlimited with mesh | AMI, HAN, BAN, IAN |
ZigBee | 250 kbps | 100+ meters | AMI, HAN |
System Configuration | EMS Approach | Control Objectives |
---|---|---|
PV/Wind/Fuel cell/Battery | Fuzzy logic (FL) [47] | Satisfy the load demand while maintaining the battery SOC and keeping the hydrogen storage tank at maximum level. Cost optimization. |
PV/Wind/Fuel cell/Battery | Linear programming (LP) [56] | Reduce the computational time. Minimize the energy cost and CO2 emissions. Increase the battery life. |
PV/Fuel cell/Battery | Model predictive control (MPC) [57] | Weather forecasts. Optimize microgrid operation while considering uncertainties of RERs and load demand. Minimize the daily generation cot and emission. |
PV/Battery/Fuel cell | Linear programming (LP) [61] | Battery overcharging protection. |
Hybrid renewable energy sources based on storage systems integrated with a grid. | Fuzzy logic (FL) [72] | Optimization and design of control strategy: power, energy efficiency, economic evaluation, environmental effects, and voltage quality. |
PV/Wind/Fuel cell/Battery | Fuzzy logic (FL) [73] | Regulate the overall system power flow. Batteries’ life cycle. |
Standalone and grid-connected system with different configurations. | HOMMER [85,86] | Making energy balance calculations on an hourly basis for a complete year. Size optimization of each component to achieve the minimum cost of energy production. |
Intelligent EMS based on SCADA system [40] (MATLAB/Simulink integrated with Modbus and Konnex) | |||
---|---|---|---|
System Element | Type | Capacity | Objective |
PV Panels | Monocrystalline | 5 kW | MPPT |
Battery | Li-ion | 6.5 kWh | Charging/discharging |
Load | 9 kW | Reveals daily consumption | |
EMS with fuzzy control for a DC microgrid system [72] (MATLAB/Simulink, LabVIEW, Rs-485/Zigbee tools) | |||
System Element | Type | Capacity | Objective |
PV panels | Monocrystalline | 5 kW | MPPT |
Wind Turbine | AWV 1500 | 1.5 kW | MPPT |
Battery | Li-ion | 1.5 kWh | SOC |
Load | 6.5 kW | ||
DC bus voltage | 380 V (±20 V) | ||
EMS for islanded microgrid based on rule-based power flow control [54] (PSCAD simulation tool) | |||
System Element | Type | Capacity | Objective |
PV panels | Monocrystalline | 30 kW | MPPT |
Wind Turbine | 3 kW | MPPT | |
Battery pack | Li-ion Lead Acid | 800 Ah | SOC |
Load | (10 kW + 15 kW) | 25 kW | |
EMS for residential microgrid system based on NN and MILP [66] (Neural network and Mixed integer linear programming algorithm) | |||
System Element | Type | Capacity | Objective |
PV panels | 6 kW | MPPT | |
Battery | Li-ion | 5.8 kWh | SOC |
EMS for real time laboratory control based on feedback & PI cascaded control [107] (MATLAB/Simulink integrated with RT-LAB tool) | |||
System Element | Type | Capacity | Objective |
PV panels | 260 W | MPPT | |
Wind Turbine | PMSG | 260 W | Speed/torque |
Battery | Lead acid | 10 Ah | SOC |
DC bus voltage | 20 V | ||
Intelligent EMS with linear programming based multi-objective optimization [108] (Artificial neural network and Fuzzy logic controller) | |||
System Element | Type | Capacity | Objective |
PV panels | 20 kW | Cost minimization | |
Wind Turbine | 25 kW | Cost minimization | |
Battery | Lead acid | 15 kWh | SOC |
Fuel cell | 15 kW | Cost minimization | |
EMS with multi-agent system [109] (MATLAB/Simulink tool) | |||
System Element | Type | Capacity | Objective |
PV panels | Titan S-60 | 100 kW | MPPT |
Wind Turbine | PMSG | 200 kW | MPPT |
Battery | Lead acid | 300 kWh | SOC |
Load | 80 kW |
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Ali, S.; Zheng, Z.; Aillerie, M.; Sawicki, J.-P.; Péra, M.-C.; Hissel, D. A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications. Energies 2021, 14, 4308. https://doi.org/10.3390/en14144308
Ali S, Zheng Z, Aillerie M, Sawicki J-P, Péra M-C, Hissel D. A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications. Energies. 2021; 14(14):4308. https://doi.org/10.3390/en14144308
Chicago/Turabian StyleAli, Sadaqat, Zhixue Zheng, Michel Aillerie, Jean-Paul Sawicki, Marie-Cécile Péra, and Daniel Hissel. 2021. "A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications" Energies 14, no. 14: 4308. https://doi.org/10.3390/en14144308
APA StyleAli, S., Zheng, Z., Aillerie, M., Sawicki, J.-P., Péra, M.-C., & Hissel, D. (2021). A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications. Energies, 14(14), 4308. https://doi.org/10.3390/en14144308