DC Soft Open Points for Resilient and Reconfigurable DC Distribution Networks
Abstract
:1. Introduction
1.1. Need for DC Network Reconfiguration
1.2. Reliability Assessment of Reconfigurable DC Microgrids
- Loss of load expectation (LOLE),
- Loss of load probability (LOLP),
- Expected energy not supplied (EENS),
- System average interruption frequency index (SAIFI),
- Customer average interruption frequency index (CAIFI),
- System average interruption duration index (SAIDI),
- Customer average interruption duration index (CAIDI).
1.3. Agent-Based Control of Reconfigurable DC Microgrids
1.4. Contributions to Knowledge
2. Materials and Methods
2.1. DC Soft Open Point (DCSOP)
2.1.1. Implementation of the DCSOP Using a Bi-Directional DC–DC Converter
2.1.2. Seamless Averaged Model for the DCSOP
2.2. Reliability Assessment Method
2.2.1. Reliability Analysis Sets
2.2.2. Reliability Indices
- SAIDI (system average interruption period index) is the average interruption period per customer served.
- CAIDI (customer average interruption period index) is the average interruption period for those customers interrupted during a year.
2.3. Agent-Based Model
2.3.1. Microgrid/Network Agent
- Calculate power flows and operational parameters based on the different measurement points in the microgrid/network.
- Communicate with DCSOP agents and other agents, and provide information and knowledge gained from overseeing the operation of the network.
- Send direct requests to particular equipment agents to act in specific ways.
- Communicate with other DCSOP, load, power flow or other network agents in order to establish joint decision making.
- Communicate with other, adjacent microgrid agents, in case there is any synergy that can be gained, such as load reduction to prevent load shedding.
2.3.2. DCSOP Agent
- Communicates with other agents (e.g., load agents) and receives messages from them on various conditions in the network.
- Makes decisions regarding the state of the network and whether there is a need to act or not.
- Communicates with other DCSOP, load, power flow or other network agents in order to establish joint decision making.
- Sends commands to the DCSOP device, enabling it to transition from the ON to the OFF state, and vice versa.
- Receives measurements on different parameters from the DCSOP in order to check on the state of the device.
2.3.3. Generator/Load Agent
- Communicates with other agents (e.g., DCSOP or microgrid agents) and sends messages to them regarding various parameters associated with their generator or load.
- Makes decisions regarding the local state of the network and whether there is a need to act or not (e.g., generator disconnection, load shedding).
- Communicates with other DCSOP, load, power flow or other network agents in order to establish joint decision making.
2.3.4. Multi-Agent System Structure
2.3.5. Agent Implementation in MATLAB/Simulink Stateflow
3. Results and Discussion
3.1. DCSOP Characterisation Case Studies
3.2. DCSOP Stress Test
3.3. Control of Current Flow Direction
3.4. Replacing NOP with DCSOP in a DC Microgrid
3.5. Replacing NOP with a Normal Conductor in a DC Microgrid
3.6. Impact of DCSOP on Reliability of Reconfigurable DC Microgrids—Case Studies
3.6.1. Case R.1: Base Case, No DCSOP
- CAIDI: 1.269,
- SAIDI: 1.0497.
3.6.2. Case R.2: One DCSOP, Location A
- CAIDI: 0.961,
- SAIDI: 0.964.
3.6.3. Case R.3: One DCSOP, Location B
- CAIDI: 0.964,
- SAIDI: 0.7461.
3.6.4. Case R.4: Two DCSOPs, Locations A and B
- CAIDI: 0.658,
- SAIDI: 0.656.
3.6.5. Summary and Comparison of Reliability Cases
3.7. Testing of Agent-Based Control of DCSOP for DC Network Reconfiguration
4. Conclusions
- Being able to control the power flows, and
- Being able to connect two buses operating at different voltages.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Appendix A
Branches | R/Ω | Loads | Rating/KW | Generators | Rating/V |
---|---|---|---|---|---|
Bus 1–Bus 2 | 1.32 | Load 1 | 100 | G1 | 400 |
Bus 2–Bus 3 | 1.32 | Load 2 | 60 | G2 | 400 |
Bus 3–Bus 4 | 1.32 | Load 3 | 60 | G3 | 400 |
Bus 4–Bus 5 | 1.32 | Load 4 | 60 | G4 | 400 |
Bus 2–Bus 6 | 1.32 | Load 5 | 30 | G5 | 400 |
Bus 6–Bus 7 | 1.32 | Load 6 | 30 | ||
Bus 2–Bus 8 | 1.32 | Load 7 | 60 | ||
Bus 8–Bus 9 | 1.32 | ||||
Bus 8–Bus 10 | 1.32 |
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DCSOP Operation Case Study | Rationale |
---|---|
(C.1) Stress test | Evaluate controller performance |
(C.2) Control of power flow | Assess the ability of the controller to regulate power flow |
(C.3) DCSOP in a microgrid | Simulate the effect of a DCSOP and evaluate potential benefits such as reduced line loading |
(C.4) Comparison between DCSOP and simple NOP | Identify differences between DCSOP functionality against a simple DC line equivalent |
Branches | Currents before Connecting the DCSOP, A | Currents after Connecting the DCSOP, A |
---|---|---|
Bus 1–Bus 2 | 72.5 | 33.3 |
Bus 2–Bus 3 | 72.5 | 33.3 |
Bus 3–Bus 4 | 0 | 72.8 |
Bus 4–Bus 5 | 0 | −72.8 |
Bus 2–Bus 6 | 217.5 | 100 |
Bus 6–Bus 7 | 102.6 | 119.4 |
Bus 2–Bus 8 | 72.5 | 33.3 |
Bus 8–Bus 9 | 0 | 0 |
Bus 8–Bus 10 | 242.3 | 242.3 |
DCSOP | 0 | −145.6 |
Generators | Currents before Connecting the DCSOP, A | Currents after Connecting the DCSOP, A |
---|---|---|
Generator 1 | 72.5 | 33.3 |
Generator 2 | 322.5 | 356.7 |
Generator 3 | −149.8 | −222.6 |
Generator 4 | 615.5 | 576.4 |
Generator 5 | 149.8 | 149.8 |
Branches | Currents with NOP, A | Currents with a Normal Conductor Closing the NOP, A |
---|---|---|
Bus 1–Bus 2 | 72.5 | 37.9 |
Bus 2–Bus 3 | 72.5 | 37.9 |
Bus 3–Bus 4 | 0 | 67.6 |
Bus 4–Bus 5 | 0 | −67.6 |
Bus 4–Bus 6 | 0 | −135.2 |
Bus 2–Bus 6 | 217.5 | 113.8 |
Bus 6–Bus 7 | 102.6 | 117.4 |
Bus 2–Bus 8 | 72.5 | 37.9 |
Bus 8–Bus 9 | 0 | 0 |
Bus 8–Bus 10 | 242.3 | 242.3 |
Generators | Currents with NOP, A | Currents with a Normal Conductor Closing the NOP, A |
---|---|---|
Generator 1 | 72.5 | 38 |
Generator 2 | 322.5 | 355.5 |
Generator 3 | −149.8 | −217.4 |
Generator 4 | 615.5 | 581 |
Generator 5 | 149.8 | 149.8 |
CAIDI | % Improvement | SAIDI | % Improvement | |
---|---|---|---|---|
Case R.1 | 1.269 | - | 1.0497 | - |
Case R.2 | 0.961 | 24.3% | 0.964 | 8.1% |
Case R.3 | 0.964 | 24.0% | 0.7461 | 28.9% |
Case R.4 | 0.658 | 48.1% | 0.656 | 37.5% |
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Ramadan, H.A.; Skarvelis-Kazakos, S. DC Soft Open Points for Resilient and Reconfigurable DC Distribution Networks. Energies 2022, 15, 5967. https://doi.org/10.3390/en15165967
Ramadan HA, Skarvelis-Kazakos S. DC Soft Open Points for Resilient and Reconfigurable DC Distribution Networks. Energies. 2022; 15(16):5967. https://doi.org/10.3390/en15165967
Chicago/Turabian StyleRamadan, Husam A., and Spyros Skarvelis-Kazakos. 2022. "DC Soft Open Points for Resilient and Reconfigurable DC Distribution Networks" Energies 15, no. 16: 5967. https://doi.org/10.3390/en15165967
APA StyleRamadan, H. A., & Skarvelis-Kazakos, S. (2022). DC Soft Open Points for Resilient and Reconfigurable DC Distribution Networks. Energies, 15(16), 5967. https://doi.org/10.3390/en15165967