Smart Control of Low Voltage Grids

1 IEEE Power & Energy Society General Meeting 2014 Panel Session: Advanced Modelling and Control of Future Low Voltage Networks Smart Control of Low Voltage Grids Christian Oerter, Nils Neusel-Lange Wuppertal University Institute of Power System Engineering Germany July 31 st 2014

2 Current Challenges on Distribution Level Integration of decentralized generation units, especially due to the German Energiewende Electrification of air conditioning for buildings (heat pumps and air cons) Future integration of electric vehicles and storage units Increased capacity utilization, overloading possible Exceedance of the permitted voltage range Voltage U r + 10% Overloading! 187,6A Length of the line 10kV/0,4kV S t,r = 400kVA 129,8A 72,1A NAYY 4x50 SE I th,max = 142A

3 Future Distribution Grids conventional or smart? Conventional grid expansion measures Problem: Grid capacity bottleneck Substitution Solution: Intelligent grid expansion Smart Grid Additional features Technology Options: Ancillary Services Smart Outage Management Smart Market Condition Monitoring Technical and economical benefits

Monitoring and Control System for LV-grids 4

5 Monitoring and Control System for LV-grids Process sequence: Cyclic operation Virtually real-time (10sec cycle interval) Optimization tasks: Robustness Performance Main modules: Grid State Identification Control Process System Initialization Update Process Values Grid State Identification Grid State Analysis Control Demand Reset Demand Control Process Set Points

6 Monitoring of LV-grids Grid State Identification Minimal sensor environment due to cost efficiency Estimation of load and feed currents to compensate the lack of information Phase-selective power flow calculation Grid Topology Identification During grid operation switching operations can be necessary Grid State Identification depends on accurate topology for power flow calculations Automatic topology-change recognition Cycle Inputs: Slack-Voltage Measured Branch Currents u Slack, i Branch,Meas R Branch X Branch L Branch Low Voltage Grid State Identification including predictive methods I/O parameters of the developed algorithm Topology I Topology II Static Grid Parameters: Topology Technical Data Unique Grid State u Load, i Branch, s Slack

7 Voltage and Power Control for LV-grids Combination of control facilities (actuators) for LV-grids, e.g. controllable transformers with OLTC, generation units, loads or storage units, in a consecutive, 3-stage control model Avoidance of active power curtailment as long as possible! 3-stage control model in an infeed-oriented scenario U r +10% 2. 3. 1. # nodes/line length U r -10% 1 st stage: controllable transformer 2 nd stage: power factor control 3 rd stage: active power control Active power curtailment necessary to resolve overloading conditions!

8 Voltage and Power Control for LV-grids Influencing parameters on actuator effectivity: Grid topology Position of the actuator within the grid Control range of the actuator Task for the control algorithm: Identification of the most suitable actuator to resolve a given critical grid state Maximum effectivity of the actuator leads to minimum influence on customers and decentralized generation units Compensation of customers/producers for curtailed active power by the grid operator German government consideration: 5% curtailment of the annual energy yield without compensation

9 Sensitivity-based Control for LV-grids Control algorithm is based on runtime sensitivity analysis Sensitivity matrix describes the influence of an actuator on the grid s nodes and branches Sensitivity matrix calculation is based on the grid s topology data, separately for power factor control and active power control Transformer with OLTC 1 2 Zone 1 3 4 5 6 7 Pr 40kW Pr 40kW Pr 40kW Cable SrT 400kVA 8 9 10 11 12 Overhead lines Zone 2 Pr 40kW Pr 40kW Pr 40kW sensor U m, I m, P m, Q m, cos φ m (m ϵ [L1, L2, L3] : phase) sensor/actuator

10 Direct Control Strategy for LV-grids System Initialization Characteristics: Sequential process sequence Short cycle times 1 set point per cycle Multiple cycles for resolving critical grid states (where necessary) Update Process Values Grid State Identification Grid State Analysis Control Demand Reset Demand Control Model Stage 1: Direct Voltage Control Control Process Control Model Stage 2: Power Factor Control Control Model Stage 3: Active Power Control Set Points

11 Optimized Control Strategy for LV-grids System Initialization Characteristics: Iterative process sequence Increased cycle times Multiple set points per cycle Resolving critical grid states within a single cycle (if technically possible) Update Process Values Grid State Identification Grid State Analysis Control Demand Reset Demand Control Model Stage 1: Direct Voltage Control Set Point Verification Control Process Control Model Stage 2: Power Factor Control Control Model Stage 3: Active Power Control Set Points

Deviation from Ur [%] 15 10 5 0-5 -10-15 12 10 8 3 6 4 2 Node # 2 1 Cycle # Initialization Set Points Scenario Setup Smart Grid System (Virtual Entity) LV-Grid Simulator (Power Flow Calculation) Simulation Results Comparison of both control strategies in case of an exceedance of the permitted voltage range Direct Control Strategy Simulation Environment Virtual Measurements 15% 10% 5% 0% -5% -10% -15% 4 Deviation from Ur [%] 15 10 5 0-5 -10-15 12 1. Set point: OLTC U 2% * 2. Set point : Power Factor cos 12 0,95 ind * 3. Set point : Power Factor cos 11 0,95 ind Optimized Control Strategy 10 8 6 Node # 4 2 Cycle # 1 2 12 15% 10% 5% 0% -5% -10% -15%

13 Field Experience Tasks: Engineering tasks, social & legal tasks Validation of the developed algorithms 11 loads 6 loads open cut-off point closed cut-off point Substation #1 27 loads 7 loads 7 loads 24 loads Node #40 5 loads Node #71 3 loads 3 loads 5 loads 3 loads Substation #2 Results: 5 loads GSI max. error: 1.5% 5 loads 3 loads 3 loads Node #214 Estimation error: 1.0% Measurement error: 0.5% Control process pretest

14 Conclusion Cost-effective system, minimum of components Avoidance of critical grid states Better utilization of existing grid capacity Avoidance/Delay of expensive grid expansion measures Extension for MV-grid application under development Lessons learned from field application: Smart Meters not useable as measurement devices a.t.m. Compensation of customers for curtailed active power Experience with power line communication technology Developed algorithms worked reliable in first tests tuning and adaption for further improvement

15 Thank you for your attention! christian.oerter@uni-wuppertal.de http://www.evt.uni-wuppertal.de