Integration of renewable energy sources and demand-side management into distribution networks by Damien Ernst – University of Li` ege dernst@ulg.ac.be EES-UETP – Porto, Portugal June 15-17, 2016
Outline Active network management Rethinking the whole decision chain - the GREDOR project GREDOR as an optimization problem Finding m ∗ , the optimal interaction model Finding i ∗ , the optimal investment strategy Finding o ∗ , the optimal operation strategy Finding r ∗ , the optimal real-time control strategy Conclusion Microgrid: an essential element for integrating renewable energy D. Ernst 2/81
Active network management Distribution networks traditionally operated according to the fit and forget doctrine . Fit and forget. Network planning is made with respect to a set of critical scenarios to ensure that sufficient operational margins are always garanteed (i.e., no over/under voltage problems, overloads) without any control over the loads or the generation sources. Shortcomings. With rapid growth of distributed generation resources, maintaining such conservative margins comes at continuously increasing network reinforcement costs. D. Ernst 3/81
The buzzwords for avoiding prohibitively reinforcement costs: active network management . Active network management. Smart modulation of generation sources, loads and storages so as to safely operate the electrical network without having to rely on significant investments in infrastructure. D. Ernst 4/81
A first example: How to maximize the PV production within a low voltage feeder without suffering over-voltages? What is currently done: The active power produced by PV panels is 100% curtailed as soon as over-voltage is observed. The curtailment is done automatically by the inverter. Objective: Why not investige better control schemes for minimizing the curtailment? D. Ernst 5/81
The low-voltage feeder example: 2 4 1 0 1 2 3 4 5 1 3 5 P B 1 ,t , Q B P B 2 ,t , Q B P B 3 ,t , Q B 1 ,t 2 ,t 3 ,t P P V 1 ,t , Q P V P P V 2 ,t , Q P V P P V 3 ,t , Q P V Infinite bus 1 ,t 2 ,t 3 ,t 0 1 2 3 V T h 1 Y T h Y 01 Y 12 Y 23 P L 1 ,t , Q L P L 2 ,t , Q L P L 3 , Q L 1 ,t 2 ,t 3 ,t D. Ernst 6/81
Control actions: At every time-step, for such a problem, various decisions can typically be taken regarding the feeder: ◮ Curtailing the PV active power / activating reactive power ◮ Charging or discharging batteries ◮ Managing the demand D. Ernst 7/81
Modeling the load (SLP) and PV production (from Belgian data) 4.5 9 data1 data1 data2 data2 data3 data3 4 8 data4 data5 data6 data7 data8 3.5 7 data9 data10 data11 data12 House consumption (kW) 3 6 data13 data14 PV production (kW) data15 data16 2.5 5 data17 data18 2 4 1.5 3 1 2 0.5 1 0 0 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 Time Time D. Ernst 8/81
The current (and basic) approach Principle: As soon as an over-voltage is observed at a bus, the corresponding inverter disconnects the PV panels from the feeder during a pre-determined period of time. � 0 if V j , t > V max , ∀ j ∈ { 1 , . . . , N } , ∀ t ∈ { 0 , . . . , T − 1 } , P PV j , t = P PV , max otherwise. j , t This control scheme, which is currently the one that is applied in practice, will be considered as the reference strategy. D. Ernst 9/81
Effects of the current (and basic) control scheme on the load and the PV production 4.5 1.1 PV16 data1 PV17 data2 PV18 data3 4 data4 1.09 data5 data6 data7 3.5 data8 data9 data10 1.08 data11 data12 3 data13 data14 PV production (kW) data15 Voltage (p.u.) data16 1.07 2.5 data17 data18 2 1.06 1.5 1.05 1 1.04 0.5 0 1.03 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 Time Time D. Ernst 10/81
The centralized optimization approach Principle: Solve an optimization problem over the set of all inverters: N � P PV , max � P PV ∗ 1 , t , Q PV ∗ 1 , t , . . . , P PV ∗ N , t , Q PV ∗ � − P PV ∈ arg min N , t j , t j , t P PV 1 , t , Q PV 1 , t ,..., P PV N , t , Q PV j =1 N , t subject to h ( P PV 1 , t , Q PV 1 , t , . . . , P PV N , t , Q PV N , t , V 1 , t , . . . , V N , t , θ 1 , t , . . . , θ N , t ) = 0 V min ≤ | V j , t | ≤ V max , j = 1 , . . . , N j , t ≤ P PV , max 0 ≤ P PV , j = 1 , . . . , N j , t | Q PV j , t | ≤ g ( P PV j , t ) , j = 1 , . . . , N D. Ernst 11/81
Effects of the centralized optimization approach on the load and the PV production Potential gain in this example: Curtailed energy with the basic approach: 31.63 kWh Curtailed energy with the centralized approach: 21.38 kWh D. Ernst 12/81
Towards decentralized approaches Principle: We are investigating control schemes that would only need local information. These control schemes work by measuring the sensitivity of the voltage (measured locally by the inverter) with respect to the injections of active and reactive power. D. Ernst 13/81
State transition diagram of the distributed control scheme: Mode A P set = P MPP Q set = Q f t > t RQ : Signal received Q set = Q f ( V tm , P set ) reached Mode E Mode B Signal received P set = P MPP P set = P MPP Q set → Q f Q set → − Q max until t = t RQ until t = t DQ No more signal for T reset t > t RP : t > t DQ and signal persists: P set = P MPP reached Q set = − Q max reached Signal received Mode D Mode C P set → P MPP P set → 0 Q set = − Q max Q set = − Q max until t = t RP until t = t DP No more signal for T reset D. Ernst 14/81
◮ The red dotted lines are the emergency control transitions while blue dashed lines are the restoring ones. ◮ t DQ (resp. t DP ) is the time needed in Mode B (resp. Mode C) to use all available reactive (resp. active) controls. ◮ T reset is the elapsed time without emergency signal for the controller to start restoring active/reactive power. ◮ t RP (resp. t RQ ) is the time needed in Mode D (resp. Mode E) to restore active (resp. reactive) power to the set point values of Mode A. ◮ P set and Q set are the active and reactive power set points of the controller. ◮ P MPP is the maximum available active power of the PV module and depends on the solar irradiation. ◮ Q max is the maximum available reactive power; it varies according to the capability curve as a function of the active power output. D. Ernst 15/81
Maximum PV active power that could be produced 35 30 25 20 (kW) 15 PV N4AB1 PV N4AB6 10 PV N4AB2 PV N4AB7 PV N4AB3 PV N4AB8 5 PV N4AB4 PV N4AB9 PV N4AB5 PV NODE4B 0 0 400 800 1200 1600 2000 2400 2800 3200 t (s) Active power produced by the PV First try to restore active power 30 Second try to restore active power 25 20 (kW) 15 10 PV N4AB1 PV N4AB6 PV N4AB2 PV N4AB7 PV N4AB3 PV N4AB8 5 PV N4AB4 PV N4AB9 PV N4AB5 PV NODE4B 0 0 400 800 1200 1600 2000 2400 2800 3200 t (s) D. Ernst 16/81
Reactive power produced by the PV PV N4AB1 PV N4AB6 0 PV N4AB2 PV N4AB7 PV N4AB3 PV N4AB8 PV N4AB4 PV N4AB9 -2 PV N4AB5 PV NODE4B -4 (kVAr) -6 -8 -10 0 400 800 1200 1600 2000 2400 2800 3200 t (s) Resulting voltages 1.1 First try to restore active power Second try to restore active power 1.08 1.06 (pu) 1.04 1.02 N4AB1 N4AB6 N4AB2 N4AB7 1 N4AB3 N4AB8 N4AB4 N4AB9 0.98 N4AB5 NODE4B 0 400 800 1200 1600 2000 2400 2800 3200 t (s) D. Ernst 17/81
Outline Active network management Rethinking the whole decision chain - the GREDOR project GREDOR as an optimization problem Finding m ∗ , the optimal interaction model Finding i ∗ , the optimal investment strategy Finding o ∗ , the optimal operation strategy Finding r ∗ , the optimal real-time control strategy Conclusion Microgrid: an essential element for integrating renewable energy D. Ernst 18/81
The GREDOR project. Redesigning in an integrated way the whole decision chain that is used for managing distribution networks in order to perform active network management optimally (i.e., maximisation of social welfare). D. Ernst 19/81
Decision chain The four stages of the decision chain for managing distribution networks: 1. Interaction models 2. Investments 3. Operational planning 4. Real-time control D. Ernst 20/81
1. Interaction models An interaction model defines the flows of information, services and money between the different actors. Defined (at least partially) in the regulation. Example: The Distribution System Operator (DSO) may curtail a wind farm at a regulated activation cost. 2. Investments Planning of the investments needed to upgrade the network. Examples: Decisions to build new cables, investing in telemeasurements, etc. D. Ernst 21/81
3. Operational planning Decisions taken a few minutes to a few days before real-time. Decisions that may interfere with energy markets. Example: Decision to buy the day-ahead load flexibility to solve overload problems. 4. Real-time control Virtually real-time decisions. In the normal mode (no emergency situation caused by an “unfortunate event”), these decisions should not modify production/consumption over a market period. Examples: modifying the reactive power injected by wind farms into the network, changing the tap setting of transformers. D. Ernst 22/81
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