Integration of renewable energy sources and demand-side management into distribution networks

Transcription

1 Integration of renewable energy sources and demand-side management into distribution networks by Damien Ernst University of Liège EES-UETP Porto, Portugal June 15-17, 2016

2 D. Ernst 2/81 Outline Active network management Rethinking the whole decision chain - the REDOR project REDOR 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

3 D. Ernst 3/81 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.

4 D. Ernst 4/81 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.

5 D. Ernst 5/81 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?

6 We also denote by Mt 2 {0, 1} N a time varying vector that specifies, at every time-step t, whether the j-th house has joined the community between t and t if house j is a member of the community, 0 otherelse. The low-voltage 8t 2 {0,...,T feeder1},mt(j) example: = Figure 2.1: raphic representation of the test network Description of each house P B 1,t, Q B 1,t We assume that each house j 2 {1,...,N} is provided with: P B 2,t, Q B 2,t P B 3,t, Q B 3,t Infinite A quarter-hourly bus electricity production using PV P1,t PV panels, Q PV 1,t; such a production P2,t PV, Q PV 2,tis modeledp using 3,t PV, Qtwo PV 3,t time series, corresponding to the active and reactive power that are injected into the network at time t 12 {0,...,T 1}. 0 We denote by Pj,t PV 1and Q PV j,t the active 2and reactive power that 3 are injected by VThthe PV panels at time t. We assume that these different values are bounded. One has: YTh Y01 Y12 Y23 8j 2 {1,...,N}, 8t 2 {0,T 1}, 0 apple Pj,t PV apple P PV,max j,t P 8j 2 {1,...,N}, 8t 1,t, L Q L 1,t 2 {0,T 1}, Q PV P2,t, L Q L 2,t j,t apple PV,max j,t P L 3, Q L 3,t Note that, by convention, we Figure assume 2.2: that Electrical the active model power of the injected network. into the network needs to be greater than 0, i.e.: 2. The active power injected 8jinto 2 {1,...,N}, the network8t needs 2 {0,T to be smaller 1},Pj,t PV than 0. the maximum amount of power that can be generated by the PV installation. Let P PV,max j,t denote this maximum amount of power. D. Ernst Also, Note that noteinthat thisq work PV,max P PV,max j,t j,t is a function is assumed of P PV,max to depend j,t. only on sunshine. This may be a quite limitative 6/81

7 D. Ernst 7/81 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

8 D. Ernst 8/81 Modeling the load (SLP) and PV production (from Belgian data) House consumption (kw) data1 data2 data3 PV production (kw) data1 data2 data3 data4 data5 data6 data7 data8 data9 data10 data11 data12 data13 data14 data15 data16 data17 data :00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 Time 0 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 Time

9 D. Ernst 9/81 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. j {1,..., N}, t {0,..., T 1}, P PV j,t = { 0 if Vj,t > V max, PV,max Pj,t otherwise. This control scheme, which is currently the one that is applied in practice, will be considered as the reference strategy.

10 D. Ernst 10/81 Effects of the current (and basic) control scheme on the load and the PV production PV production (kw) PV16 PV17 PV18 Voltage (p.u.) data1 data2 data3 data4 data5 data6 data7 data8 data9 data10 data11 data12 data13 data14 data15 data16 data17 data :00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 Time :00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 Time

11 D. Ernst 11/81 The centralized optimization approach Principle: Solve an optimization problem over the set of all inverters: ( P PV 1,t, Q PV 1,t,..., PN,t PV, QN,t PV ) arg min P PV 1,t,QPV 1,t,...,PPV N,t,QPV N,t N P j=1 PV,max j,t P PV j,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, 0 P PV j,t Q PV j,t g(p PV j,t ), j = 1,..., N PV,max Pj,t, j = 1,..., N j = 1,..., N

12 D. Ernst 12/81 Effects of the centralized optimization approach on the load and the PV production Potential gain in this example: Curtailed energy with the basic approach: kwh Curtailed energy with the centralized approach: kwh

13 D. Ernst 13/81 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.

14 State transition diagram of the distributed control scheme: D. Ernst 14/81 t > t RQ : Q set = Q f (V tm, P set ) reached Mode A P set = P MPP Q set = Q f Signal received Mode E P set = P MPP Q set Q f until t = t RQ Signal received Mode B P set = P MPP Q set Q max until t = t DQ No more signal for T reset t > t RP : P set = P MPP reached Mode D P set P MPP Q set = Q max until t = t RP Signal received No more signal for T reset Mode C P set 0 Q set = Q max until t = t DP t > t DQ and signal persists: Q set = Q max reached

15 D. Ernst 15/81 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.

16 Maximum PV active power that could be produced (kw) PV N4AB1 PV N4AB2 PV N4AB6 PV N4AB7 5 PV N4AB3 PV N4AB4 PV N4AB8 PV N4AB9 0 PV N4AB5 PV NODE4B Active power produced by the PV t (s) First try to restore active power Second try to restore active power 20 (kw) PV N4AB1 PV N4AB6 PV N4AB2 PV N4AB7 5 PV N4AB3 PV N4AB4 PV N4AB8 PV N4AB9 0 PV N4AB5 PV NODE4B t (s) D. Ernst 16/81

17 Reactive power produced by the PV (kvar) Resulting voltages PV N4AB1 PV N4AB2 PV N4AB3 PV N4AB4 PV N4AB5 PV N4AB6 PV N4AB7 PV N4AB8 PV N4AB9 PV NODE4B t (s) First try to restore active power Second try to restore active power 1.06 (pu) N4AB1 N4AB2 N4AB3 N4AB4 N4AB5 N4AB6 N4AB7 N4AB8 N4AB9 NODE4B t (s) D. Ernst 17/81

18 D. Ernst 18/81 Outline Active network management Rethinking the whole decision chain - the REDOR project REDOR 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

19 The REDOR 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

20 D. Ernst 20/81 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

21 D. Ernst 21/81 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.

22 D. Ernst 22/81 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.

23 D. Ernst 23/81 REDOR as an optimization problem M : Set of possible models of interaction I : Set of possible investment strategies O : Set of possible operational planning strategies R : Set of possible real-time control strategies Solve: arg max social welfare(m, i, o, r) (m,i,o,r) M I O R

24 D. Ernst 24/81 A simple example M: Reduced to one single element. Interaction model mainly defined by these two components: 1. The DSO can buy the day-ahead load flexibility service. 2. Between Uncertain the beginning variables! of every market... 1 period,! T it can decide to curtail generation for the next market period or activate... System state the load flexibility service. x 0 x 1 x 2 Curtailment decisions x T-1 x T have a cost. Control actions u 1... u T u 0 Time 0h00 24h00 Period 1 2 T... Stage T Day-ahead Intra-day

25 D. Ernst 25/81 I: Made of two elements. Either to invest in an asset A or not to invest in it. O: The set of operational strategies is the set of all algorithms that: (i) In the day-ahead process information available to the DSO to decide which flexible loads to buy (ii) Process before every market period this information to decide how to modulate the flexible loads how to curtail generation. R: Empty set. No real-time control implemented. social welfare(m, i, o, r): The (expected) costs for the DSO.

26 D. Ernst 26/81 The optimal operational strategy Let o be an optimal operational strategy. Such a strategy has the following characteristics: 1. For every market period, it leads to a safe operating point of the network (no overloads, no voltage problems). 2. There are no strategies in O leading to a safe operating point and having a lower (expected) total cost than o. This cost is defined as the cost of buying flexiblity plus the costs for curtailing generation. It can be shown that the optimal operation strategy can be written as a stochastic sequential optimization problem. Solving this problem is challenging. etting even a good suboptimal solution may be particularly difficult for large distribution networks and/or when there is strong uncertainty on the power injected/withdrawn day-ahead.

27 D. Ernst 27/81 Illustrative problem HV MV Bus 1 Bus 3 Bus 2 Bus 4 Bus 5 Wind Solar aggregated Residential aggregated Industrial When Distributed eneration (D) sources produce a lot of power: overvoltage problem at Bus 4, congestion problem on the MV/HV transformer. Two flexible loads; only three market periods; possibility to curtail the two D sources before every market period (at a cost).

28 Information available to the DSO on the day-ahead The flexible loads offer: Load in MW increase the dimensions of the problem. In the following sections, we describe results obtained on a small test system, with a short time horizon and a moderate number of scenarios. In the concluding section, we discuss pitfalls and avenues for solving realistic scale instances. Case Study We analyze issues arising at the MV level in some Belgian distribution systems (Figure Periods 5). Often, wind-farms are Periods directly connected to the HV/MV transformer, as modeled in our test system by the generator connected to bus 2. Power off-takes and injections induced by residential consumers are aggregated at busindustrial 4 by a load and (right). a generator representing the total production of PV panels. Finally, the load connected to bus 5 represents an industrial consumer. Residential aggregated (left) and Load in MW Scenario tree for representing 6 uncertainty: D-A W = 80% S = 20% W = 60% S = 10% W = 70% S = 40% W = 40% S = 30% W = 35% S = 30% W = 20% S = 20% W = 80% S = 70% W = 65% S = 50% W = 60% S = 60% W = 45% S = 40% W = 35% S = 60% W = 25% S = 40% W = 20% S = 50% W = 10% S = 30% (a) Scenario tree used for the case study. The nodes show the values of the random variables and the label on the edges define the transition W probabilities = Wind; between nodes. S = Sun. HV Additional information: MV Bus 1 a load-flow model of the network; the price (per MWh) for curtailing generation. Bus 3 Bus 2 Bus 4 Bus 5 otal generation of D units in MW D. Ernst 28/

29 Decisions output by o The day-ahead: To buy flexibility offer from the residential aggregated load. Before every market period: We report results when generation follows this scenario. Results: eneration never curtailed. Load modulated as follows: D-A W = 80% S = 20% W = 60% S = 10% W = 70% S = 40% W = 40% S = 30% W = 35% S = 30% W = 20% S = 20% W = 80% S = 70% W = 65% S = 50% W = 60% S = 60% W = 45% S = 40% W = 35% S = 60% W = 25% S = 40% W = 20% S = 50% W = 10% S = 30% Load in MW Periods D. Ernst 29/81

30 D. Ernst 30/81 On the importance of managing uncertainty well E{cost} max cost min cost std dev. o 46$ 379$ 30$ 72$ MSA 73$ 770$ 0$ 174$ where MSA stands for Mean Scenario Strategy. Observations: Managing uncertainty well leads to lower expected costs than working along a mean scenario. More results in: Q. emine, E. Karangelos, D. Ernst and B. Cornélusse. Active network management: planning under uncertainty for exploiting load modulation.

31 D. Ernst 31/81 The optimal investment strategy Remember that we had to choose between making investment A or not. Let AP be the recovery period and cost A the cost of investment A. The optimal investment strategy can be defined as follows: 1. Simulate using operational strategy o the distribution network with element A several times over a period of AP years. Extract from the simulations the expected cost of using o during AP years. Let cost o with A be this cost. 2. Simulate using operational strategy o the distribution network without element A several times over a period of AP years. Extract from the simulations the expected cost of using o during AP years. Let cost o without A be this cost. 3. If cost A + cost o with A cost o without A, do investment A. Otherwise, not.

32 D. Ernst 32/81 Solving the REDOR optimization problem Solving the complete optimization problem arg max social welfare(m, i, o, r) (m,i,o,r) M I O R in a single step is too challenging. Therefore, the problem has been decomposed in 4 subproblems: 1. Finding m, the optimal interaction model, 2. Finding i, the optimal investment strategy, 3. Finding o, the optimal operation strategy, 4. Finding r, the optimal real-time control strategy.

33 D. Ernst 33/81 Finding m The set M of interaction models is only limited by our imagination. In the REDOR project, we have selected four interaction models to study in more detail. These interaction models are defined by: 1. The type of access contract between the users of the grid and the DSO, 2. The financial compensation of flexibility services. For simplicity, we focus only on the access contract feature of the interaction models in this presentation. More information S. Mathieu, Q. Louveaux, D. Ernst, and B. Cornélusse, DSIMA: A testbed for the quantitative analysis of interaction models within distribution networks.

34 D. Ernst 34/81 Access agreement The interaction models are based on access contracts. The grid user requests access to a given bus. The DSO grants a full access range and a flexible access range. The width of these ranges depends on the interaction model.

35 Flow of interactions One method to obtain social welfare(m, i, o, r) is to simulate the distribution system with all its actors and compute the surpluses and costs of each of them. This simulation requires us to: 1. Define all decision stages as function of m, 2. Simulate the reaction of each actor to m. Producers & Retailers lobal baseline Flexibility contracts TSO Local baselines Flexibility offers Settlement DSO Flexibility needs Flexibility activation requests Flexibility platform Time D. Ernst 35/81

36 One day in the life of a producer selling flexibility services A producer performs the following actions: 1. Sends its baseline to the TSO at the high-voltage level. I will produce 15MWh in distribution network 42 between 8am and 9am. 2. Sends its baseline to the DSO at the medium-voltage level. I will produce 5MWh in bus 20 between 8am and 9am. 3. Obtains flexibility needs of the flexibility services users. The DSO needs 3MWh downward in bus 20 between 8am and 9am. 4. Proposes flexibility offers. I can curtail my production by 2MWh in bus 20 between 8am and 9am. 5. Receives activation requests for the contracted services. Curtail production by 1MWh in bus 20 between 8am and 9am. 6. Decides the final realizations. Produce 4MWh, or 5MWh if more profitable, in bus 20 between 8am and 9am. D. Ernst 36/81

37 In this presentation, we assume that these flexibility services are paid by the DSO at a cost which compensates the imbalance created by the activation of the service. D. Ernst 37/81 Parameters of the interaction models The implementation of the models are based on 3 access contracts: Unrestricted access: Allow the grid users access to the network without restriction. Restricted access: Restrict the grid users so that no problems can occur. Flexible access: Allow the users to produce/consume as they wish but if they are in the flexible range, they are obliged to propose flexibility services to the DSO.

38 Effects of the access range on the baseline of an actor The access restriction of the DSO is shown by the red dotted line. Power Power Figure : Unrestricted access Power Time Figure : Restricted access Time Flexible access range Full access range Time Figure : Flexible access - The filled areas represents the energy curtailed by the DSO by the activation of mandatory flexibility services. D. Ernst 38/81

39 D. Ernst 39/81 Back to our optimization problem These models are studied for a given investment, operation planning and real-time control strategy, i.e. one strategy (i, o, r). arg max social welfare(m, i, o, r) (m,i,o,r) M I O R Consider the simplified subset of interaction models M = { unrestricted, restricted, flexible }. social welfare(m, i, o, r): the sum of the surpluses minus the costs of all actors and a cost given by the protection scheme of the real-time control strategy.

40 D. Ernst 40/81 Open-source testbed The testbed evaluating interaction models is available as an open source code at the address It is based on an agent-based model where every agent solves an optimization problem for each decision stage.

41 Key messages Unrestricted: Too much renewable production leading to high protections cost. Who would pay this cost? Restricted: Little allowed renewable generation but a secure network. Flexible: Large amount of renewable generation but still requiring a few sheddings due to coordination problems. D. Ernst 41/81 Comparison of the interaction models Simulation of a 75 bus system in an expected 2025 year with 3 producers and 3 retailers owning assets connected to the DN. Interaction model Unrestricted Restricted Flexible Welfare e Protections cost e TSO surplus e DSO costs e Producers surplus e Retailers surplus e Table : Mean daily welfare and its distribution between the actors.

42 D. Ernst 42/81 Coordination problem The model flexible suffers from the lack of coordination between the DSO and the TSO. Assume that the flow exceeds the capacity of line 3 by 1MW. To solve this issue, the DSO curtails a windmill by 1MW. In the same time, assume that the TSO asks a storage unit to inject 0.4MW. These activations leads to a remaining congestion of 0.4MW.

43 D. Ernst 43/81 Finding i The investment strategy i is divided in two parts: 1. Announcing the capacity of renewables that may be connected to the network: lobal Capacity ANnouncement. 2. Determining the target optimal network: Investment planning tool.

44 D. Ernst 44/81 CAN: lobal Capacity ANnouncement CAN is a tool that determines the maximum hosting capacity of a medium voltage distribution network. Features of CAN: Determines the capacity of each bus. Accounts for the future of the system. Relies on the tools that are routinely used by DSOs (repeated power flows). Results may be published in appropriate form (tabular, map, through the regulator, etc.). CAN is not meant to be a replacement for more detailed computations for generation connection projects.

45 D. Ernst 45/81 CAN procedure The procedure is implemented in a rolling horizon manner. The results are refreshed at each step of the planning horizon. More information: B. Cornélusse, D. Vangulick, M. lavic, D. Ernst: lobal capacity announcement of electrical distribution systems: A pragmatic approach.

46 D. Ernst 46/81 CAN results Subst. Feeder Voltage ener. ener. name name (kv) (MW) type 99 FN PV 2064 FN PV Black squares in one-line diagram indicate generation substations.

47 D. Ernst 47/81 Investment planning tool Main software tools Smart Sizing determines the main features of the ideal network. Rating of cables, number of substations, etc. Smart Planning development of grid expansion plans. Change cable between bus 16 and 17 in Supporting software tools Smart Operation mimics the grid operation. Proxy of o. Smart Sampling provides exogenous data such as load profiles. Smart Sampling Smart Sizing Smart Operation Smart Planning output

48 Figure : A large set of profiles is reduced to 3 profiles. D. Ernst 48/81 Smart sampling Smart Sampling creates calibrated time series models able to generate synthetic load and generation profiles mimicking the statistical properties of real measurements. Advantages Compactness: as they are represented by mathematical formula with a few parameters. Information reduction: computational burden can be reduced by working on a reduced statistically relevant data set.

49 Smart Sizing A tool for long-term planning to find least cost features of the distribution network, taking into account CAPEX/OPEX while meeting voltage constraint given targets of load and D penetration. Smart sizing evaluates the traditional aspects just like any traditional planning tool (number of transformer, infrastructure cost, cost due to losses, etc.), the benefits of flexibility and the impact of distributed generation on grid costs. D. Ernst 49/81

50 D. Ernst 50/81 Smart Planning The multistage investment planning problem is hard to tackle as planning decisions are subject to uncertainty. The smart planning tool schedules optimal investment plans from today to target architecture (with smart planning) integrating the optimal future system operation (with smart operation). It decides: The type of grid investment and optimal year of investment, Install a cable between node A and B in The optimal use of available flexibility, Load shifting, PV curtailment. Reactive power support. From PV/storage.

51 Smart Planning - Overview D. Ernst 51/81

52 D. Ernst 52/81 Finding o - oal iven an electrical distribution system, described by: N and L, the network infrastructure; D, the electrical devices connected to the network; C, a set of operational limits; T, the set of time periods in the planning horizon. We want the best strategy o which defines the set of power injections of the devices {(P d, Q d ) d D} to be such that the operational constraints are respected for all t T. {g c ( ) 0 c C}

53 D. Ernst 53/81 Control actions - curtailment A curtailment instruction, i.e. an upper limit on the production level of a generator, can be imposed for some distributed generators. Curtailment instruction The DSO has to compensate for the energy that could not be produced because of its curtailment instructions, at a price that is proportional to the amount of curtailed energy.

54 D. Ernst 54/81 Control actions - load modulation The consumption of the flexible loads can be modulated, as described by a modulation signal over a certain time period. Load modulation instruction Load modulation signal The activation of a flexible load is acquired in exchange for a fee that is defined by the flexibility provider.

55 D. Ernst 55/81 Decisional Framework We rely on a Markov Decision Process framework for modeling and decision-making purposes. At each time-step t, the system is described by its state s t and the control decisions of the DSO are gathered in a t. The evolution of the system is governed by: s t+1 p( s t, a t ), which models that the next state of the system follows a probability distribution that is conditional on the current state and on the actions taken at the corresponding time step.

56 D. Ernst 56/81 Decisional Framework A cost function evaluates the efficiency of control actions for a given transition of the system: cost(s t, a t, s t+1 ) = curtailment costs + flex. activation costs + penalties for violated op. constraints. Finally, we associate the operational planning problem with the minimization of the expected sum of the costs that are accumulated over a T -long trajectory of the system: min a 1,...,a T E s 1,...,s T ( T 1 t=1 cost(s t, a t, s t+1 ) )

57 D. Ernst 57/81 Computational Challenge Finding an optimal sequence of control actions is challenging because of many computational obstacles. The figures illustrate a simple lookahead policy on an ANM simulator. This simulator and a 77-buses test system are available at More information Q. emine, D. Ernst, B. Cornélusse. Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution.

58 Centralized real-time controller The role of the real-time controller is to handle limit violations observed or predicted close to real-time. Over/under-voltage, thermal overload. To bring the system to a safe state, the controller controls the D units outputs and adjusts the transformers tap positions. operational planning 0 P %&', Q %&' Measurements (refreshed every ~ 10 s) Set-points (updated every ~ 10 s) Near-future schedules (communicated every ~ 15 min) real-time controller 1109 (voltage set-point of LTC) P, Q, V Ext. rid P, Q, V P ", Q D. Ernst " 58/81 P, Q, V P ", Q " V P ", Q " NO LOAD BUS LOAD BUS D UNIT

59 Multistep optimization problem Consider a set of control horizon periods T. For each time step k we solve the following problem: min π P P g (k + i) P ref (k + i) 2 + π C Q g (k + i) Q ref (k + i) 2 P g,q g i T i T where π P and π C are coefficients prioritizing active over reactive control. Linearized system evolution For all i T, V (k + i k) = V (k + i 1 k) + S V [u(k + i 1) u(k + i 2)] I (k + i k) = I (k + i 1 k) + S I [u(k + i 1) u(k + i 2)] where S V and S I are sensitivities matrices of voltages and currents with respect to control changes. Operational constraints For all i T, V low (k + i) V (k + i k) V up (k + i) For all i T, I (k + i k) I up (k + i) u min u(k + i k) u max u min u(k + i k) u(k + i 1 k) u max D. Ernst 59/81

60 D. Ernst 60/81 Network behavior without real-time corrective control Operational planning! #$% real-time controller! " Ext. rid NO LOAD BUS LOAD BUS D UNIT

61 Figure : Voltages D. Ernst 61/81 Network behaviour with real-time corrective control Figure : Active power Figure : Reactive power

62 D. Ernst 62/81 Four main challenges of the REDOR project Data: Difficulties for DSOs to gather the right data for building the decision models (especially for real-time control). Computational challenges: Many of the optimization problems in REDOR are out of reach of state-of-the-art techniques. Definition of social welfare(,,, ) function: Difficulties to reach a consensus on what is social welfare, especially given that actors in the electrical sector have conflicting interests. Human factor: Engineers from distribution companies have to break away from their traditional practices. They need incentives to change their working habits.

63 D. Ernst 63/81 Acknowledgements 1. To all the partners of the REDOR project: 2. To the Public Service of Wallonia - Department of Energy and Sustainable Building for funding this research.

64 D. Ernst 64/81 REDOR project website More information, as well as the list of all our published scientific papers are available at the address:

65 D. Ernst 65/81 Outline Active network management Rethinking the whole decision chain - the REDOR project REDOR 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

66 Microgrid A microgrid is an electrical system that includes multiple loads and distributed energy resources that can be operated in parallel with the broader utility grid or as an electrical island. Essential objects for integrating large amount of renewable energy into distribution networks. D. Ernst 66/81

67 Microgrids and storage Many authors claim that microgrids should come with two types of storage device: A short-term storage capacity (typically batteries), A long-term storage capacity (e.g., hydrogen). Here we study the sizing and the operation of a microgrid powered by PV panels and having batteries and a long-term storage device working with hydrogen. D. Ernst 67/81

68 D. Ernst 68/81 Formalization and problem statement: exogenous variables where: E t = (c t, i t, µ t, et PV, et B, e H 2 t ) E, t T and with E = R + 2 I E PV E B E H 2, c t [W ] R + is the electricity demand within the microgrid; i t [W /m or W /W p ] R + denotes the solar irradiance incident to the PV panels; µ t I represents the model of interaction; e PV t e B t E PV models the photovoltaic technology; E B models the battery technology; e H 2 t E H 2 models the hydrogen storage technology; T = {1, 2,..., T } represents the discrete time steps.

69 D. Ernst 69/81 Formalization and problem statement: state space Let s t S denotes a time varying vector characterizing the microgrid s state at time t T : s t = (s (i) t, s (o) t ) S, t T and with S = S (i) S (o), where s (i) t S (i) and s (o) t S (o) represent the state information related to the infrastructure and to the operation of the microgrid, respectively. s (i) t = (x PV t, x B t, x H2 t, L PV t, L B t, L H2 t, D B t, P B t, R H2 t, η PV t, η B t, η H2 t, ζt B, ζt H2, rt B, rt H2 ) S (i) t T and with S (i) = R +9 ]0, 1] 7, s (o) t = (s B t, s H 2 t ) S (o), t T and with S (o) = R + 2

70 D. Ernst 70/81 Formalization and problem statement: action space As for the state space, each component of the action vector a t A can be related to either notion of the sizing or control, the former affecting the infrastructure of the microgrid, while the latter affects its operation. We define the action vector as: a t = (a (i) t, a (o) t ) A t, t T and with A t = A (i) A (o) t, where a (i) t A (i) relates to sizing actions and a (o) t control actions. A (o) t to

71 D. Ernst 71/81 A microgrid featuring PV, battery and storage using H 2 has two control variables that correspond to the power exchanges between the battery, the hydrogen storage, and the rest of the system: a (o) t = (pt B, p H 2 t ) A (o) t, t T, where p B t [W ] is the power provided to the battery and with p H 2 t [W ] the power provided to the hydrogen storage device. We have, t T : ( ) ( A (o) t = [ ζt B st B, xb t sb t ] [ P B ηt B t, Pt B ] [ ζt H2 s H2 t, RH 2 t s H 2 t η H 2 t ) ] [ xt H2, xt H2 ], which expresses that the bounds on the power flows of the storing devices are, at each time step t T, the most constraining among the ones induced by the charge levels and the power limits.

72 D. Ernst 72/81 Robust sizing of a microgrid Let C be a function defined over the triplet (state, action, environment) such that C(s t, a t, E t ) is the sum of investment costs and operating costs related to the microgrid over the period t till t + 1. Let E = {(E 1 t ) t=1...t,..., (E N t ) t=1...t } with Et i E, t T, i {1,..., N} be a set of plausible scenarios for the exogeneous variables. We define the robust optimization of the sizing of a microgrid where investments can only be made at t = 1 by: max min i {1,...,N} a i,t A i,t,s i,t S, t T C(s i,t, a i,t, Et i ) t T s.t. s i,t = f (s i,t 1, a i,t 1 ), t T \{1} s.t. s.t. a (i) i,t = 0, t T \{1} a (i) j,1 = a(i) k,1, j, k {1,..., N}, s(o) i,1 = 0

73 D. Ernst 73/81 Levelized Energy Cost (LEC) iven a microgrid trajectory (s t, a t, s t+1 ) t T and an environment trajectory (E t ) t T, the LEC r is computed as follows: LEC r = n I y M y y=1 n ɛ y y=1 (1+r) y (1+r) y + I 0 where n = Life of the system (years) I y = Investment expenditures in the year y M y = Operational revenues in the year y ɛ y = Electricity consumption in the year y r = Discount rate which may refer to the interest rate or discounted cash flow The LEC r represents the price at which electricity must be generated to break even over the lifetime of the project.

74 D. Ernst 74/81 Investment costs The overall investment cost I y can be written as the sum of the investments in the PV panels, the battery and the hydrogen: I y = Iy PV + Iy B + I H 2 y

75 Operational costs We will now associate to each time step a reward function ρ t that is a function of the net demand for electricity and the actions: ρ t : (a t, d t ) R From the reward function ρ t, we obtain the operational revenues over year y defined as: M y = t τ y ρ t where τ y is the set of time steps belonging to year y. We now introduce two variables: φ t [W ] R + as the local production of electricity that refers to the photovoltaic production given by: φ t = x PV i t d t [W ] R as the net demand for electricity that is the difference between the consumption and the production: d t = c t φ t D. Ernst 75/81

76 D. Ernst 76/81 Operational costs In the case where the microgrid is fully off-grid, we consider that the microgrid has no possibility to generate any income. The reward function is therefore equal to the penalty induced by the energy that was not supplied to follow the demand: { k Et l, Et l < 0 ρ t = 0, otherwise where Et l < 0 is the quantity of energy not supplied at time t and k is the cost endured per kwh. The quantity of energy that the microgrid alone lacks to cover the consumption is given by: Et l = pt R d t, t T R {B,H 2 }

77 D. Ernst 77/81 Linear programming In the fully off-grid case, the overall optimization problem can be written as: T 1 Minimize LEC = t=0 n y=1 k Ft (1+r) + y I0 ɛy (1+r) y with y = ceil( t 365 ) With 0 s B t x B, t [0, T ] 0 pt B,+ P B, t [0, T 1] P B t pt B, 0, t [0, T 1] 0 st H2 R H2, t [0, T ] 0 pt H2,+ x H2, t [0, T 1] x H2 pt H2, 0, t [0, T 1] s B t = st 1 B + ηt B p B,+ t 1 + pb, t 1 ζt 1 B, t [1, T ] st H2 = s H2 t 1 + ηb t p H2,+ t 1 Ft dt p B,+ t pt B, + ph2, t 1, t [1, T ] ζ H2 t 1 pt H2,+ pt H2,, t [1, T ] Ft 0, t [1, T ]

78 D. Ernst 78/81 Results - Belgium Figure : LEC in Belgium over 20 years for different investment strategies as a function of the cost endured per kwh.

79 D. Ernst 79/81 Results - Belgium Figure : LEC in Belgium over 20 years for a value of loss load of 2e/kWh as a function of a unique price drop for all the constitutive elements of the microgrid.

80 D. Ernst 80/81 Results - Spain Figure : LEC (r = 2%) in Spain over 20 years for different investment strategies as a function of the cost endured per kwh not supplied within the microgrid.

81 D. Ernst 81/81 Bibliography F. Olivier, P. Aristidou, D. Ernst, and T. Van Cutsem, Active management of low-voltage networks for mitigating overvoltages due to photovoltaic units, IEEE Transactions on Smart rid, vol. 2, no. 7, pp , 2016 S. Mathieu, Q. Louveaux, D. Ernst, and B. Cornélusse, DSIMA: a testbed for the quantitative analysis of interaction models within distribution networks, Sustainable Energy, rids and Networks, vol. 5, pp , 2016 B. Cornélusse, D. Vangulick, M. lavic, and D. Ernst, lobal capacity announcement of electrical distribution systems: a pragmatic approach, Sustainable Energy, rids and Networks, vol. 4, pp , 2015 Q. emine, D. Ernst, and B. Cornélusse, Active network management for electrical distribution systems: problem formulation and benchmark, arxiv preprint arxiv: , 2014 H. Soleimani Bidgoli, M. lavic, and T. Van Cutsem, Model predictive control of congestion and voltage problems in active distribution networks, in CIRED Workshop 2014 Challenges of implementing Active Distribution System Management, 2014 V. François-Lavet, Q. emine, D. Ernst, and R. Fonteneau, Towards the minimization of the levelized energy costs of microgrids using both long-term and short-term storage devices, in Smart rid: Networking, Data Management, and Business Models. CRC Press, 2016, pp