REAL TIME CONTROL OF ELECTRICAL GRIDS WITH EXPLICIT POWER SETPOINTS - - PowerPoint PPT Presentation

REAL‐TIME CONTROL OF ELECTRICAL GRIDS WITH EXPLICIT POWER SETPOINTS

Dagstuhl Seminar Feb 23‐27, 2015 Jean‐Yves Le Boudec Mario Paolone joint work with

• Dr. Andrey Bernstein and Lorenzo Reyes, EPFL

Laboratory for Communications and Applications and Distributed Electrical Systems Laboratory

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References

[Commelec] Andrey Bernstein, Lorenzo Reyes‐Chamorro , Jean‐Yves Le Boudec , Mario Paolone, “A Composable Method for Real‐Time Control of Active Distribution Networks with Explicit Power Setpoints”, arXiv:1403.2407 (http://arxiv.org/abs/1403.2407) http://smartgrid.epfl.ch [Campus smart grid] M. Pignati et al ,“Real‐Time State Estimation of the EPFL‐ Campus Medium‐Voltage Grid by Using PMUs”, to appear at Innovative Smart Grid Technologies (ISGT2015)

• 1. Motivation: Real Time Control of

Electrical Grids

Electrical grids are controlled in real‐time to ensure Energy balance + Quality of Service Generators react to frequency variations (droop control) Issue: inertia‐less systems (DC/AC converters : wind mills, PVs)

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The problem of inertia‐less systems at distribution level

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2003 blackout in Italy frequency trend

Source: UCTE Interim Report of the Investigation Committee on the 28 September 2003 Blackout in Italy

2009 blackout during the islanding maneuver of an active distribution network

Source: A. Borghetti, C. A. Nucci, M. Paolone,

• G. Ciappi, A. Solari, “Synchronized Phasors

Monitoring During the Islanding Maneuver of an Active Distribution Network”, IEEE Trans. On Smart Grid, vol. 2 , issue: 1, march, 2011, pp: 70 – 79.

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Short‐term volatility of Solar PV Measured on EPFL Roof

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Current methods for real time control of electrical grids do not work well with a high penetration of intermittent distributed generation (e.g. solar photovoltaics, combined heat and power)

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The same PV peak does not always have the same effect…

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10:05 Voltage surge PV power =160 kW 14:40 No voltage surge PV power =180 kW

14:40 load absorbs the peak 10:05: consumption is small

Chandolon

Controling a Distribution Network or a Microgrid

Traditional Upgrade lines and transformers Fast ramping fuel generators Explicit control + storage Storage can be real or virtual: storage, intelligent buildings, e‐cars

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The EPFL Commelec Project Grid Real Time control

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Battery Grid Intelligent building produce I can either consume or produce I would like to consume [0 – 50 kw] Produce 423 kW Consume 10kW Consume 20 kW Consume 20 kW Consume 50kW Consume 50kW Grid Agent Building Agent Battery Agent

Problems with Explicit Control

inexpensive platforms (embedded controllers) scalability do not build a monster of complexity ‐ bug‐free Such a system must be

scalable

and

composable

(i.e. built with identical small elements)

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• 2. COMMELEC’s Architecture

Software Agents associated with devices

load, generators, storage grids

Grid agent sends explicit power setpoints to devices’ agents Leader and follower

resource agent is follower

• r grid agent

e.g. LV grid agent is follower of MV agent

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The Commelec Protocol

Every agent advertises its state (every ms) as PQt profile, virtual cost and belief function Grid agent computes optimal setpoints and sends setpoint requests to agents Communication is over D‐TLS and IPRP – details not discussed today

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A Uniform, Simple Model

Every resource agent exports ‐ constraints on active and reactive power setpoints (PQt profile) ‐ virtual cost ‐ belief function

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BA GA

I can do , It costs you (virtually) ,

Examples of PQt profiles

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Examples of PQt profiles: the case of a battery

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• requirement: compute the internal limits that the

battery must respect for the next time step.

• HP: the state of charge SoC is fixed between two

setpoints implementations (correct if Δt is small enough) battery model is extremely simple

Rt  V dc I dc  Vt

dc Vtt dc

It

dc  Itt dc

Et  Vt

dc  RtIt dc

Et Rt Vt

dc

It

dc

Examples of PQt profiles: the case of a battery

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• limits on DC: Vdc and Idc need to respect specific

battery constraints Vmin ≤ Vdc ≤Vmax and Imin ≤ Idc≤ Imax , therefore, the DC limits in power are:

P

min dc  max

Vmax Et Vmax

 

Rt , Et  RtImin

 Imin

      ,P

max dc  min P max Vdc,P max Idc

 

P

max Vdc 

Et

2 4Rt, if Et 2

  Vmin

Vmin Et Vmin

 

Rt

• therwise

       ,P

max Idc 

Et

2 4Rt, if Et

2Rt

 

   Imax

Et  RtImax

 Imax otherwise

    

Examples of PQt profiles: the case of a battery

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• limits on AC: the battery is interfaced with a power

converter of rated power Sr and efficiency η

P

min ac  P min dc

P

max ac  P max dc

     if Pdc  0 P

min ac  P min dc 

P

max ac  P max dc

      if Pdc  0 P

t ac

 

2  Qt ac

 

2  Sr

Virtual cost act as proxy for Internal Constraints

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BA GA

I can do , It costs you (virtually) , If state of charge is 0.7, I am willing to inject power If state of charge is 0.3, I am interested in consuming power

Examples of Virtual Costs

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Commelec Protocol: Belief Function

Say grid agent requests setpoint

• from a resource;

actual setpoint will, in general, differ. Belief function is exported by resource agent with the semantic: resource implements

• Essential for safe operation

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Examples of Belief Function

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PQt profile = setpoints that this resource is willing to receive Belief function = actual operation points that may result from receiving a setpoint

Grid Agent’s job

Leader agent (grid agent) computes setpoints for followers based on

state estimation advertisements received requested setpoint from leader agent

Grid Agent attempts to minimize

• Grid Agent does not see the details of resources

a grid is a collection of devices that export PQt profiles, virtual costs and belief functions and has some penalty function problem solved by grid agent is always the same

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virtual cost of resource penalty function keeps voltages close to 1 p.u. and currents within bounds cost of power flow at point

• f common connection

Grid Agent’s algorithm

Given estimated (measured) state

• computed next

setpoint is where is a vector opposed to gradient of overall objective Proj is the projection on the set of safe electrical states This is a randomized algorithm to minimize

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Aggregation (Composability)

A system, including its grid, can be abstracted as a single component

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I can do , It costs you (virtually) , given PQt profiles of S, S, solve load flow and compute possible , + overall cost ,

Aggregation Example

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boiler microhydro PV battery non controlled load

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Aggregated PQt profile safe approximation (subset of true aggregated PQt profile)

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Aggregated Belief safe approximation (superset of true aggregated belief)

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Traditional control Commelec

Simulations –Results

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Local power management

Boiler WB2 starts because WB1 stops at mid power due to line congestion Boiler WB2 charges at full power because PV3 produces

Separation of Concerns

Resource Agents Device dependent Simple:

translate internal state (soc) into virtual cost Implement setpoint received from a grid agent

Grid Agents Complex and real time But: all identical

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Reliability and Security

Grid Agent development uses Prof Sifakis’s rigorous system development approach and the BIP framework Grid Agent are triplicated, Resource Agents use voting Communication used authentication (D‐TLS) and real time reliability protocols

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An Operating System for Electrical Grids

Resource control uses the Commelec API (publicly available) and does not need to be aware of the grid

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Intelligent Building Application E-car PVs

Commelec

API

Commelec API

Commelec Grid Agent Commelec API

EPFL Campus Smart Grid smartgrid.epfl.ch

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System architecture Sensors and Phasor Measurement Units

‐ Voltage and current sensors ‐ Class 0.1 ‐ Nodal voltages and injected currents ‐ Phasor Measurement Units ‐ GPS synchronization ‐ Synchrophasor estimation based on the enhanced IpDFT algorithm [1] running on a FPGA ‐ Encapsulation and streaming according to IEEE c37.118.2‐2011

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Conclusion

Commelec is a practical method for automatic control of a grid

exploits available resources (storage, demand response) to avoid curtailing renewables while maintaining safe operation

Method is designed to be robust and scalable

separation of concerns between resource agents (simple, device specific) and grid agents (all identical) a simple, unified protocol that hides specifics of resources aggregation for scalability

We have started to develop the method on EPFL campus to show grid autopilot

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