Update of Microgrid EMS Trends

Panel Session: Microgrid Control

Prof. Daniel Olivares

Pontifical Catholic University of Chile

Contributors: J.D. Lara, C. Cañizares, and M. Kazerani (U of Waterloo)

IEEE-PES GM 2014

Washington D.C., USA

July 29, 2014

1

Agenda

• Overview of Microgrid EMS.

• EMS Architectures.

• Trends in EMS for Microgrids (from TF Paper).

• Recent Contributions in EMS Design.

• Uncertainty Management in EMS for

Microgrids.

• Final Remarks.

2

Microgrid EMS

• A microgrid EMS is a secondary control system

responsible for the economic and reliable operation

of the microgrid.

• It provides set points to the primary controllers,

located at the device level.

• The EMS distinctive function is to determine a

suitable active power dispatch of controllable units.

3

Microgrid EMS

4

• The EMS can receive

supervisory control signals

based on requirements of

the Main Grid.

• EMS operation policy

depends on the microgrid

ownership structure,

connection status and

market model.

EMS Architectures

5

Centralized Decentralized

Trends in EMS (from TF paper [1])

• Real-time dispatch optimization for centralized

architectures.

• Multi-agent systems in decentralized

architectures.

• Uncertainty management through model

predictive control and (incipient) use of

stochastic optimization techniques.

• Increasing modelling detail.

6

Recent Contributions in EMS

• In the last 2 years, a number of new contributions

has been made with respect to EMS models and

operation, including:

– Privacy-preserving strategies [2].

– Hybrid EMS architectures [3][4].

– Highly detailed network models [5][6].

– Sophisticated approaches to uncertainty management [7]-

[9].

7

Privacy-preserving Approaches

• Concerns with respect to privacy have been raised regarding the

exchange of information about consumption patterns with a central

controller or neighbours.

• Insights on people’s activities and schedules can be obtained from load

profiles and willingness to shift/shed load at certain times.

• Privacy-preserving approaches limit the sharing of information by using

problem decomposition and distributed optimization techniques.

8

Hybrid EMS architectures

• Hybrid approaches recognize the advantages of the

centralized and decentralized approaches in that:

– The centralized approach better deals with multi-step operation

planning and uncertainty management.

– The decentralized (MAS) approach increases autonomy of DGs and is

closer to plug-and-play.

• Thus, hybrid approaches use centralized control for day-ahead

(or hours-ahead) scheduling and other tasks requiring multi-

step coordination, and a decentralized approach is used for

the final dispatch of units based on bids.

9

High-detail network models

• Increased modelling detail of the microgrid has been used in recent publications

(e.g., three-phase network models), in order to account for the effect of network

constraints on the EMS calculations.

• It has been shown that, under certain scenarios (e.g., high loading),

voltage/reactive power limits and system imbalance can render simplified dispatch

solutions infesible.

• Such effect is more apparent in remote microgrids (permanently isolated) due to

the lack of active and reactive power support from main grid.

• Thisissue can also be addressed including a set of dispatching rules as additional

constraints to the energy management problem; however, it requires prior

knowledge of the system.

10

Uncertainty Management in EMS

• Managing uncertainty in microgrid’s operation is usually addressed in

literature by performing frequent dispatch command updates using an

MPC approach.

• As discussed in the TF paper, this might not be enough with large

penetrations of highly variable, hard to predict RE-sources.

• Recent contributions and ongoing research focus on the combination of

uncertainty-aware problem formulations and MPC.

11

Uncertainty Management in EMS

• In addition to availability of RE resources, the sources of uncertainty to be

considered by the EMS may also include:

– Electricity prices from the main grid (in grid-connected operation).

– Load profile.

• Two main alternatives are identified for problem formulation: stochastic

programming and robust optimization.

• Robust optimization aims at minimizing the cost of the worst-case

scenario, which is characterized by a parameter called budget of

uncertainty.

• Stochastic programming aims at minimizing the expected operation cost,

where uncertainty is characterized by a PDF, or a number of possible

scenarios.

12

Uncertainty Management in EMS

Stochastic Programming Robust Optimization

13

Gamma can be interpreted as the number of time-steps in a prediction time series that deviate from the forecasted value.

For example, for =24, each hour of a 24 hour forecast deviates (in a pre-specified value) from the forecasted value.

Each scenario is a possible realization of an uncertain resource profile (time series)

EMS with MPC + Stochastic

Programming

14

Stochastic

EMS

Forecast

Scenario Generation

Hedged

Dispatch Solution

EMS with MPC + Stochastic

Programming

15

Reserve levels are increased with respect to a deterministic EMS formulation.

Hedged solutions produce lower cost solutions due to avoidance of expensive load shedding.

In this particular case, hedged solutions also yield lower fuel costs.

By design, the stochastic EMS formulation

should produce higher commitment costs (first-

stage decisions) and lower dispatch costs

(second-stage decisions), yielding lower total

costs in the long term.

Final Remarks

• EMS design and models have continued to evolve in recent years in terms

of modelling detail, architecture, and uncertainty management, according

to the trends identified in the TF paper.

• New challenges concerning privacy preserving issues have been identified

and incipient solutions using decomposition techniques have appeared.

• Uncertainty-aware EMS formulations using stochastic and robust

optimization techniques are starting to appear in technical literature,

showing great potential to deal with uncertainty of RE sources, electricity

prices, and loads.

• A particular Stochastic-MPC approach to EMS design was introduced, and

some of its advantages in terms of appropriate reserve levels and lower

operation costs have been presented.

16

References

Olivares, D.E., Mehrizi-Sani, A, Etemadi, AH., Canizares, C.A, Iravani, R., Kazerani, M., Hajimiragha, A., Gomis- Bellmunt, O.,

Saeedifard, M., Palma-Behnke, R., Jimenez-Estevez, G.A, and Hatziargyriou, N.D., "Trends in Microgrid Control," IEEE Trans.

on Smart Grid, vol.5, no.4, pp.1905-1919, July 2014.

Zhe Wang; Kai Yang; Xiaodong Wang, "Privacy-Preserving Energy Scheduling in Microgrid Systems," IEEE Trans. on Smart

Grid, , vol.4, no.4, pp.1810-1820, Dec. 2013.

Mao, M., Jin, P., Hatziargyriou, N.D., and Chang, L., "Multiagent-Based Hybrid Energy Management System for Microgrids,"

IEEE Trans. on Sustainable Energy, vol.5, no.3, pp.938-946, July 2014.

Chun-xia Dou and Bin Liu, "Multi-Agent Based Hierarchical Hybrid Control for Smart Microgrid," IEEE Trans. on Smart Grid,

vol.4, no.2, pp.771-778, June 2013.

Olivares, D.E., Canizares, C.A, and Kazerani, M., "A Centralized Energy Management System for Isolated Microgrids," IEEE

Trans. on Smart Grid,, vol.5, no.4, pp.1864-1875, July 2014.

Levron, Y., Guerrero, J.M., and Beck, Y., "Optimal Power Flow in Microgrids With Energy Storage," IEEE Trans. on Power

Systems,, vol.28, no.3, pp.3226-3234, Aug. 2013.

Yu Zhang, Gatsis, N., and Giannakis, G.B., "Robust Energy Management for Microgrids With High-Penetration Renewables,"

IEEE Trans. on Sustainable Energy, vol.4, no.4, pp.944-953, Oct. 2013.

Su, W., Wang, J., and Roh, J., "Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources,"

IEEE Trans. on Smart Grid, vol.5, no.4, pp.1876-1883, July 2014.

Olivares, D.E., Lara, J.D., Canizares, C.A, and Kazerani, M., “Stochastic-Predictive Energy Management System for Isolated

Microgrids," Submitted to IEEE Trans. on Smart Grid (Under Review).

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