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Control Concepts for Microgrids

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Schematic representation of a microgrid.

The worldwide use of renewable energies has increased significantly in recent years. Most renewable energy sources (RES) are relatively small-sized in terms of generation power and therefore often connected to the power system at the medium and low voltage levels. In addition, they are typically interfaced to the network via AC inverters. These are power electronic devices, which possess significantly different physical characteristics from synchronous generators. To facilitate the integration of a sizeable number of renewable distributed generation units, the concept of microgrids has become increasingly popular. They represent locally controllable parts of a larger electrical network, consisting of several generation units, storage devices and loads. Typically, microgrids can be operated both in grid-connected and islanded mode.

Primary Control

Droop control, a (usually heuristically parametrized) simple decentralized scheme has been a popular low-level control for microgrids. Conditions for stability and power sharing under droop control were derived in [1] and [2] for inverter-based microgrids and in [3] for microgrids with mixed rotational and electronic-interfaced DG units. In [4], the analysis of [1] and [2] was extended to models considering inaccurate clocks, respectively time delays, induced by the fact that each individual inverter is operated with its own processor. Furthermore, the analysis in [1] and [2] revealed that droop control is not suitable to achieve the objective of reactive power sharing. As a consequence, in [5] and [6] a consensus-based distributed voltage control was proposed, which guarantees reactive power sharing in meshed inverter-based microgrids with dominantly inductive power lines and arbitrary electrical topology. An alternative to droop control in inverter-based networks has been provided in [7] by means of a decentralized control design based on linear matrix inequalities. The procedure proposed therein guarantees overall network stability, while accounting for power sharing. Further, in [8] and [9] the stability of droop controlled microgrids in the presence of delays was investigated.

Energy Management

The top control level for microgrids is commonly referred to as operational control. When compared to a conventional power system, microgrids are characterized by the intermittency of many RES and the more prominent use of storage units. The main task of operational control is then to provide setpoints for, and thus to coordinate the infeed of the different units in an optimal manner, while ensuring robustness with respect to variations in load and RES infeed. In [10], assuming that the uncertainty is within a bounded region along a given load and RES trajectory prediction, the problem was posed as a worst-case hybrid optimal control problem. The resulting control inputs are applied in a receding horizon fashion, leading to a model predictive controller. In [11], this scheme was extended by taking into account the dependence of predicted controls on the state forecasts in the optimization procedure, which leads to higher RES and lower thermal infeed. In [12], the operation costs could be decreased even further by employing the probability distributions of load and RES in a scenario-based model predictive control approach.

People Involved

Cooperations

Funding

Publications

  1. 1.0 1.1 1.2
    J. Schiffer, R. Ortega, A. Astolfi, J. Raisch, T. Sezi. Conditions for Stability of Droop-Controlled Inverter-Based Microgrids. Automatica, 50 (10):2457–2469, 2014.
  2. 2.0 2.1 2.2
    J. Schiffer, R. Ortega, A. Astolfi, J. Raisch, T. Sezi. Stability of Synchronized Motions of Inverter-Based Microgrids Under Droop Control. In 19th IFAC World Congress, pages 6361–6367, Cape Town, South Africa, 2014.
  3. J. Schiffer, D. Goldin, J. Raisch, T. Sezi. Synchronization of Droop-Controlled Microgrids with Distributed Rotational and Electronic Generation. In 52nd IEEE Conference on Decision and Control (CDC), pages 2334–2339, 2013.
  4. J. Schiffer, R. Ortega, C. A. Hans, J. Raisch. Droop-Controlled Inverter-Based Microgrids are Robust to Clock Drifts. In American Control Conference (ACC), Chicago, IL, USA, 2015.
  5. J. Schiffer, T. Seel, J. Raisch, T. Sezi. Voltage Stability and Reactive Power Sharing in Inverter-Based Microgrids with Consensus-Based Distributed Voltage Control. IEEE Transactions on Control Systems Technology, 2015.
  6. J. Schiffer., T. Seel, J. Raisch, T. Sezi. A Consensus-Based Distributed Voltage Control for Reactive Power Sharing in Microgrids. In European Control Conference (ECC), pages 1299–1305, Strasbourg, France, 2014.
  7. J. Schiffer, A. Anta, T. D. Trung, J. Raisch, T. Sezi. On power sharing and stability in autonomous inverter-based microgrids. In 51st IEEE Conference on Decision and Control (CDC), 2012, pages 1105–1110, 2012.
  8. J. Schiffer, E. Fridman, R. Ortega. Stability of a class of delayed port-Hamiltonian systems with application to droop-controlled microgrids. In 54th IEEE Conference on Decision and Control (CDC), Osaka, Japan, 2015.
  9. D. Efimov, R. Ortega, J. Schiffer. ISS of multistable systems with delays: application to droop-controlled inverter-based microgrids. In American Control Conference (ACC), Chicago, IL, USA, 2015.
  10. C. A. Hans, V. Nenchev, J. Raisch, C. Reincke-Collon. Minimax Model Predictive Operation Control of Microgrids. In 19th IFAC World Congress, pages 10287–10292, Cape Town, South Africa, 2014.
  11. C. A. Hans, V. Nenchev, J. Raisch, C. Reincke-Collon. Approximate Closed-Loop Minimax Model Predictive Operation Control of Microgrids. In European Control Conference (ECC), pages 241–246, Linz, Austria, 2015.
  12. C. A. Hans, P. Sopasakis, A. Bemporad, J. Raisch, C. Reincke-Collon. Scenario-Based Model Predictive Operation Control of Islanded Microgrids. In 54th IEEE Conference on Decision and Control (CDC), page 3272–3277, Osaka, Japan, 2015.

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