Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

https://ieeexplore.ieee.org/ielaam/5165411/8075182/7990560-aam.pdf

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

Model predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems [1], [2]. The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control objectives have made it an attractive multivariable constrained control approach [1]. MPC (a.k.a. receding-horizon control) solves an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner [3]. The OCP is solved over a finite sequence of control actions {u0,u1,f,uN- 1} at every sampling time instant that the current state of the system is measured. The first element of the sequence of optimal control actions is applied to the system, and the computations are then repeated at the next sampling time. Thus, MPC replaces a feedback control law p(m), which can have formidable offline computation, with the repeated solution of an open-loop OCP [2]. In fact, repeated solution of the OCP confers an "implicit" feedback action to MPC to cope with system uncertainties and disturbances. Alternatively, explicit MPC approaches circumvent the need to solve an OCP online by deriving relationships for the optimal control actions in terms of an "explicit" function of the state and reference vectors. However, explicit MPC is not typically intended to replace standard MPC but, rather, to extend its area of application [4]-[6].

/pdf/stochastic-model-predictive-control-an-overview-and-1kyy9166uh.pdf

Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

/pdf/online-short-term-solar-power-forecasting-1ljgpbshkb.pdf

Online Short-term Solar Power Forecasting

Communication infrastructure (CI) in microgrids (MGs) allows for the application of different control architectures for the secondary control (SC) layer. The use of new SC architectures involving CI is motivated by the need to increase MG resilience and handle the intermittent nature of distributed generation units. The structure of SC is classified into three main categories, including centralized SC (CSC) with a CI, distributed SC (DISC) generally with a low-data-rate CI, and decentralized SC (DESC) with communication-free infrastructure. To meet the MGs’ operational constraints and optimize performance, control and communication must be utilized simultaneously in different control layers. In this survey, we review and classify all types of SC policies from CI-based methods to communication-free policies, including CSC, averaging-based DISC, consensus-based DISC methods, containment pinning consensus, event-triggered DISC, washout-filter-based DESC, and state-estimation-based DESC. Each structure is scrutinized from the viewpoint of the relevant literature. Challenges such as clock drifts, cyber-security threats, and the advantage of event-triggered approaches are presented. Fully decentralized approaches based on state-estimation and observation methods are also addressed. Although these approaches eliminate the need of any CI for the voltage and frequency restoration, during black start process or other functionalities related to the tertiary layer, a CI is required. Power hardware-in-the-loop experimental tests are carried out to compare the merits and applicability of different SC structures.

/pdf/on-the-secondary-control-architectures-of-ac-microgrids-an-151dix7731.pdf

On the Secondary Control Architectures of AC Microgrids: An Overview

https://hal-centralesupelec.archives-ouvertes.fr/hal-01633758/document

A survey on modeling of microgrids-From fundamental physics to phasors and voltage sources

In this work, we propose a hierarchical distributed model predictive control strategy to operate interconnected microgrids (MGs) with the goal of increasing the overall infeed of renewable energy sources. In particular, we investigate how renewable infeed of MGs can be increased by using a transmission network allowing the exchange of energy. To obtain an model predictive control scheme, which is scalable with respect to the number of MGs and preserves their independent structure, we make use of the alternating direction method of multipliers leading to local controllers communicating through a central entity. This entity is in charge of the power lines and ensures that the constraints on the transmission capacities are met. The results are illustrated in a numerical case study.

/pdf/hierarchical-distributed-model-predictive-control-of-3ykhjljc3d.pdf

Hierarchical Distributed Model Predictive Control of Interconnected Microgrids

We propose a model predictive control (MPC) approach for the operation of islanded microgrids that takes into account the stochasticity of wind and load forecasts. In comparison to worst case approaches, the probability distribution of the prediction is used to optimize the operation of the microgrid, leading to less conservative solutions. Suitable models for time series forecast are derived and employed to create scenarios. These scenarios and the system measurements are used as inputs for a stochastic MPC, wherein a mixed-integer problem is solved to derive the optimal controls. In the provided case study, the stochastic MPC yields an increase of wind power generation and decrease of conventional generation.

/pdf/scenario-based-model-predictive-operation-control-of-3em4hoc6nm.pdf

Scenario-based model predictive operation control of islanded microgrids

Clock drift in digital controllers is of great relevance in many applications. Since almost all real clocks exhibit drifts, this applies in particular to networks composed of several individual units, each of which being operated with its individual clock. In the present work, we investigate the effect of clock drifts in inverter-based microgrids. Via a suitable model that incorporates this phenomenon, we prove that clock inaccuracies hamper synchronization in microgrids, in which the individual inverters are operated with a fixed uniform constant electrical frequency. In addition, we show that the well-known frequency droop control renders stability of a lossless microgrid robust with respect to clock inaccuracies. This claim is established by using stability results reported previously by the authors for lossless inverter-based microgrids with ideal clocks. We also discuss the effect of clock drifts on active power sharing. The analysis is illustrated via a simulation example.

/pdf/droop-controlled-inverter-based-microgrids-are-robust-to-1i30b8hhg2.pdf

Droop-controlled inverter-based microgrids are robust to clock drifts

Clock drift in digital controllers is of great relevance in many applications. Since almost all real clocks exhibit drifts, this applies in particular to networks composed of several individual units, each of which being operated with its individual clock. In the present paper, we demonstrate via extensive experiments on a microgrid in the megawatt range that clock drifts may impair frequency synchronization in low-inertia power systems. The experiments also show that—in the absence of a common clock—the standard model of an inverter as an ideal voltage source does not capture this phenomenon. As a consequence, we derive a suitably modified model of an inverter-interfaced unit that incorporates the phenomenon of clock drifts. By using the derived model, we investigate the effects of clock drifts on the performance of droop-controlled grid-forming inverters with regard to frequency synchronization and active power sharing. The modeling and analysis is validated via extensive experiments on a microgrid in the megawatt range.

/pdf/modeling-analysis-and-experimental-validation-of-clock-drift-568508hepn.pdf

Modeling, Analysis, and Experimental Validation of Clock Drift Effects in Low-Inertia Power Systems

http://www.control.tu-berlin.de/wiki/images/2/2e/christian_ifac2014.pdf

Christian A. Hans

Papers

Hierarchical Distributed Model Predictive Control of Interconnected Microgrids

Scenario-based model predictive operation control of islanded microgrids

Droop-controlled inverter-based microgrids are robust to clock drifts

Modeling, Analysis, and Experimental Validation of Clock Drift Effects in Low-Inertia Power Systems

Minimax Model Predictive Operation Control of Microgrids