A distributed algorithm for controlling continuous and discrete variables in a radial distribution grid

The increased integration of distributed energy resources (DERs) in the distribution network with intermitent generation profiles will likely make voltage regulation a difficult task. However, DERs bring both challenges and opportunities, as they can provide renewable forms of local energy and act as voltage regulating components. The DERs are usually interfaced with power electronic devices, in which both their active and reactive power outputs can be regulated and treated as continuous control variables. In contrast, other voltage regulatory devices such as On-Load Tap-Changing (OLTC) transformers are often controlled by making discrete tap changes. Thus, appropriate control strategies are required to control and coordinate the DERs with other voltage regulatory devices. In this work, a distributed control strategy based on the Alternating Direction Method of Multipliers (ADMM) is developed, which controls both the continuous and the discrete variables in a distribution grid. The proposed control strategy is compared to a centralized and a local control architecture, where optimal parameters have been computed for the local controllers. Finally, a simulation study is made for the three different control architectures using a modified CIGRE medium voltage network. The results showed significant improvements in the daily voltage profiles while also reducing the power losses by over 30% when using an optimal control strategy.A distributed algorithm for controlling continuous and discrete variables in a radial distribution gridpublishedVersio

MPC-based Voltage Control with Reactive Power from High-Power Charging Stations for EVs

The recent development of EVs with high-capacity batteries and high charging power capabilities leads to an increased demand for fast-charging stations (FCS). However, FCS can cause power quality issues such as voltage drops in distribution grids with limited power capacity. Grid reinforcements are a standard solution for solving power quality issues. However, these can be costly. An alternative approach is to install bi-directional chargers at FCS and use this flexibility source to provide voltage support in peak-load periods by injecting reactive power to the grid. This paper proposes a model predictive controller (MPC) to control and coordinate such high-power chargers. The MPC maximizes the charging rate for the EVs while ensuring that the voltage level stays within the allowable limits. The control system has been evaluated through simulations on a realistic grid model, and the results show that both the FCS and the grid can benefit from utilizing the reactive power.acceptedVersio

Review of Grid Interconnection Requirements and Synchronization Controllers for Dispersed Minigrids

As of 2019, the world population without electricity access is estimated to 770 million with most of these communities residing in sub-Saharan Africa. Nevertheless, between 2000 and 2019 the Indian population with electricity access has grown from 43% to 99%. Minigrids have played a major role in the efforts of increasing access to electricity in rural areas. However, interconnecting minigrids to each-other or to the main grid remains still a challenge both due to lack of clear protocols and of technically matured controllers to manage the synchronization. In this paper, a review of existing interconnection guidelines is presented and their relevance for the interconnection of minigrids is assessed. Furthermore, existing synchronization controllers are reviewed highlighting their applicability for minigrids.acceptedVersio

Decentralized Energy Management Concept for Urban Charging Hubs with Multiple V2G Aggregators

This work introduces a decentralized management concept for the urban charging hubs (UCHs) where electric vehicles (EVs) can access multiple charger clusters, each controlled by an aggregator. The given day ahead schedules (DASs) and peak power limits (PPLs) of the aggregators providing grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services can constrain the energy supply. A suitable energy management concept is required to prevent the impact of supply limitations on EV users. In the proposed approach, an electromobility operator (EMO) acting as an authorized entity, allocates incoming EVs into the charger clusters in the UCH. The EMO executes a smart routing (SR) algorithm that jointly optimizes the cluster allocations and charging schedules, minimizing the charging cost for the given dynamic price signals produced by the aggregators. For real-time charging control (RTC) of the charging units, each aggregator solves an optimization problem with periodically updated parameters given by the DAS/PPLs and charging commitments. This work demonstrates the effectiveness of the proposed concept through comparisons against benchmark strategies without SR and RTC. The results highlight that the proposed concept reduces the deviations from the DASs and the violations of PPLs while significantly decreasing unfulfilled charging demand and unscheduled discharge from EV batteries.Decentralized Energy Management Concept for Urban Charging Hubs with Multiple V2G AggregatorsacceptedVersio

The value of multiple data sources in machine learning models for power system event prediction

We describe a method for assessing the value of additional data sources used in the prediction of unwanted events (voltage dips, earth faults) in the power system. Using this method, machine learning models for event prediction using (combinations of) different data sources are developed. The value of each data source is the improvement in model performance it brings. In addition, feature importance is retrieved using SHapley Additive exPlanations (SHAP). The methodology is applied to models that predict faults based on power quality and weather data. We find that models that combine sources outperform models using either in isolation. They predict ground faults and voltage dips with AUCs (Area Under Curve) of 0.74 and 0.80, respectively. Meteorological data appears more valuable than power quality data and the most important features are dew point, month of the year, and the power spectral density at 4.7 HzacceptedVersio

Topics in the optimal operation of process plants

An increasingly competitive global market, together with stricter environmental and safety regulations make it necessary for chemical process plants to operate close to its optimum. As a result, there has been a growing interest in online optimization methods, e.g., model predictive control (MPC), real-time optimization (RTO), and economic MPC (EMPC). However, implementing such techniques remains challenging, mainly due to the computational complexity and lack of accurate dynamic models. Another approach is to use simple control structures that keep specific controlled variables (CVs) at a constant value, also known as selfoptimizing control [122]. The central idea of self-optimizing control is to select CVs such that in the presence of disturbances, the loss is minimized by holding them at constant set-points. Besides using single measurements, selecting linear combinations of measurements as CVs will further improve the self-optimizing control performance. Using all measurements available will, in theory, give the smallest loss, but increases the risk of getting sensor failures and makes implementing the control structure more di cult. Instead, it is preferable to nd an optimal measurement subset using the branch and bound method derived in [26] or the mixed-integer quadratic programming (MIQP) approach in [149]. However, when using decentralized control, it is often desirable to impose some structural constraints on the CVs. E.g., by only combining manipulated variables (MVs) with CVs associated with certain units or parts of the process. Unfortunately, when structural constraints are included, it makes the underlying optimization problem non-convex and thus, finding the optimal solution is di cult. In the rst part of this thesis, an alternating direction method of multipliers (ADMM) algorithm is proposed for incorporating structural constraints on the CVs. The resulting algorithm is computationally very efficient and able to find local solutions that give similar or better performance when compared to existing methods. Self-optimizing control focuses on the steady-state operation, and therefore, the CVs are typically calculated using only steady-state models of the process. As a consequence, little attention has been put on the dynamic performance when selecting measurement combinations, where the general approach is to first compute the optimal CVs and then design their respective controllers. The optimal measurement combinations, can often (especially if many measurements are used) result in very dynamically complex systems, that makes designing the feedback controllers di cult. If dynamic models of the process are available, then it should be possible to also consider how using a measurement combination as CVs will affect the dynamics of the system. In the second part of the thesis, PI controllers and measurement combinations are simultaneously obtained with the aim to find an optimal trade-o between minimizing the steady-state loss and the transient response for the resulting closed-loop system. A solution can be found by solving a bilinear matrix inequality (BMI), which becomes a linear matrix inequality (LMI) by specifying a stabilizing state feedback gain. The resulting control structures were evaluated on several case studies that consisted of di erent distillation column models. The simulations showed that the resulting control structures could give comparable performance to model predictive controllers (MPC) as long the parameters for the PI controllers and the CVs had been chosen appropriately. In the ideal case, it would be su cient to only use self-optimizing control variables with feedback controllers, since the operation would remain near-optimal without needing to change the set-points despite there being disturbances present. However, self-optimizing control alone is unlikely going to achieve truly optimal operation, and will probably require the inclusion of some online optimization method. Common for most of these algorithms (e.g., RTO, EMPC) is that they require more information about the current states and disturbances of the process. Measuring all the relevant states and disturbances is in general not possible, and thus, they must be estimated using appropriate state estimators. For chemical processes and other large-scale systems, using centralized state estimators are in general not favorable due to the high computational complexity. In addition, developing and maintaining a set of local models will, in general, be a lot easier compared to using a single global model. Therefore, it would be preferable to decompose them into multiple different local estimators, that uses a local model and the locally available measurements. From the different local estimates, it should be possible to reconstruct a more accurate global state vector using some appropriate fusion method. However, most existing fusion methods are limited to fusing only two state vectors of the same size, where each locally computed estimate refers to the same states. However, chemical processes are usually composed of di erent units where the dynamic model for each unit contain their own set of states, with some being shared between each other. Therefore, for the last part of this thesis, a fusion algorithm is proposed that is able to fuse multiple state estimates, where parts of the local state vectors are overlapping each other. The resulting algorithm was able to provide a fused estimate with a lower estimation error compared to existing fusion methods

Controller Design and Sparse Measurement Selection in Self-optimizing control

Self-optimizing control focuses on minimizing loss for processes in the presence of disturbances by holding selected controlled variables at constant set-points. A measurement combination can be found, using the Null-space method, which further reduces the loss. Since self-optimizing control focuses on the steady-state operation, little attention has been put on the dynamic performance when selecting measurement combinations. In this work, an iterative LMI approach is combined with the sparsity promoting weighted l1-norm, to find a measurement subset together with PI controllers for the Null-space method. The measurement combination and the controllers are designed such that, the dynamic response is improved when the process is facing disturbances. The proposed method is illustrated on a Petlyuk column case study

An ADMM algorithm for incorporating structural constraints in self-optimizing control

An ADMM algorithm is proposed for selecting structurally constrained measurement combinations as controlled variables (CVs). The CV selection is based on the self-optimizing control principle, where the goal is to choose CVs such that the steady-state operation is optimized when they are kept at constant set-point. When CV selection incorporates structural constraints, it becomes a non-convex optimization problem and thus, finding the optimal solution is difficult. However, using an ADMM algorithm for a given measurement set together with specified structural constraints, a local solution can be obtained. The resulting CVs seem to give similar or better performance when compared to other existing methods. The proposed method was evaluated on case studies, consisting of a binary distillation column and an evaporator

An Iterative LMI Approach to Controller Design and Measurement Selection in Self-Optimizing Control

Self-optimizing control focuses on minimizing loss for processes in the presence of disturbances by holding selected controlled variables at constant set-points. The loss can further be reduced by controlling measurement combinations to constant values. Two methods for finding appropriate measurement combinations are the Null-space and the Exact local method. Both approaches offer sets with an infinite number of solutions that give the same loss. Since self-optimizing control is mainly concerned with minimizing the steady-state loss, little attention has been put on the dynamic performance when selecting measurement combinations

Accounting for dynamics in self-optimizing control

Self-optimizing control focuses on minimizing the steady-state loss for processes in the presence of disturbances by holding selected controlled variables at constant set-points. The loss can further be reduced by controlling linear measurement combinations that have been obtained with the purpose of minimizing either the worst-case loss or the average loss. Since self-optimizing control mainly focuses on the steady-state operation, little emphasis has been put on the dynamic behaviour of the resulting closed-loop system. The general approach is to first compute the optimal controlled variables and then design their respective controllers. However, the optimal measurement combinations, can often (especially if many measurements are used) result in very dynamically complex systems, that makes designing the feedback controllers difficult. In this work, PI controllers and measurement combinations are simultaneously obtained with the aim to find an optimal trade-off between minimizing the steady-state loss and the transient response for the resulting closed-loop system. A solution can be found by solving a bilinear matrix inequality (BMI), which becomes a linear matrix inequality (LMI) by specifying a stabilizing state feedback gain. The optimization problem can also be combined with the sparsity promoting weighted l1-norm, which penalizes the number measurements used and thus, attempts to find an optimal measurement subset. The proposed method requires solving a BMI, for which an iterative LMI approach can be used to find a local optimum, which often seems to give good results, as illustrated on two case studies, consisting of a binary and a Kaibel distillation column