This paper proposes a data-driven distributed voltage control approach based on the spectrum clustering and the enhanced multi-agent deep reinforcement learning (MADRL) algorithm. Via the unsupervised clustering, the whole distribution system can be decomposed into several sub-networks according to the voltage and reactive power sensitivity. Then, the distributed control problem of each sub-network is modeled as Markov games and solved by the enhanced MADRL algorithm, where each sub-network is modeled as an adaptive agent. Deep neural networks are used in each agent to approximate the policy function and the action value function. All agents are centrally trained to learn the optimal coordinated voltage regulation strategy while executed in a distributed manner to make decisions based on only local information. The proposed method can significantly reduce the requirements of communications and knowledge of system parameters. It also effectively deals with uncertainties and can provide online coordinated control based on the latest local information. Comparison results with other existing model-based and data-driven methods on IEEE 33-bus and 123-bus systems demonstrate the effectiveness and benefits of the proposed approach.
With the large scale penetration of electric vehicles (EVs) and the advent of bidirectional chargers, EV aggregators will become a major player in the voltage regulation market. This paper proposes a novel false data injection attack (FDIA) against the voltage regulation capacity estimation of EV charging stations, the process that underpins voltage regulation in distribution system. The proposed FDIA takes into account the uncertainty in EV mobility and network conditions. The attack vector with the largest expected adverse impact is the solution of a stochastic optimization problem subject t…
High penetrations of photovoltaic (PV) systems can cause severe voltage quality problems in distribution networks. This paper proposes a distributed control strategy based on the dynamic formation of coalitions to coordinate a large number of PV inverters for voltage regulation. In this strategy, a rule-based coalition formation scheme deals with the zonal voltage difference caused by the uneven integration of PV capacity. Under this scheme, PV inverters form into separate voltage regulation coalitions autonomously according to local, neighbor as well as coalition voltage magnitude and regulat…
This paper proposes a new distributed consensus-based control method for voltage and frequency control of fully inverter-based islanded microgrids (MGs). The proposed method includes the active power sharing in voltage control to improve the reactive power sharing accuracy and thus generalizes some existing secondary frequency and voltage control methods. Firstly, frequency is regulated by distributed secondary frequency control. Secondly, voltage is regulated by distributed average voltage control and decentralized individual voltage control. It offers a tunable trade-off between voltage regu…
This paper proposes a disturbance-observer-based control (DOBC) scheme for frequency and voltage regulation for a renewable energy sources (RES) integrated power systems. The proposed approach acts a feed-forward control which improves the dynamic performance of the conventional proportional-integral-derivative (PID) controller. Robustness of the proposed control scheme has been validated through simulations under worst-case and stochastic uncertainties to mitigate real-time variability in RES output and load. The performance of the proposed control technique is compared to well established te…
Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in the modern distribution power grids. Faced with uncertain or even unknown distribution grid models and fast-changing power injections, model-free deep reinforcement learning (DRL) algorithms have been proposed to find the reactive power injections for inverters while optimizing the voltage profiles. However, such data-driven controllers can not guarantee satisfaction of the hard operational constraints, such as maintaining voltage profiles within a certain range o…
This paper proposes a new single input multiple output power supply by integrating a flyback converter and several buck converters. The flyback converter works as the main regulator, and the buck converters provide series voltage compensation with the aim of tight regulation. A time multiplexing switching scheme is proposed to deliver multiple output voltage levels via a two winding transformer and to eliminate the cross regulation between output channels. This configuration reduces the size of flyback transformer and filter capacitors, and consequently improves the overall form factor. A deta…
Rapid developments in power distribution systems and renewable energy have widened the applications of dc--dc buck--boost converters in dc voltage regulation. Applications include vehicular power systems, renewable energy sources that generate power at a low voltage, and dc microgrids. It is noted that the cascade--connection of converters in these applications may cause instability due to the fact that converters acting as loads have a constant power load (CPL) behavior. In this paper, the output voltage regulation problem of a buck--boost converter feeding a CPL is addressed. The constructio…
In a purely inverter-based microgrid, both grid-forming (GFM) and grid-following (GFL) inverters will have a crucial role to play in frequency/voltage regulation and maintaining power sharing through their grid support capabilities. Consequently, the coordination between these two technologies becomes increasingly important for optimal system performance. However, the existing work does not consider GFL's potential to participate in a secondary control in coordination with GFM, thus not able to utilize the full capability of inverter resources. In this paper, we show that it is possible to ful…
Photovoltaic (PV) smart inverters can improve the voltage profile of distribution networks. A multi-objective optimization framework for coordination of reactive power injection of smart inverters and tap operations of on-load tap changers (OLTCs) for multi-phase unbalanced distribution systems is proposed. The optimization objective is to minimize voltage deviations and the number of tap operations simultaneously. A novel linearization method is proposed to linearize power flow equations and to convexify the problem, which guarantees convergence of the optimization and less computation costs.…
In this paper, we propose a novel four-stage distributed controller for a DC microgrid that achieves power sharing and average voltage regulation for the voltages at actuated and unactuated buses. The controller is presented for a DC microgrid comprising multiple distributed generating units (DGUs) with time-varying actuation states; dynamic RLC lines; nonlinear constant impedance, current and power (ZIP) loads and a time-varying network topology. The controller comprising a nonlinear gain, PI controllers, and two dynamic distributed averaging stages is designed for asymptotic stability. This …
A voltage regulation method for slow voltage variations at distribution level is proposed, based on a view of the loads, generators and storage along a distribution line as point weights. The "centers of mass" of the absorbed and injected currents (loads & generation, respectively) are compensated by minimizing the distance between them, through proper re-dispatching of the power of the available units and interruptible loads. The technique is recursively applied to lesser parts of the distribution line to address local phenomena and is assumed to be offered as ancillary service to the system …
In this paper we consider the problem of voltage regulation of a proton exchange membrane fuel cell connected to an uncertain load through a boost converter. We show that, in spite of the inherent nonlinearities in the current-voltage behaviour of the fuel cell, the voltage of a fuel cell/boost converter system can be regulated with a simple proportional-integral (PI) action designed following the {Passivity-based Control (PBC) approach. The system under consideration consists of a DC-DC converter interfacing a fuel cell with a resistive load. We show that the output voltage of the converter c…
This paper proposes a disturbance observer-based control (DOBC) method for frequency and voltage regulation of a solar photovoltaic (PV)-diesel generator(DG) based hybrid microgrid during islanding conditions. The DOBC is integrated as a feed-forward control to the synchronous generator based DG, which handles real-time power mismatches and regulates the microgrid frequency and voltage under islanding. To substantiate the operational robustness of the developed controller under real-time uncertainties arising due to variability in PV output and load, the controller has been tested under worstc…
This paper proposes a decentralised secondary voltage control strategy that has several benefits over the existing centralised strategies. For that, a new structure for the control is proposed in terms of an inner and outer loops for each generator. The individual generators of a particular zone participate in the secondary voltage control by aligning their reactive powers with respect to the pilot point voltage reference. The decentralised nature of the proposed control strategy enables plug and play operation: run with different numbers of generators without any need of regulators reconfigur…
Power distribution grids with high PV generation are exposed to voltage disturbances due to the unpredictable nature of renewable resources. Smart PV inverters, if controlled in coordination with each other and continuously adapted to the real-time conditions of the generation and load, can effectively regulate nodal voltages across the feeder. This is a fairly new concept and requires communication and a distributed control logic to realize a fair utilization of reactive power across all PV systems. In this paper, a collaborative reactive power optimization is proposed to minimize voltage dev…
High-voltage direct-current (HVDC) systems for constant or intermittent power delivery have recently been developed further to support grid frequency regulation (GFR). This paper proposes a new control strategy for a line-commutated converter-based (LCC) HVDC system, wherein the DC-link voltage and current are optimally regulated to improve real-time GFR in both rectifier- and inverter-side AC networks. A dynamic model of an LCC HVDC system is developed using the DC voltage and current as input variables, and is integrated with feedback loops for inertia emulation and droop control. A linear q…
This paper presents a distributed voltage regulation method based on multi-agent system control and network self-organization for a large distribution network. The network autonomously organizes itself into small subnetworks through the epsilon decomposition of the sensitivity matrix, and agents group themselves into these subnetworks with the communication links being autonomously determined. Each subnetwork controls its voltage by locating the closest local distributed generation and optimizing their outputs. This simplifies and reduces the size of the optimization problem and the interactio…
In this paper, a dual heuristic programming controller is proposed to control a boost converter. Conventional controllers such as proportional integral derivative (PID) or proportional integral (PI) are designed based on the linearized small-signal model near the operating point. Therefore, the performance of the controller during start up, load change, or input voltage variation is not optimal since the system model changes by varying the operating point. The dual heuristic programming controller optimally controls the boost converter by following the approximate dynamic programming. The adva…
In this paper, a neural network predictive controller (NNPC) is proposed to control a buck converter. Conventional controllers such as proportional integral (PI) or proportional integral derivative (PID) are designed based on the linearized small signal model near the operating point. Therefore, the performance of the controller in the start up, load change, or reference change is not optimal since the system model changes by changing the operating point. The neural network predictive controller optimally controls the buck converter by following the concept of the traditional model predictive …
University of California San Diego（加州大学圣地亚哥分校）；NEC Laboratories America（NEC美国实验室）
Increasing adoption of solar photovoltaic (PV) presents new challenges to modern power grid due to its variable and intermittent nature. Fluctuating outputs from PV generation can cause the grid violating voltage operation limits. PV smart inverters (SIs) provide a fast-response method to regulate voltage by modulating real and/or reactive power at the connection point. Yet existing local autonomous control scheme of SIs is based on local information without coordination, which can lead to suboptimal performance. In this paper, a deep reinforcement learning (DRL) based algorithm is developed a…
We develop a system-level design for the provision of Ancillary Service (AS) for control of electric power grids by in-vehicle batteries, suitably applied to Electric Vehicles (EVs) operated in a sharing service. An architecture for cooperation between transportation and energy management systems is introduced that enables us to design an autonomous Vehicle-to-Grid (V2G) for the provision of multi-objective AS: primary frequency control in a transmission grid and voltage amplitude regulation in a distribution grid connected to EVs. The design is based on the ordinary differential equation mode…
The suspension regulation is critical to the operation of medium-low-speed maglev trains (mlsMTs). Due to uncertain environment, strong disturbances and high nonlinearity of the system dynamics, this problem cannot be well solved by most of the model-based controllers. In this paper, we propose a model-free controller by reformulating it as a continuous-state, continuous-action Markov decision process (MDP) with unknown transition probabilities. With the deterministic policy gradient and neural network approximation, we design reinforcement learning (RL) algorithms to solve the MDP and obtain …
Suppression and Regulation of Thermal Birefringence in Optical Voltage Sensor with Isomerism Electrodes and Arbitrary Electric Field Direction Modulation
Pseudo-gradient Based Local Voltage Control in Distribution Networks
College of Engineering and Applied Sciences, University of Colorado, Boulder（科罗拉多大学博尔德分校工程与应用科学学院）；Department of Electrical Engineering, Caltech, Pasadena（加州理工学院电气工程系）
Recent advances in data sensing and processing technologies enable data-driven control of high-voltage direct-current (HVDC) systems for improving the operational stability of interfacing power grids. This paper proposes an optimal data-driven control strategy for an HVDC system with line-commutated converters (LCCs), wherein the dc-link voltage and current are optimally regulated at distinct HVDC terminals to improve frequency regulation (FR) in both rectifier- and inverter-side grids. Each HVDC converter is integrated with feedback loops for regulation of grid frequency and dc-link voltage i…
The occurrence of voltage violations are a major deterrent for absorbing more roof-top solar power to smart Low Voltage Distribution Grids (LVDG). Recent studies have focused on decentralized control methods to solve this problem due to the high computational time in performing load flows in centralized control techniques. To address this issue a novel sensitivity matrix is developed to estimate voltages of the network by replacing load flow simulations. In this paper, a Centralized Active, Reactive Power Management System (CARPMS) is proposed to optimally utilize the reactive power capability…
Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage controls, but this is difficult to obtain in practice. This paper develops a model-free approach based on the surrogate model and deep reinforcement learning (DRL). We have also extended it to deal with unbalanced three-phase scenarios. The key idea is to learn a surrogate model to capture the relationship between the power injections and voltage fluctuation of each node from historical data instead of using the original inaccurate model affected by errors and uncertainties. This allows us…
