Voltage Control In Networks Research Articles

Coordinated scheduling of 5G base station energy storage for voltage regulation in distribution networks

With the rapid development of 5G base station construction, significant energy storage is installed to ensure stable communication. However, these storage resources often remain idle, leading to inefficiency. To enhance the utilization of base station energy storage (BSES), this paper proposes a co-regulation method for distribution network (DN) voltage control, enabling BSES participation in grid interactions. In this paper, firstly, an energy consumption prediction model based on long and short-term memory neural network (LSTM) is established to accurately predict the daily load changes of base stations. Secondly, a BSES aggregation model is constructed by using the power feasible domain maximal inner approximation method and Minkowski summation to evaluate the charging and discharging potential and adjustable capacity of BSES clusters. Subsequently, a BSES demand assessment and optimal scheduling model for low voltage regulation in DN is developed. This model optimizes the charging and discharging strategies of BSES to alleviate low voltage problems in DN. Finally, the simulation results effectively verify the feasibility of the proposed optimal scheduling method of BSES for voltage regulation in DN.

Voltage regulation and energy loss minimization for distribution networks with high photovoltaic penetration and EV charging stations using dual-stage model predictive control

The widespread integration of photovoltaic (PV) units and controllable loads like electric vehicles (EVs) might cause uncertain voltage fluctuations quite frequently. The reactive power from distributed generation (DG) units and EV charging stations (EVCSs) can effectively be used along with conventional devices like on-load tap changer (OLTC) to successfully control network voltages in real-time, with multi-time scale coordination. However, the control of a large number of available resources, with reliable communication among them becomes a complex task. Moreover, the substantial real-time data set amassed through the deployment of measurement devices across the network increases both cost and computational intricacies. This paper presents a novel approach, employing a time decomposition-based dual-stage model predictive control (MPC) with a reduced model control framework for voltage control and energy loss minimization in active distribution networks (ADNs), by significantly reducing the number of measuring devices. A minimum global set of available control resources is identified for real-time control, aiming to mitigate control complexity and minimize the demand for heavy communication. The proposed control strategy is validated on the 33-bus distribution network and modified IEEE 123-bus distributed network, under high PV penetration and EV charging stations. It is seen that the proposed reduced model framework with very limited measuring devices and control equipment can effectively regulate the voltages with a standard deviation of 0.0059 p.u. and 0.0035 p.u. as compared to the full order system model, for 33-bus network and IEEE 123-bus network, respectively. Furthermore, there is a net 23.24% reduction in energy losses when power loss minimization is considered along with the minimization of voltage deviations in the 33-bus network.

A new voltage sensitivity-based distributed feedback online optimization for voltage control in active distribution networks

Rising sustainability, environmental, and decarbonization considerations have prompted a recent increase in the integration of inverter-based distributed energy resources (DER), such as photovoltaics (PV), into the power grid. This trend raises concerns about potential voltage violations within distribution systems, emphasizing the growing relevance of effective voltage regulation. Utilizing smart inverters for active and reactive power control proves highly efficient in addressing voltage rise issues within active distribution networks (ADN). This work proposes a distributed feedback online optimization (FOO) strategy for voltage regulation in ADNs. The FOO strategy utilizes online recursive least squares (RLS) sensitivity-based feedback, relying on local measurements to iteratively compute optimal reactive power set points for DERs, ensuring bus voltage regulation to within acceptable limits. The distributed control algorithm employs a primal-dual update approach, enabling coordinated information exchange between neighboring controllers. This online feedback-based control offers a promising solution for real-time coordinated voltage control in distribution grids, particularly with the increasing integration of inverter-based DERs at the grid's edge. The approach demonstrates robustness under varying conditions, communication dynamics, and topology changes, validated using real distribution feeders with actual solar PV production and consumption data. Advancements in sensing, communication systems, and smart grid tools further enhance the feasibility and scalability of this strategy for evolving power systems.

Distributed MPC-Based Voltage Control for Active Distribution Networks Considering Uncertainty of Distributed Energy Resources

Due to the uncertainty of distributed energy resources (DERs), the effectiveness of voltage control in distribution networks faces significant challenges. Aiming at this problem, a novel distributed stochastic model predictive control (DSMPC) scheme was proposed in this paper to achieve voltage regulation considering the uncertainty of DERs. In the proposed control scheme, based on the photovoltaic (PV) prediction error model, multiple operation scenarios are selected to characterize the uncertainty of PV generation. Then, based on these selected scenarios, a DSMPC approach is developed to minimize the cost of control actions by coordinating the PV inverters and battery energy storage systems. Simulation involving the modified IEEE 34-bus verified that the proposed method can work effectively under the uncertainty of DERs and achieves a comparable control performance with a well-designed centralized controller.

Cooperative voltage control in distribution networks considering multiple uncertainties in communication

Delays, jitter, and packet loss in communication networks can impact the performance of cooperative voltage control systems in distribution networks. In distribution systems with a high penetration of renewable energy sources that do not respond promptly, these issues can even lead to system destabilization when voltage surges occur. Considering the interdependence of delay, jitter, and packet loss, the current direct approach of accumulating information entropy may result in the deterioration of dynamic control performance. Based on Copula entropy theory, a new multivariate communication uncertainty metric model is proposed. Using the multivariate Epanechnikov kernel function model, a method has been developed to estimate the multivariate non-independent uncertainty of a communication system. Accurate state estimation is integrated into event-triggered sliding mode control (ETSMC) of the distribution network to facilitate coordinated voltage control and enhance resilience against communication uncertainty. Design criteria for the controller and observer parameters are provided based on Lyapunov stability theory. Simulation results confirm that the proposed ETSMC offers significant improvements in control performance and system resilience to external power disturbances and multivariate communication uncertainty events.

Distribution network voltage control considering virtual power plants cooperative optimization with transactive energy

The integration of massive distributed energy resources (DERs) brings new challenges for the distribution networks (DNs) operation control. To control DERs effectively, virtual power plants (VPPs) are introduced into DNs. Conventionally, the control strategy of VPPs is formulated as an optimization model without considering voltage control and transactive energy (TE) in DNs. In this article, multiple VPPs cooperative optimization with TE is studied and cast as a nonlinear programming model. It aims to optimize the voltage control of DNs and the operation profits of VPPs. To dynamically capture the optimal operation state of VPPs in DNs, the distribution locational marginal pricing and DN partition are incorporated into the model. In general, this type of model is computationally challenging and mainly solved by approximate algorithms with low efficiency. Recently, multi-agent deep reinforcement learning (MADRL) is emerging as a scalable and promising data-driven approach. We propose an augmented MADRL solution to this problem. In the training stage, one soft actor-critic learning agent augmented by parameter sharing strategy is employed to simultaneously capture the state-action patterns of multiple VPPs. The prioritized experience replay technique is applied to improve learning efficiency and speed. Numerical test results demonstrate the superiority of the proposed method over benchmark methods.

Multi-agent deep reinforcement learning with enhanced collaboration for distribution network voltage control

Due to the increasing high penetration of Photovoltaic (PV), it brings great challenge for voltage control issue of distribution network. To address this problem, this paper presents an improved collaborative Multi-Agent Reinforcement Learning (MARL) approach for proactive voltage control, aimed at mitigating voltage violations and minimizing power losses. The self-attention mechanism is embedded into the multi-agent soft actor-critic (MASAC) algorithm to enhance the collaboration of multi-agent system, which can well improve the learning efficiency to ensure the voltage safety of Distribution Network. In addition, the proposed learning approach is implemented on IEEE 33-bus, 141-bus and 322-bus systems, and the simulation results reveal that the proposed approach can control the voltage into safety domain as well as reduce power losses.

Design of Radial Distribution System to Study Load-Flow and Short Circuit Analysis Using ETAP Software

This work's analysis provides information on fault analysis and load flow studies, which help to understand how radial distribution networks operate both in normal and emergency circumstances. The research explores optimization methods, describing capacitor placement, transformer size, and feeder reconfiguration in order to lower losses and increase system efficacy. Moreover, the work looks at reliability-focused maintenance techniques, stressing the need of predictive maintenance and condition monitoring in raising the dependability of radial distribution networks. Modern electrical power networks depend heavily on their radial distribution system, which effectively and dependable distributes electricity to customers. Many radial distribution system-related topics are covered in detail on this website, including analytical methods, optimization processes, and dependability enhancement strategies. Power flow, voltage control, and losses in radial networks are investigated in this paper under a range of operational situations. The last and crucial stage of a power system, the electrical distribution system makes sure that customers have a steady supply of electricity. Regretfully, because it is often neglected and exposed to environmental risks, our distribution network has a number of problems. Our distribution feeder is rigorously analyzed for load flow and short-circuits in order to overcome these challenges and find and fix problems. The main topics of this study are the load flow and short-circuit analysis of a radial distribution feeder, which is mostly constructed with (ETAP). The final and crucial part of a power system, the electrical distribution system ensures that consumers receive an uninterrupted and steady supply of electricity. Our distribution feeder undergoes a rigorous and cautious process to address these issues front on because of the uncontrollable nature and susceptibility of our network to environmental hazards. This paper delves deeply into the issues of short-circuiting and load flow in radial distribution feeders. Both symmetrical and asymmetrical faults are thoroughly investigated by mathematical computations and ETAP simulations; the results of the software simulations are contrasted with those of the mathematical analysis. These discoveries will definitely influence the development and management of our system and point us in the direction of workable and practical solutions.

Analytical target cascading based real-time distributed voltage control for MV and LV active distribution networks

This paper proposes a new real-time distributed voltage control (RTDVC) scheme for medium and low voltage (MV&LV) distribution networks (DNs) based on the analytical target cascading (ATC) method. In this scheme, photovoltaic (PV) units at the LV level, on-load tap changer (OLTC) and distributed generation (DG) clusters at the MV level are optimally coordinated to regulate the bus voltages in MV&LV DNs to be close to the nominal value while minimizing the number of operations of OLTC and smoothing the reactive power outputs of DGs. Based on the ATC method, the large-scale strongly coupled voltage control problem of MV&LV DNs is decomposed into several subproblems while guaranteeing the optimality of the primal voltage optimization problem. The decomposed subproblems are iteratively solved in parallel in the MV and LV controllers, which achieves optimal voltage control in a distributed manner and effectively reduces the computation burden. An MV&LV test system with two 20 kV feeders and 39 0.4 kV LV grids was simulated to demonstrate the control performance of the proposed distributed voltage control scheme.

Dynamic Control and Time-Delayed Channel Scheduling Co-Design for Voltage Control in Active Distribution Networks

Dynamic Control and Time-Delayed Channel Scheduling Co-Design for Voltage Control in Active Distribution Networks

Power electronics applied to voltage control in rural distribution networks with penetration of distributed generation

In rural distribution networks with widely dispersed loads, characterized by the utilization of long overhead lines, voltage profiles with big variations appear along the line due to the significant variations of the load between peak hours and valley hours. This paper describes the problems that the integration of distributed generation based on renewable resources could generate in that scenario, and presents solutions that are commonly used to mitigate these problems and proposes new solutions to solve them. The integration of distributed generation (DG) based on renewable resources in these networks increases the variability of the voltage, and in some cases the power flow of the network also can be bidirectional. Usually this integration of distributed generation is limited by the network operator limiting the maximum voltage variation produced by the distributed generation in the point of common coupling (PCC). In this paper different traditional methods for voltage control are described and analyzed for a widely dispersed rural network scenario. The impact of the DG in these methods of voltage control and the advantages of power electronics in this new scenario are exposed. Power Electronic devices applied to these distribution networks allow more efficient management of the network increasing the integration of distributed generation maintaining the security levels.

Model-Free Distributed Voltage Control for Distribution Networks Based on State Space Mapping and Super-Linear Feedback

Model-Free Distributed Voltage Control for Distribution Networks Based on State Space Mapping and Super-Linear Feedback

A Joint Distributed Optimization Framework for Voltage Control and Emergency Energy Storage Vehicle Scheduling in Community Distribution Networks

A Joint Distributed Optimization Framework for Voltage Control and Emergency Energy Storage Vehicle Scheduling in Community Distribution Networks

An advanced secondary voltage control strategy for future power systems

The network voltage control together with the regulation of reactive power, has always been recognized as vital feature of power systems. It is aimed at containing the bus voltage within acceptable limits for guaranteeing system security, first and then assessing power quality. The voltage control is a key field in which several Transmission System Operators are currently investing resources to be well prepared in managing power systems with a high share of renewable energy sources, overcoming the drawbacks of the existing architectures. As is well known the voltage control is realized through a hierarchical structure, which is based on classical primary control, secondary control, and tertiary optimization. The secondary control is concerned with the regional level, implying proper coordination at both the geographical and temporal level. Due to its central role in power system operation, the secondary voltage control cannot be excluded from the effects of the dramatic changes that power systems are experiencing worldwide, even though regulators work by implementing well consolidated methodologies and strategies which show excellent performances. The motivation for this study in fact relies on the observation of some oscillatory phenomena in some areas of the Italian power system characterized by a high number of regulating resources highly coupled each other on the basis of the grid topology: despite these oscillatory phenomena are not so frequent, with the increasing of regulating resources enslaved by secondary regulation (such as renewable-based power plants) could potentially be worsened in the near future. Thus, in this paper, an optimal control strategy is proposed by tailoring a proper linear quadratic integral control which allows guaranteeing the desired profile for pilot buses voltage, ensuring a time response of aperiodic type without any oscillations. The aim of the proposed approach is to preserve the strengths of the existing secondary voltage regulation while enhancing the overall stability so enabling the progressive large penetration of renewable energy sources-based power plants and minimize the occurrence of oscillatory phenomena especially in highly electrically coupled areas. The robustness of the proposed regulation is verified first with respect to the WSCC 9-bus test system and successively on an actual regional area of Italian transmission network.

A voltage control method for distribution networks based on TCN and MPGA under cloud edge collaborative architecture

A distribution network voltage control method based on TCN and MPGA under cloud edge collaborative architecture is proposed to address the issues of heavy computational burden and inability to balance economic efficiency in traditional decentralized and centralized voltage control methods. Firstly, under the cloud edge collaboration architecture, the power prediction model based on time convolutional networks is trained in the cloud, and the edge predicts the load demand and new energy active output in each region based on the TCN Attention power prediction model issued by the cloud. Then, considering safety and economy comprehensively, with the goal of minimizing the daily network loss and voltage regulation cost in the distribution network, the Pareto solution set for the optimal operation point of each voltage regulation equipment in the entire distribution network was solved based on an improved multi population genetic algorithm at the distribution cloud main station. Finally, the optimal distribution network voltage control scheme is obtained based on the Pareto solution set. Based on the simulation system, experimental testing was conducted on the proposed method, and the results showed that its daily voltage regulation cost and network loss were 863.4 yuan and 43.73 MW h, respectively. The average absolute percentage error of the prediction model was 4.51 %, which was superior to other comparative methods.

Stochastic MPC based double-time-scale voltage regulation for unbalanced distribution networks with distributed generators

With the increasing penetration of renewable energy resources, the uncertainty of renewable energy resources output introduces challenges to voltage control in distribution networks (DNs). To solve these problems, this paper proposes a double-time-scale voltage control scheme for unbalanced DNs based on stochastic model predictive control (SMPC) to coordinate multiple voltage control devices. In fast-time-scale control, the active and reactive power outputs of distributed generators (DGs) are optimized to minimize voltage deviation, active power curtailment and reactive power fluctuation. In the slow-time-scale control, optimal tap changes of on-load tap changers (OLTC), step voltage regulators (SVR), and switched capacitor banks (CBs) are calculated to reduce voltage deviation and tap operations. Additionally, in the fast-time-scale control, the SMPC-based scheme adopts a scenario generation approach to represent the uncertainty of DG output. Considering the temporal correlation of the DG output, different prediction scenarios and scenario probabilities are obtained by the empirical cumulative distribution function fitted by the historical measurement data. In this paper, the effectiveness of the proposed double-time-scale voltage control scheme is verified by the unbalanced IEEE123 node system. The case study demonstrates that the proposed voltage scheme can effectively keep the three-phase voltages in unbalanced DN within the secure operation range and achieve better voltage profiles.

Deep Neural Network-Based Autonomous Voltage Control for Power Distribution Networks with DGs and EVs

This paper makes use of machine learning as a tool for voltage regulation in distribution networks that contain electric vehicles and a large production from distributed generation. The methods of voltage regulation considered in this study are electronic on-load tap changers and line voltage regulators. The analyzed study-case represents a real-life feeder which operates at 10 kV. It has 9 photovoltaic systems with various peak installed powers, 2 electric vehicle charging stations, and 41 secondary substations, each with an equivalent load. Measurement data of loads and irradiation data of photovoltaic systems were collected hourly for two years. Those data are used as inputs in the feeder’s model in DigSilent PowerFactory where Quasi-Dynamic simulations are run. That will provide the correct tap positions as outputs. These inputs and outputs will then serve to train a Deep Neural Network which later will be used to predict the correct tap positions on input data it has not seen before. Results show that ML in general and DNN specifically show usefulness and robustness in predicting correct tap positions with very small computational requirements.

Demand response‐based autonomous voltage control for transmission and active distribution networks using modified partially observed Markov decision process model

AbstractTo fully utilize the voltage regulation capacity and interaction characteristics of the Transmission and Distribution (T&D) system, a novel Modified Partially Observed Markov Decision Process (MPOMDP)‐based Reinforcement Learning (RL) scheme for Autonomous Voltage Control is proposed, which incorporates Demand Response (DR) and cooperation with the Transmission Network . The proposed scheme consists of two vital components: an MPOMDP block and a Modified Asynchronous Advantage Actor‐Critic‐based RL block. The MPOMDP block innovatively exploits the confidence interval of the observed state to make a better perception of the precise system state by introducing two new probability vectors. Then the MPOMDP block is fed into the underlying architecture of the RL block for asynchronously capturing features and optimal decision‐making, where the solving framework additionally brings in a public data buffer to realize boundary information sharing. Case studies are conducted on a modified T&D system considering N‐1 contingencies, with a training dataset from a district in Suzhou, China. Simulation results demonstrate that the proposed scheme can achieve significant voltage optimization while ensuring fast convergence speed.

An Efficient Modular Optimization Scheme for Unbalanced Active Distribution Networks With Uncertain EV and PV Penetrations

Integrated control strategies with Network Reconfiguration (NR), Demand Response (DR), and voltage control can reduce peak demand, energy loss, and system-wide unbalances in modern three-phase active Electric Distribution Networks (EDNs). However, simultaneous handling of these strategies is computationally complex and challenging. This paper presents a stochastic optimization formulation that slices the problem, solves the sub-problems separately, and splices them back to find optimal solutions efficiently. At the outset, all constraint-violating configurations are eliminated using a graph-theory-based edge traversal search developed from the classic Knuth’s Algorithm-S. Subsequently, deploying suitable indices, cyclic NR-DR assignments are performed to find near-optimal topologies and load schedules concerning the minimum loss, peak load, and unbalances. Further, Voltage Regulators (VRs) are set to achieve loss and unbalance reduction with minimal tap operations. The proposed scheme is tested on the modified IEEE 123-node test feeder with extensive EV and PV penetrations. The results show that this modular scheme provides superior solutions with an almost 75% reduction in time than the conventional co-optimization method. Various other case studies illustrate the effectiveness of the proposed scheme.

A Hierarchical Multi-Stage Coordination of Inverters for Voltage Control in Active Distribution Networks with a µPMU-PDC

This paper proposes a hierarchical multi-stage approach based on a distributed level phasor measurement unit (µPMU) at local controllers and a phasor data concentrator (PDC) at the central control unit to restore system voltage when it exceeds the limits recommended by the IEEE 1547-2018 standard. The proposed algorithm does not need an accurate system model or employ optimization solutions. Therefore, it has less implementation complexity and would be popular among distribution network service providers (DNSPs) and distribution network operators (DNOs) as it does not suffer from cost and computational complexity limitations. A PMU-PDC-based communication platform has been developed as a more efficient alternative to the supervisory control and data acquisition (SCADA) system, and provides superior characteristics, including a higher sample rate, higher data resolutions, and faster communication. The proposed coordinated algorithm aims to postpone power generation curtailment in distributed energy resources (DERs) with overvoltage problems (local DERs) by incorporating all the DERs that are not subjected to voltage violation (remote DERs) by absorbing their maximum reactive power capacity. If the system voltage has not recovered after absorbing all of the reactive power capacity of all the DERs, a reduced active power curtailment proposed by the algorithm is then applied to the system to control the voltage. The proposed strategy has been simulated in MATLAB and applied to IEEE 13-bus and IEEE 33-bus radial distribution benchmark systems to validate the performance of the system, in terms of its ability to coordinate voltage control and the accuracy of the PMU-PDC-based communication interface.