Voltage Control In Distribution Networks Research Articles

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.

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.

Centralized and decentralized combined voltage control for distribution network with high penetration PV connected

With the high penetration of distributed photovoltaics (DPVs) integrated into the distribution network, the voltage problem becomes a critical problem. At present, the resources in the current distribution network hardly satisfy the requirements of voltage regulation. However, the huge amount of DPVs in the distribution network could be a potential voltage regulation resource due to the controllable active power and reactive power. In this paper, a day-ahead centralized and intraday decentralized combined voltage control method is proposed for high penetration DPVs connected distribution Networks. In the part of day-ahead control, centralized compensation of the distribution network voltage is achieved by controlling the gear ratio of the capacitor/reactor group on an hourly scale. The intraday control part consists of decentralized local control based on DPVs and centralized control based on energy storage (ES) equipment. The reactive and active power of DPVs is controlled in a decentralized way by using local priority sensitivity control based on the coordination with capacitor optimization. Finally, simulations are performed to verify the performance of the method. The results indicate that this method can effectively solve the voltage problem caused by DPVs.

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

Coordinated Voltage Control of Distribution Network Considering Multiple Types of Electric Vehicles

Coordinated Voltage Control of Distribution Network Considering Multiple Types of Electric Vehicles

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.

A continuous-time voltage control method based on hierarchical coordination for high PV-penetrated distribution networks

The photovoltaic (PV) integration brings both fast photovoltaic generation (PVG) variations and a large number of PV inverters to the distribution networks (DNs). The management of the PVG variation and the PV inverter is an urgent problem. The discrete-time model can’t describe PVG variations during the schedule interval and the regulation potential of the PV inverter is limited by a fixed dispatching value. This paper proposes a continuous-time voltage control method based on hierarchical coordination for high PV-penetrated DNs. In the central schedule, based on the Bernstein polynomial method, continuous-time models of PVGs are established and reference values of local inverter control curves are dispatched as continuous-time functions in a sub-hourly timescale. In the local control, reactive power and active power curtailment of PV inverters are set as the reference values, i.e., the corresponding values of the continuous-time functions, every 30 s to optimize the central dispatching objective under the voltage constrains, and deviate from the reference values according to both the local control curve and voltage measurement only when the real-time bus voltage violation occurs. In addition, the cooperation between inverter and mechanical equipment in the continuous-time framework is also considered, and the Bernstein polynomial method is developed to the continuous-time modeling of voltage control in DNs. The simulation shows that the proposed method has lower maximum voltage magnitude and lower energy loss compared with discrete-time central schedule method.

Research on Optimal Voltage Control of Distribution Network with the Participation of EVs and PVs

With the accelerating penetration of photovoltaics (PVs) and electric vehicles (EVs), distribution networks face the risks of voltage violations and fluctuations. On the one hand, conventional voltage regulation resources like OLTC transformers and capacitor banks feature slow response and limited lifetime duration, making them incapable of quickly responding to the temporary voltage issues created by PVs and EVs. On the other hand, EVs and PVs interact with the power grid via fully controllable power electronic converters capable of real-time adjusting their operating settings, making them ideal voltage support resources. To exploit the voltage support capability of PVs and EVs, this paper proposes a two-stage control scheme for the voltage regulation of distribution networks, consisting of the day-ahead and intraday control stages. The day-ahead control mitigates potential voltage violations via day-ahead scheduling of the operation settings for OLTC transformers and capacitor banks. The intraday control further alleviates voltage deviations and voltage fluctuations based on the reactive power support of PV systems and the rational EV charging/discharging scheduling. A rolling optimization-based control technique is proposed in the intraday control stage to achieve real-time control of EVs and PVs with the stochastic nature of EV charging behaviors inherently considered. The proposed two-stage voltage regulation scheme is validated via case studies performed on the IEEE 123-node test feeder integrated with PVs and EVs.

A bi-level optimization method for voltage control in distribution networks using batteries and smart inverters with high wind and photovoltaic penetrations

In electrical distribution systems, the voltage quality problem considers the most restrictive issue that hinders high photovoltaic (PV) and wind integration. Therefore, this study aims at providing a reliable control method for the voltage problem in distribution networks embedded with high PV and wind sources. The proposed voltage management strategy consists of two controlling stages. The first stage uses battery energy storage systems (BESS), while the second stage employs the smart PV inverters' reactive power injection capability. For BESS operation, a bi-level optimization method based on metaheuristic optimization algorithms (MOA) is developed to regulate the voltage levels by governing the batteries charging/discharging rates. The bi-level optimization problem aims to maximize PV and wind energy exploitation while improving the voltage profile. In this study, a detailed comparison is conducted between three MOA: Social Spider Optimization, Particle Swarm Optimization, and Cuckoo Search Optimization. In addition, considering the uncertainty associated with PV system generation, a PV generation forecast is embedded in the voltage control strategy to reinforce the decision-making process of BESS operation. The efficacy of the proposed method is deployed on a modified IEEE-34 bus test feeder with real-world load and PV and wind power generation data. Experimental results show the effectiveness of the proposed method and indicate that proper coordination between the BESS and smart PV inverters is beneficial for distribution system operations that seamlessly integrate PV and wind energy. Overall, the proposed voltage management strategy can be used to control the voltage to any desired range at different locations.

Hierarchical Central-Local Inverter-Based Voltage Control in Distribution Networks Considering Stochastic PV Power Admissible Range

This paper proposes a hierarchical distribution network voltage control method considering active and reactive power coordination of PV units in both central and local control stages. In contrast to the traditional local control methods, the proposed method defines the admissible range (AR) of PV power and determines it via centralized optimization to realize active PV power curtailments in the local control stage. The affine decision rule (ADR) is adopted to control the reactive power with respect to active power within AR. A distributionally robust chance constraint is designed, based on statistical indices of active PV power, to assess the probability that active PV power generation would fall inside AR. A two-stage optimization problem that can simultaneously provide central and local control strategies is proposed, which is transformed into tractable formulas based on the Gauss Inequality as well as ADR and binary expansion techniques. The proposed method solutions are compared with those of the theoretically optimal method, a robust central-local control method, and a local control method to show the value of hierarchical inverter control with AR in reducing PV power curtailment and ensuring nodal voltages within limits.

Deep Reinforcement Learning for Charging Scheduling of Electric Vehicles Considering Distribution Network Voltage Stability.

The rapid development of electric vehicle (EV) technology and the consequent charging demand have brought challenges to the stable operation of distribution networks (DNs). The problem of the collaborative optimization of the charging scheduling of EVs and voltage control of the DN is intractable because the uncertainties of both EVs and the DN need to be considered. In this paper, we propose a deep reinforcement learning (DRL) approach to coordinate EV charging scheduling and distribution network voltage control. The DRL-based strategy contains two layers, the upper layer aims to reduce the operating costs of power generation of distributed generators and power consumption of EVs, and the lower layer controls the Volt/Var devices to maintain the voltage stability of the distribution network. We model the coordinate EV charging scheduling and voltage control problem in the distribution network as a Markov decision process (MDP). The model considers uncertainties of charging process caused by the charging behavior of EV users, as well as the uncertainty of uncontrollable load, system dynamic electricity price and renewable energy generation. Since the model has a dynamic state space and mixed action outputs, a framework of deep deterministic policy gradient (DDPG) is adopted to train the two-layer agent and the policy network is designed to output discrete and continuous control actions. Simulation and numerical results on the IEEE-33 bus test system demonstrate the effectiveness of the proposed method in collaborative EV charging scheduling and distribution network voltage stabilization.

Modern Active Voltage Control in Distribution Networks, including Distributed Generation, Using the Hardware-in-the-Loop Technique

Voltage constraints usually place restrictions on how distributed generation (DG) can be connected to weak distribution networks. As DG capacity increases, active voltage control techniques are needed. Active approaches can greatly lower connection costs while boosting the capacity of connectable DG when used in place of the passive strategy. In this article, a modified active voltage control algorithm is used on an IEEE 33 bus system to test the robustness and reliability of the control algorithm under severe conditions. The simulations are carried out using the hardware-in-the-loop (HIL) method. Real-time simulations are used to test data transfer and the reliability of the control algorithm’s execution. The analysis is based on a three-phase symmetric power system.

Research on Reactive Voltage Control of Distribution Network with Distributed Photovoltaic Sources

Aiming at the problem that the voltage of multiple nodes exceeds the limit after the distributed photovoltaic source is connected, a reactive voltage control method of distribution network is proposed. It modeled the distribution network system with distributed photovoltaic sources, determined the maximum photovoltaic output and adjustable amount, and calculated the power flow. The line loss can be obtained from the node voltage and rated power, and the power distribution of each branch of the whole network can be further obtained. This paper analyzed the influence of photovoltaic power access on the voltage quality of the distribution network from the perspective of power. It calculated the reactive output power of reactive power compensation equipment of upstream and downstream nodes. In this paper, the active power of distributed generation is locally reduced at each out-of-limit node to control the voltage. The experimental results show that the proposed method can effectively improve the relationship between photovoltaic output and voltage, and realize the correction of photovoltaic regulation. Therefore, it can reduce the risk of voltage out of limit and improve the voltage quality.

Federated Reinforcement Learning for Decentralized Voltage Control in Distribution Networks

Multi-agent reinforcement learning (MARL) with “centralized training & decentralized execution” framework has been widely investigated to implement decentralized voltage control for distribution networks (DNs). However, a centralized training solution encounters privacy and scalability issues for large-scale DNs with multiple virtual power plants. In this letter, a decomposition & coordination reinforcement learning algorithm is proposed based on a federated framework. This decentralized training algorithm not only enhances scalability and privacy but also has a similar learning convergence with centralized ones.

Local voltage control in distribution networks using PI control of active and reactive power

Overvoltage is becoming increasingly prevalent in distribution networks with high penetration of renewable distributed energy sources (DERs). Local control of converter-based resources is a flexible and scalable method to prevent this growing issue. Reactive power is used for voltage control in many local control schemes. However, the typical range of R/X ratios for distribution power lines indicates that mitigation of overvoltage often requires excessive amounts of reactive power. Complete reliance on reactive power thus limits the effectiveness of local control strategies. In this work we instead propose a method that combines enhanced power factor voltage control with upper voltage limit tracking using PI control. We develop a modelling framework and demonstrate the stability of the proposed method. We then simulate the nonlinear operation of two parallel PI controllers in a medium voltage test system.

Cluster Partition-Based Zonal Voltage Control for Distribution Networks With High Penetrated PVs

With the high penetrated photovoltaics (PVs) accessing distribution networks (DNs) in the future, the overvoltage of distribution network will be more serious, and the voltage control will be more complex. To solve this problem, a cluster partition-based zonal voltage control method for DN is proposed in this study. First, a cluster partition method using a comprehensive performance index is proposed: in terms of DN structure, a global density quality function index is introduced to measure the coupling degree of nodes in clusters. In terms of cluster function, the inconsistency coefficient index is presented to reflect the PV output characteristics, and a node membership function index is presented to limit the scale of the cluster. Then, the DN cluster partition is carried out under the fast Newman (FN) algorithm. Second, a second-order cone programming (SOCP) model of voltage control is established to maximize the PV consumption in each cluster. To achieve the coordinated optimization of clusters, an iterative optimization strategy among clusters is proposed. Finally, the effectiveness and rationality of the proposed method are verified in a 10 kV actual feeder system in Zhejiang Province, China.

Voltage control loss factors for quantifying DG reactive power control impacts on losses and curtailment

Distributed Generators that use reactive power for voltage control in distribution networks reduce renewable curtailment but can significantly increase network losses, undermining the effectiveness of this control. This paper proposes Voltage Control Loss Factors (VCLFs) as a means of understanding the interactions between reactive power flows, losses and curtailment, focusing on commercial-scale generators in radial systems. The metric uses a substitution-based method, whereby a system with voltage control is compared against a counterfactual with no such control. The proposed method studies this metric by coupling numerically precise black-box simulations with analytic results from a Two-Bus network representation. The latter provides a physical explanation for the numerical simulation results in terms of power, voltage and impedance parameters, providing clear explainability which is absent in traditional approaches for determining distribution loss factors. The whole solution space of the Two-Bus system is explored, and VCLFs are calculated for six cases on three unbalanced test networks to illustrate the approach. Relative losses as high as 30% are found in a system with high branch resistance-reactance ratio and large voltage rise. The results have implications for the design of loss allocation algorithms in distribution networks, and the optimal sizing of power-electronic interfaced Distributed Generators.

Cost-Effective Coordinated Voltage Control in Active Distribution Networks With Photovoltaics and Mobile Energy Storage Systems

High penetration of intermittent renewable energy resources (RES) will impose great challenges on the conventional voltage control in distribution networks. This paper proposes a two-stage cost-effective coordinated voltage control strategy to mitigate fast voltage violations while minimizing the total voltage regulation cost. In the first stage, the optimal power flow (OPF) is performed to determine the day-ahead dispatch results of on-load tap changers (OLTC) and capacitor banks (CBs). The hourly charging schedule of mobile energy storage systems (MESS) is also obtained through a spatial-temporal decision model embedded in the optimization problem. In the second stage, the real-time voltage control strategy is implemented to regulate the power output of photovoltaics (PVs) and MESS with one minute resolution. A novel fine-tuning control method is proposed to adjust the power output, which can further reduce voltage regulation cost within a certain voltage dead band. To reserve more fast-response power resources for PVs and MESS, the voltage regulation cost curves are designed as a piecewise linear function with different per unit cost in each range. The proposed approach is tested on the IEEE 33-bus and 123-bus distribution systems and comparative simulation results show the enhanced control effect in mitigating voltage violations and saving costs.

Distributed Coordinated Voltage Control for Distribution Networks With DG and OLTC Based on MPC and Gradient Projection

This paper proposes a distributed coordinated voltage control scheme for distribution networks with distributed generation (DG) and on-load tap changer (OLTC). In this scheme, static synchronous compensators (STATCOMs), DG units and OLTC are coordinated to regulate voltages of all buses to be close to the nominal value in the distribution network, mitigate voltage fluctuations, and minimize the number of operations of OLTC while considering different temporal characteristics of voltage regulation devices. The optimization problem of coordinating DG units and STATCOMs is decomposed by the gradient projection (GP) method. The local controller optimizes the reactive power outputs of DGs and STATCOMs according to local voltage and reactive power measurements, and still achieves the optimal coordination of DG units and STATCOMS in a decentralized manner without a central controller or communication between local controllers. The OLTC control scheme is designed to correct the long-term voltage deviations based on model predictive control (MPC) while minimizing the number of operations. The local controllers send the calculated reactive power references of DG and STATCOMs to the OLTC controller, which achieves distributed coordinated voltage control and mitigates the computation burden. A distribution network with two 20 kV feeders and 8 DG units was used to validate the control performance of the proposed coordinated voltage control scheme.