Voltage Regulation In Distribution Networks Research Articles

Incentivizing Prosumer Voltage Regulation for Unbalanced Radial Distribution Networks

Voltage regulation in distribution networks is becoming extremely challenging with the increased penetration of rooftop photovoltaic (PV) generation. Many recent studies demonstrate the effectiveness of power control of smart inverters for voltage regulation purposes. However, how to fully incentivize demand-side participation in voltage regulation is yet to be answered. This paper seeks to address this challenge by designing a comprehensive voltage regulation incentive scheme for customers to control their smart inverters in unbalanced radial distribution networks. A three-phase residential network from a Distributed Network Service Provider (DNSP) in Australia is modelled in the open-source distribution system simulator OpenDSS with real load and PV data, and an control algorithm to optimize the households' smart inverters in a distributed fashion is developed using game theory. The proposed algorithm is implemented in MATLAB, which is guaranteed to approach the optimal solution. Simulation results show that the proposed scheme achieves better voltage regulation performance as the widely adopted localized voltage-var droop control method and, at the same time, with lower costs to the prosumers.

Graph Learning-Based Voltage Regulation in Distribution Networks With Multi-Microgrids

Microgrids (MGs), as localized small power systems, can effectively provide voltage regulation services for distribution networks by integrating and managing various distributed energy resources. Existing literature employs model-based optimization approaches to formulate the voltage regulation problem of multi-MGs, which require complete system models. However, this assumption is normally impractical due to time-varying environment and privacy issues. To fill this research gap, this paper suggests a data-driven decentralized framework for the cost-effective voltage regulation of a distribution network with multi-MGs. A novel multi-agent reinforcement learning method featuring an augmented graph convolutional network and a proximal policy optimization algorithm is proposed to solve this problem. Furthermore, the techniques of critical bus and electrical distance enhance the capability of feature extractions from the distribution network, allowing for the decentralized training with privacy preserving. Simulation results based on modified IEEE 33-bus, 69-bus, and 123-bus networks are developed to validate the effectiveness of the proposed method in enabling multi-MGs to provide distribution network voltage regulation.

Real-time Strategy for Voltage Regulation of Distribution Networks Considering the Incomplete Response of Distributed Resources

To solve the problem of nodes whose voltage exceeds the security constraint in the scenario of massive distributed generation access, a real-time strategy for voltage regulation of the distribution network considering the incomplete response of distributed resources is proposed. The strategy includes two parts: a real-time strategy for voltage regulation of the distribution network and the improvement of the strategy to deal with the incomplete response of distributed resources. Firstly, the real-time voltage autonomy of the distribution network is realized through the distributed resource strategy for voltage regulation; Then, aiming at the incomplete response of distributed resources, the strategy for voltage regulation is improved, and the margin iteration and feedback regulation are added; Finally, based on the IEEE33 node model, we verify the strategy for voltage regulation which is proposed in this paper. The results of the experiment show that the strategy for voltage regulation can effectively solve the problem of distribution network voltage out of limit, and has good anti-interference ability for the incomplete response of distributed resources.

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.

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.

Safety-Aware Semi-End-to-End Coordinated Decision Model for Voltage Regulation in Active Distribution Network

Prediction plays a vital role in the active distribution network voltage regulation under the high penetration of photovoltaics. Current prediction models aim at minimizing individual prediction errors but overlook their collective impacts on downstream decision-making. Hence, this paper proposes a safety-aware semi-end-to-end coordinated decision model to bridge the gap from the downstream voltage regulation to the upstream multiple prediction models in a coordinated differential way. The semi-end-to-end model maps the input features to the optimal var decisions via prediction, decision-making, and decision-evaluating layers. It leverages the neural network and the second-order cone program (SOCP) to formulate the stochastic PV/load predictions and the var decision-making/evaluating separately. Then the var decision quality is evaluated via the weighted sum of the power loss for economy and the voltage violation penalty for safety, denoted by regulation loss. Based on the regulation loss and prediction errors, this paper proposes the hybrid loss and hybrid stochastic gradient descent algorithm to back-propagate the gradients of the hybrid loss with respect to multiple predictions for enhancing decision quality. Case studies verify the effectiveness of the proposed model with lower power loss for economy and lower voltage violation rate for safety awareness.

A Consensus-Based Distributed Temperature Priority Control of Air Conditioner Clusters for Voltage Regulation in Distribution Networks

High penetration of Photovoltaic (PV) to the distribution network may bring under-voltage and over-voltage issues, limiting the PV hosting capacity. Air conditioners (AC) in grid-interactive buildings can support voltage regulation by manipulating flexible energy consumption. This paper developed a novel voltage control strategy to regulate the AC clusters’ on/off states for distribution network voltage regulation under high PV penetrations. The novelty lies in the distributed formulation of temperature priority-based on/off control (TPC) of AC clusters and the strategic selection and permutation of demand response technologies, including the real-time optimal demand response resources dispatch, distributed sensing of ACs based on average consensus algorithm, and the local implementation of TPC strategy and trial calculation scheme for flexibility capacity estimation. Finally, the distributed TPC is validated to be effective for system rebalancing with no comfort violations and an acceptable ON/OFF switching frequency. The theoretical and numerical analysis also proves its scalability and robustness to communication delays and link failures. It is then incorporated into a novel hierarchical control framework for smart grid voltage control in a four-bus three-phase test grid, considering the voltage sensitivities to power injections in different locations and phases.

Deep reinforcement learning based topology-aware voltage regulation of distribution networks with distributed energy storage

Both the high penetration of clean energy with strong fluctuation and the complicated variable operation condition bring great challenges to the voltage regulation of the distribution network. To deal with the problem, a topology-aware voltage regulation multi-agent deep reinforcement learning (MADRL) algorithm is proposed. The distributed energy storages (DESs) are modeled as agents to regulate voltage autonomously in real-time, which could fast adapt to dynamic topological scenarios. Firstly, taking the minimization of voltage fluctuation and maximization of reserve capacity as the target, the optimal voltage regulation model is established. Secondly, a topology extraction method considering voltage sensitivity is proposed for dynamic topology clustering, and the obtained typical topology is added to the observation set of agents. Then, the optimal voltage regulation model is formulated to the decentralized partially observable Markov decision process (Dec-POMDP) framework, in which only local information is required for the agent during the test process to decision-making to realize the hierarchical and partitioned control of voltage. Finally, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is used to solve the Dec-POMDP model. The feasibility and superiority of the proposed algorithm are verified and analyzed in the simulation under different scenarios.

Distributionally robust OPF in distribution network considering CVaR-averse voltage security

The uncertain generations of renewable energy sources (RES) bring considerable challenges to voltage security in distribution networks. This paper proposes a conditional value at risk (CVaR)-averse distributionally robust chance constrained optimal power flow (CVaR-DRCC-OPF) which considers the voltage regulation from a risk perspective. The CVaR of voltage violation is proposed to act as an additional penalty term in the objective. Compared to the conventional chance-constrained OPF, the CVaR-averse penalty term quantifies the voltage violation better, especially for the uncertainty distribution function with a long tail. In addition, this paper develops a moment-based ambiguous set of the uncertainty distribution functions. By applying the duality theory, we reformulate the proposed OPF as a second-order cone programming (SOCP), which can be solved in polynomial processing time. Simulation results on the modified 118-bus distribution system show the efficiency and superiority of the proposed CVaR-DRCC-OPF in distribution network voltage regulation over other benchmark models.

The effect of SoC management on economic performance for battery energy storage system in providing voltage regulation in distribution networks

Nowadays, distribution networks experience voltage deviation and violation issues owing to the increased penetration of photovoltaic (PV) generation. The battery energy storage system (BESS) deployment is a promising solution in providing voltage regulation. However, the economic performance of BESS for voltage regulation provision cannot be ensured when state of charge (SoC) management is considered. Therefore, this study assessed the effect of SoC management on economic performance for a BESS in providing voltage regulation in a distribution network. Five different SoC management scenarios proposed in the literature were implemented with a droop-based voltage regulation strategy. A techno-economic model was developed to determine the advantages and disadvantages of those SoC management scenarios based on cost-attractiveness and profitability. In addition, a sensitivity analysis was conducted to examine the extent to which the net present value (NPV) was affected by changes in SoC restoration thresholds. The results show that the NPV increased substantially when the SoC management scenarios (except the dead-band SoC restoration) were considered. Nevertheless, the levelized cost of energy (LCOE) did not display the economic feasibility of the BESS projects because the remuneration of capacity performance was omitted. Moreover, a significant increase in NPV was indicated for wider SoC restoration thresholds.

Stackelberg-Game-Based Demand Response for Voltage Regulation in Distribution Network with High Penetration of Electric Vehicles

With the development of the economy, electricity demand continues to increase, and the time for electricity consumption is concentrated, which leads to increasing pressure on the voltage regulation of the distribution network. For example, a large number of electric vehicles charging during a low-price period may cause the problem of under-voltage of the distribution network. On the other hand, the penetration of distributed power generation of renewable energy may cause over-voltage problems in the distribution network. This study proposes a Stackelberg game model between the distribution system operator and the load aggregator. In the Stackelberg game model, the distribution system operator affects the users’ electricity consumption time by issuing subsidies to decrease the frequency of voltage violations. As the representative of users, the load aggregator helps the users schedule the demand during the subsidized period to maximize profits. Case studies are carried out on the IEEE 33-bus power distribution system. The results show that the time of the subsidy can be optimized based on the Stackelberg game model. Both the distribution system operator and the load aggregator can obtain the optimal economic profits and then comprehensively improve the operating reliability and economy of the power distribution system.

Adaptive Coalition Formation-Based Coordinated Voltage Regulation in Distribution Networks

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 regulation capacity information. To coordinate control within each coalition, we develop a feedback-based leader-follower consensus algorithm which eliminates the voltage violations caused by the fast fluctuations of load and PV generation. This algorithm allocates the required reactive power contribution among the PV inverters according to their maximum available capacity to promote an effective and fair use of the overall voltage regulation capacity. Case studies based on realistic distribution networks and field-recorded data validate the effectiveness of the proposed control strategy. Moreover, comparison with a centralized network decomposition-based scheme shows the flexibility of coalition formation in organizing the distributed PV inverters. The robustness and generalizability of the proposed strategy are also demonstrated.

Weak Grid-Induced Stability Problems and Solutions of Distributed Static Compensators with Voltage Droop Support

Distributed static compensators (DSTATCOMs) are grid-connected power electronic equipment dedicated to compensating reactive power as well as improving voltage regulation in distribution networks. They eclipse conventional compensation approaches, such as capacitor banks, in terms of flexibility and effectiveness. Despite their identified advantages, STATCOMs with voltage droop are subject to weak grid-induced stability problems, as first revealed by this paper. Specifically, the voltage droop controller that couples the amplitude of point of common coupling (PCC) voltages to the reactive current reference creates a local control loop. Such a loop greatly deteriorates system stability in weak grids, which feature large and variable grid impedances. To address such stability problems, we propose a novel virtual resistance control scheme, which improves system stability through mitigation of local control loop gains in the low-frequency band. Experimental results obtained from a DSTATCOM prototype clearly demonstrate the correctness of stability analysis and the effectiveness of stability improvement.

Active Exploration by Chance-Constrained Optimization for Voltage Regulation with Reinforcement Learning

Voltage regulation in distribution networks encounters a challenge of handling uncertainties caused by the high penetration of photovoltaics (PV). This research proposes an active exploration (AE) method based on reinforcement learning (RL) to respond to the uncertainties by regulating the voltage of a distribution network with battery energy storage systems (BESS). The proposed method integrates engineering knowledge to accelerate the training process of RL. The engineering knowledge is the chance-constrained optimization. We formulate the problem in a chance-constrained optimization with a linear load flow approximation. The optimization results are used to guide the action selection of the exploration for improving training efficiency and reducing the conserveness characteristic. The comparison of methods focuses on how BESSs are used, training efficiency, and robustness under varying uncertainties and BESS sizes. We implement the proposed algorithm, a chance-constrained optimization, and a traditional Q-learning in the IEEE 13 Node Test Feeder. Our evaluation shows that the proposed AE method has a better response to the training efficiency compared to traditional Q-learning. Meanwhile, the proposed method has advantages in BESS usage in conserveness compared to the chance-constrained optimization.

Research of current distribution by phases in asynchronous electric motor with a combined winding

THE PURPOSE. To consider the features of power supply system of oil and gas production complex, variety of layout of electrical complexes of producing wells. To develop a universal methodology for determining optimal voltage value in power supply center, that is, on substation power bus.METHODS. When calculating voltage in power supply center, which provides a certain amount of voltage on stator of most remote electric motor, method of equalizing potentials in nodes of outgoing line was used when calculating loads of elements of electrical complexes of producing wells. The development of a methodology for calculating optimal voltage of power supply center was carried out using the method of cognition, which was called ascent from simple to complex. The search for optimal voltage value of power supply center must be carried out by numerical methods with involvement of a software product that allows use of search algorithms.RESULTS. The article proposes a method for calculating optimal voltage of power supply center of outgoing line of an oil and gas producing enterprise. The developed technique can be applied under a wide range of energy optimization criteria and for any configuration of outgoing line circuit, takes into account technological features of the process of mechanized oil production and ensures a reduction in electricity consumption.CONCLUSION. The calculation method considered in article makes it possible to develop an optimal list of organizational and technical measures for voltage regulation in distribution network in order to reduce power consumption.

An Incentivized and Optimized Dynamic Mechanism for Demand Response for Managing Voltage in Distribution Networks

The voltage regulation in distribution networks is one of the major obstacles when increasing the penetration of distributed generators (DGs) such as solar photovoltaics (PV), specially during cloud transients, causing potential stress on network voltage regulations. Residential demand response (DR) is one of the cost-effective solutions for voltage management in distribution networks. However, the main barriers of DR implementation are the complexities of controlling a large number and different types of residential loads, satisfying customers’ preferences and providing them fair incentives while identifying the optimum DR implementation locations and sizing as well as cooperating with the existing network equipment for the effective voltage management in the networks. A holistic and practical approach of DR implementation is missing in the literature. This study proposes a dynamic fair incentive mechanism using a multi-scheme load control algorithm for a large number of DR participants coordinating with the existing network equipment for managing voltage at medium voltage (MV) networks. The multi-scheme load control is comprised of short-interval (10-minute) and long-interval (2-hour) DR schemes. The dynamic incentive rates are optimised based on the energy contribution of DR participating consumers, their influence on the network voltage and total power loss improvement. The proposed method minimises the DR implementation cost and size, fairly incentivises the consumers participating in the DR and priorities their consumption preferences while reduces the network power losses and DGs’ reactive power contributions to effectively manage the voltage in the MV networks. An improved hybrid particle swarm optimisation algorithm (IHPSO) is proposed for the load controller to provide fast convergence and robust optimisation results. The proposed approach is comprehensively tested using the IEEE 33-bus and IEEE 69-bus networks with several scenarios considering a large number of DR participants coordinated with the DGs and on-load tap changer (OLTC) in the networks.

A Distributed MPC to Exploit Reactive Power V2G for Real-Time Voltage Regulation in Distribution Networks

It has been demonstrated theoretically and experimentally that the Vehicle-to-Grid (V2G) enabled electric vehicle (EV) charger is of a reactive power compensation ability with a battery or capacitor connected. To exploit the aggregated reactive power V2G abilities of massively dispersed EV chargers, a distributed model predictive control (DMPC) strategy applying to both balanced and unbalanced distribution networks (DNs) is proposed to integrate them into real-time DN voltage regulation. Firstly, based on the instantaneous power theory and voltage sensitivity matrices, a voltage regulation model considering the reactive response of EV chargers is established without violating the normal EV active charging demands. Then, a completely distributed framework is achieved by DMPC, in which prediction information and calculation results are shared in a Peer-to-Peer (P2P) way to realize the asynchronous broadcast. The proposed model and techniques are validated by numerical results obtained from the IEEE European low voltage test feeder system. The case studies indicate that the proposed DMPC is robust to communication latency (CML) and works effectively in both balanced and unbalanced DNs without any control center, which is a significant advantage for the promotion of real-time reactive power V2G in DNs with irregular user integration and relatively poor communication infrastructure.

Stochastic Analysis-Based Volt–Var Curve of Smart Inverters for Combined Voltage Regulation in Distribution Networks

The proliferation of renewable energy resources (RES), especially solar photovoltaic (PV) generation resources, causes overvoltage and line overloading in distribution networks. This study proposes a two-level volt–var control method based on multiple timescales. The on-load tap changer (OLTC) operates on an hourly timescale, to regulate the voltage on the secondary winding. In the 15-minutes timescale, PV-connected smart inverters and static var compensators (SVCs) are obliged to compensate the reactive power for the voltage control at the point of common coupling. In the multi-timescale voltage control framework, this study proposes a new multi-sectional volt–var curve (MSVVC) of a PV inverter. The objective of the MSVVC is to minimize the energy loss in the network, improve the voltage profile, and obtain the operational margin of other reactive power compensation devices. In the process of determining the optimal parameters of the MSVVC, stochastic modeling-based load flow analysis is utilized to consider the intermittency and uncertainty of RES generation. The effectiveness of the proposed method is verified on the IEEE 33-bus system in comparison with the conventional volt–var curve cases.

Optimized Energy Storage System Configuration for Voltage Regulation of Distribution Network With PV Access

With the large-scale integration of renewable energy such as wind power and PV, it is necessary to maintain the voltage stability of power systems while increasing the use of intermittent renewable energy sources. The rapid development of energy storage technologies permits the deployment of energy storage systems (ESS) for voltage regulation support. This paper develops an ESS optimization method to estimate the optimal capacity and locations of distributed ESS supporting the voltage regulation of a distribution network. The electrical elements of the network integrated with PV and ESS are first modelled to simulate the voltage profile of the network. Then an improved multi-objective particle swarm optimization (PSO) algorithm is employed to minimise a weighted sum of the overall nodal voltage deviation from the nominal level across the network and across the time horizon and the energy capacity of ESS reflecting the associated investment. The improved PSO algorithm adaptively adjusts the inertia weight associated with each particle based on its distance from the best known particle of the population and introduces the cross-mutation operation for a small distance to avoid falling into local optimal solutions. Then the dynamic dense distance arrangement is taken to update the non-inferior solution set and indicate potential global optimal solutions so as to keep the scale and uniformity of the optimal Pareto solution set. To mitigate the impact of decision makers’ preference, the information entropy based technique for order of preference by similarity to ideal solution is used to select the optimal combination of the ESS access scheme and capacity from the Pareto solution set. The proposed ESS optimization method is tested based on the IEEE 24-bus system with additional imports from high-voltage power supply. The voltage profile of the network simulated without the ESS or with the random or optimized ESS placement is compared to illustrate the effectiveness of the optimized ESS in performing voltage regulation under normal operation and supporting emergency power supply during high-voltage transmission failures.

Distributed Control of Multi-Energy Storage Systems for Voltage Regulation in Distribution Networks: A Back-and-Forth Communication Framework

Distributed storage systems (DESSs) are widely utilized to regulate voltages in active distribution networks with high penetration of volatile renewable energy. In this paper, the distributed multi-energy storage systems (MESSs) are integrated into the active distribution network to enhance the capability of voltage regulation by exploiting interactions among multi-energy loads. A novel distributed control strategy based on back-and-forth communication (BFC) framework is developed to optimally coordinate multiple MESSs for multi-time-step voltage regulation. To achieve the independent control of MESSs at each bus, the BFC is proposed to determine the global sensitivities of voltage violation in the whole network with respect to the power injection at each bus in only one-step iteration. These sensitivities updated in real time during BFC quantify the capability of the single MESS to mitigate voltage fluctuations across all buses, which can be used to individually inform the operation of each MESS. Distinct from traditional distributed control methods that achieve voltage regulation in a single timeslot, the proposed control strategy regulates MESSs in a multi-time-step manner to alleviate anticipated voltage violation in advance. The simulation based on a modified 11-bus radial distribution network demonstrates the effectiveness of the proposed distributed control strategy.