Smart loads for voltage control in distribution networks

This paper shows that the smart loads (SLs) could be effective in mitigating voltage problems caused by photovoltaic (PV) generation and electric vehicle (EV) charging in low-voltage (LV) distribution networks. Limitations of the previously reported SL configuration with only series reactive compensator (SLQ) (one converter) is highlighted in this paper. To overcome these limitations, an additional shunt converter is used in back-to-back (B2B) configuration to support the active power exchanged by the series converter, which increases the flexibility of the SL without requiring any energy storage. Simulation results on a typical U.K. LV distribution network are presented to compare the effectiveness of an SL with B2B converters (SLBCs) against an SLQ in tackling under- and over-voltage problems caused by EV or PV. It is shown that SLBCs can regulate the main voltage more effectively than SLQs especially under over-voltage condition. Although two converters are required for each SLBC, it is shown that the apparent power capacity of each converter is required to be significantly less than that of an equivalent SLQ.

Voltage control in distribution networks becomes increasingly important with an increasing penetration of distributed generation (DG). As distribution networks are much more resistive than transmission networks, the conventional technique of reactive power compensation in order to control the voltage cannot be applied very well. For that reason other voltage control techniques have to be applied. In this contribution several options for voltage control in distribution networks are analyzed and related to important grid parameters such as the short-circuit ratio and the X/R ratio of the grid. For networks with a high X/R ratio reactive power can be used for voltage control. For networks with a low X/R ratio several new voltage control techniques are proposed. These techniques are voltage control with active power, inserting a controllable inductance in the grid and series compensation by DG unit converters.

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

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.

This paper studies the participation of doubly-fed induction wind generator (DFIG), as a continuous variable, in dynamic optimization of reactive power and voltage control in the distribution network by obtaining the reactive power capability through controlling the power electronic interfaces and taking DFIG rated parameters into accounts. Given the fluctuation in the DFIG output active power, a time partition method based on the variance of the DFIG output active power has been proposed. According to the results of the time partition method, the operational requirements and the characteristics of the control variables in the distribution network, different dynamic optimization algorithms have been applied in different optimization time. The simulation based on the improved IEEE 33-bus system has been conducted to validate the effectiveness of the proposed optimization strategy.

This paper proposes a multi-agent control scheme in order to coordinate between distributed battery energy storage systems (BESS) and renewable distributed generation (DG) units in distribution networks. The proposed control scheme aims to utilize BESSs as distributed voltage controllers across the distribution network, in such a way to overcome the intermittent high-ramp rate of renewable DG units. To that end, the voltage control in distribution networks is formulated as a distributed constraint satisfaction problem (DisCSP). An asynchronous weak commitment approach is utilized to solve the formulated problem. Several case studies are carried out to test the effectiveness and performance of the proposed algorithm under different operating conditions.

The possibility of high Photovoltaic (PV) and Electric Vehicle (EV) penetration in Low-Voltage (LV) Distribution Networks (DN) is examined in this research utilising a typical DN found in Malta as a case study. The European Union (EU) has established goals and directives that make it quite probable that DNs will include a lot of PVs, causing overvoltages, and a lot of EVs, charging erratically, causing undervoltages. A DN may encounter both unfavourable scenarios on the same day since PV generation and EV charging often occurs simultaneously, particularly in residential case studies. Utilising dispersed or centralised storage effectively is one method to address these problems, however, doing so may postpone the need for costly DN expenditures. The paper demonstrates the level of automatic mitigation achieved in Malta residential areas. The information provided is mostly by actual measurements taken at the start of the LV feeder.

Over the last decade governments in Australia and around the globe have heavily promoted the installation of rooftop solar photovoltaics (PV) in residential low voltage (LV) distribution networks. While distributed solar PV offers a range of benefits, they have not significantly reduced distribution network demand during critical periods due to the incongruity between the residential load profile and the solar PV generation curve. The residential load profile has load demand in the morning, moderate demand during mid-morning, low demand during the middle of the day and peak demand in the evenings. In winter the mid-morning demand is greater than in summer leading to instances where there are two peak demand periods during the day. Solar PV generation is low during the morning and steadily increases until the middle of the day and then decreases until the evening. Peak solar PV generation typically occurs when demand in the network is low. This entails that solar PV resources are poorly utilised and high concentrations of solar PV generation in LV distribution networks can often lead to the degradation of power quality through overvoltage and instances of reverse power flow. Moreover, the addition of solar PV to existing LV networks with unbalanced phases can often exacerbate existing power quality issues in a particular overloaded phase. Corrective measures to address power quality issues may require costly augmentations to the electricity network.

Network partition-based hierarchical decentralised voltage control for distribution networks with distributed PV systems

A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid particle swarm optimisation (PSO) is proposed. The objective is to minimise comprehensive cost, consisting of power loss and operation cost of transformers and capacitors, and subject to constraints such as minimum and maximum reactive power limits of distributed generators, maximum deviation of bus voltages and maximum allowable daily switching operation number. PSO is used to solve the corresponding mixed integer non‐linear programming problem and the hybrid PSO (HPSO) method, consisting of three PSO variants, is presented. In order to mitigate the local convergence problem, fuzzy adaptive inference is used to improve the searching process and the final fuzzy adaptive inference‐based HPSO is proposed. The proposed algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33‐node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods.

Distribution networks have been experiencing significant changes under the pressure of the energy transition. The high integration of renewable energy sources combined with the electrification process introduces new challenges in managing distribution networks. Innovative solutions aimed at optimizing the control of complex problems, starting from historical data instead of a detailed system model, have been growing due to rapid development in artificial intelligence and machine learning. This paper proposes a Q-learning algorithm to control the tap setting of the on-load tap changer installed in primary substation transformers. The ultimate goal is to maintain voltage magnitudes at all busses of the medium-voltage distribution network within a safe range, simultaneously optimizing on-load tap changer operations. As a case study, the effectiveness of the proposed algorithm is assessed using a real medium-voltage distribution network with high penetration of renewable energy sources that supplies more than 2500 users/prosumers. The ability of the proposed algorithm to control bus voltages is tested in several scenarios characterized by significant variability and uncertainty. Outcomes show that the proposed algorithm is suitable for optimizing voltage control in distribution networks using a data-driven approach.

It is the responsibility of the distribution network operator (DNO) to maintain voltage profiles within statutory limits across a range of conditions such as varying load or intermittent output of distributed generation. An effective device for voltage control in distribution networks is the on-load transformer tap changer equipped with automatic voltage control (AVC) relays. In order to increase security of supply, it is common practise for the DNO to parallel transformers. A number of factors that can influence the optimum tap position are primary voltage level, load level, power factor, distributed generation and circulating current. This paper presents, firstly, an overview of automatic voltage control methods for paralleled transformers and related standard DNO practice. Secondly, a detailed description of the established transformer automatic paralleling package (TAPP) schemes is presented. Finally, modelling and analysis of the TAPP scheme is performed for a range of distribution network case studies in order to determine and evaluate the effect of the above factors on optimum tap position. Results from the modelling and analysis of the TAPP scheme and conclusions are also presented.

Clean energy incentives and the continuous fall in the cost of photovoltaic (PV) installations have led to a steady growth in residential PV systems. One of the main consequences of this high PV penetration in low voltage (LV) distribution networks is the overvoltage issue. Active power curtailment of PV inverters has been previously used to curtail the output power of the inverters below its operating point to prevent such overvoltages. This technique, however, uses a constant droop-based approach to curtail the power, based on the difference between the measured voltage and a critical voltage level. In this paper, this technique is implemented in a typical LV distribution network in North America with high PV penetration level. The simulation results show that the system undergoes excessive curtailment resulting in unnecessary energy loss. An adaptive droop-based approach using adaptive dynamic programming (ADP) is proposed as a possible solution to minimize the total energy loss in the system while keeping the system voltage under the critical operating limits. The energy loss due to curtailment has decreased by 25% after implementing the adaptive-droop based approach using ADP.

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.

Electric vehicles (EV) have gained global attention due to increasing oil prices and rising concerns about transportation-related urban air pollution and climate change. While mass adoption of EVs has several economic and environmental benefits, large-scale deployment of EVs on the low-voltage (LV) urban distribution networks will also result in technical challenges. This paper proposes a simple and easy to implement single-phase EV charging coordination strategy with three-phase network supply, in which chargers connect EVs to the less loaded phase of their feeder at the beginning of the charging process. Hence, network unbalance is mitigated and, as a result, EV hosting capacity is increased. A new concept, called Maximum EV Hosting Capacity (HC max ) of low voltage distribution networks, is introduced to objectively assess and quantify the enhancement that the proposed phase-shifting strategy could bring to distribution networks. The resulting performance improvement has been demonstrated over three real UK residential networks through a comprehensive Monte Carlo simulation study using Matlab and OpenDSS tools. With the same EV penetration level, the under-voltage probability was reduced in the first network from 100% to 54% and in the second network from 100% to 48%. Furthermore, percentage voltage unbalance factors in the networks were successfully restored to their original values before any EV connection.