Unbalanced Low Voltage Distribution Networks Research Articles

Discrete optimal designs for distributed energy systems with nonconvex multiphase optimal power flow

The optimal selection, sizing, and location of small-scale technologies within a grid-connected distributed energy system (DES) can contribute to reducing carbon emissions, consumer costs, and network imbalances. There is a significant lack of studies on how DES designs, especially those with electrified heating systems, impact unbalanced low-voltage distribution networks to which most DES are connected. This is the first study to present an optimisation framework for obtaining discrete technology sizing and selection for grid-connected DES design, while simultaneously considering multiphase optimal power flow (MOPF) constraints to accurately represent unbalanced low-voltage distribution networks. An algorithm is developed to solve the resulting Mixed-Integer Nonlinear Programming (MINLP) formulation. It employs a decomposition based on Mixed-Integer Linear Programming (MILP) and Nonlinear Programming (NLP) and uses integer cuts and complementarity reformulations to obtain discrete designs that are also feasible with respect to the network constraints. A heuristic modification to the original algorithm is also proposed to improve computational speed. Improved formulations for selecting feasible combinations of air source heat pumps (ASHPs) and hot water storage tanks are also presented. Two networks of varying size are used to test the optimisation methods. Designs with electrified heating (ASHPs and tanks) are compared to those with conventional gas boilers. The algorithms outperform one of the existing state-of-the-art commercial deterministic MINLP solvers, which fails to find any solutions in two instances within specified time limits. While feasible solutions were obtained for all cases, convergence was not achieved for all, especially for those involving the larger network. Where converged, the algorithm with the heuristic modification has achieved results up to 70% faster than the original algorithm. Results for case studies suggest that including ASHPs can support up to 16% higher renewable generation capacity compared to gas boilers, albeit with higher ASHP investment costs, as local generation and consumption minimises network violations associated with excess power export. The results also show the importance of including nonlinear power flow constraints in DES design problems. The optimisation framework and results can be used to inform stakeholders such as policymakers and network operators, to increase renewable energy capacity and aid the decarbonisation of domestic heating systems.

Performance comparison of optimum power flow based on the sequential second‐order cone programming in unbalanced low voltage distribution networks with distributed generators

A solution technique using sequential second-order cone programming to solve the optimum power flow problem in low voltage (LV) distribution networks with distributed generation is developed. A novel bound tightening method is suggested to get exact solutions with few iterations. A novel approximation method is suggested to increase exactness by approximating phase angle dependent components. The performance of the suggested solution method is compared with linear programming, genetic algorithm, particle swarm, sequential quadratic programming with multiple start points, and global search-based optimization methods. The exactness of the generated solutions is validated after comparison with a load flow. The proposed algorithm provides better performance in optimality, execution time, and exactness compared to other methods.

Deep Reinforcement Scheduling of Energy Storage Systems for Real-Time Voltage Regulation in Unbalanced LV Networks With High PV Penetration

The ever-growing higher penetration of distributed energy resources (DERs) in low-voltage (LV) distribution systems brings both opportunities and challenges to voltage support and regulation. This paper proposes a deep reinforcement learning (DRL)-based scheduling scheme of energy storage systems (ESSs) to mitigate system voltage deviations in unbalanced LV distribution networks. The ESS-based voltage regulation problem is formulated as a multi-stage quadratic stochastic program, with the objective of minimizing the expected total daily voltage regulation cost while satisfying operational constraints. While existing voltage regulation methods are mostly focused on one-time-step control, this paper explores a day-horizon system-wide voltage regulation problem. In other words, the size of action and state spaces are extremely high-dimensional and need to be delicately handled. Furthermore, in order to overcome the difficulty of modeling uncertainties and develop a real-time solution, a learn-to-schedule feedback control framework is proposed by adapting the problem to a model-free DRL setting. The proposed algorithm is tested on a customized 6-bus system and a modified IEEE 34-bus system. Simulation results validate the effectiveness and near-optimality of voltage regulation by ESS in comparison with a deterministic quadratic program solution.

Energy management in unbalanced low voltage distribution networks with microgeneration and storage by using a multi-objective optimization algorithm

The ongoing increase of PhotoVoltaic (PV) microgeneration, pointing to high levels of penetration, can lead to the occurrence of overvoltages. Moreover, the inherent unbalanced nature of the LV distribution grid is a difficulty that must be considered when designing countermeasures. On the other hand, storage systems are suitable devices that can help the process of voltage control. This paper proposes a control system for a real-time energy management system, using Multi-Objective Particle Swarm Optimization (MOPSO) on a highly unbalanced LV distribution network, with batteries and a high share of PV. The purpose of the control system is the optimization of four objectives: Voltage Unbalance Factor (VUF), voltage deviation, active power losses and PV curtailment, wherein four scenarios were considered for assessment. This study is conducted using an unbalanced three-phase power flow algorithm. A comparison of residential and community storage is performed. The results show that the inclusion of storage devices near the prosumers’ PV systems is the recommended solution to enhance voltage stability and balance. Moreover, a significant reduction of active power losses in the LV network is observed.

On the impact of different voltage unbalance metrics in distribution system optimization

With increasing penetrations of single-phase, rooftop solar PV installations, the relative variations in per-phase loading and associated voltage unbalance are expected to increase. High voltage unbalance may increase network losses and lead to failure of three-phase equipment such as motor loads. However, solar PV panels are connected to the grid through inverters, which can provide reactive power support and may mitigate some of these negative effects. In this paper, we utilize a three-phase AC optimal power flow (OPF) formulation to minimize voltage unbalance using reactive power from solar PV inverters. When considering actions to reduce voltage unbalance, it is important to recognize that various organizations such as IEC, NEMA and IEEE provide different and partially inconsistent definitions of voltage unbalance in their power quality standards. This paper analyzes the impact of the different voltage unbalance metrics using different combinations of voltage unbalance objectives and constraints. For our analysis, the optimization scheme is tested on two unbalanced low-voltage distribution networks. We observe that minimizing voltage unbalance defined by one standard might actually increase voltage unbalance as defined by another standard, potentially resulting in equipment damage. We also observe that minimizing voltage unbalance does not always lead to lower network losses. However, considerable reduction in voltage unbalance with low network losses can be achieved by minimizing the losses while simultaneously enforcing limits on multiple definitions of voltage unbalance.

Voltage Quality Improvement in Low Voltage Distribution Networks Using Reactive Power Capability of Single-Phase PV Inverters

The rapid growth of residential rooftop photovoltaic (PV) systems may cause significant power quality problems, such as severe voltage fluctuation and unbalance which can restrict further PV integrations to the network. In this paper, the potential capability of residential PV inverters is investigated to develop a distributed reactive power compensation scheme for voltage regulation in three-phase four-wire unbalanced low voltage distribution networks (LVDNs). For this purpose, an effective method is proposed by means of single-phase PV inverters arbitrarily connected among different phases. The aims are improving voltage profile along the feeder and reducing the voltage unbalances. The PV inverters are connected in such a way that they form distributed delta and wye configurations to compensate the negative and zero sequence components of voltage, respectively and to regulate voltage within the allowed limits as well. The proposed consensus-based algorithm considers each PV system as a smart agent and updates its reactive power control setting based on only local measurements and data communications with neighboring agents. The effectiveness of the proposed strategy in terms of voltage regulation and unbalance and energy losses is shown through detailed simulations over a 24-hr period on a three-phase four-wire unbalanced LVDN with realistic data.

A two-layer control strategy for voltage regulation of active unbalanced LV distribution networks

In this paper, a coordinated control strategy is proposed to effectively address the voltage regulation issue in active unbalanced low-voltage (LV) distribution networks. The proposed control scheme is based on a two-layer centralized approach, consisting of the local and the overlaying control layers. The former is embedded at the hardware level of the distributed renewable energy sources (DRESs). Therefore, it is continuously active, aiming to prevent network overvoltages and to mitigate voltage unbalances based explicitly on local measurements. The overlaying control strategy is implemented at a central controller, at the distribution system operator (DSO) level, acting supplementary to the local control scheme to increase the total injected green energy and to implement DSO policies for the reallocation of the active power curtailment. The validity of the proposed two-layer control strategy is demonstrated on a real Slovenian LV network by means of time-series simulations. Additionally, the impact of the proposed control on the maximum DRESs penetration level is thoroughly evaluated, while its performance is compared against other conventional control strategies, revealing its superiority in terms of voltage regulation, voltage unbalance mitigation and increased green energy production.

Smart Home Energy Management in Unbalanced Active Distribution Networks Considering Reactive Power Dispatch and Voltage Control

This paper presents a new optimization algorithm for home energy management systems (HEMSs) in three-phase unbalanced low-voltage (LV) distribution networks. Compared with conventional HEMS optimization methods, which consider only the active power consumption scheduling for smart home appliances and distributed energy resources (DERs) (e.g., solar photovoltaic (PV) systems and energy storage systems (ESSs)), the novelty of the proposed approach is to consider: i) both active and reactive power consumption schedulings of home appliances and DERs, ii) realistic three-phase unbalanced LV distribution networks with voltage-dependent load models, and iii) voltage control using an on-load tap changer transformer and smart inverters of PV system and ESS at the households. The proposed HEMS optimization algorithm, which is formulated using mixed-integer linear programming, is tested in the modified CIGRE LV distribution network. Numerical examples demonstrate the performance of the proposed algorithm in terms of active/reactive power consumption, three-phase voltage magnitudes, and the total cost of electricity.

Voltage Sensitivity Analysis of a Laboratory Distribution Grid With Incomplete Data

New voltage control algorithms are necessary to cope with the increasing amount of distributed generation and electric vehicles in distribution networks. Many of the newly proposed voltage control algorithms are based on linearized dependencies between the voltage magnitude, and the active and reactive power consumption. These linearized dependencies are normally obtained by algorithms, which rely on accurate grid topology information. Due to the traditionally passive operation of low voltage (LV) distribution networks, this information is typically missing, incomplete, or inaccurate. Therefore, this paper introduces a method to extract these linear dependencies based on historical smart meter data only. No information about the grid topology is required. The model adapts to the changing load conditions in the network. The algorithm has a low complexity and is applied to an unbalanced LV distribution network. Data of a practical laboratory setup is used to validate the proposed method in real-life conditions. With the obtained voltage sensitivity factors a voltage management strategy was implemented for the laboratory grid.