Unbalanced Distribution Networks Research Articles

An Effective Bi-Stage Method for Renewable Energy Sources Integration into Unbalanced Distribution Systems Considering Uncertainty

The output generations of renewable energy sources (RES) depend basically on climatic conditions, which are the main reason for their uncertain nature. As a result, the performance and security of distribution systems can be significantly worsened with high RES penetration. To address these issues, an analytical study was carried out by considering different penetration strategies for RES in the radial distribution system. Moreover, a bi-stage procedure was proposed for optimal planning of RES penetration. The first stage was concerned with calculating the optimal RES locations and sites. This stage aimed to minimize the voltage variations in the distribution system. In turn, the second stage was concerned with obtaining the optimal setting of the voltage control devices to improve the voltage profile. The multi-objective cat swarm optimization (MO-CSO) algorithm was proposed to solve the bi-stages optimization problems for enhancing the distribution system performance. Furthermore, the impact of the RES penetration level and their uncertainty on a distribution system voltage were studied. The proposed method was tested on the IEEE 34-bus unbalanced distribution test system, which was analyzed using backward/forward sweep power flow for unbalanced radial distribution systems. The proposed method provided satisfactory results for increasing the penetration level of RES in unbalanced distribution networks.

On the Solution of the Optimal Power Flow for Three-Phase Radial Distribution Networks With Energy Storage

We study a multiperiod optimal power flow (OPF) problem in unbalanced three-phase radial distribution networks with batteries. To address this problem, we first take the resistance-line battery model that treats lossy batteries by adding resistive lines and virtual buses. Then, we derive a linearization for the power flow equations, called generalized three-phase simplified DistFlow, which accounts for shunt elements. Also, it is exact at a freely chosen point while having globally good performance. Using it, we extend the heuristic iterative OPF algorithm, CoDistFlow, to unbalanced three-phase networks. At convergence, three-phase CoDistFlow gives a solution that satisfies the exact nonlinear power-flow equations and the exact security constraints. To theoretically guarantee the convergence of three-phase CoDistFlow, we develop the forced convergent three-phase CoDistFlow. Moreover, we show how to adapt the three-phase CoDistFlow to solve a scenario-based OPF in the presence of uncertainties, whereas other existing OPF solution methods may not offer this possibility. Finally, we perform evaluations on the IEEE 123-bus test feeder and comparisons with Ipopt.

The Added Value of Coordinating Inverter Control

Coordinated photovoltaic inverter control with centralized coordination of curtailment can increase the amount of energy sent from low-voltage (LV) distribution networks to the grid while respecting voltage constraints. First, this paper quantifies the improvement of such an approach relative to autonomous droop control, in terms of PV curtailment and line losses in balanced networks. It then extends the coordinated inverter control to unbalanced distribution networks. Finally, it formulates a control algorithm for different objectives such as the fairer distribution of PV curtailment and rewarding PV customers for utilizing the excess power locally. The coordinated inverter control algorithm is tested on the 114-node and 906-bus LV European test feeders with cable sizes between 50mm^2 and 240mm^2 and validated with reference to OpenDSS. The results demonstrate that coordinated inverter control is superior when applied to high impedance LV networks and LV networks constrained by the distribution transformer capacity limits compared to autonomous inverters. On the 95mm^2 overhead line, it yields a 2% increase on average in the utilized PV output with up to 5% increase for some PV locations at higher penetration levels. Up to a 20% increase in PV hosting capacity was observed for location scenarios with PV system clustering.

Optimal allocation of energy storage system and its benefit analysis for unbalanced distribution network with wind generation

Battery energy storage system (BESS) can play a major role to overcome the challenges with renewable energy sources, especially in the context of Smart Grid (SG). In this manuscript, BESS optimal location and capacitance of wind power penetrations, and charging/discharging dispatches of BESS are determined using inherited competitive swarm optimization (ICSO) algorithm while improving the performance of the unbalanced distribution network subject to technical constraints. This approach utilizes the average feeder load from decisive criterion for charging or discharging battery. The wind turbine generator (WTG) charging and sustainable average load (SAL) charging methods for BESS are compared considering conservative mode of discharging. The proposed methodology is investigated on IEEE 37 bus unbalanced radial distribution system. The application outcomes displays that proposed methodology enhances various performance objectives of distribution system by optimally coordinating the dispatches of BESS.

An Efficient Backward/Forward Sweep Algorithm for Power Flow Analysis through a Novel Tree-Like Structure for Unbalanced Distribution Networks

The increase of distributed energy resources (DERs) in low voltage (LV) distribution networks requires the ability to perform an accurate power flow analysis (PFA) in unbalanced systems. The characteristics of a well performing power flow algorithm are the production of accurate results, robustness and quick convergence. The current study proposes an improvement to an already used backward-forward sweep (BFS) power flow algorithm for unbalanced three-phase distribution networks. The proposed power flow algorithm can be implemented in large systems producing accurate results in a small amount of time using as little computational resources as possible. In this version of the algorithm, the network is represented in a tree-like structure, instead of an incidence matrix, avoiding the use of redundant computations and the storing of unnecessary data. An implementation of the method was developed in Python programming language and tested for 3 IEEE feeder test cases (the 4 bus feeder, the 13 bus feeder and the European Low Voltage test feeder), ranging from a low (4) to a very high (907) buses number, while including a wide variety of components witnessed in LV distribution networks.

Sequential and Comprehensive BESSs Placement in Unbalanced Active Distribution Networks Considering the Impacts of BESS Dual Attributes on Sensitivity

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</i> The placement of battery energy storage system (BESS) determines whether its capabilities can be effectively exploited. However, existing BESS placement studies are commonly based on a balanced network model while practical distribution networks are essentially unbalanced. Besides, the power injection-based loss sensitivities are widely used to reduce the computation and memory burdens for complicated placement applications, which nevertheless cannot fully consider the impacts of both the BESS charging and discharging attributes and tends to generate less cost-effective clustered solutions. To address the challenges above, this study firstly proposes a multi-objective BESS placement optimization model for unbalanced active distribution networks, considering the time-varying load and renewable profiles as well as electricity prices, to minimize the cost of primary investment and operation/maintenance while maximizing the saving by loss reduction and load shifting. Then, a sequential BESS placement strategy is presented, based on the definition of a comprehensive loss sensitivity index (CLSI) considering the dual attributes of BESS charging and discharging, for more cost-effective and reasonable placement solutions. Finally, the proposed CLSI based sequential BESS placement is tested by detailed simulations on a real Australian distribution network of high PV penetrations.

A new algorithm for impacts of multiple DGs and D-STATCOM in unbalanced radial distribution networks

A major research is going on distributed generation (DG) which utilise renewable energy sources such as solar, wind, fuel cells, etc., for electric power generation. As the renewable energy sources are geographically widely spread and of low capacity, they can be connected to distribution networks. This paper proposes a three-phase power flow algorithm (PFA) for unbalanced radial distribution networks (URDN) with multiple installations of DGs and distribution static synchronous compensator (D-STATCOM). The proposed PFA utilised the special topology of radial distribution networks (RDN) to divide the network into different sections. The bus numbers and branch numbers of these sections are stored in row entries of BUS_NUM and BRANCH_NUM matrices, respectively. These matrices make the implementation of PFA simple. The accuracy of the three-phase PFA without the integration of DGs and D-STATCOM is tested on IEEE-13 bus URDN and the results are found to be very accurate with the literature. Several case studies are carried on IEEE-13 bus URDN with integration of DGs and D-STATCOM. The test results show that the proposed PFA can effectively handle different DG modelling and D-STATCOM and overall energy saving, reduced power loss and improved voltage profile are achieved with these integrations.

A new algorithm for impacts of multiple DGs and D-STATCOM in unbalanced radial distribution networks

A major research is going on distributed generation (DG) which utilise renewable energy sources such as solar, wind, fuel cells, etc., for electric power generation. As the renewable energy sources are geographically widely spread and of low capacity, they can be connected to distribution networks. This paper proposes a three-phase power flow algorithm (PFA) for unbalanced radial distribution networks (URDN) with multiple installations of DGs and distribution static synchronous compensator (D-STATCOM). The proposed PFA utilised the special topology of radial distribution networks (RDN) to divide the network into different sections. The bus numbers and branch numbers of these sections are stored in row entries of BUS_NUM and BRANCH_NUM matrices, respectively. These matrices make the implementation of PFA simple. The accuracy of the three-phase PFA without the integration of DGs and D-STATCOM is tested on IEEE-13 bus URDN and the results are found to be very accurate with the literature. Several case studies are carried on IEEE-13 bus URDN with integration of DGs and D-STATCOM. The test results show that the proposed PFA can effectively handle different DG modelling and D-STATCOM and overall energy saving, reduced power loss and improved voltage profile are achieved with these integrations.

Modeling of Electric Springs and Their Multi-Objective Voltage Control Based on Continuous Genetic Algorithm for Unbalanced Distribution Networks

Modeling of Electric Springs and Their Multi-Objective Voltage Control Based on Continuous Genetic Algorithm for Unbalanced Distribution Networks

Reducing the active power curtailment of distributed generation based on soft open points in unbalanced distribution networks

Voltage profiles may exceed the upper limit in a distribution network with a high penetration of distributed generation (DG). Voltage can be effectively regulated by curtailing the active power of DG and adjusting the reactive power. Soft open points (SOPs) have flexible active power transfer and reactive power regulation capabilities that can help reduce the active power curtailment of DG. Asymmetric network parameters, single-phase loads, and DGs cause voltage unbalance in distribution networks. This study proposes an optimal power control model of SOPs and DG converters in unbalanced distribution networks (UDNs). First, the individual phase power control characteristics of SOPs and DG converters in UDNs are modelled. Second, an optimisation model is constructed with the minimum active power curtailment of DG and the total power loss and voltage unbalance of UDNs as the objective function and with consideration of the networks and SOP operation constraints. The original optimisation model with non-convex and non-linear characteristics is transformed using power flow linearisation and linear relaxation into a linear programming model that can be efficiently solved. Finally, the feasibility and efficiency of the proposed model are verified using an IEEE 33-node system.

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.

New phase‐changing soft open point and impacts on optimising unbalanced power distribution networks

Three-phase unbalanced conditions in distribution networks are conventionally caused by load imbalance, asymmetrical fault conditions of transformers and impedances of three phases. The uneven integration of single-phase distributed generation (DG) worsens the imbalance situation. These unbalanced conditions result in financial losses, inefficient utilisation of assets and security risks to the network infrastructure. In this study, a phase-changing soft open point (PC-SOP) is proposed as a new way of connecting soft open points (SOPs) to balance the power flows among three phases by controlling active power and reactive power. Then an operational strategy based on PC-SOPs is presented for three-phase four-wire unbalanced systems. By optimising the regulation of SOPs, optimal energy storage systems dispatch and DG curtailment, the proposed strategy can reduce power losses and three-phase imbalance. Second-order cone programming (SOCP) relaxation is utilised to convert the original non-convex and non-linear model into an SOCP model which can be solved efficiently by commercial solvers. Case studies are conducted on a modified IEEE 34-node three-phase four-wire system and the IEEE 123-node test feeder to verify the effectiveness, efficiency and scalability of the proposed PC-SOP concept and its operational strategy.

Improving Hosting Capacity of Unbalanced Distribution Networks via Robust Allocation of Battery Energy Storage Systems

Distribution system operators aim to improve hosting capacity (HC) of distribution networks (DNs) to accommodate more distributed rooftop photovoltaics (PVs). Although PV power generation delivers numerous benefits, power unbalance and voltage rise are two major obstacles that limit the network HC. To mitigate these issues, battery energy storage systems (BESSs) can be applied. Thus, this paper proposes a robustly optimal allocation method for BESSs, which aims to reduce the power unbalance and alleviate the voltage rise, and thus improve the HC of the unbalanced three-phase DNs. Considering that locations and capacities of distributed rooftop PVs are determined by customers, future PV installations are regarded as uncertainties. In addition, to deal with the uncertainties, the proposed BESS allocation problem is formulated as an adaptive robust optimization (ARO) model with integer recourse variables. Accordingly, a solution algorithm which integrates an alternating optimization procedure into a column-and-constraint generation algorithm is developed to efficiently solve the ARO model. With the proposed BESS allocation method, a new perspective on HC improvement is provided, which not only considers the worst power unbalance situation but also satisfies the allowed maximum PV capacity. The simulation results verify high efficiency and solution robustness of the proposed allocation method.

Coordinated optimisation of PEV charging with the support of reactive discharging and phase switching in unbalanced active distribution networks

Plug-in electric vehicles (PEVs) are being widely deployed, especially in the residential premises. Random PEV charging causes negative impacts on the power grid, a coordinated PEV charging is essential. Currently, PEV charging studies mainly focus on the active power control of PEVs while there is a risk of worse operation, due to the transferring of charging load instead of eliminating. Besides, existing PEV charging studies are commonly based on a three-phase balanced network model, but practical distribution networks, especially on the low-voltage level, are unbalanced. This study proposes a novel coordination strategy of PEV charging in unbalanced distribution networks, with the support of reactive discharging and phase switching. The optimisation model of PEV charging is firstly proposed to minimise the network loss and charging cost while maximising users’ benefit from ancillary reactive service. The optimal power flow problem defined above is solved by a combined solver of discrete particle swarm optimisation and direct load flow. Then, a coordination strategy of PEV charging, reactive discharging and phase switching is presented to apply the proposed optimisation over 24 h. Finally, detailed simulations are conducted on a real Australian distribution network, to test the feasibility and effectiveness of the proposed optimisation and coordination strategy of PEV charging in unbalanced distribution networks.

Load pattern‐based voltage stability analysis in unbalanced distribution networks considering maximum penetration level of distributed generation

Stability analysis in the power system is becoming more important than ever as more distributed energy resources penetrate in the system. This study presents a novel load pattern voltage stability index (LPVSI) applicable to transmission and distribution systems. By considering the nominal value of voltages, the power network is converted into a two-bus equivalent system. Then, LPVSI is derived by only the real-time measurement of the voltage and deviation of active and reactive power loads. Also, the assessment of distributed generation's penetration level on unbalanced systems, with maximum loadability and power loss reduction constraints, is performed with regard to daily load variations. The accuracy and efficiency of the proposed indicator are tested on an unbalanced 34-node radial distribution system. Obtained results in comparison with some other papers in the literature demonstrate that the proposed voltage stability index is fast and effective in identifying non-trivial instabilities in the power system networks.

Optimal distributed generation allocation in unbalanced radial distribution networks via empirical discrete metaheuristic and steepest descent method

Loss minimization and voltage improvement through distributed generation (DG) planning is a well-established problem. However, a careful review of the literature shows that there is still room for the development of efficient algorithms for this purpose. In special, hybridization between optimization techniques is suitable for this complex problem, as it allows taking advantage of the positive features of different approaches. In this work, a novel empirical discrete metaheuristic (EDM) is presented and merged with the steepest descent method to solve the DG allocation problem. The allocation is broken into two subproblems: sitting the DGs and sizing them. The EDM deals with the first subproblem, while the second one is solved by the steepest descent method in an interchangeable optimization structure. The EDM tackles some key limitations of metaheuristic family methods. Relatively, it shows: low results variability in different executions; low initial conditions dependency; and few number parameters to tune. All simulations are performed in a communication scheme using the softwares Matlab and OpenDSS. The obtained results with IEEE 34-bus and IEEE 123-bus distribution test systems were compared to the literature and other metaheuristics, attesting the quality of the proposed approach.

Optimised TSO–DSO interaction in unbalanced networks through frequency‐responsive EV clusters in virtual power plants

The increased penetration of distributed energy resources (DERs) installed in distribution networks via power electronic converters reduces the overall rotating inertia of the power system, causing faster frequency dynamics after a large disturbance. However, these DERs coupled with energy storage systems (ESSs) can be managed to provide valuable grid support functions such as fast frequency response (FFR) and assist the transmission system operator (TSO) in frequency control. In this study, single- and three-phase clusters of electric vehicles (EVs), acting as mobile-ESSs, are grouped together as virtual power plants (VPPs) to centralise the provision of FFR for the distribution system operator (DSO) whilst considering network unbalance. Furthermore, the parameters of the EV clusters within each VPP are optimised to minimise the inertia-weighed maximum frequency deviation following a disturbance whilst adhering to network security and power quality constraints. To this aim, a variant of the metaheuristic optimisation algorithm called improved harmony search is used for the optimisation process. Finally, the merits of the proposed methodology are shown with an illustrative example of an unbalanced three-phase transmission and distribution network modelled in DIgSILENT PowerFactory. The results show that clusters of EVs grouped as VPPs effectively improve frequency support via the TSO–DSO interaction.

A Multi-Stage Coordinated Volt-Var Optimization for Integrated and Unbalanced Radial Distribution Networks

The growing penetrations of rooftop photovoltaics (PVs) into low-voltage (LV) distribution networks are challenging voltage regulation. Developing an effective volt-var (VV) control has been the focus of many researchers with various approaches proposed so far. However, assuming a single voltage level and balanced network model, widely adopted in existing literatures, tends to cause inaccurate and even infeasible control solutions. Besides, existing distribution VV control studies are usually based on the day-ahead predictions of PV generations and loads, introducing inevitable and non-negligible errors. To address the challenges above, this paper proposes a VV co-optimization across unbalanced medium-voltage (MV) and LV networks, by traditional and emerging techniques, to ensure the network operation with the required power quality. Specifically, the operation of MV delta-connected switched capacitors and LV distributed PV inverters is coordinated, under a three-stage strategy that suits integrated and unbalanced radial distribution networks. The proposal aims to simultaneously improve voltage magnitude and balance profiles while reducing network power loss, at the least controlling cost. To effectively solve the proposed VV optimization problem, a joint solver of the modified particle swarm optimization and the improved direct load flow is employed. Finally, the proposal is evaluated by simulations on real Australian distribution networks over 24 h.

Probabilistic Voltage Sensitivity Analysis to Quantify Impact of High PV Penetration on Unbalanced Distribution System

From an operational and planning perspective, it is important to quantify the impact of increasing penetration of photovoltaics on the distribution system. Most existing impact assessment studies are scenario-based where derived results are scenario specific and not generalizable. Moreover, stochasticity in the temporal behavior of spatially distributed PVs requires a large number of scenarios that increase with the size of the network and the level of penetration. Therefore, we propose a new computationally efficient analytical framework of voltage sensitivity analysis that allows for stochastic analysis of voltage change due to random changes in PV generation. We first derive an analytical approximation for voltage change at any node of the network due to change in power at other nodes in an unbalanced distribution network. The quality of this approximation is reinforced via bounds on the approximation error. Then, we derive the probability distribution of voltage change at a certain node due to random changes in power injections/consumptions at multiple locations of the network. The accuracy of the proposed PVSA is illustrated using a modified version of the IEEE 37 bus test system. The proposed PVSA can serve as a powerful tool for proactive monitoring/control and ease the computational burden associated with perturbation based cybersecurity mechanisms.

PMUs Enabled Tellegen's Theorem-Based Fault Identification Method for Unbalanced Active Distribution Network Using RTDS

The correct identification of the fault in the distribution systems can help in the healing process, system restoration, and thus lessen the duration of the outage. With the latest trend of penetrating the DG with the distribution network, the conventional fault identification (FI) methods become inappropriate for the promising active distribution network. Hence, the FI methods in the multisource, unbalanced, and weak distribution system is a challenge. In this article, the entire network is divided into the number of zones with each phase representing the two-port network. Further, the adjoint network matrices are formed for each of the two-port networks. The Tellegen's theorem based on adjoint circuit concept is then, used to identify the open-circuit section or the faulted node in the network using the input and output port information only. The voltage and current phasor information of the ports are acquired from the phasor measurement units based on the DFT. The methodology has been extensively verified on the IEEE-13 and IEEE-34 bus distribution systems integrated with the DGs, for all types of open and short circuit faults, with very encouraging results, with the real-time digital simulator.