Power Flow In Distribution Networks Research Articles

An overvoltage adjustment strategy Based on Integrated Voltage Sensitivity in the Active Distribution Network

With the growing proportion of renewable energy resources represented by distributed photovoltaics in the distribution network, the overvoltage problem is becoming more and more serious owing to the photovoltaic reverse power flow in the active distribution network. This paper begins with the quantification and analysis of node voltage sensitivity, and the influence calculation formula of photovoltaic injection on the voltage sensitivity of the target node and other nodes is deduced; next, a general conclusion on the change of voltage sensitivity caused by active and reactive power of Photovoltaic is given. To solve the possible low control efficiency of control strategy simply depends on voltage sensitivity, a definition of so-called Integrated voltage sensitivity is proposed, and an optimized overvoltage control scheme is utilized under the distribution network structure. The effectiveness of the proposed control strategy is validated by a simulation example, namely, on the premise of not affecting the active power output of the photovoltaic as much as possible, the same overvoltage regulation effect can be obtained with smaller adjustments in reactive power output, further, rapid overvoltage regulation is achieved.

Distribution network power flow calculation based on the BPNN optimized by GA‐ADAM

AbstractPower system operation and control are based on power flow calculations. In order to solve the uncertainty of the increasing penetration of renewable energy, the voltage fluctuation at the load point increases in the distribution network, and the inaccuracy of the power flow calculation due to the insufficient power flow data collection capability of the traditional power system. In this paper, a data‐driven power flow analysis model is proposed, a back propagation neural network combined with genetic algorithm (GA) and adaptive moment estimation (ADAM) optimization algorithm model is constructed to analyze the power flow calculation method of distribution networks under stochasticity. Firstly, the power flow initial value information, topology characteristics, and power factor index are introduced to construct a training set, and the mapping relationship between bus voltage and power is fully explored by training the regression model. Second, the GA‐ADAM algorithm is used to optimize the initial values and weight parameters of the model. Finally, it is verified based on IEEE‐33 bus distribution model, and the model is used for power flow calculation, and compared with other methods through each relevant error evaluation indicators. The results show that the model constructed in this paper has small error indicators and high accuracy, which improves the efficiency and accuracy of power flow calculation.

Research on Integrated Energy Distributed Sharing in Distribution Network Considering AC Power Flow and Demand Response

Under the constraint of the AC power flow architecture considering reactive power regulation, the examination of integrated energy distributed transactions among multiple subsystems can promote the research in the field of energy sharing. It is difficult to fully cover the consideration of AC power flow, demand response, integrated energy, and other factors in traditional related research. In response, a study is therefore conducted in this paper on integrated energy sharing in the distribution network. First, this paper introduces the system operation framework of the proposed distribution network model, and explains the interaction between all the players. Secondly, a distribution network power flow model and an integrated energy subsystem model are respectively. In particular, the subsystem model specifically considers new energy, demand response, integrated energy, and other factors. Then, a cooperative game model is constructed based on the cooperative relationship among subsystems in the distribution network system, followed by the analysis of the benefits brought by cooperation to the distribution network and the subsystems themselves. Finally, a distributed solution flow is established for the model based on the Alternating Direction Method of Multipliers (ADMM) algorithm. The results of the example analysis reveal the effectiveness of the model proposed in increasing the degree of energy utilization and further absorbing new energy in the distribution network system, each subsystem can generate up to 12% more absorption capacity than it would otherwise operate separately to accommodate more renewable energy in the distribution system.

Bipartite graph-based community-to-community matching in local energy market considering socially networked prosumers

The mutual influences among different energy prosumers caused by the socially connected relationship have not been modeled explicitly in the current local energy market. Hence, we first develop an iterative market clearing framework to bridge the data-driven and model-driven models of heterogeneous market participants. Then, we consider the behavior difficulty, communication channel, depth of information process, social influence, and observed peer effects in the multi-agent deep reinforcement learning (MADRL) framework in the data-driven models. Finally, we calculate the marginal market value of each C2C energy transaction with a linearized distribution network power flow model and formulate the C2C matching as a bipartite graph. We propose a recurrent Hungarian algorithm to solve the best matching schemes of C2C trading. Case studies verify that the distribution network power flow model considered in C2C energy trading successfully constrain the unit values of nodal voltages between 0.9 and 1.1. The power flow values in distribution network are constrained within the upper limits of transmission capacity for each branch. The finite iteration number of MADRL in hybrid market-clearing algorithm guarantees the normal P2P energy market operation. Moreover, the social influences for the P2P energy trading results are verified by comparing P2P trading results in different scenarios. Our proposed model can better depict the trading behaviors in communities.

Second-Order Cone Programming (SOCP) Model for Three Phase Optimal Power Flow (OPF) in Active Distribution Networks

With the high penetration of distributed generations (DGs), modern distribution systems require novel and efficient optimal power flow (OPF) models. This paper proposes a second-order cone programming (SOCP) based AC-OPF model for three-phase radial power distribution networks. Mutual coupling effects are generally ignored in the existing multi-phase SOCP AC-OPF models. To overcome this critical issue, the proposed SOCP-OPF model introduces a coupling coefficient for the mutual coupling effects on the three-phase unbalanced lines. The derivation of the coupling coefficients has been illustrated with the required proof that the relaxation is tight and the solution from the proposed OPF model is optimal for an unbalanced multi-phase distribution network. The SOCP-OPF model is evaluated on several IEEE standard distribution networks without and with high penetration of Distributed Energy Resources (DERs). It has been shown that the proposed OPF model provides an optimal global solution for convex objective functions. Also, this OPF model is scalable and computationally efficient compared to Nonlinear Programming (NLP) and other convex OPF models.

Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods

Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the “Dual-Carbon” target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations.

Optimal scheduling method of air source heat pump heating station considering optimal power flow of distribution network

Participation of adjustable loads in demand response can alleviate power supply and demand challenges. As a typical adjustable load, this paper gives an optimal scheduling method considering optimal power flow in distribution networks of air source heat pumps, and studies adjustable load power allocation schemes for multiple distribution network nodes. The scheme with the goal of the lowest network loss determines the scheduling amount of the corresponding node heat supply stations to achieve optimal power flow dispatch, and the genetic algorithm is used to solve the model. Specific examples analysis shows that the proposed decision-making method can reasonably allocate power adjustments, prevent node voltage overruns in distribution networks, and reduce active power losses.

Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation

With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam charging is introduced and the dynamic loading process of traffic flow is described using a cellular transmission model. The charging load is related to traffic flow and serves as a bond between the power distribution network and traffic network. The traffic flow achieves balanced allocation under dynamic user equilibrium conditions, and cooperatively optimizes the power flow of the power distribution network in conjunction with charging loads. Numerical analysis shows that this model can accurately depict the congestion situation during peak travel periods, and alleviate traffic congestion and distribution network voltage out of range.

A multiconductor approach to study power flows in asymmetric and unbalanced electric distribution networks

A multiconductor approach to study power flows in asymmetric and unbalanced electric distribution networks

Position-Capacity Optimization Method for Distributed Photovoltaic Accessing to Distribution Network Based on PV Load Uncertainty Quantification

As the proportion of photovoltaic grid-connected systems increases year by year, the uncertainty of photovoltaic output and load has an increasingly significant impact on the stable operation of the distribution network. In this paper, a photovoltaic access location-capacity optimization method in distribution lines based on the quantification of photovoltaic-load uncertainty is proposed, and the uncertainty propagation of the PV load model parameters in the distribution network power flow calculation node voltage output model is analyzed. This paper proposed an improved Sobol’s method based on polynomial development to quantitatively evaluate the uncertain impact of photovoltaic load parameters, constructed a photovoltaic access location-capacity evaluation model for distribution lines based on uncertainty quantitative assessment, optimized the access capacity of nodes by using the goal function of line loss minimization, and finally verified the feasibility and effectiveness of the proposed method by improving the IEEE33 node.

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field.

Accurate optimal power flow for active distribution networks via floating tangent surface

Accurate optimal power flow for active distribution networks via floating tangent surface

Dynamic Optimal Power Flow of Active Distribution Network Based on LSOCR and Its Application Scenarios

Optimal power flow (OPF) is a crucial aspect of distribution network planning and operation. Conventional heuristic algorithms fail to meet the system requirements for speed and accuracy, while linearized OPF approaches are inadequate for distribution networks with high R/X ratios. To address these issues and cater to multi-period scenarios, this study proposes a dynamic linearized second-order cone programming-based (SOCP) OPF model. The model is built by first establishing a dynamic OPF model based on linearized second-order conic relaxation (LSOCR-DOPF). The components of the active distribution network, such as renewable energy power generation units, energy storage units, on-load-tap-changers, static var compensators, and capacitor banks, are then separately modeled. The model is implemented in MATLAB and solved by YALMIP and GUROBI. Finally, three representative scenarios are used to evaluate the model accuracy and effectiveness. The results show that the proposed LSOCR-DOPF model can ensure calculation time within 3 min, voltage stability, and error control within 10−6 for all three applications. This method has strong practical value in the fields of active distribution network day-ahead dispatch, accurate modeling of ZIP load, and real-time operation.

Generalized linear‐constrained optimal power flow for distribution networks

AbstractOptimal power flow (OPF) plays an important role in the secure and economical operation of the distribution network. This paper proposes a novel generalized linear‐constrained optimal power flow (GLOPF) model for both radial and mesh distribution networks, which iteratively approaches the optimal solution to the original OPF problem. The first‐order Taylor series approximation method is applied to linearize the non‐linear and non‐convex power flow constraints for a given Taylor expansion point (TEP). In addition, the objective function of the GLOPF model is only added with an easy‐compute second‐order penalty on state variables, which is at least positive semi‐definite and guarantees the convexity of the quadratic optimization. Then a TEP‐based iterative (TEP‐I) method is proposed in the GLOPF model to construct a series of linear‐constrained convex quadratic optimizations, which avoids the complex calculation of quasi‐Hessian or Hessian matrices in traditional sequential programming methods. The converged solution to the GLOPF model is proved to satisfy the Karush–Kuhn–Tucker conditions of the original OPF problem. Furthermore, the GLOPF model at first and second iterations can be implemented as the ‘cold‐start’ and ‘warm‐start’ LOPF models, respectively. Case studies verify the super‐linear convergence and high accuracy of the proposed GLOPF model.

Research on real-time power flow calculation method in closed-loop distribution networks

It is less intelligent to calculate the closed-loop power flow of the distribution network by manually inputting the closed-loop power flow parameters. Therefore, it is necessary to study the real-time calculation method of the loop power flow in the distribution network. In this paper, a real-time calculation method is put forward based on the Thévenin’s Theorem. Forward/backward sweep algorithm is used to calculate the power flow of the closed-loop distribution network with the aid of the equivalent impedance and the maximum inrush current. A real-time automatic analysis tool for the closed-loop power flow is developed as well, based on the autonomous decentralized concept. The closed-loop power flow calculation is embedded in the DSCADA system to evaluate the feasibility of the loop-closing operation. Test results show that the proposed method can effectively identify the limit violation conditions within the distribution network, and that the calculation error in closed-loop power flow is small.

Optimal power flow in four-wire distribution networks: Formulation and benchmarking

In recent years, several applications have been proposed in the context of distribution networks. Many of these can be formulated as an optimal power flow problem, a mathematical optimization program which includes a model of the steady-state physics of the electricity network. If the network loading is balanced and the lines are transposed, the network model can be simplified to a single-phase equivalent model. However, these assumptions do not apply to low-voltage distribution networks, so the network model should model the effects of phase unbalance correctly. In many parts of the world, the low-voltage distribution network has four conductors, i.e. three phases and a neutral. This paper develops OPF formulations for such networks, including transformers, shunts and voltage-dependent loads, in two variable spaces, i.e. current–voltage and power–voltage, and compares them for robustness and scalability. A case study across 128 low-voltage networks also quantifies the modelling error introduced by Kron reductions and its impact on the solve time. This work highlights the advantages of formulations in current–voltage variables over power–voltage, for four-wire networks. • We develop two quadratic optimization formulations for unbalanced four-wire grids. • The current–voltage formulation is superior to the power–voltage formulation. • Four-wire optimization models require about 30% more time to solve than three-wire ones. • Numerical results support the proposed relaxation and approximation hierarchies.

Research on distribution network operation state division method based on big data technology

In the traditional reliability calculation of distribution network, the distribution of operation scenarios is ignored in data sampling. It is possible to sample too much data for a certain type of scenario, resulting in too extreme or conservative calculation results. To solve this problem, this paper analyzes the random characteristics and spatio-temporal correlation of distributed generation and electric vehicle load, and combined with the characteristics of power flow and power load of distribution network, a distribution network big data generation technology based on distribution network operation mode and time correlation is proposed. Then, using k-means algorithm as the clustering algorithm of scene extraction, the distribution network operation state distribution model based on clustering technology is established, and the distribution network operation state division method is proposed. Practical implementations are also conducted, which verifies the effectiveness of the proposed solution.

Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm

Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system’s demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study’s main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators’ operation, due to the penetration of renewable energy sources into the studied networks.

A three-phase power flow algorithm for ungrounded network based on constraints of zero-sequence components

The existing research on three-phase power flow in ungrounded distribution networks primarily adopts the methods of presetting zero-sequence reference voltages and neutral point reference voltages. This makes it impossible to calculate the power flow in ungrounded network with non-zero neutral point voltages or zero-sequence voltages. Furthermore, a unified zero-sequence components constraint method of ungrounded distribution network does not exist. In this paper, a three-phase power flow algorithm is proposed to calculate three-phase power flow in an ungrounded distribution network. First, power flow equation variables, transformer admittance matrices and zero-sequence components are used to analyze the characteristic of three-phase power flow in an ungrounded distribution network. Second, a three-phase power flow algorithm for ungrounded distribution network is designed by constraining the zero-sequence currents of the ungrounded winding nodes to zero. Third, theoretical analyses and mathematical derivations are used to prove the applicability of the two methods, one of which constrains zero-sequence voltages of ungrounded winding nodes to zero and the other constrains squares of zero-sequence voltages and currents to zero. Last, a five-nodes network, modified IEEE123 network and practical network are used to demonstrate that the proposed method has a high accuracy and good applicability in calculating the three-phase power flow of ungrounded distribution networks.

Research on optimal power flow of active distribution network based on soft open point

The uncertainty and fluctuation of distributed energy output and grid connection cause the problem of voltage out of limit or offset in distribution network. Soft open point is is an intelligent soft switch, which is composed of power electronic devices. Compared with ordinary tie switch, the intelligent soft switch can adjust the power flow of the feeders on both sides and optimize the power flow distribution of the distribution network.. In this paper, a power flow optimization method of active distribution network based on intelligent soft switch is proposed, and a dynamic optimal power flow model based on second-order cone programming is established. Through this method, the network loss and voltage deviation of the network containing a large amount of active management elements are significantly reduced.The study case shows that the proposed optimization method has a certain influence on the optimal power flow of active distribution network.