OPF Algorithm Research Articles

Optimal Pumping Flow Algorithm to Improve Pumping Station Operations in Irrigation Systems

In Spain, irrigated agriculture is the most water-intensive sector, consuming around of 80% of water resources. Moreover, irrigation water distribution systems are the infrastructure by which one-third of water resource losses take place. Monitoring and controlling operations in irrigation canals are essential for mitigating leakages and water waste in operational actions. On the other hand, energy consumption by agriculture is around 5% of usage in developed countries and even higher in undeveloped countries. Although it is a small part of the total energy supply for a country, energy waste reduces the competitiveness of the agriculture sector, which continually reduces profit margins in an economic sector with very low profit margins already. The tool developed in this paper aims to increase the efficiency of water and energy management in the agricultural sector and is included in an overall control diagram for scheduled irrigation management. This tool, the optimal pumping flow (OPF algorithm), optimizes the pumping flow from the irrigation canal to the irrigation reservoir in terms of water level at the canal and reservoir, crop flow demand, system constraints, and energy prices. Regarding the results, the OPF algorithm can calculate the optimum pumping operations, being able to optimize water resource usage and energy expenses by ensuring that the water level at reservoirs remains within a specified range and that pump flow never exceeds a threshold. Further, it allows for the management of pump operations outside of peak hours. On the other hand, the OPF algorithm is also integrated into the overall control diagram in a second test. Here, the OPF algorithm collaborates with a control canal algorithm such as the GoRoSo algorithm to optimize canal gates and pump operations, respectively. In this scenario, OPF reduces cumulative energy expenses by 58% compared to the scenario where the pump station operates only when the reservoir water level is below a certain threshold.

A Hierarchical OPF Algorithm with Improved Gradient Evaluation in Three-Phase Networks

A Hierarchical OPF Algorithm with Improved Gradient Evaluation in Three-Phase Networks

A Reinforcement Learning-based Approach for Active Optimization of High Proportional Distributed Power Supply Access to Rural Distribution Networks

In the process of actively implementing the rural revitalization strategy and the “double carbon” goal, distributed power sources are connected to the rural distribution network on a large scale, resulting in a gradual transition to a new type of active distribution network, which has a greater impact on the distribution network in terms of voltage, network loss, and protection, thus posing greater demands on the distribution network in terms of planning and control, power quality management, and operation. This significantly impacts the voltage, network loss, and protection of the distribution network, thus putting higher requirements on the planning and control, power quality management, operation, and maintenance of distribution networks. Since the traditional OPF algorithm has great defects in solving large-scale decentralized, time-series, and stochastic nonlinear problems, in order to realize real-time control of distribution networks under more complex network conditions, this paper proposes an optimization algorithm based on reinforcement learning and applies it to real-time control of active distribution networks, taking the optimal network loss as the objective function and considering network voltage, line load factor, and distributed power supply. As a de-modeling artificial intelligence method, reinforcement learning has great advantages in solving large problems with complex mathematical models. The method’s control effect and convergence time can be improved by introducing imitation learning and migration learning, expanding the knowledge matrix, and introducing risk assessment mechanisms. Based on MATLAB R2017a simulation platform for the IEEE 33-node improvement model, the simulation results show that the reinforcement learning method has a better control effect in solving the real-time control problem of active distribution network, significantly alleviates the voltage crossing limit, effectively reduces the network loss, and has good real-time characteristics.

A stochastic distributed control approach for load restoration of networked microgrids with mobile energy storage systems

In response to extreme events, substantial research efforts have focused on developing load restoration strategies for networked microgrids (MGs). However, existing distributed control approaches only consider independent time periods, which cannot capture the time-coupled flexibility of storage units. Additionally, mobile energy storage systems (MESSs) have been deployed for resilience enhancement due to their advantages in mobility and flexibility. However, existing research on networked MGs utilizes simplistic energy management approaches for MG modeling without detailed network structures, which cannot capture the mobility of MESSs. To address these issues, this paper proposes a three-stage stochastic distributed control approach based on rolling optimization to enhance the resilience of networked MGs with MESSs. Specifically, a stochastic linearized OPF is formulated in the first stage to capture the flexibility of MESSs and uncertainties, while a consensus algorithm is utilized in the second stage to calculate the power exchange among MGs. Finally, a detailed non-linear AC OPF algorithm is employed in the third stage to capture technical constraints relating to stability properties towards accurate optimization results. Case studies considering load distinction, multiple line outages, and limited generation resources are developed to demonstrate the effectiveness of the proposed distributed control approach in accurate and timely decision-making.

A resilience enhancement strategy for networked microgrids incorporating electricity and transport and utilizing a stochastic hierarchical control approach

High-impact and low-probability (HILP) events can cause severe damage to power systems. Networked microgrids (MGs) with distributed generation resources provide a viable solution for the resilience enhancement of power systems. However, most literature utilizes centralized control methods based on energy management systems to model networked MGs and employs static storage units to enhance resilience, which are both unrealistic. In this paper, a hierarchical control approach based on detailed AC OPF algorithm is developed to capture technical constraints relating to voltage, angle and power loss as well as obtaining accurate solutions of power exchange between MGs, while the routing of electric vehicles (EVs) is incorporated into the model to reduce load shedding during extreme events. Uncertainties relating to load profiles and renewable energy sources are captured via stochastic programming. The impacts of limited generation resources and different levels of contingencies (including multiple line faults) are captured in the model to mimic a realistic scenario and verify the effectiveness of the proposed resilience enhancement strategy. Appropriate sensitivity analyses are suggested to investigate the influence of uncertain event occurrence time, tie-line capacity and EV scheduling horizon.

Optimal Power Flow Solution Based on Jellyfish Search Optimization Considering Uncertainty of Renewable Energy Sources

Today’s electrical power system became more complex interconnected network that is expanding every day. The transmission lines of the power system are more severely loaded than ever before. Hence, the power system is facing many problems such as power losses increasing, voltage instability, line overloads, etc. The optimization of real and reactive powers due to the installation of energy resources at appropriate buses can minimize the losses and improve the voltage profile especially, for congested networks. As a result, the optimal power flow problem (OPF) is considered more important tool for the processes of planning and operation of power systems. OPF is a very significant tool for power system operators to meet the electricity demand of the consumers efficiently, and for the reliable operation of the power system. However, the incorporation of renewable energy sources (RESs) into the electrical grid is a very challenging problem due to their intermittent nature. In this paper, the proposed power flow model contains three different types of energy sources: thermal power generators representing the conventional energy sources, wind power generators (WPGs), and solar photovoltaic generators (SPGs) representing RESs. Uncertain output powers from WPGs and SPGs are forecasted with the aid of Weibull and lognormal probability distribution functions (PDF), respectively. The under and overestimation output powers of RESs are taken into consideration while formulating the objective function through adding a penalty and reserve cost, respectively. Moreover, carbon tax is imposed to the main objective function to help in reducing carbon emissions. A jellyfish search optimizer (JS) is employed to reach optimization in the modified IEEE 30-bus test system to validate its feasibility. To examine the effectiveness of the proposed JS algorithm, its simulation results are compared with the results of four other nature-inspired global optimization algorithms. The developed OPF algorithm considers several practical cases such as generation uncertainty of renewable energy sources, time-varying load and the ramp rate limits of thermal generators. The simulation results show the effectiveness of the JS algorithm in solving the OPF problem in terms of minimization of total generation cost and solution convergence.

Distributed online optimal power flow for distribution system

This paper proposes a distributed online OPF algorithm for distribution systems with high integration of electronic-interfaced distributed energy resources (DERs). The algorithm can coordinate the update of DER set-points on fast time scales only depending on local measurements and boundary information for each distribution area. Comparing with the existing distributed online algorithms that require some special setups (e.g., without considering the constraints on currents), the proposed algorithm can cope with various setups for the objective functions and constraints. The algorithm is based on solving the Taylor approximations of OPF problems by a distributed solution. In this solution, we design a strategy to formulate the second-order Taylor information in a distributed way by decomposing the sensitivities of the OPF targets among different areas; moreover, we design a method to simplify the constraints on DER outputs to accelerate the solution. The validity of the algorithm is verified by case studies, which show that it can continuously drive DER outputs towards the time-varying OPF targets composed of power losses and operation costs of DERs while satisfying the constraints on voltages and currents.

Remote Sensing Applied to the Extraction of Road Geometric Features Based on Optimum Path Forest Classifiers, Northeastern Brazil

One of the principal difficulties related to road safety management in Brazil is the lack of data on road projects, especially those on rural roads, which makes it difficult to use road safety studies and models from other countries as a reference. Updating road networks through the use of hyperspectral remote sensing images can be a good alternative. However, accurately recognizing and extracting hyperspectral images from roads has been recognized as a challenging task in the processing of hyperspectral data. In order to solve the aforementioned challenges, Hyperion hyperspectral images were combined with the Optimum Forest Path (OPF) algorithm for supervised classification of rural roads and the effectiveness of the OPF and SVM classifiers when applied to these areas was compared. Both classifiers produced reasonable results, however, the OPF algorithm outperformed SVM. The higher classification accuracy obtained by the OPF was mainly attributed to the ability to better distinguish between regions of exposed soil and unpaved roads.

Microgrid Optimal Power Flow Using the Generalized Benders Decomposition Approach

The future distribution system is envisioned to be a network of distributed energy resources (DER), being able to operate in both the grid connected and islanded modes. This paper presents an optimal power flow algorithm based on the generalized benders decomposition (GBD) method for scheduling DER and voltage regulation device operations to meet economic and network security objectives. Among the key model features, the proposed algorithm performs multi-interval scheduling studies on the unbalanced distribution system network model with conservation voltage reduction. It is also able to support the microgrid operation in the grid-connected and islanded modes. The GBD-based OPF algorithm has been successfully tested on the IEEE 123-node test feeder model and demonstrated potential for large-scale system applications.

Optimal Power Flow and Unified Control Strategy for Multi-Terminal HVDC Systems

This paper presents an automation strategy for multi-terminal HVDC (MT-HVDC) systems combining a dc optimal power flow (dc OPF) routine and a unified reference controller (URC). In the presented automatic framework, the dc OPF algorithm is implemented at the power dispatch center (PDC) of the MT-HVDC system to find optimal reference operation points of the power converters to minimize the losses during the operation of the MT-HVDC grid and solves the contradiction between minimizing losses and preventing commutation failure. At the local control systems, the operating points of the voltage-source converter (VSC) stations are tuned based on the calculations executed in the PDC, which enables fast response to power fluctuation and ensures a stable dc voltage. However, if the communication between the two control layers is lost, the MT-HVDC grid remains stable based on the pre-defined V-P droop characteristics for the power converter stations till the connection establishes again, and a set of new operating points is generated and sent. The static and dynamic simulations conducted on the CIGRE B4 HVDC test grid establish the efficient and effective grid control performance with the proposed automation strategy. The analysis shows that the proposed control scheme achieves the desired minimum losses while, at the same time, satisfying the system constraints.

AC OPF in radial distribution networks – Part II: An augmented Lagrangian-based OPF algorithm, distributable via primal decomposition

In the first part of this two-part paper we show that the branch-flow convexification of the OPF problem is not exact and that the ADMM-based decomposition of the OPF fails to converge in specific scenarios. Therefore, there is a need to develop algorithms for the solution of the non-approximated OPF problem that remains inherently non-convex. To overcome the limitations of recent approaches for the solution of the OPF problem, we propose in this paper, a specific algorithm for the solution of a non-approximated, non-convex AC OPF problem in radial distribution systems. It is based on the method of multipliers, as well as on a primal decomposition of the OPF problem. We provide a centralized version, as well as a distributed asynchronous version of the algorithm. We show that the centralized OPF algorithm converges to a local minimum of the global OPF problem and that the distributed version of the algorithm converges to the same solution as the centralized one. Here, in this second part of the two-part paper, we provide the formulation of the proposed algorithm and we evaluate its performance by using both small-scale electrical networks, as well as a modified IEEE 13-node test feeder.

Application of Intelligent Firefly Algorithm to Solve OPF with STATCOM

Voltage profile has been a major concern for power system utilities. Lack of voltage profile control may cause voltage collapse and lead to complete power cut in the system. Several events of voltage collapse in the recent past in the country are some example. Flexible AC Transmission Systems (FACTS) devices are increasingly used to improve voltage profile and power flow control in many utilities. However, owing to the considerable cost of FACTS devices involved, it is important to find the optimal location and sizing of these devices in a power system to obtain maximum benefits of these devices. In the recent works, to find optimal location and sizing of STATCOM, PSO (Particle Swarm Optimization), Genetic Algorithm (GA) and many other intelligent algorithms was used. In this research work new intelligent algorithm known as firefly OPF algorithm is used to find best location and size of STATCOM. The algorithm is implemented for finding optimal location and sizing of STATCOM for voltage profile improvement in a benchmark IEEE 30-bus system.

Transient stability constrained optimal power flow using independent dynamic simulation

Transient stability constrained optimal power flow (TSC-OPF) is originally a non-linear optimisation problem with variables and constraints in time domain, which is not easy to deal with directly because of its huge dimension. This study presents an efficient approach to realise TSC-OPF by introducing an independent dynamics simulation algorithm into the optimisation procedure. In the new approach, the simulation algorithm is used to realise the dynamics constraints and to deduce the transient stability constraint, whereas the optimisation algorithm verifies the steady state and the transient stability constraints together. The new TSC-OPF has just one more constraint than that of a conventional OPF and can be solved by a conventional OPF algorithm with small modification. Moreover, the new approach makes it easy to improve the accuracy and efficiency of TSC-OPF, because of its flexibility in choosing machine models and simulation methods, which is important for large power systems. In the study, the proposed approach is tested with two small power systems, where the two-axis machine model and a mixed time step simulation method are used to assess system transient stability with a 1-ms time resolution.

A global approach to transient stability constrained optimal power flow using a machine detailed model

Transient stability constrained optimal power flow (TSC-OPF) is originally a nonlinear optimization problem with variables and constraints in time domain, which is not easy to deal with because of its huge dimension, especially for systems with detailed machine models. This paper presents an efficient approach to realize TSC-OPF by introducing an independent dynamics simulation algorithm into the optimization procedure. In the new approach, the simulation algorithm is used to realize the dynamic constraints and to deduce the transient stability constraint, while the optimization algorithm verifies the steady state and the transient stability constraints together. The new TSC-OPF has just one more constraint than that of a conventional OPF and can be solved by a conventional OPF algorithm with small modification. In the new approach, there is no limitation for the machine model and the simulation method. The nonlinearity of the power system is taken fully into account. In the paper, the proposed approach is verified with a small three-machines system. The simulation results show the machine model influences greatly the system transient stability and the TSC-OPF results. The widely used machine classical model in the TSC-OPF over-estimates the system transient stability and under-estimates the TSC-OPF costs.

Unit commitment taking into account reactive power

AbstractIn this paper, the Voltage Magnitude Constrained OPF (VMC‐OPF) algorithm to determine the reactive power injection from compensating equipment to load bus is developed. By introducing the proposed VMC‐OPF to the real power related Unit Commitment (UC) [1], a novel daily UC method taking into account the reactive power to secure bus voltage magnitude is presented. The Transmission Access Charge (TAC) and Ancillary Services Charge (ASC) are included in the objective function for considering costs of using transmission network and supplying reactive power to maintain the voltage. Using the proposed UC, the difference between two schedules based on benefit maximization and cost minimization is also discussed. To ascertain the effectiveness of the proposed UC method, we demonstrate various simulation results for a model power system of IEEE‐118 bus system. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(3): 17–26, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20590

A Comparative Study on Particle Swarm Optimization for Optimal Steady-State Performance of Power Systems

In this paper, three new particle swarm optimization (PSO) algorithms are compared with the state of the art PSO algorithms for the optimal steady-state performance of power systems, namely, the reactive power and voltage control. Two of the three introduced, the enhanced GPAC PSO and LPAC PSO, are based on the global and local-neighborhood variant PSOs, respectively. They are hybridized with the constriction factor approach together with a new operator, reflecting the physical force of passive congregation observed in swarms. The third one is based on a new concept of coordinated aggregation (CA) and simulates how the achievements of particles can be distributed in the swarm affecting its manipulation. Specifically, each particle in the swarm is attracted only by particles with better achievements than its own, with the exception of the particle with the best achievement, which moves randomly as a crazy agent. The obtained results by the enhanced general passive congregation (GPAC), local passive congregation (LPAC), and CA on the IEEE 30-bus and IEEE 118-bus systems are compared with an interior point (IP)-based OPF algorithm, a conventional PSO algorithm, and an evolutionary algorithm (EA), demonstrating the excellent performance of the proposed PSO algorithms

無効電力を考慮した発電機起動停止計画法

In this paper, the Voltage Magnitude Constrained OPF (VMC-OPF) algorithm to determine the reactive power injection from compensating equipment to load bus is developed. By introducing the proposed VMC-OPF to the real power related Unit Commitment (UC) [1], a novel daily UC method taking into account the reactive power to secure bus voltage magnitude is presented. The Transmission Access Charge (TAC) and Ancillary Services Charge (ASC) are included in the objective function for considering costs of using transmission network and supplying reactive power to maintain the voltage. Using the proposed UC, the difference between two schedules based on benefit maximization and cost minimization is also discussed. To ascertain the effectiveness of the proposed UC method, we demonstrate various simulation results for a model power system of IEEE-118 bus system. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(3): 17–26, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20590

Genetic-algorithm-based optimal power flow for security enhancement

Power-system-security enhancement deals with the task of taking remedial action against possible network overloads in the system following the occurrence of contingencies. Line overload can be removed by means of generation redispatching and by adjustment of phase-shifting transformers. The paper presents a genetic-algorithm (GA) based OPF algorithm for identifying the optimal values of generator active-power output and the angle of the phase-shifting transformer. The locations of phase shifters are selected based on sensitivity analysis. To overcome the shortcomings associated with the representation of real and integer variables using the binary string in the GA population, the control variables are represented in their natural form. Also, crossover and mutation operators which can deal directly with integers and floating-point numbers are used. Simulation results on IEEE 30-bus and IEEE 118-bus test systems are presented and compared with the results of other approaches.

A comprehensive approach to transient stability control: part I-near optimal preventive control

A general approach to real-time transient stability control is proposed, and two complementary techniques are devised: one for preventive, the other for emergency control. In this paper, the general transient stability control approach is first revisited then applied to real-time preventive control. The technique consists of shifting active power generation. The amount of power and the machines from which to shift it are methodically determined, and various patterns of generation decrease/increase are considered. Further, a standard OPF algorithm is combined with this control technique to get a real-time transient stability-constrained OPF software, able to meet power system security and electricity market requirements. Simulations conducted on the 88-machine EPRI system and the Mexican interconnected power system illustrate the various techniques, highlight their specifics, and assess their performance.

Modeling of the Generalized Unified Power Flow Controller (GUPFC) in a Nonlinear Interior Point OPF

With the progress of installing the latest generation of FACTS devices, namely, the convertible static compensator (CSC) [1], several innovative operating concepts have been introduced to the historic development and application of FACTS. One of the novel concepts is the generalized unified power flow controller (GUPFC), or multiline UPFC, which can control bus voltage and power flows of more than one line or even a subnetwork. The GUPFC should have stronger control capability than the UPFC. A mathematical model for the GUPFC consisting of one shunt converter and two or more series converters is developed and implemented in a nonlinear interior point OPF algorithm. Numerical results with various GUPFC devices based on the IEEE 30 bus system and IEEE 118 bus system demonstrate the feasibility as well as the effectiveness of the GUPFC model established and the OPF method proposed.