Real-time Optimal Power Flow Research Articles

Topology-Transferable Physics-Guided Graph Neural Network for Real-Time Optimal Power Flow

Topology-Transferable Physics-Guided Graph Neural Network for Real-Time Optimal Power Flow

Real-Time Testing Optimal Power Flow in Smart-Transformer-Based Meshed Hybrid Microgrids: Design and Validation

The smart transformer (ST) is a multiport and multi-stage converter that allows for the formation of meshed hybrid microgrids (MHMs) by enabling AC-DC ports in medium and low voltage. This type of microgrid has advantages over the performance of conventional hybrid AC-DC microgrids (HMGs); however, the number of degrees of freedom of the ST increases the complexity of the energy management systems (EMSs), which require adequate and accurate modeling of the power flow of the converters and the MG to find the feasible solution of optimal power flow (OPF) problems in the MHM. An ST’s equivalent power flow model is proposed for formulating the MHM OPF problem and developing low-frequency equivalent models integrated with a decoupled hierarchical control architecture under a real-time simulation approach to the ST-based MHM. A simulation model of the MHM in the Simulink® environment of Matlab® 9.12 is developed and implemented under a digital real-time simulation (DRTS) approach on the OPAL-RT® platform. This model allows for determining the accuracy of the developed equivalent models, both low-frequency and power flow, and determining the MHM performance based on optimal day-ahead scheduling. Simulation test results demonstrated the ST equivalent model’s accuracy and the MHM’s accuracy for OPF problems with an optimal day-ahead scheduling horizon based on the model-in-the-loop (MIL) and DRTS approach.

Real-Time Optimal Power Flow With Linguistic Stipulations: Integrating GPT-Agent and Deep Reinforcement Learning

Real-Time Optimal Power Flow With Linguistic Stipulations: Integrating GPT-Agent and Deep Reinforcement Learning

Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flow

Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flow

Physics-Informed Reinforcement Learning for Real-Time Optimal Power Flow with Renewable Energy Resources

Physics-Informed Reinforcement Learning for Real-Time Optimal Power Flow with Renewable Energy Resources

Real-Time Optimal Power Flow Method via Safe Deep Reinforcement Learning Based on Primal-Dual and Prior Knowledge Guidance

Real-Time Optimal Power Flow Method via Safe Deep Reinforcement Learning Based on Primal-Dual and Prior Knowledge Guidance

A Real-Time Adaptive Energy Optimization Method for Urban Rail Flexible Traction Power Supply System

In urban rail traction power supply systems, the optimal power flow method is mostly utilized for energy optimization. Conventional power flow optimization adopts offline calculation methods to obtain the optimal strategy under given scenarios. However, this method cannot continuously realize the ideal optimization effect for the time-varying flexible traction power supply system. For this reason, this paper proposes a real-time adaptive energy optimization method to solve the problem above. Firstly, the control objective of the optimal power flow is determined based on analyzing the system power flow. Secondly, a hierarchical control system is designed to achieve adaptive optimal control for the flexible traction power supply system. Moreover, a swarm intelligence optimization algorithm-based optimal power flow calculation method is employed to realize the real-time optimal power flow. Finally, some simulations are performed to verify the effectiveness and accuracy of the proposed real-time optimal power flow. It is demonstrated that the real-time optimal power flow method makes it possible to consider the low delay and high accuracy of control commands at the same time. Compared with the conventional offline method, the proposed real-time method can reduce the input electricity consumption by 1.47%, which is valuable in practical applications.

Fast Optimal Power Flow With Guarantees via an Unsupervised Generative Model

Climate change concerns are driving the widespread integration of renewable generation sources, storage systems, electric vehicles and diverse consumer loads. Inherent variabilities of these power entities introduce uncertainties that affect the economical operations and integrity of the electrical grid. Thus, optimal power flow (OPF) studies must be conducted at high granularity in order to account for these fluctuations. However, due to the non-convex nature of the OPF problem, solving it in real-time still remains an open challenge. In this paper, a novel data-driven approach that combines generative learning, information theory and domain knowledge is proposed to enable real-time OPF studies. In order to train this model, only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feasible</i> datapoints are necessary - not optimal values. Once trained, the time required to compute the solution is extremely fast and falls within the sub-second range. No information pertaining to the topology or power flow equations is required once the model is trained. Theoretical guarantees on optimality and feasibility of the solutions generated by the proposed ML model are established. Comprehensive practical and comparative studies conducted demonstrate the efficacy of the proposed data-driven approach for solving OPF in real-time.

Equilibrium analysis of a peer-to-peer energy trading market with shared energy storage in a power transmission grid

With the increasing penetration of renewable energy resources in power systems, energy storage is expected to play a more active role in system regulation. Shared use of energy storage is an emerging business model, and its impact on the power grid needs thorough analysis. This paper proposes a two-layer equilibrium model to study the grid impact of peer-to-peer (P2P) energy trading considering shared energy storage (SES). The upper layer describes the P2P energy transaction among generators and SES units. The generators can freely choose to sell electricity to the SES units or power grid while the SES units can choose to charge or discharge. The physical power transmission grid is considered in the lower layer. It determines the locational marginal price through a real-time optimal power flow model. The locational marginal price in turn affects the decisions of generators and thus the equilibrium of the P2P market in the upper layer. Based on Karush-Kuhn-Tucker (KKT) optimality conditions and the Big-M method, a mixed-integer linear program is developed to compute the equilibrium of the P2P market in the upper layer, and an iteration algorithm is proposed to search the equilibrium point in the transmission grid with the P2P market. Numerical results demonstrate that both generators and SES units can benefit from the P2P energy transaction.

Distributed Real-time Optimal Power Flow Strategy for DC Microgrid Under Stochastic Communication Networks

Distributed Real-time Optimal Power Flow Strategy for DC Microgrid Under Stochastic Communication Networks

A feasible reduced space method for real-time optimal power flow

We propose a novel feasible-path algorithm to solve the optimal power flow (OPF) problem for real-time use cases. The method augments the seminal work of Dommel and Tinney with second-order derivatives to work directly in the reduced space induced by the power flow equations. In the reduced space, the optimization problem includes only inequality constraints corresponding to the operational constraints. While the reduced formulation directly enforces the physical constraints, the operational constraints are softly enforced through Augmented Lagrangian penalty terms. In contrast to interior-point algorithms (state-of-the art for solving OPF), our algorithm maintains feasibility at each iteration, which makes it suitable for real-time application. By exploiting accelerator hardware (Graphic Processing Units) to compute the reduced Hessian, we show that the second-order method is numerically tractable and is effective to solve both static and real-time OPF problems.

Real-Time Optimal Power Flow of South Sulawesi Network System That Integrated Wind Power Plant Based on Artificial Intelligence

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Evaluation of Data-Enhanced Hierarchical Control for Distribution Feeders With High PV Penetration

Reliable and resilient grid operations with high penetration levels of distributed energy resources (DERs) can be achieved with improved situational awareness and seamless integration of DERs with utility enterprise controls. This paper presents the details of the development of a data-enhanced hierarchical control (DEHC) architecture and the results of its evaluation. The DEHC is a hybrid control framework that enables the efficient, reliable, and secure operation of distribution grids with extremely high penetrations of solar photovoltaic (PV) generation by seamlessly integrating centralized utility controls, distributed controls for DERs, and autonomous grid-edge controls. In the DEHC architecture, the advanced distribution management system (ADMS) controls the legacy devices (such as load tap changers and capacitor banks), the PV smart inverters are dispatched by real-time optimal power flow, and the grid-edge devices regulate local voltages in coordination with each other. The DEHC is demonstrated using a commercial ADMS platform, real utility distribution feeder models, and grid-edge devices. The performance of the DEHC architecture is evaluated using simulations and hardware-in-the-loop experiments with voltage regulation as the control objective. The results show that the DEHC enables high penetration levels of PV in distribution feeders by effectively managing system voltages through the synergistic operation of ADMS, distributed PV smart inverter controls, and secondary-level grid-edge device control.

Deep Reinforcement Learning Based Real-time AC Optimal Power Flow Considering Uncertainties

Modern power systems are experiencing larger fluctuations and more uncertainties caused by increased penetration of renewable energy sources (RESs) and power electronics equipment. Therefore, fast and accurate corrective control actions in real time are needed to ensure the system security and economics. This paper presents a novel method to derive real-time alternating current (AC) optimal power flow (OPF) solutions considering the uncertainties including varying renewable energy and topology changes by using state-of-the-art deep rein-forcement learning (DRL) algorithm, which can effectively assist grid operators in making rapid and effective real-time decisions. The presented DRL-based approach first adopts a super-vised-learning method from deep learning to generate good initial weights for neural networks, and then the proximal policy optimization (PPO) algorithm is applied to train and test the artificial intelligence (AI) agents for stable and robust performance. An ancillary classifier is designed to identify the feasibility of the AC OPF problem. Case studies conducted on the Illi-nois 200-bus system with wind generation variation and N-1 topology changes validate the effectiveness of the proposed method and demonstrate its great potential in promoting sustainable energy integration into the power system.

(DA-DOPF): A Day-Ahead Dynamic Optimal Power Flow With Renewable Energy Integration in Smart Grids

The performance of a power system can be measured and evaluated by its power flow analysis. Along with the penetration of renewable energies such as wind and solar, the power flow problem has become a complex optimization problem. In addition to this, constraint handling is another challenging task of this problem. The main critical problem of this dynamic power system having such variable energy sources is the intermittency of these VESs and complexity of constraint handling for a real-time optimal power flow (RT-OPF) problem. Therefore, optimal scheduling of generation sources with constraint satisfaction is the main goal of this study. Hence, a renewable energy forecasting–based, day-ahead dynamic optimal power flow (DA-DOPF) is presented in this paper with the forecasting of solar and wind patterns by using artificial neural networks. Moreover, contribution factors are calculated using triangular fuzzy membership function (T-FMF) in the sub-interval time slots. Furthermore, the superiority of feasible (SF) solution constraint handling approach is used to avoid the constraint violation of inequality constraints of optimal power flow. The IEEE 30-bus transmission network has been amended to integrate a solar photovoltaic and wind farm in different buses. In this approach, the computing program is based on MATPOWER which is a tool of MATLAB for load flow analysis which uses the Newton–Raphson technique because of its rapid convergence. Meteorological information has been gathered during the time frame January 1, 2015, to December 31, 2017, from Danyore Weather Station (DWS) at Hunza, Pakistan. A Levenberg–Marquardt calculation–based artificial neural network model is utilized to foresee the breeze speed and sunlight-based irradiance in light of its versatile nature. Finally, the results are discussed analytically to select the best generation schedule and control variable values.

A Progressive Period Optimal Power Flow for Systems with High Penetration of Variable Renewable Energy Sources

Renewable energy sources including wind farms and solar sites, have been rapidly integrated within power systems for economic and environmental reasons. Unfortunately, many renewable energy sources suffer from variability and uncertainty, which may jeopardize security and stability of the power system. To face this challenge, it is necessary to develop new methods to manage increasing supply-side uncertainty within operational strategies. In modern power system operations, the optimal power flow (OPF) is essential to all stages of the system operational horizon; underlying both day-ahead scheduling and real-time dispatch decisions. The dispatch levels determined are then implemented for the duration of the dispatch interval, with the expectation that frequency response and balancing reserves are sufficient to manage intra-interval deviations. To achieve more accurate generation schedules and better reliability with increasing renewable resources, the OPF must be solved faster and with better accuracy within continuous time intervals, in both day-ahead scheduling and real-time dispatch. To this end, we formulate a multi-period dispatch framework, that is, progressive period optimal power flow (PPOPF), which builds on an interval optimal power flow (IOPF), which leverages median and endpoints on the interval to develop coherent coordinations between day-ahead and real-time period optimal power flow (POPF). Simulation case studies on a practical PEGASE 13,659-bus transmission system in Europe have demonstrated implementation of the proposed PPOPF within multi-stage power system operations, resulting in zero dispatch error and violation compared with traditional OPF.

Coordinated Inverter Control to Increase Dynamic PV Hosting Capacity: A Real-Time Optimal Power Flow Approach

High penetrations of distributed photovoltaics (PV) could cause adverse grid impacts, such as voltage violations. The recent development in inverter technologies provides the opportunity to develop control systems to realize effective PV governance, and thus, to improve dynamic PV hosting capacity for distribution grids. In this article, a novel distributed energy resource management system (DERMS) solution is proposed by adopting the real-time optimal power flow approach for coordinated control of the distributed PV inverters. The proposed approach eliminates the dependence on load knowledge via measurement feedback correction, and it can be implemented in real time. One challenge is that the technique is sensitive to the data availability and integrity of voltage measurements. Therefore, a decentralized DERMS approach is developed by leveraging the concepts of state estimation. The framework and the effectiveness of the solution approach are numerically demonstrated on a real distribution feeder in Southern California.

A real-time multi-objective optimization framework for wind farm integrated power systems

Wind energy is a rising industry that is rapidly influencing the global energy environment, leading to pollutant mitigation and ultimately helping to shift the climate change paradigm. The incorporation of wind energy into power grids poses new challenges from the point of view of operating performance for transmission network operators. The inconsistent nature is the most significant issue with wind energy. Wind power differs from time to time and the operator must choose the operation strategy accordingly. In this study, a real-time multiple objective optimal power flow framework is proposed to solve this issue. The related dynamic optimization problem is solved online and the control variables are optimized simultaneously. To guarantee both the feasibility and performance of the proposed method, a multi-objective particle swarm optimization with decomposition is developed. Using modified IEEE 30-bus and IEEE 57-bus test systems, the validation of the proposed approach is described in this research.

Dynamic optimal power flow with cross entropy covariance matrix adaption evolutionary strategy for systems with electric vehicles and renewable generators

Modern power systems which integrate renewable energy sources (RESs), such as wind or solar energy sources and plug-in electric vehicles (PEVs), need to carry out the uncertainty due to the access of demanded or injected power. Therefore, it is necessary to consider uncertainty costs in optimal power flow problems. To develop the uncertainty cost functions, log-normal probability distribution function (PDF) for solar irradiance, normal PDF for unloading and loading behavior of PEVs, and Rayleigh PDF for wind speed are considered. In this work, a dynamic optimal power flow (DOPF) model is formulated by considering the uncertainties of RESs and PEVs. A newly hybrid metaheuristic algorithm named cross entropy (CE) covariance matrix adaption evolutionary strategy (CMAES) is proposed to evaluate the DOPF problem in a modified IEEE 118-bus system. The application of adaptive step length in the CMAES method improves the local exploitation capability, while the CE method is used in the initial stage for global exploration due to its fast convergence. Simulation results indicate that the proposed technique can solve the DOPF problems with RESs and PEVs effectively and can provide high-quality solutions compared to different techniques. The conventional statistical method called ANOVA test is performed for comparative analysis of different techniques. The results of this test show the validation of CE-CMAES compared to different optimization techniques. Therefore, the proposed method could be easily and efficiently applied by power system operators in real-time optimal power flow operations.

Real-time optimal power flow solution for wind farm integrated power system using evolutionary programming algorithm

In order to satisfy the increasing demand for electricity, a huge burden is levied on the conventional coal-fired thermal power generation units, which affects the environment and human health adversely. To tackle this problem, the clean renewable sources of energy have gained more attention. With time, wind power is expected to contribute more significantly and should be used as much as possible. This has provided much focus to the wind farm integrated power system. But, the unsteady behavior of wind energy has made the operation of wind farm integrated power system more complex. Scenario generation methods are used for tackling the unsteady behavior of wind energy. A persistence method of scenario generation is proposed for solving real-time optimal power flow problem in wind farm integrated power system. The proposed persistence method performs better compared to other recent methods in the literature.