Optimal power flow control optimisation problem incorporating conventional and renewable generation sources: a review

In recent decades, the energy market around the world has been reshaped to accommodate the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system is performed using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of simulation and statistical results of EO with other optimization techniques showed that EO is more effective and superior and provides the lowest optimization value in term of electric power generation, real power loss, emission index and voltage deviation.

This paper proposes a novel approach for Optimal Power Flow (OPF) problems in power systems by integrating the optimal placement of Flexible Alternating Current Transmission System (FACTS) devices with a hybrid renewable energy system comprising wind and photovoltaic (PV). The objective is to optimize the placement of FACTS devices to simultaneously minimize power loss, voltage deviation and reduce gross cost. To achieve this goal, the Enhanced Multi – Strategies Sparrow Search Algorithm (EMSSA) is employed, which combines multiple optimization strategies inspired by the collaborative foraging behavior of sparrows. By considering multiple objectives, including power loss reduction, voltage deviation reduction, and gross cost minimization, the proposed methodoloty aims to identify optimal locations for installing FACTS devices such as SVC (Static Var Compensator), TCSC (Thyristor Controlled Seris Compensator), TCPS (Thyristor Controlled Phase Shifter). The results showcase the capability of the proposed methodology to achieve significant improvements in power system performance, thereby contributing to the efficient and sustainable operation of modern power grids. This research also provides valuable insights for power system operators and planners to make decisions regarding the deployment of FACTS devices and the integration of renewable energy sources.

The optimal power flow (OPF) problem is one of the most popular and important issues that researchers need to solve in power systems. In this study, a modified weighted teaching-learning-based optimization (WTLBO) algorithm was designed and implemented for the OPF problem, regarding the teaching-learning-based optimization (TLBO) algorithm, which is one of the meta-heuristic algorithms. To demonstrate the effectiveness of TLBO and developed WTLBO algorithms in OPF solutions, six different single/multi-objective functions consisting of targets, such as fuel cost, total active power losses, and voltage deviation were tested on standard IEEE 30 and 57 bus systems. Multi-objective functions were transformed into a single objective function using the weighted sum method. Analysis results were compared with different optimization algorithms used in the literature. When the developed WTLBO algorithm is compared according to the single/multi-objective functions in OPF solutions, it has been proven that it performs better than the original TLBO algorithm and other algorithms in the literature. As a result of the analysis, according to the TLBO algorithm, in the IEEE 30 bus power system, in the proposed WTLBO algorithm, a decrease of 0.05% in the total fuel cost, 2.20% in active power losses, and 13.37% in voltage deviation are observed.

Optimal power flow (OPF) is an ideal method of optimally utilizing power system resources. Its effect further enhances the presence of FACTS devices. Performing OPF in combination with a FACTS device may also be helpful for the improvement of power system stability during outage conditions. In the present work, a combined index-based strategy for the optimal placement of Thyristor-Controlled Series Compensator (TCSC) and optimal tuning of generators using the harmony search algorithm is proposed for improving the system stability. The projected technique is verified and implemented on IEEE 30 bus system. The system is tested at both normal and contingency conditions. The contingency analysis is done using a new method, namely rapid contingency ranking technique (RCRT). The TCSC has been placed on the basis of an index which is a combination of line utilization factor (LUF) and fast voltage stability index (FVSI). A multi-objective function has been chosen for tuning the generators. The multi-dimensional function includes deviation in voltage, cost of power generation, and loss of transmission line. The outcomes of the proposed method are also compared to a method, i.e., genetic algorithm.

The increasing penetration of wind generation in electric power systems has made evident the necessity of a unified energy infrastructure interdependency analysis, in order to explore the optimal use of fossil fuel-based generation and the security constraints associated with different energy infrastructures. In this context, the optimal generation dispatch of online generating units is important in the daily operation planning. This paper proposes a multi-energy day-ahead optimal power flow (OPF) approach in order to assess the wind energy penetration in an electric power system and its impact on the fossil fuel-based generation management. The applicability of the proposed approach is demonstrated by integrating a 15-node natural gas network and a 4-node coal distribution system with the IEEE 118-bus power system.

Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement

Flexible AC transmission systems (FACTS) and optimal power-flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the power generation profiles from renewable energy sources, solar and wind systems, and determining the optimal locations and sizes of FACTS devices increase the complexity of the OPF problems in modern power network models, such as transmission power loss, power generation operation cost and voltage deviation, as a highly nonlinear-nonconvex optimization problem. Therefore, this article introduces and employs four new independent, reliable and efficient optimization algorithms inspired by nature and biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem-based Optimization (AEO), Marine Predators Algorithm (MPA) and Jellyfish Search (JS), for solving both multi- and single-OPF objective problems for a power network incorporating FACTS and stochastic renewable energy sources. The proposed new metaheuristic optimization techniques are compared to the common and available alternatives in the literature, Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO) and Grey Wolf Optimizer (GWO), using IEEE 30-bus test system. To consider and address the challenges of the OPF in modern power network models, the proposed optimization techniques tested under different operation cases such as an increasing in the load, with and without FCTAS and renewable energy sources, different renewable energy sources locations on the network. The result showed that the MPA, SMA, JS and AEO algorithms are more effective solvers for the OPF problems cases compared to the PSO, GWO and MFO algorithms. For example, the AEO obtained 0.0844 p.u. in case of minimizing the voltage deviation compared to 0.1155 p.u. for PSO, which means that the AEO algorithm improved the voltage deviation term by 27% compared to the PSO algorithm.

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model’s better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

Optimal power flow with transient stability (OTS) is a nonlinear optimisation problem in functional space that can be transformed to a static optimisation problem via constraint transcription techniques. However, the solution of the transformed OTS by ordinary numerical methods involves a massive calculation due to the stability constraints. Hence, the authors propose a penalty-based approach for OTS, in which the adjoint equation method is applied to evaluate the gradient of the penalty term associated with the stability constraints. It is demonstrated that the implementation of the adjoint equation method significantly reduces the computational burden in solving the OTS. A numerical test on a power system using prototype code is presented.

This work describes the benefits arising from the introduction of a hybrid wind - pumped storage unit into a non interconnected power system such as Rhodes' Island power system. Optimal power flow (OPF) is one of the main tools for optimal operation and planning of modern power systems. The OPF was applied to the Rhodes' power system which consists of thermal power plants and Renewable Energy Sources (RES, mainly wind turbines). In order to increase the RES penetration, pumped storage is applied. The pumped storage unit is considered to be connected to grid bus where a wind park exists. Actual data regarding the wind speed, load, thermal stations, lines, transformers and RES were imported in Matlab environment for the simulation of the power system. Various scenarios about the operation of the hybrid unit were examined. The scope was to achieve peak shaving during the high load demand period. Finally, the benefits of the investment were examined and evaluated. (9 pages)

Distributed generators (DGs) are nowadays most commonly used in distribution networks to meet the rising demand. The distribution network power flow is altered as more distributed generations are integrated with the distribution network. Analyzing and researching the network's power flow with distributed generation is an essential element of analyzing the distributed generation's influence on the networks. This study proposed new sites in the test network for the purpose of connecting DGs. Good results were obtained in improving the necessary network elements. Firstly, applying a Newton-Raphson method to measure the distribution network's power flow without distributed generations. Then the optimal power flow approach with DGs is set up based on many goals such as minimizing fuel cost, minimizing losses, voltage deviation, increasing efficiency, and the incremental fuel cost in the network appeared. Eventually, applying Particle Swarm to overcome the optimization problems. In MATLAB software environment and programing MATPOWER version 6.0, the simulation is evaluated and validated using the IEEE30 node method. The test results indicate that optimal power flow analysis with distributed generation will accomplish many networks aims and boost system economy.

Purpose The purpose of this study is to address the efficiency of power losses representation while still reducing the computational burden of an optimal power flow (OPF) model in transmission expansion planning (TEP) studies. Design/methodology/approach A modified TEP model is formulated with inclusions of linearized approximation of power losses for a large-scale power system with renewable energy sources. The multi-objectives function determines the effect of transmission line losses on the optimal power generation dispatch in the power system with and without inclusion of renewable energy sources with emphasis on minimizing the investment and operation costs, emission and the power losses. Findings This study investigates the impact of renewable energy sources on system operating characteristics such as transmission power losses and voltage profile. Sensitivity analysis of the performance for the developed deterministic quadratic programming models was analyzed based on optimal generated power and losses on the system. Research limitations/implications In the future, a comparison of the alternating current OPF and direct current (DC) OPF models based on the proposed mathematical formulations can be carried out to determine the efficiency and reduction of computation process of the two models. Practical implications This paper proposed an accurate way of computing transmission losses in DC OPF for a TEP context with a view of achieving a minimal computation time. Originality/value This paper addresses the following objectives: develop a modified DC OPF with a linearized approximation of power losses in TEP problem with large integration of RES. Investigate the impact of RES on system operating characteristics such as transmission power losses and voltage profile.

Consensus-based distributed optimal power flow using gradient tracking technique for short-term power fluctuations

With transportation electrification, the power systems and traffic systems are more and more interdependent on each other. To analysis this interdependence more concisely, an optimal traffic power flow problem is formulated with the help of alternating current (AC) optimal power flow and vehicle routing problem (VPR). These two problems are integrated into an unified time-space network (TSN), as a mixed integer programming problem. To solve the meso-scale level problems with multiple oriented destination pairs, the random permuted alternating direction method of multipliers is adopted to solve the formulated problem distributed. The case study is performed on the modified IEEE-30 and Sioux fall networks, and the results verify the effectiveness of the proposed scheme.

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.