Power System Problems Research Articles

Reinforcement Learning Model-Based and Model-Free Paradigms for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions

This paper reviews recent works related to applications of reinforcement learning in power system optimal control problems. Based on an extensive analysis of works in the recent literature, we attempt to better understand the gap between reinforcement learning methods that rely on complete or incomplete information about the model dynamics and data-driven reinforcement learning approaches. More specifically we ask how such models change based on the application or the algorithm, what the currently open theoretical and numerical challenges are in each of the leading applications, and which reinforcement-based control strategies will rise in the following years. The reviewed research works are divided into “model-based” methods and “model-free” methods in order to highlight the current developments and trends within each of these two groups. The optimal control problems reviewed are energy markets, grid stability and control, energy management in buildings, electrical vehicles, and energy storage.

Reliability evaluation of direct current gathering system in onshore wind farm based on reliability block diagram-sequential monte carlo

The full DC wind power generation system has effectively overcome the harmonic resonance, reactive power transmission, and other problems of the traditional AC wind power system, which has broad prospects for development. As a key component of the mentioned system, the reliability of the collection system is critical to the safe and stable operation of the entire onshore wind farm. Firstly, this paper investigates the key equipment and topology of the onshore wind farm DC collection system. Secondly, considering both the internal components and external environment of the wind farm, a component outage probability model based on weather factors is constructed to provide accurate data for the reliability evaluation of the DC collection system of the wind farm. The Reliability Block Diagram is used to analyze the internal logical connection of different topologies of onshore wind farm DC collection systems in detail. Then, a reliability evaluation method of an onshore full DC wind farm collection system based on Reliability Block Diagram-Sequential Monte Carlo is proposed. Finally, a 50 MW onshore wind farm is studied as a sample to compare and analyze the assessment results of the reliability of different collection system topologies. The results show that the reliability of the DC collection system of onshore wind farms has significant advantages.

Stochastic dual dynamic programming for optimal power flow problems under uncertainty

Planning in the power sector has to take into account the physical laws of alternating current (AC) power flows as well as uncertainty in the data of the problems, both of which greatly complicate optimal decision making. We propose a computationally tractable framework to solve multi-stage stochastic optimal power flow (OPF) problems in AC power systems. Our approach uses recent results on dual convex semi-definite programming (SDP) relaxations of OPF problems in order to adapt the stochastic dual dynamic programming (SDDP) algorithm for problems with a Markovian structure. We show that the usual SDDP lower bound remains valid and that the algorithm converges to a globally optimal policy of the stochastic AC-OPF problem as long as the SDP relaxations are tight. To test the practical viability of our approach, we set up a case study of a storage siting, sizing, and operations problem. We show that the convex SDP relaxation of the stochastic problem is usually tight and discuss ways to obtain near-optimal physically feasible solutions when this is not the case. The algorithm finds a physically feasible policy with an optimality gap of 3% and yields a significant added value of 27% over a rolling deterministic policy, which leads to overly optimistic policies and underinvestment in flexibility. This suggests that the common industry practice of assuming direct current and deterministic problems should be reevaluated by considering models that incorporate realistic AC flows and stochastic elements in the data as potentially more realistic alternatives.

Model‐free adaptive load frequency control for power systems with wind penetration under deregulation environment

AbstractWith the gradual deregulation of the power system by the power department, the power system has developed into a large‐scale and multiregional control system. Because of the power system internal complexity enhancing, the stable operation of power system becomes increasingly difficult. This paper analyzes the load frequency control problem of multiregional interconnected power system with wind energy. This study designs an improved model‐free adaptive control algorithm based on I/O data. It avoids model establishment of the multiregional power system. It also effectively solves the problem of frequency stability control under the influence of load change, introducing the generation participation matrix to simulate bilateral contracts under the power market. The dynamic evolution relationship of the system with the generation participation matrix is established, taking a three‐regional power system with wind energy as an example. Frequency fluctuations in all three regions are between . Convergence times of frequency deviation are all within 30 s, much less than the response time of load frequency control. The simulation results further demonstrate the effectiveness of the proposed algorithm, comparing the control algorithm proposed in this paper with other algorithms, which proves that the proposed algorithm has good control performance.

Application on power system economic dispatch of marine predator algorithm improved by asymmetric information exchange

The solution to the economic dispatch (ED) problem for power systems allows the power sector to reduce operating costs. However, the ED problem is a complex nonlinear and nonconvex optimization problem whose solution requires powerful algorithms. We propose a new version of the Marine Predator Algorithm (MPA), called IMPA, for solving complex ED problems. The algorithm introduces an asymmetric information exchange (AIE) mechanism, which not only accelerates to escape of local optima but also enriches the diversity of search. In this work, 12 benchmark functions were used to test the performance of the proposed algorithm IMPA. Then, the IMPA was used to solve the ED engineering problem of power system containing of 6, 13, 40, and complex 140 units. The minimum and average costs searched by IMPA are 1657962.7265$/h and 1657962.7265$/h, and they are much lower than the results of the MPA and NMPA, which means that our proposed improved IMPA improves the performance of MPA for solving the economic dispatch problem of large-scale power systems. The results show that the solutions obtained by IMPA are more competitive than those of MPA and NMPA, which provides an additional solution for cost reduction of the power system.

Intelligent distribution network fault monitoring integrating differential evolution and chaos whale optimization algorithm

Faced with rapid development and increasingly complex power grid structure structure of the power grid, it is necessary to accurately locate faults in modern intelligent distribution networks in order to ensure power supply reliability and improve power supply service quality. Aiming at the low positioning accuracy, long time consumption, and limited coverage types in existing positioning algorithms, a new intelligent distribution network fault positioning algorithm is designed by combining two algorithms to complete the monitoring task. Firstly, intelligent distribution network fault location methods under different distributed power grid connection methods are analyzed. Then, considering the distributed power grid connection, a fault location algorithm is designed by combining differential evolution algorithm, Sine chaotic mapping, and whale algorithm. The research results indicated that the designed method had good benchmark performance, with accuracy and recall values as high as 0.98 and 0.97, respectively. During the training process, it only required 175 iterations to reach a stable state. In practical applications, the accuracy of this algorithm in testing three types of faulty power grids was 98.90%, 98.41%, and 98.25%, respectively. The method can effectively improve the fault location accuracy, providing better positioning technology for fault monitoring problems in power systems.

A deep generative model for selecting representative periods in renewable energy-integrated power systems

The extensive integration of renewable energies into power systems has led to a challenging and computationally demanding scenario for the system planning. This is due to the increased number of time series involved and the greater complexity of time series data integrated into power systems. In this paper, a new deep learning-based time aggregation method is proposed for selecting representative periods for renewable energy-integrated power system datasets where multiple variable energy resources are present. Relying on the capabilities of deep generative models, especially Generative Adversarial Network (GAN), the proposed method selects a representative period, including one or more representative days, obtained from multiple time series with spatio-temporal correlations among them. The proposed approach contains the Long Short-Term Memory (LSTM) Network in both generator and discriminator parts. Also, it includes a loss term, specifically designed for clustering tasks, in the minimax objective of the vanilla GAN loss function to enhance the clustering performance in the latent space. Furthermore, the learning rate of the proposed model, as the most important parameter of the learning algorithm, is adaptively fine-tuned during the training process, based on the training error, to enhance its learning performance. Two real-world test cases with various datasets and time series are used for data-based and model-based evaluations. Results obtained on these two real-world test cases confirm the superiority of the proposed model with respect to the state-of-the-art conventional and deep learning-based models, obtaining the performance improvement in the range of [38.55 %, 46.14 %] in terms of Mean Absolute Percentage Error (MAPE), in the range of [45.75 %, 70.16 %] in terms of Root Mean Squared Error (RMSE), and in the range of [65.81 %, 74.58 %] in terms of Mean Absolute Error (MAE). The proposed model can significantly enhance the tractability and scalability of the planning problem of renewable energy-integrated power systems, facilitating renewable energy integration into power systems which is a key issue for net zero transitioning of communities.

Impact of electric vehicles and wave energy systems on OPF of power networks using hybrid Osprey-PSO approach

In this article, a novel optimization algorithm named hybrid Osprey Optimization and Particle Swarm Optimization (OOPSO) is proposed to solve the Probabilistic Optimal Power Flow (POPF) problem in power systems with high penetration of renewable energy resources (RERs), such as photovoltaic, wind, and wave energy and integration of electric vehicles (EVs). The algorithm's results are compared with established optimization algorithms like Osprey Optimization Algorithm (OOA), Particle Swarm Optimization (PSO), and Heap Optimization. Applied to the 30 and 118-bus IEEE test systems, the OOPSO algorithm efficiently deals with the uncertainties produced by RERs generation. This research study includes EV charging profiles for weekdays and weekends. It considers home and work-level charging scenarios. The RERs and EVs penetration ensure system reliability while minimizing generation costs and satisfying power flow constraints. Compared to existing algorithms, the OOPSO algorithm achieves superior performance, faster convergence, and higher solution accuracy. The simulation results show the effectiveness of OOPSO. It appears to be a robust tool for POPF, integrating RERs in power systems with EVs. This work highlights the potential of OOPSO as a feasible approach for power system optimization in the context of RERs and EV integration, paving the way for further research into unconventional grid optimization techniques.

A four-stage strategy for solving AC transmission expansion planning problem in large power system based on differential evolution algorithm and teaching–learning-based optimization algorithm

A four-stage strategy for solving AC transmission expansion planning problem in large power system based on differential evolution algorithm and teaching–learning-based optimization algorithm

Continuous Switch Model and Heuristics for Mixed-Integer Nonlinear Problems in Power Systems

Continuous Switch Model and Heuristics for Mixed-Integer Nonlinear Problems in Power Systems

Applications of Multi-Objective OPF Solutions with Optimal Placement of Multiple and Multi-Type FACTS Units to IEEE System: Comparison of Different Approaches

Optimal power flow (OPF) problem and its implications for power system stability and efficiency is investigated in this study. OPF, a restricted optimization query with non-linearity and non-convexity, is one of the most challenging and fascinating problems in the recent power system. Based on these parameters, researchers have been working hard over the past few decades to identify the best solutions to the OPF issue that maintain system stability. This work presents multi-objective OPF solutions utilizing Newton's technique with numerous multi-type FACTS units. First, the GA is applied to identify the perfect size and location of the FACTS units. Next, the generator and FACTS settings are optimized. In this instance, four scenarios are taken into consideration and three OFs are employed to see how the OFs affect the positioning and dimensions of FACTS devices. The OF is suggested to consider the reduction of both generation costs and transmission losses while also optimizing the power transfer capacity of designated corridors. A full analysis relating to the IEEE-30 bus system is presented and analyzed.

Design & Fabrication of Water Optimized - Solar Panel Cleaning Robot

Abstract: Maintaining a power capacity of solar cell is the main thing for exercising solar power system. Most common problems in solar power system are dust, beach, creature’s pollen and crops burning. These challenges have a huge impact on the power generation capacity of solar panel. The thing of the robot's conception design is to make a machine that's easy to install, adaptable in terms of configuration, and requires lower labourers and functional charges. The robot is equipped with helical comber skirmishes which are driven by dc motor so that the robot is enforced with a water sprinkle in order to clean the panels far and wide it moves through. In this design, a system contains a length dimension unit and bedded control schemes in order to drive a movement directly and to localize the positions of the robot.

A surrogate-assisted evolutionary algorithm with dual restricted Boltzmann machines and reinforcement learning-based adaptive strategy selection

To improve the effectiveness of surrogate-assisted evolutionary algorithms (SAEAs) in solving high-dimensional expensive optimization problems with multi-polar and multi-variable coupling properties, a new approach called DRBM-ASRL is proposed. This approach leverages restricted Boltzmann machines (RBMs) for feature learning and reinforcement learning for adaptive strategy selection. DRBM-ASRL integrates four search strategies based on three heterogeneous surrogate modeling approaches, each catering to different preferences. Two of these strategies focus on generative sampling in the subspaces with varying dimensions, while the other two aim to explore the local and global landscapes in the high-dimensional source space. This allows for more effective tradeoffs between exploration and exploitation in the solution space. Reinforcement learning is employed to adaptively prioritize the search strategies during optimization , based on the online feedback information from the optimal solution. In addition, to enhance the representation of potentially optimal samples in the solution space, two task-driven RBMs are separately trained to construct a feature subspace and reconstruct the features of the source space. DRBM-ASRL has been evaluated on various high-dimensional benchmarks ranging from 50 to 200 dimensions, as well as 14 CEC 2013 complex benchmark problems with 100 dimensions and a power system problem with 118 dimensions. Experimental results demonstrate its superior convergence performance and optimization efficiency compared to eight state-of-the-art SAEAs.

AC–DC security-constrained optimal power flow for the Australian National Electricity Market

This paper presents a new methodology to solve the integrated ac–dc security-constrained optimal power flow (SCOPF) problem for large-scale power systems, with a focus on the Australian National Electricity Market (NEM). The proposed SCOPF is based on a decomposition scheme that significantly reduces the dimensionality of the problem and thus improves the computational performance. It further supports a contingency ranking, evaluation, and non-dominated filtering algorithm, cyclic selection of filtered contingencies, careful choice of slack variables, and a tailored warm start to the nonlinear problem. The methodology is validated against the two-stage mathematical programming model that uses an explicit nonlinear model of both the base case and the contingency cases, at a slightly higher cost but significantly faster execution. Moreover, the NEM power system, is studied addressing different scenarios including load variations, renewable energy zone integration, and additional reinforcements like point-to-point and meshed HVDC. The proposed approaches provides preventive solutions in less than 5 min thus affirming the model’s effectiveness in real-time applications and planning studies.

Conceptual framework for determining the treewidth of distribution grids

While distribution grids are often operated radially, they are typically designed to be more redundant, so that each load has multiple connections to the main grid. For complex networks like these, the notion of treewidth can be used to quantify their complexity. In this paper, we propose a new conceptual framework and derive an exact formula for computing treewidth with the help of our constructs. We argue that our framework effectively captures complexities in the structure of distribution grids and has a potential to simplify the calculation of treewidth. After analysing our findings, we hypothesise that the treewidth of distribution grids will typically be low implying that some difficult power system problems can be solved on them in parameterised polynomial time with dynamic programming. We demonstrate this with an example problem of dividing a distribution grid into tree-like operational subgraphs around the primary substations so that no voltage violations occur.

VSG-LFC on multi-area power system under unsecured communication channel using hybrid trigger mechanism

This paper studies the frequency fluctuation problems of a multi-area power system with renewable energy sources (RESs) under denial-of-service (DoS) attacks. (i) Due to the decoupling of the RESs from power system using electronic devices, the RESs have no natural frequency response capability, which result in decreasing of entire power system equivalent inertia with large penetration of RESs. (ii) DoS attacks cause the insecurity of network communication, which can vanish data transmission and yield data packet dropout. To address the above issues, a virtual synchronous generator (VSG) incorporated with load frequency control (LFC) method (VSG-LFC) is proposed by introducing a hybrid-triggered mechanism (HTM) to (i) reduce the system sensitivity to the penetration level of RESs; (ii) compromise between frequency regulation performance and the reasonable usage of network resource under unsecured communication environments. Then, a switched system model is constructed considering non-identical DoS attack situations. Furthermore, theoretical analyses using Lyapunov functional related methods are carry out for asymptotical mean-square stability with H∞ performance on frequency regulation. Simulation evaluations of power system verify the effectiveness of this development. It shows that in a two-area power system frequency derivation performance indicators (IAE, ITAT, ISE) in Area1 are reduced by (82.996%, 86.273%, 85.823%) and in Area2 are reduces by (77.996%, 83.613%, 82.315%).

Investigation of reciprocal rank method for automatic generation control in two-area interconnected power system

In this article, the design and performance analysis of Jaya-assisted reciprocal rank-based proportional–integral–derivative controller with derivative filter (PIDn) is proposed for the automatic generation control (AGC) problem of two-area interconnected power systems. The filter with a derivative gain is used to nullify the impact of noise in the input signal. The integral of time multiplied square error (ITSE) of frequency deviations, tie-line power deviations, and area-control errors is the foundation of the goal function developed for tuning controller parameters (ACEs). A single overall objective function is created by combining these sub-objectives. ITSEs of two areas, ITSEs of tie-line power deviation, and ITSEs of ACEs of two areas are weighted and summarized to form the overall goal function. Each sub-relative goal’s importance in the control design is evaluated using the weights in the overall objective function. The weights in this article are determined using the reciprocal rank approach in a methodical manner, in contrast to earlier techniques where weights were either assumed equally by ignoring the relative relevance of sub-objectives or chosen at random. The resultant total objective function is minimized using the Jaya method. Six different test cases involving various load disturbances in the interconnected areas are used to evaluate the performance of the suggested Jaya optimizer-assisted reciprocal rank technique-based controller. Additionally, the performance of various controllers tuned with the sine cosine algorithm (SCA), salp swarm algorithm (SSA), symbiotic organisms search (SOS), Nelder–Mead simplex (NMS), and Luus–Jaakkola (LJ) algorithms is compared with that of the Jaya-tuned controller. The time domain specifications are provided for each of the six test scenarios. To display the differences in frequencies and tie-line power, the results are also shown. Statistical analysis is also provided to assess the suggested controller’s overall efficacy.

Optimizing ELD in power systems applying GWO: A Practical Approach

Addressing the Economic load Dispatch (ELD) Problem in power systems is crucial for minimizing generation cost and transmission losses while meeting the load demand. This research explores the application of Grey Wolf Optimization (GWO) to solve the ELD problem, leveraging ‘GWO’s inspiration from grey wolf social behavior. Through simulation, ‘GWO’s superior convergence speed and solution quality compared to traditional techniques is demonstrated. The finding highlights ‘GWO’s effectiveness in enhancing the economic and operational efficiency of power systems, offering promising avenues for sustainable energy management strategies.

Optimal power dispatch in hybrid power system for medium- and large-scale practical power systems using self-adaptive bonobo optimizer algorithm

Incorporating renewable energy sources (RESs) introduces a notable amount of uncertainty in the optimal planning and operation of electrical power grids. Under these circumstances, this paper proposes the application of a recently introduced metaheuristic optimization technique to solve the stochastic optimal power flow (OPF) problem involving wind and solar power sources. The self-adaptive bonobo optimizer (SaBO) is used to minimize three distinct objective functions: (i) Total generation cost (TGC) minimization, including both thermal and wind/solar generation costs, (ii) Power loss minimization, (iii) Combined generation cost and emissions effect minimization. The costs associated with the stochastic generation of wind and solar power included direct costs, reserves and penalty costs from the overestimation and underestimation of available wind and solar power, respectively. The performance of the proposed algorithm is evaluated on two power systems: the modified IEEE 30-bus and the Algerian DZA 114-bus test systems. To demonstrate the efficacy of the SaBO, the obtained results have been compared with those obtained from the Kepler optimization algorithm (KOA) and other recently published optimizers under the same case studies and constraints. The comparative results clearly show the superiority of the SaBO algorithm over all other well-known optimization algorithms provided in the literature for solving the OPF problem. This is evidenced by minimizing total generation costs of 781.2363 $/h for the modified IEEE 30-bus and 16,706.1630 $/h for the Algerian DZA-114-bus system. Furthermore, the integration of RES led to a notable 2.33% and 11.67% reduction in total generation cost for the IEEE 30-bus and Algerian DZA 114-bus systems, respectively, compared to their initial configurations without RESs. The promising findings highlight the powerful of the optimizer to solve non-linear and complex optimization problems in power systems.

Boosting cuckoo optimization algorithm via Gaussian mixture model for optimal power flow problem in a hybrid power system with solar and wind renewable energies

This paper presents a novel approach, the Gaussian Mixture Method-enhanced Cuckoo Optimization Algorithm (GMMCOA), designed to optimize power flow decision parameters, with a specific focus on minimizing fuel cost, emissions, network loss, and voltage deviation. GMMCOA integrates the strengths of COA and GMM while mitigating their individual limitations. While COA offers robust search capabilities, it suffers from initial parameter dependency and the risk of getting trapped in local optima. Conversely, GMM delivers high-speed performance but requires guidance to identify the best solution. By combining these methods, GMMCOA achieves an intelligent approach characterized by reduced parameter dependence and enhanced convergence speed. The effectiveness of GMMCOA is demonstrated through extensive testing on both the IEEE 30-bus and the large-scale 118-bus test systems. Notably, for the 118-bus test system, GMMCOA achieved a minimum cost of $129,534.7529 per hour and $103,382.9225 per hour in cases with and without the consideration of renewable energies, respectively, surpassing outcomes produced by alternative algorithms. Furthermore, the proposed method is benchmarked against the CEC 2017 test functions. Comparative analysis with state-of-the-art algorithms, under consistent conditions, highlights the superior performance of GMMCOA across various optimization functions. Remarkably, GMMCOA consistently outperforms its competitors, as evidenced by simulation results and Friedman examination outcomes. With its remarkable performance across diverse functions, GMMCOA emerges as the preferred choice for solving optimization problems, emphasizing its potential for real-world applications.