Power System Problems Research Articles

Optimization of Electrical Power Systems Using Hybrid PSO-GA Computational Algorithm: a Review

Since the intelligent optimization algorithms has been successful in solving tested functions, the electrical power system researchers have done many studies and applications using optimal strategies. Any topic of power system has been optimized using those algorithms such as the quality, the stability, the voltage profile, the reliability, the smart grid, and the economic generations. With the increasing demand for electricity, the traditional methods to solve the optimization problems cannot meet the demand, and with the deepening of research, many emerging technologies are applied to the solution of power system optimization problems. However, when solving practical engineering problems, there are still many challenges. Genetic Algorithm and Particle Swarm Optimization are two of the most famous strategies that are applied to solve the power system problems. This paper focuses on the advantages of advance hybrid computational algorithm in order to optimize the performance of electrical power system. The detail of the hybrid of classical intelligent algorithm Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), which have been used to solve the power system problems, has been briefly summarized. In addition, the hybrid algorithm is classified into several types depending on the method of the computation between them. The parallel, the Serial, and the impeded hybrid are the main types of this classification. Finally, future suggestions of the important power system applications using the hybrid GA-PSO are introduced according to the corresponding methods, which have given good results for engineering systems.

AGGREGATIVE-DECOMPOSITION APPROACH FOR OPTIMAL CONTROL OF A HIERARCHICAL ERGATIC ELECTRIC POWER SYSTEM

This article discusses the optimization problems of energy associated with the optimal management of a complex ergatic electric power system. The search for solutions to these problems is proposed to be carried out on the basis of extremization of the target functional specified using the statistical model. In order to analyze the control process of a complex ergatic electric power system, it is proposed to apply the aggregative - decomposition approach, according to which models of complex power supply systems are described by compositions of interconnected units. When searching for a solution to the optimization problem of controlling a complex electric power system, it is proposed to apply the method of stochastic programming, as well as the method of stochastic quasigradients. From the point of view of the object-oriented approach, it is proposed to consider complex ergatic electric power systems as a class of multi-level multi-purpose systems, which are the subject of cybernetics research. Moreover, the adequacy of algorithmic models for managing these systems is ensured by the implementation of compromise methods. To implement control functions at the highest hierarchical levels of complex electric power systems, it is proposed to create aggregated models of these systems using the descriptive aggregation procedure. The principles of coordination of mathematical programming problems of electric power systems are also considered, according to which local admissibility and consistency predetermines global admissibility.

A Genetic Based Algorithm for Short-Term Thermal Generating Unit Commitment.(Dept.E)

Unit Commitment is one of the most complex and difficult optimization problem in power systems. The objective of the optimal commitment is to determine the on/off states of the units in the system to meet the load demand and spinning reserve requirements at each time period, such that the overall cost of generation is minimized, while satisfying the various operational constraints. This paper presents the application of an improved genetic algorithm (GA) with crossover, mutation, and advanced operators such as repair and swap mutation to determine the commitment order of the thermal units in power generation. The proposed GA has been successfully applied to 10 and 26 generating-unit systems. Robustness of the proposed GA is demonstrated by comparison to the dynamic programming algorithm. The comparison results show the effectiveness of the proposed GA in solving the unit commitment problem with sufficient accuracy and low computational time.

Solution to the economic dispatch problem of the Nigerian power system using genetic algorithm

This paper aims to solve the economic dispatch problem of the 28-bus Nigerian power system using genetic algorithm. The power flow solution of the network is first obtained using Newton-Raphson technique; the solution thus obtained is used to determine the loss coefficients of the network. For this study, a forecasted load demand of 2000MW will be considered, MATLAB's genetic algorithm optimization toolbox is used to obtain the optimum generation level of each unit. The optimal power output of each scheduled generating units was obtained after 200 iterations at a minimal generation cost of ₦136,370.205/hr. A power loss of 11.32MW in the network was also obtained using Kron's loss formula. Keywords: Genetic Algorithm, Economic Dispatch, loss coefficient, Optimization, Constraint

Distributed machine learning for energy trading in electric distribution system of the future

Machine Learning (ML) has seen a great potential to solve many power system problems along with its transition into Smart Grid. Specifically, electric distribution systems have witnessed a rapid integration of distributed energy resources (DERs), including photovoltaic (PV) panels, electric vehicles (EV), and smart appliances, etc. Electricity consumers, equipped with such DERs and advanced metering/sensing/computing devices, are becoming self-interested prosumers who can behave more actively for their electric energy consumption. In this paper, the potential of distributed ML in solving the energy trading problem among prosumers of a future electric distribution system - building DC grid cell, is explored, while considering the limited computation, communication, and data privacy issues of the edge entities. A fully distributed energy trading framework based on ML is proposed to optimize the load and price prediction accuracy and energy trading efficiency. Computation resource allocation, communication schemes, ML task scheduling, as well as user sensitive data preserving issues in the distributed ML framework are addressed with consideration of all the economic and physical constraints of the electric distribution systems.

Peer Review of Renewable Energy-based Adaptive Protection(s) & Relay Coordination Optimization Techniques

Background: Distributed Sustainable Energy Generation (DSEG) has become a leading trend in modern R&D to achieve practical, financial, and environmental benefits. With respect to the power system, the benefits are minimization of power failures, enhancement of energy quality & reliability, congestion relief, and smart/micro grid demand response. The widespread use of modern technologies encourages the use of DSEG for safe power production and power loss reduction. Besides, it also poses new challenges such as optimal placement of relays, protection device settings, and voltage control problems in power systems. : Among various solutions reported in the literature, a widely accepted solution seems to be the deployment of Fault Current Limiter (FCL) to minimize DSEG impacts. However, FCLs are an expensive option to restore relay coordination. The adaptive protection scheme provides benefits to the smart grid's advanced features and serves as an efficient solution to address new DSEG and FCL challenges. Objective: This paper presents an extensive review of the effect of DSEG on distribution systems from the safety point of view, different optimization techniques used for adaptive protections, optimal relay coordination, and merits & demerits of FCL. Methods: The paper discusses various recent optimization and hybrid optimization techniques used for solving the Optimal Relay Coordination model (ORC). Adaptive and FCL based non-adaptive techniques are also discussed to solve this model. An attempt has also been made to present a comparative study of various optimization techniques in tabular form, considering different bus models and their outcomes. Conclusion: Comparative study of these techniques shows that Water cycle optimization, hybrid whale-GWO and GSA-SQP algorithms give better results in solving the ORC problem. FCL based non-adaptive technique was found better as compared with adaptive techniques because a greater number of DGs can be integrated on a grid with less complexity.

Internal Benchmarking of Thermal Power Plants оf Electric Power Systems

Improving the operational efficiency (OE) of thermal power plants is one of the most important problems of electric power systems (EPS). According to modern concepts, efficiency is the simultaneous consideration of three properties of objects, viz. economy, reliability and safety. The methodology of their joint assessment assumes that the service life of the main equipment does not exceed the standard value, but this condition is now met by less than half of the production enterprises of a lot of EPS. In order to increase OE, it is necessary, first of all, to learn how to objectively compare the performance of objects both of the same type – in a given time interval, and unique ones – in adjacent intervals. Existing methods for calculating integrated performance indicators do not fully take into account the random nature of technical and economic indicators (TEI). The article presents a new method for comparing the OE of EPS objects, the essence of which is to switch from joint consideration of TEI to analysis of their relative changes in comparison with the factory default value (nominal value). Relative values of indicators characterize the amount of wear or residual life. In this case, for example, the arithmetic mean of the relative values of the TEI determines the average wear of the object. This physical representation enlivens integral indicators, and their comparison and ranking ceases to be science-intensive. It is proposed to take into account also the degree of variation of relative deviations (wear), which is adequate to the object’s misalignment. It manifests itself in a significant change (deterioration) of one or (less often) two relative values of the TEI in the calculated time interval (month) and is characterized by such statistical indicators as the geometric mean and the coefficient of variation of relative deviations. Herewith, if the arithmetic mean value of the object’s wear is restored during major repairs, then the misalignment is eliminated much faster – during current repairs. A necessary condition for the feasibility of using these or those integral indicators is their functional and statistical independence. The results of the studies performed using the simulation method made it possible to establish that the smallest correlation occurs between the integral indicator calculated as the arithmetic mean of random variables and the integral indicator calculated as the coefficient of variation of the same random variables. Comparison of correlation fields clearly confirms these conclusions.

Tap changer optimisation using embedded differential evolutionary programming technique for loss control in power system

Over-compensation and under-compensation phenomena are two undesirable results in power system compensation. This will be not a good option in power system planning and operation. The non-optimal values of the compensating parameters subjected to a power system have contributed to these phenomena. Thus, a reliable optimization technique is mandatory to alleviate this issue. This paper presents a stochastic optimization technique used to fix the power loss control in a high demand power system due to the load increase, which causes the voltage decay problems leading to current increase and system loss increment. A new optimization technique termed as embedded differential evolutionary programming (EDEP) is proposed, which integrates the traditional differential evolution (DE) and evolutionary programming (EP). Consequently, EDEP was for solving optimizations problem in power system through the tap changer optimizations scheme. Results obtained from this study are significantly superior compared to the traditional EP with implementation on the IEEE 30-bus reliability test system (RTS) for the loss minimization scheme.

Economical operation strategy of an integrated energy system with wind power and power to gas technology – a DRL‐based approach

With the rapid development of artificial intelligence, adopting advanced deep reinforcement learning (DRL) methodologies to solve the optimisation problem in power systems has become more effective. This study proposes a novel energy control method associated with DRL to solve the economical optimisation problems in an integrated energy system with wind power and power-to-gas technology. To consider the randomness of wind power and the flexibility of upper-level energy prices, the economical optimisation problem is formulated as a finite Markov decision-making process. Cycling decay learning rate deep deterministic policy gradient (CDLR-DDPG) algorithm is proposed to obtain the optimal operation strategy. A comparison among different benchmark methods is provided to demonstrate the superiority of CDLR-DDPG algorithm in optimising an economical problem for the considered system.

Impact of grid partitioning algorithms on combined distributed AC optimal power flow and parallel dynamic power grid simulation

The complexity of most power grid simulation algorithms scales with the network size, which corresponds to the number of buses and branches in the grid. Parallel and distributed computing is one approach that can be used to achieve improved scalability. However, the efficiency of these algorithms requires an optimal grid partitioning strategy. To obtain the requisite power grid partitionings, the authors first apply several graph theory based partitioning algorithms, such as the Karlsruhe fast flow partitioner (KaFFPa), spectral clustering, and METIS. The goal of this study is an examination and evaluation of the impact of grid partitioning on power system problems. To this end, the computational performance of AC optimal power flow (OPF) and dynamic power grid simulation are tested. The partitioned OPF-problem is solved using the augmented Lagrangian based alternating direction inexact Newton method, whose solution is the basis for the initialisation step in the partitioned dynamic simulation problem. The computational performance of the partitioned systems in the implemented parallel and distributed algorithms is tested using various IEEE standard benchmark test networks. KaFFPa not only outperforms other partitioning algorithms for the AC OPF problem, but also for dynamic power grid simulation with respect to computational speed and scalability.

Key technical parameters of a new distributed physical energy storage system

The energy storage technology is one of the advanced technologies to solve the problems of modern power system, and also an indispensable key technology for the construction of smart city power grid. A mechanical elastic energy storage (MEES) technology is proposed in this context. As a new physical energy storage technology, the MEES is still in the principle verification stage compared with other physical energy storage technologies researched and developed for many years. In this paper, the MEES system is introduced from the composition, the principle of energy storage/power generation, and the key technical parameters of energy storage. The advantages and disadvantages of MEES system are pointed out, and the future development direction of the MEES technology is clear.

Optimal coordinated design of PSS and UPFC-POD using DEO algorithm to enhance damping performance

Low-frequency oscillations (LFO) are an inevitable problem of power systems and they have a great effect on the capability of transfer and power system stability. The power system stabilizers (PSSs) as well as flexible AC transmission system (FACTS) devices can help to damp LFO. The target of this study is to tackle the problem of a dual-coordinated design between PSS and unified power flow controller (UPFC) implementing the task of power oscillation damping (POD) controller in a single machine infinite bus (SMIB) system. So, dolphin echolocation optimization (DEO) technique is utilized as an optimization tool to search for optimal parameter tunings based on objective function for enhancing the dynamic stability performance for a SMIB. DEO an algorithm has a few parameters, simple rules, provides the optimum result and is applicable to a wide range of problems like other meta-heuristic algorithms. Use DEO gave the best results in damping LFO compared to particle swarm optimization (PSO) algorithm. From the comparison results between PSO and DEO, it was shown that DEO provides faster settling time, less overshoot, higher damping oscillations and greatly improves system stability. Also, the comparison results prove that the multiple stabilizers show supremacy over independent controllers in mitigationg LFO of a SMIB.

A Classification Method for Power-Quality Disturbances Using Hilbert–Huang Transform and LSTM Recurrent Neural Networks

Power quality disturbances are one of the main problems in an electric power system, where deviations in the voltage and current signals can be evidenced. These sudden changes are potential causes of malfunctions and could affect equipment performance at different demand locations. For this reason, a classification strategy is essential to provide relevant information related to the occurrence of the disturbance. Nevertheless, traditional data extraction and detection methods have failed to carry out the classification process with the performance required, in terms of accuracy and efficiency, due to the presence of a non-stationary and non-linear dynamics, specific of these signals. This paper proposes a hybrid approach that involves the implementation of the Hilbert–Huang Transform (HHT) and long short-term memory (LSTM), recurrent neural networks (RNN) to detect and classify power quality disturbances. Nine types of synthetic signals were reproduced and pre-processed taking into account the mathematical models and their specifications established in the IEEE 1159 standard. In order to eliminate the presence of mode mixing, the ensemble empirical decomposition (EEMD) and masking signal methods were implemented. Additionally, based on the successful benefits of LSTM RNNs reported in the literature, associated to the high accuracy rates achieved at learning long short-term dependencies, this classification technique is implemented to analyze the sequences obtained from the HHT. Based on the experimental results, it is possible to show that the ensemble recognition approach using the EEMD yields a better classification accuracy rate (98.85%) compared with the masking signal and the traditional HHT approach.

Sizing energy storage to reduce renewable power curtailment considering network power flows: a distributionally robust optimisation approach

The limited reserve of fossil fuels and public awareness of environmental issues prompt the rapid development of renewable energy generation. However, the centralised utilisation of renewable energy in bulk power systems is impeded mainly by its volatile nature and transmission congestion, leading to the spillage of renewable power. The energy storage unit is expected to be a promising measure to smooth the output of renewable plants and reduce the curtailment rate. This study addresses the energy storage sizing problem in bulk power systems. To capture the operating status of the power system more accurately, the authors use a dedicated power flow model which involves voltage and reactive power. The uncertainty of renewable generation is described via inexact probability distributions encapsulated in a data-driven Wasserstein-metric based ambiguity set, based on which the renewable energy curtailment rate is formulated as a distributionally robust chance constraint. The objective is to minimise the total investment cost, and the optimal sizing problem gives rise to a distributionally robust chance-constrained program, and is reformulated as a tractable linear program via conservative approximation. Case studies conducted on the modified IEEE 30-bus and 118-bus systems demonstrate the effectiveness and performance of the proposed approach.

A multi-objective approach for solving transmission expansion planning problem considering wind power uncertainty

Security-constrained transmission expansion planning (SCTEP) is a vital power system problem. In last years, to attain energy sustainability, wind power has received significant attention as one of the popular renewable energy resources. This paper solves multi-objective optimization of TEP considering uncertainty of wind power. The uncertainty is modelled by Weibull probability distribution function and Monte-Carlo simulation is employed to consider the uncertainty into the problem. In the problem, the objectives are investment cost of new lines and expected energy not supplied obtained by N-1 security constraint criterion. Since SCTEP is a complex, large-scale, mixed-integer and non-linear combinatorial optimization problem, multi-objective crow search algorithm and multi-objective particle swarm optimization are utilized to find Pareto front. On modified 24-bus IEEE system, the optimization framework is investigated and the results are discussed.

Power Systems Resilience Metrics: A Comprehensive Review of Challenges and Outlook

Recently, there has been a focus on natural and man-made disasters with a high-impact low-frequency (HILF) property in electric power systems. A power system must be built with “resilience” or the ability to withstand, adapt and recover from disasters. The resilience metrics (RMs) are tools to measure the resilience level of a power system, normally employed for resilience cost–benefit in planning and operation. While numerous RMs have been presented in the power system literature; there is still a lack of comprehensive framework regarding the different types of the RMs in the electric power system, and existing frameworks have essential shortcomings. In this paper, after an extensive overview of the literature, a conceptual framework is suggested to identify the key variables, factors and ideas of RMs in power systems and define their relationships. The proposed framework is compared with the existing ones, and existing power system RMs are also allocated to the framework’s groups to validate the inclusivity and usefulness of the proposed framework, as a tool for academic and industrial researchers to choose the most appropriate RM in different power system problems and pinpoint the potential need for the future metrics.

Optimal power flow solution with stochastic wind power using the Lévy coyote optimization algorithm

Optimal power flow (OPF) is one of the most fundamental single/multi-objective, nonlinear, and non-convex optimization problems in modern power systems. Renewable energy sources are integrated into power systems to provide environmental sustainability and to reduce emissions and fuel costs. Therefore, some conventional thermal generators are being replaced with wind power sources. Although wind power is a widely used renewable energy source, it is intermittent in nature and wind speed is uncertain at any given time. For this reason, the Weibull probability density function is one of the important methods used in calculating available wind power. This paper presents an improved method based on the Levy Coyote optimization algorithm (LCOA) for solving the OPF problem with stochastic wind power. In the proposed LCOA, Levy Flights were added to the Coyote optimization algorithm to avoid local optima and to improve the ability to focus on optimal solutions. To show the effect of the novel contribution to the algorithm, the LCOA method was tested using the Congress on Evolutionary Computation-2005 benchmark test functions. Subsequently, the solution to the OPF problem with stochastic wind power was tested via the LCOA and other heuristic optimization algorithms in IEEE 30-bus, 57-bus, and 118-bus test systems. Eighteen different cases were executed including fuel cost, emissions, active power loss, voltage profile, and voltage stability, in single- and multi-objective optimization. The results showed that the LCOA was more effective than the other optimization methods at reaching an optimal solution to the OPF problem with stochastic wind power.

Distributed economic MPC for LFC of multi-area power system with wind power plants in power market environment

The rising penetration of renewable energy and the diverse power transaction mechanisms in power market environment (PME) make the control problem of the power system more difficult. Considering environmental pressure and market competition pressure, it is more important to ensure the economic operation of power system in the control process. This paper presents a novel distributed economic model predictive control (DEMPC) strategy for the load frequency control (LFC) problem of a practical multi-area power system incorporating wind power plants (WPPs) in PME. Meanwhile, the real time economic optimization is achieved by integrating the economic factors into the objective function. A distribution company (DISCO) participation matrix (DPM) is applied to visualize the liberalization of power transactions between the DISCOs and dispatchable generation companies (DGENCOs). Area participation factors (apfs) are introduced to present the allocation of uncontracted load demands. The DEMPC controllers solve the optimization problem of power system with external disturbances and constraints including frequency fluctuation constraint, control input constraints, output power of WPP constraint and pitch angle constraint of the wind turbine. Response results for a three-area power system including WPPs and thermal power plants demonstrate the certain improvement of the system performance with the participation of wind turbines and the high-efficiency of the proposed DEMPC strategy.

Novel design of artificial ecosystem optimizer for large-scale optimal reactive power dispatch problem with application to Algerian electricity grid

Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics, and its complexity increases as a number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimization algorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including non-living elements such as sunlight, water, and air. The main merit of this optimizer is its high flexibility that leads to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, three-objective version of ORPD problem is considered involving active power losses minimization and voltage deviation and voltage stability index. The proposed optimizer was examined on medium- and large-scale IEEE test systems, including 30 bus, 118 bus, 300 bus and Algerian electricity grid DZA 114 bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques. Also, the results of comparison show that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem.

A New Aggrandized Class Topper Optimization Algorithm to Solve Economic Load Dispatch Problem in a Power System.

Optimization techniques are widely being used to solve large and complex economical load dispatch (ELD) and combined emission economical dispatch (CEED) problems in power systems. These techniques can solve these problems in a short computational time. In this article, a new human intelligence-based metaheuristic optimization technique, that is, aggrandized class topper optimization (CTO), is proposed to solve ELD and CEED problems. This proposed algorithm is an upgraded form of classical CTO in which the concept of remedial classes is incorporated to enhance the learning ability of weak students of a class. To validate the exploration, exploitation, convergence, and local minima avoidance capabilities of the proposed algorithm, 29 benchmark functions are considered. Furthermore, seven different test cases for the ELD problem and four test cases for a CEED problem are considered to test the effectiveness of the proposed algorithm to solve these complex problems. The result analysis proves that the proposed algorithm provides better and effective results in almost each test case.