Unit Test System Research Articles

On Employing a Constrained Nonlinear Optimizer to Constrained Economic Dispatch Problems

Recently, different metaheuristic techniques, their variants, and hybrid forms have been extensively used to solve economic load dispatch (ELD) problems with and without valve point loading (VPL) effects. Due to the randomization involved in these metaheuristic techniques, one has to perform extensive runs for each experiment to get an optimal solution. The process may sometimes become laborious and time-consuming to converge to an optimal solution. On the other hand, advanced calculus-based techniques, being deterministic, perform iteration systematically and come up with the same solution on each run of the experiment. Since ELD problems are constrained optimization problems, we are proposing the constrained (deterministic) optimization algorithm for their solutions. Various 13-unit, 38-unit, and 40-unit thermal test systems are considered. Valve point loading (VPL) effects are also considered in some cases. Computer-based numerical results depict that the constrained optimization algorithm shows evidence of being almost as competitive in a total fuel cost as the metaheuristic optimization techniques, especially for the less-constrained ELD problems but with far reduced computation time. This finding validates the application of the constrained optimization technique to solve the economic dispatch problem.

Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units

The unit commitment problem is a complex and essential problem in the power generation field, which is solved to obtain the schedule of a large number of generating units to minimize the operating cost and the fulfillment of consumer load demand. The present work solves the unit commitment problem using quantum-inspired evolutionary algorithms with a novel elitist local search method (QIEA-ELS). The proposed algorithm solves the unit commitment problem efficiently and its applicability is verified on various unit test systems. The constraints are satisfied efficiently to find a feasible solution, the novel elitist search method is used to locally explore the search area around the fittest individual to find a better solution in its vicinity in genotype space represent by qubits. The solution of the unit commitment is carried out considering two small population sizes as suggested in earlier work by other authors using QIEA, though it can be extended using larger population size also. The computational time is also reduced by using the suggested method with a novel elitist local search (ELS) method. The results obtained after applying the proposed algorithm are found to better as compared to other well-known solution techniques.

Dynamic Economic Environmental Dispatch of Power System Using An Improved Whale Algorithm

With the global energy crisis and environmental problems becoming increasingly serious, the construction of a new power system containing multiple renewable energy sources for power generation has become a current research direction. In this study, a dynamic economic environmental dispatch model for power systems containing wind and thermal power generation is proposed to promote renewable energy consumption while ensuring system power balance. Firstly, a comprehensive objective function that integrates system operating costs and pollutant emission targets is established, and system active balance constraints and unit output constraints are considered. Secondly, an improved whale optimization algorithm is proposed to deal with the solution problem of the dynamic economic environmental dispatch model by introducing circle chaos mapping, dynamic sine cosine factor, and polynomial variation strategy. Finally, the optimization capability of the proposed model is verified by running a unit test system. The proposed model promotes the consumption and utilization of wind and photovoltaic resources and positively impacts achieving carbon peaking and carbon neutrality.

Command Authentication Using Multiagent System for Attacks on the Economic Dispatch Problem

Over the past decade, cyberattack events on the electricity grid are rising and have severely impacted grid operation. These attacks are becoming more intelligent as they can falsify critical data and bypass existing protection protocols. This could result in the system to operate in a nonoptimal condition and suffer substantial economic losses over a prolonged period. Hence, it is crucial to develop tools to defend the system against such attacks to minimize the cost of malicious operations. This article aims to develop a novel command authentication strategy to detect and mitigate false data injection attacks (FDIAs) targeting the system centralized economic dispatch (ED) control signals. First, we describe the ED problem based on Singapore’s deregulated market context. We then perform a risk assessment and formulate a stealthy FDIA vector targeting the ED control signals. Subsequently, we propose a novel authentication technique leveraging on the multiagent system, which includes detection and mitigation algorithms to verify the ED control commands. Last, we evaluate the efficiency and latency of the proposed technique on the 5-bus, 3-generating unit, and 15-generating unit test system. Simulation and experiment results using a real-time hardware-in-the-loop system proves that the authentication technique can detect and mitigate the stealthy FDIA vector with high accuracy.

Solution of Economic Dispatch Problem Using Hybrid Multi-Verse Optimizer

Economic Dispatch (ED) is one of the important problems among numerous vital operational problems of power economics. ED is a non-convex, non-drivable, and non-linear optimization issue along with diverse constraints. In this research work, a neoteric natural influential optimization tool Multi-verse Optimizer (MVO) hybridized with sequential quadratic programming (SQP) termed as Hybrid MVO (MVO-SQP) is proposed for the solution of multi-constrained ED problem. This contemporary effort contemplates on ED problem in the milieu of convergence features, numerical proficiency, and stoutness.The non-convex ED problem is encrypted at Hybrid Multi-verse Optimizer (HMVO) framework and employed in MATLAB for 30 trials, 300 iterations, and 30 exploration universes. The presented mechanism is verified on typical standard test systems comprising of 6, 13, 15, and 40 unit test systems, and experimental outcomes are juxtaposed with the reported approaches in the literature. The statistical analysis shows that the proposed approach saves 2.927 $/h for 6 units test system, 9.0618 $/h for 13 units test system, 1.831 $/h for 15 units test system, and 113.515 $/hr for 40 units test system with losses. It is envisioned that this work will equip the researchers with an improved tool to solve ED problem.

Value creation through insourcing – Additive Manufacturing as efficient in-house production technology

AVL List GmbH (AVL) is the world’s largest independent company for development, simulation and testing in the automotive industry, and in other sectors. The business unit Instrumentation and Test Systems (ITS) is focusing on the development, realization and manufacturing of high-precision measurement, development and test solutions for automotive components and systems. While the focus of in-house production at ITS is on assembly processes, manufacturing of the required components is mainly outsourced to external suppliers.With the foundation of the AVL Additive Manufacturing (AM) Initiative in 2017, manufacturing was partially in-sourced again. Since then, several activities have been conducted to successfully integrate AM as enabling technology. This paper presents how AM was methodically implemented and how AM contributes to the ITS value chain. For research and (pre-) development, Design for Additive Manufacturing (DfAM) enables novel and innovative measurement concepts with advanced functionalities. Feasibility studies, including experimental results on key performance indicators, are described and the proof of concept is discussed. Going further, design departments benefit from rapid realization of prototypes with in-house AM production. Especially since the Covid-19 pandemic, long delivery times from external suppliers influence the development process. This paper shows how AM has contributed during this phase in terms of speed, innovation and costs but also the importance of relevant know-how to start an AM transformation. Besides rapid prototyping, the existing AM machines already produce 65 serial parts for AVL’s measurement devices. First results show that not only delivery times were reduced by 80% but also substantial cost savings of up to 40% - compared to the external purchasing - could be achieved. With that in mind, the in-house AM production ecosystem will be further expanded to bring value and manufacturing know-how into the company.

Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

Economic Load Dispatch (ELD) plays a pivotal role in sustainable operation planning in a smart power system by reducing the fuel cost and by fulfilling the load demand in an efficient manner. In this work, the ELD problem is solved by using hybridized robust techniques that combine the Genetic Algorithm and Artificial Fish Swarm Algorithm, termed the Hybrid Genetic–Artificial Fish Swarm Algorithm (HGAFSA). The objective of this paper is threefold. First, the multi-objective ELD problem incorporating the effects of multiple fuels and valve-point loading and involving higher-order cost functions is optimally solved by HGAFSA. Secondly, the efficacy of HGAFSA is demonstrated using five standard generating unit test systems (13, 40, 110, 140, and 160). Finally, an extra-large system is formed by combining the five test systems, which result in a 463 generating unit system. The performance of the developed HGAFSA-based ELD algorithm is then tested on the six systems including the 463-unit system. Annual savings in fuel costs of $3.254 m, $0.38235 m, $2135.7, $9.5563 m, and $1.1588 m are achieved for the 13, 40, 110, 140, and 160 standard generating units, respectively, compared to costs mentioned in the available literature. The HGAFSA-based ELD optimization curves obtained during the optimization process are also presented.

Hybrid Genetic Algorithm-Gravitational Search Algorithm to Optimize Multi-Scale Load Dispatch

Genetic algorithm (GA) and gravitational search algorithm (GSA) both have successfully been applied in solving ELD problems of electrical power generation systems. Each of these algorithms has their limitations and advantage. GA's global search and GSA's local search capability are their strong points while long execution period of GA and premature of convergence of GSA hinders the possibility of optimum result when applied separately in ELD problems. To mitigate these limitations, experiment is done for the first time by combining GA and GSA suitably and applying the hybrid in non-linear ELD problems of 6, 15, and 40 unit test systems. The paper reports the details of this study including comparative analysis considering similar hybrid algorithms. The result strongly attests the quality, consistency, and overall effectiveness of the GA-GSA hybrid in ELD problems.

A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints

Dynamic economic load dispatch (DELD), which determines the best generation scheduling for different power plants during the next 24 hours in order to meet the electricity demand as well as minimize the total energy cost, is a highly complex non-linear and non-convex problem. This study proposes an efficient modified teaching-learning-based optimization algorithm, called MTLBO, to effectively solve the DELD optimization problem in a power system containing thermal generations and wind power. The planning problem comprises the total fuel cost function with valve-point loading effect and the transmission power losses. Also, the uncertainties of wind power and load demand along with the various equality and inequality operational constraints such as power generation limits, ramp rate limits, prohibited operating zones and power balance are considered in the problem. Additionally, in the proposed MTLBO algorithm, the learning phase is properly integrated into the teaching phase to improve the convergence characteristic of the original TLBO. Moreover, in order to enhance the feature of local optima avoidance, interaction of up to five students are incorporated into the learning phase to improve the knowledge of each student. To exhibit the effectiveness of the proposed approach, the algorithm is applied into the 14 real test functions. In addition, several cases with 10 and 30 unit test systems are investigated over the planning period. Compared to the main TLBO algorithm which is mostly used optimization algorithms proposed in prior studies, the simulation results demonstrate the efficiency and superiority of the proposed optimization approach in terms of consistency, robustness, convergence rate and finding better plausible optimal solutions.

Artificial Algae Algorithm with Multi-Light Source Movement for Economic Dispatch of Thermal Generation

Economic Dispatch (ED) is one of the major concerns for the efficient and economical operation of the modern power system. Actual ED problem is non-convex in nature due to Ramp Rate Limits (RRL), Valve-Point Loading Effects (VPLE), and Prohibited Operating Zones (POZs). It is generally converted into a convex problem as mathematical programming based approaches cannot handle the non-convex cost functions except dynamic programming, which also suffers from the curse of dimensionality. Heuristic techniques are potential solution methodologies for solving the non-convex ED problem. Artificial Algae Algorithm (AAA), a recent meta-heuristic optimization approach showed remarkable results on certain MATLAB benchmark functions but its application on industrial problem such as ED is yet to be explored. In this paper, AAA is used to investigate convex and non-convex ED problem due to valve-point effects and POZs while considering the transmission losses. The robustness and effectiveness of the proposed approach are validated by implementing it on IEEE standard test systems (3, 6, 13 and 40 unit Test Systems), which are widely addressed in the literature. The simulation results are promising when compared with other well-known evolutionary algorithms, showing the potential and stability of this algorithm.

Combined economic emission dispatch using grasshopper optimization algorithm

In this paper, grasshopper optimization algorithm is proposed to solve the combined economic emission dispatch (CEED) problem with power flow constraints. In CEED problem the price penalty factor plays a cardinal role to work out the test system. There are various types of price penalty factor and they are applied in the 6 unit test system. The leading requirement of the test system is to minimize the total cost, fuel cost and emission. The numerical and statistical results confirm the high quality of the solutions founded by GOA and its superiority is compared to other existing techniques which are employed in solving CEED problems.

Application of Modified ALO to Economic Load Dispatch for Coal Fired Stations

This manuscript explores the novel Modified Ant Lion (MALO) Optimization to resolve the load dispatch problem optimally. The primary role of Optimal Economic Load Dispatch (OELD) is to obtain an efficient and economical operation of a power system network. The main aim of performing the OELD is for minimizing the fuel cost of the real power generation when the losses are neglected. An Modified Ant Lion (MALO) Optimization is a narrative nature-inspired algorithm and apes the tracking mechanism of ant lions in life. The random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps are the five main steps involved in the hunting process. The principal objective of OELD is to curtail the total generation cost while honoring effective constraints of accessible generating resources. The anticipated Modified ALO technique is utilized on three and six unit test system to various load demands for solving the load dispatch problem economically. A statistical result clarifies that the anticipated MALO method has lowest fuel cost and superior in quality of solution than other optimization techniques accounted in most latest literature.

Demand response-integrated economic dispatch incorporating renewable energy sources using ameliorated dragonfly algorithm

This paper presents an integrated demand response (DR) and dynamic economic dispatch (DED) model consisting of renewable energy sources (RESs) and shunt FACTS (flexible AC transmission systems) devices with focus on reducing thermal energy consumption. RESs are environment friendly and can be located close to the load centers, which helps in reducing T&D losses as well as harmful emissions. DR helps in reducing peak power consumption and generation cost of thermal power plants. Shunt FACTS device, static synchronous compensator (STATCOM), helps in improving voltage profile of the network. Analyses are carried out using the proposed ameliorated dragonfly algorithm (ADFA). The efficiency of developed ADFA is proved by comparing the obtained results with many other methods. Static economic dispatch is carried out on 6-, 17- and 40-thermal generator test systems using ADFA, and the results are compared with other existing methods available in the literature. Further, DED is carried out on 6- and 17-thermal generator test systems. Later, a DED is performed on a real-time 17-thermal generator (South Indian 86-bus system) system for four seasons. Solar irradiance and wind speed are modeled using beta distribution and Weibull distribution functions by collecting the historical data of the test system. The developed demand response program is implemented on a real-time 17 thermal unit and 86-bus test system located in Tamil Nadu, southern part of India. The developed DR model alters time-independent loads so that burden on generating stations during the peak load is minimized. The effect of DR, RES and STATCOM in the test system is studied individually. Further, a comparative analysis considering DR, RES and STATCOM together is presented. The results obtained are promising, and it can be interpreted that by using the developed model there will be improvement in the penetration levels of RES which in turn reduces the demand for thermal energy.

A Novel Hybrid Moth Flame Optimization with Sequential Quadratic Programming Algorithm for Solving Economic Load Dispatch Problem

The insufficiency of energy resources, increased cost of generation and rising load demand necessitate optimized economic dispatch. The real world ED (Economic Dispatch) is highly non-convex, nonlinear and discontinuous problem with different equality and inequality constraints. In this research paper, a novel hybrid MFO-SQP (Moth Flame Optimization with Sequential Quadratic Programming) is proposed to solve the ED problem. The MFO is stochastic searching algorithm minimizes by random search and SQP is definite in nature that refines the local search in vicinity of local minima. Proposed technique has been implemented on 6, 15 and 40 units test system with different constraints like valve point loading effect, transmission loss, prohibited zones, generator capacity limits and power balance. Results, obtained from proposed technique are compared with those of the techniques reported in the literature, are proven better in terms of fuel cost and convergence.

Binary grey wolf optimizer models for profit based unit commitment of price-taking GENCO in electricity market

Abstract The restructuration of electric power sector has renovated the power system operational planning. In the deregulated market, electricity is treated as an entity unlike in the traditional vertically integrated market model where it is treated as a service provided by the generation companies (GENCOs). The task of GENCO is to perform self-scheduling of available units so as to achieve maximum profit in restructured power sector. Therefore, the problem of profit based unit commitment (PBUC) in deregulated markets can be formulated as a commitment and generation allocation through self-scheduling procedure. The commitment schedule optimization, i.e whether the status of a thermal unit is to be changed to on or off state, is a binary problem. Thus, the PBUC problem requires binary optimization for commitment and real valued optimization for generation allocation. In this paper, three binary grey wolf optimizer (BGWO) models are presented to solve the profit based self-scheduling problem of GENCO. The BGWO models proposed in this paper differ with respect to the transformation function used to map real valued wolf position into a binary variable. The binary mapping of commitment status is carried out using sigmoid and tangent hyperbolic transfer functions. Also, in the sigmoidal transfer function, two binary transformation procedures, namely crossover and conventional sigmoidal transfer function, are presented. The effectiveness of the proposed models in improving the solution quality of PBUC problem is examined using two test systems, a 3 unit and a 10 unit test system. In addition, two cases of GENCO market participation with and without reserve market participation are simulated. In the test case with reserve market participation, two commonly used reserve payment models are examined. Simulation results are presented and compared to existing approaches that have been used to solve the PBUC problem. The simulation results and statistical analysis demonstrate the improved solution quality (profit or fitness value) of the PBUC problem and statistical significance of the BGWO models with respect to solution quality obtained as compared to other established meta-heuristic approaches.

Unit test system of online English course based on SSH Framework

Aiming at the problems of poor test effect and imperfect system function in the existing English course unit test system, an online English course unit test system based on SSH framework is designed to accurately recommend the required online English course resources for students and better assist

Economic and Emission Load Dispatch Solution via Artificial Bee Colony Algorithm

The Economic Load Dispatch (ELD) is considered to minimize the only fuel cost in power system planning and operation. Due to environmental issues, the emission amount released by thermal power generation should be considered in minimizing the total cost generation as well. This make the power dispatch problem become multi-objective problem that called Economic and Emission Load Dispatch (EELD). This paper investigates the performances of Artificial Bee colony (ABC) algorithm for determining optimal solution for EELD problem. In this paper, the EELD problem is formulated as a single objective problem by taking minimization of total cost and emission level as separated objective. The cost characteristic of thermal generator considered the effect of opening and closing of the valve for practical application. The ABC algorithm has been tested on standard test systems which is 10-generating unit test system in order to validate the effectiveness of proposed ABC algorithm. The results obtained have been compared with reported result by other methods in literature. From the result, it found that ABC algorithm is better than other methods for minimizing fuel cost of thermal power generation and obtained comparative results in reducing the emission level.

Symbiotic Organisms Search algorithm for economic load dispatch problem with valve-point effect

Symbiotic Organisms Search (SOS) is a brand new and effective metaheuristic optimization algorithm. This paper proposes the SOS algorithm to solve the Economic Load Dispatch (ELD) problem with valve-point effect, which is one of the essential optimization problems in modern power systems. The proposed algorithm is tested on five different test cases consisting 3-machines 6-bus, IEEE 5-machines 14-bus, IEEE 6-machines 30-bus, 13 and 40 unit test systems including both transmission loss and without transmission loss. These test cases show that SOS is able to converge to the global optima successfully. Moreover results obtained from proposed algorithm are compared through different methods used in solving the ELD problem existing in the literature. According to these results, SOS produces better values than all.

Hybrid dynamic economic emission dispatch of thermal, wind, and photovoltaic power using the hybrid backtracking search algorithm with sequential quadratic programming

Increasing environmental concerns have led to a push for renewable energy sources to be extensively used to reduce emissions. In this paper, we investigate a hybrid dynamic economic emission dispatch (HDEED) problem involving thermal, wind, and photovoltaic (PV) generation systems. Our formulation considers the stochastic nature of both wind and PV generated power, and differences because of mismatches between the actual and allocated wind and PV power. A hybrid backtracking search algorithm with sequential quadratic programming was used to minimize the total operational costs and emissions, while dispatching power to the committed generation units subject to all operational constraints. To verify the efficacy, we applied the proposed technique to solve the dynamic economic emission dispatch problem on five and ten unit test systems. The proposed technique was also used to solve the HDEED problem on IEEE 30 bus, 6-unit and IEEE 57 bus, 7-unit test systems, with and without renewable generation. The results of our numerical simulations show the efficiency of the proposed technique with respect to reducing operational costs and emissions. Moreover, our results show that by incorporating renewable energy into existing power systems, we can reduce operational costs and emissions.

Market based procurement of energy and ancillary services from Renewable Energy Sources in deregulated environment

An increasing utilization of Renewable Energy Sources (RES) in electricity generation increases the need of Ancillary Services (AS) in power system. This paper proposes a market based approach for participation of Renewable Power Producers (RPP) to clear both energy and AS in day-ahead market under disaggregated framework. This approach is based on sequential clearing of Energy Market (EM) and Ancillary Service Market (ASM) such that the cost of procuring these services is minimized and feasible solution is obtained. This optimization problem is formulated and solved using Optimal Power Flow (OPF) technique which considers all transmission constraints and power flow limits. The AS considered in the present work is limited to Reserves Market (RM) such as Ten Minute Spinning Reserve (TMSR) and Thirty Minute Replacement Reserves (TMRR) only. The ability of RPPs like Wind Power Plant (WPP) and Photo-Voltaic Plant (PVP) with conventional power producers to provide both energy and AS has been demonstrated by considering a modified 7 unit test system.