Dispatch Problem In Power Systems Research Articles

Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-ω

The renewable energy sources (RESs)-based economic dispatch problem (EDP) is of vital importance for modern power systems. Environmental pollution, climatic degradation, and rapidly growing prices of continuously depleting fossil fuels have encouraged researchers to consider mechanisms for RES implementation and optimal operations. This paper presents a quasi-oppositional population-based global particle swarm optimizer with inertial weights (QPGPSO-ω) to solve environment friendly EDPs. The optimization technique is applied to solve the EDP under different scenarios including cases where only renewable energy sources (RESs) are used and the cases where combined emission–economic dispatch (CEED) problem is taken into account. The scenario for RESs includes a combination of six wind, five solar PV, and four biofuel systems for power generation. EDPs are considered without any constraints, and the variability of resources is depicted over time, along with the regional load-sharing dispatch (RLSD). The case of CEED considers ten thermal units with the valve point loading (VPL) effect and transmission losses. The results obtained by the proposed QPGPSO-ω algorithm are better than the reported results employing other optimization methods. This is shown by the lower costs achieved up to USD 8026.1439 for the case of only RES-based EDPs, USD 1346.8 for the case of RES-based EDPs with RLSD, and USD 111,533.59 for the case of CEED. Thus, the proposed QPGPSO-ω algorithm was effective in solving the various adopted power dispatch problems in power system.

Projection-based distributed economic dispatch algorithm considering communication delays under switching topologies

In this paper, a projection-based distributed algorithm is proposed to solve the economic dispatch problem of power system with communication delays under switching topologies. The algorithm is suitable for economic dispatch problem with strongly convex cost function, and ensures that the output power of each generator is always kept within its output limit. In addition, the topology is only required to be jointly connected. Firstly, the relationship between the equilibrium point and the optimal solution of the algorithm is analyzed, and then a common Lyapunov function is introduced for a set of switching topologies. According to the Lyapunov stability theory and Cauchy convergence criterion, LMIs-based sufficient conditions for the convergence of the algorithm are obtained. Finally, the effectiveness of the algorithm under different cases are verified by MATLAB simulation results.

Hybrid Optimization Technique for Solving Combined Economic Emission Dispatch Problem of Power Systems

Hybrid Optimization Technique for Solving Combined Economic Emission Dispatch Problem of Power Systems

Oppositional Pigeon-Inspired Optimizer for Solving the Non-Convex Economic Load Dispatch Problem in Power Systems

Economic Load Dispatch (ELD) belongs to a non-convex optimization problem that aims to reduce total power generation cost by satisfying demand constraints. However, solving the ELD problem is a challenging task, because of its parity and disparity constraints. The Pigeon-Inspired Optimizer (PIO) is a recently proposed optimization algorithm, which belongs to the family of swarm intelligence algorithms. The PIO algorithm has the benefit of conceptual simplicity, and provides better outcomes for various real-world problems. However, this algorithm has the drawback of premature convergence and local stagnation. Therefore, we propose an Oppositional Pigeon-Inspired Optimizer (OPIO) algorithm—to overcome these deficiencies. The proposed algorithm employs Oppositional-Based Learning (OBL) to enhance the quality of the individual, by exploring the global search space. The proposed algorithm would be used to determine the load demand of a power system, by sustaining the various equality and inequality constraints, to diminish the overall generation cost. In this work, the OPIO algorithm was applied to solve the ELD problem of small- (13-unit, 40-unit), medium- (140-unit, 160-unit) and large-scale (320-unit, 640-unit) test systems. The experimental results of the proposed OPIO algorithm demonstrate its efficiency over the conventional PIO algorithm, and other state-of-the-art approaches in the literature. The comparative results demonstrate that the proposed algorithm provides better results—in terms of improved accuracy, higher convergence rate, less computation time, and reduced fuel cost—than the other approaches.

An Efficient Algorithm for Economic Load Dispatch Considering Valve Point Loading Effect

A simple and efficient algorithm is proposed for solving the economic dispatch problem of power system with valve point discontinuities employing a particle swarm optimization based approach. Evolutionary methods such as GA and PSO are known to perform better than conventional gradient based optimization methods for non convex optimization problems. This reactive power management in economic load dispatches plays a vital role in improving power quality of the system. The power compensation and economic load dispatches is major problem in distribution network. The power is maintain the state of the UPQC (Unified power quality conditioner). The UPAC controlled by the STATCOM or DSTATCOM. The performance of the proposed method has been compared with conventional PSO. The effectiveness of the algorithm has been tested on a test system having three generating units.

The Extension of Auxiliary Problem Principle for Solving Dynamic Economic Dispatch Problem in Power Systems

In this paper, we study the convex optimization problem with linear constraint, and the objective function is composed of m separable convex functions. Considering the special case where the objective function is composed of two separable convex functions, the auxiliary problem principle (APP) is an effective parallel distributed algorithm for solving the special case. Inspired by the principle of APP, a natural idea to solve separable convex optimization problem with m ≥ 3 is to extend the method of APP, resulting in the APP-like algorithm. The convergence of the APP-like algorithm is not clear yet. In this paper, we give a sufficient condition for the convergence of the APP-like algorithm. Specifically, the APP algorithm is a special case of the APP-like algorithm when m = 2. However, simulation results show that the convergence efficiency of the APP-like algorithm is affected by the selection of penalty parameter. Therefore, we propose an improved APP-like algorithm in this paper. Simulation results show that the improved APP-like algorithm is robust to the selection of penalty parameter and that the convergence efficiency of the improved APP-like algorithm is better when compared with the APP-like algorithm.

A Cost-Effective Solution for Non-Convex Economic Load Dispatch Problems in Power Systems Using Slime Mould Algorithm

Slime Mould Algorithm (SMA) is a newly designed meat-heuristic search that mimics the nature of slime mould during the oscillation phase. This is demonstrated in a unique mathematical formulation that utilizes adjustable weights to influence the sequence of both negative and positive propagation waves to develop a method to link food supply with intensive exploration capacity and exploitation affinity. The study shows the usage of the SM algorithm to solve a non-convex and cost-effective Load Dispatch Problem (ELD) in an electric power system. The effectiveness of SMA is investigated for single area economic load dispatch on large-, medium-, and small-scale power systems, with 3-, 5-, 6-, 10-, 13-, 15-, 20-, 38-, and 40-unit test systems, and the results are substantiated by finding the difference between other well-known meta-heuristic algorithms. The SMA is more efficient than other standard, heuristic, and meta-heuristic search strategies in granting extremely ambitious outputs according to the comparison records.

Arithmetic optimization algorithm based on elementary function disturbance for solving economic load dispatch problem in power system

Arithmetic optimization algorithm based on elementary function disturbance for solving economic load dispatch problem in power system

An adaptive chaotic class topper optimization technique to solve economic load dispatch and emission economic dispatch problem in power system

An adaptive chaotic class topper optimization technique to solve economic load dispatch and emission economic dispatch problem in power system

Chaotic theory incorporated with PSO algorithm for solving optimal reactive power dispatch problem of power system

In this paper, the chaotic particle swarm optimization (CPSO) algorithm is combined with MATPOWER toolbox and used as an optimization tool for attaining solving the optimal reactive power dispatch (RPD) problem, by finding the optimal adjustment of reactive power control variables like a voltage of generator buses (VG), capacitor banks (QC) and transformer taps (Tap) while satisfying some of equality and inequality constraints at the same time. CPSO and Simple PSO algorithms will be checked in a large system such as IEEE node -118. CPSO and Simple PSO algorithms have been implemented and simulated in the MATLAB program, version (R2013b/m-file). Then compassion these results with the results obtained in the other algorithms in the literature like the comprehensive learning particle swarm optimization (CLPSO) algorithm. The simulation results confirm that the CPSO algorithm has high efficiency and ability in terms of decrease real power losses (P loss), and improve voltage profile compared with the obtained by using the simple (PSO) algorithm and (CLPSO) at light load.

Hybrid Mean Variance Mapping Optimization Algorithm for Solving Stochastic Based Dynamic Economic Dispatch Incorporating Wind Power Uncertainty

This paper presents a model for studying the dynamic economic load dispatch problem in power system including conventional thermal generators and wind turbines. The problem is solved using the hybrid variant Mean Variance Mapping Optimization technique. Due to the stochastic nature of wind speed, the Weibull distribution parameters of wind speed are obtained for different time intervals (24 hours). Consequently, the probability density function for the output wind power is achieved for each hour separately. The constraints for the thermal generators are considered including generation limits, ramp rate limits, valve-point loading and spinning reserve. The optimization problem is solved for different scenarios and total operating cost obtained shows the effect of wind power penetration as well as the overestimate and underestimate power probabilities.

Research on economic dispatch based on improved firefly ag-orithm

An improved firefly algorithm for solving the economic dispatch problem of power system is proposed. The algorithm considers the climbing rate, the dead zone and other constraints, and calculates the network loss. This algorithm is based on the firefly algorithm, a new repair strategy for active correction of the violation of the constraint condition of all kinds of fireflies, and combined with the penalty function technique, the fireflies in the feasible region or as close as possible to the feasible solution search in the area. The accuracy and speed of the algorithm can be effectively improved by greatly reducing the probability of finding the optimal location of the firefly in the infeasible solution region. The simulation results show that the algorithm has the characteristics of high speed, high accuracy and good convergence.

The Random ADMM and Its Application to Convex Economic Dispatch Problems of Power Systems

A new random alternating direction method of multipliers (ADMM) was designed to solve convex economic dispatch problems in power systems. The convergence of the random ADMM was analyzed. Under some mild assumptions, the random ADMM, according to the cycle update rule and the random selection update rule, was proved to converge to an optimal solution of the convex economic dispatch problem. The numerical experimental results show that, the proposed method is effective to solve convex economic dispatch problems.

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.

Distributed Resource Allocation Over Dynamic Networks With Uncertainty

Motivated by broad applications in various fields of engineering, we study a network resource allocation problem where the goal is to optimally allocate a fixed quantity of the resources over a network of nodes. We consider large scale networks with complex interconnection structures, thus any solution must be implemented in parallel and based only on local data resulting in a need for distributed algorithms. In this article, we study a distributed Lagrangian method for such problems. By utilizing the so-called distributed subgradient methods to solve the dual problem, our approach eliminates the need for central coordination in updating the dual variables, which is often required in classic Lagrangian methods. Our focus is to understand the performance of this distributed algorithm when the number of resources is unknown and may be time-varying. In particular, we obtain an upper bound on the convergence rate of the algorithm to the optimal value, in expectation, as a function of network topology. The effectiveness of the proposed method is demonstrated by its application to the economic dispatch problem in power systems, with simulations completed on the benchmark IEEE-14 and IEEE-118 bus test systems.

Comparison of Algorithms to Solve Multi-objective Optimal Reactive Power Dispatch Problems in Power Systems with Nonlinear Models and a Mixture of Discrete and Continuous Variables

Optimal reactive power dispatch (ORPD) is a way to improve power system performance. Determination of the optimal value of the control variable can reduce the objective function to be achieved. The optimization of the two objective functions simultaneously is called multi-objective ORPD (MORPD). This research has a major contribution in proposing and utilizing original ideas from new algorithms and new ideas from the old algorithm to solve more complex variables and challenging ORPD problems. The ORPD problem is formulated as a nonlinear model with variables consisting of continuous and discrete. The proposed multi-objective algorithm is time-varying particle optimization (MOTVPSO), ant lion objective (MOALO), dragonfly algorithm (MODA), grey wolf optimizer (MOGWO), and multi-objective multi-verse optimization (MOMVO). To measure the effectiveness of those algorithms, testing is performed on the IEEE 57-bus. The simulation results show that the MOTVPSO algorithm can contribute more dominantly from the statistical tests conducted compared to previous studies and all four algorithms in this work to minimize real power loss. Whereas the MOMVO has an advantage in computational time efficiency.

Stochastic economic dispatch of power system with multiple wind farms and pumped‐storage hydro stations using approximate dynamic programming

The stochastic economic dispatch problem of power system with multiple wind farms and pumped-storage hydro stations is formulated as a specific stochastic dynamic programming (DP) model, i.e. stochastic storage model, it is impossible to obtain an accurate solution due to the curse of dimensionality. Based on the approximate DP (ADP) method, the stochastic storage model can be transformed into a series of mixed-integer linear programming (MILP) models by describing the approximate value functions (AVFs) as convex piecewise linear functions in post-decision states. The AVFs are first initialised using the results of the deterministic model under a forecast scenario of wind farm output and then trained by scanning stochastic sampling scenarios consecutively with the successive projective approximation routine algorithm. To obtain a near-optimal day-ahead dispatch scheme, the forecast scenario is substituted into the MILP models expressed by the trained AVFs and is solved forward through each time interval. The network constraints are incorporated by the while-loop detection of critical lines. Test results on an actual provincial power system and the modified IEEE 39-bus system, including the comparison among the ADP, DP, scenario-based and chance-constrained programming methods, demonstrate the feasibility and efficiency of the proposed model and algorithm.

Multi-area economic dispatch with stochastic wind power using Salp Swarm Algorithm

Abstract Salp Swarm Algorithm (SSA) is a newly developed swarm intelligence based optimization method. Its analytical model mimics self-organized swarming and foraging behavior of salps. Salps are found in the ocean and they form a swarm called salp chain for their organized movement and foraging. Since its inception in 2017, SSA has been applied to many engineering domains. This paper presents the solution of complex constrained multi-area economic dispatch problem of power system with and without stochastic wind integration using SSA. Simulation analysis has been carried on four different constrained test cases with diverse dimensions/complexity level. They are (i) four area system with sixteen thermal units (ii) four area system with forty thermal units (iii) two area system with forty thermal units and (iv) two area forty-unit system with wind integration. Comparison of simulation results obtained by SSA with other methods reported in the recent literature has proven SSA’s capability of constrained handling and offered promising and efficient solutions.

Economic Dispatch Problem in Power System Using Hybrid PSO and Enhanced Bat Optimization Algorithm

Economic Dispatch Problem in Power System Using Hybrid PSO and Enhanced Bat Optimization Algorithm

Distributed Event-Triggered Communication Scheme for Economic Dispatch Problem in Power System With Uncoordinated Step-Sizes

Inspired by the distributed economic dispatch problem (EDP) in power system, this paper considers a problem of optimizing a sum of m local convex cost functions on an undirected network of m agents. Each agent in the network privately knows its own local convex objective function and is subjected to both coupling linear constraint and individual box constraints. To be able to reduce the requirements for information exchange among agents, we propose a novel fully event-triggered based distributed primal-dual algorithm for the convex optimization problem. Our algorithm allows the use of uncoordinated step-sizes and assumes that each agent communicates with its neighboring agents (the corresponding variables are updated) only at some independent event-triggered sampling time instants. Under some relatively standard assumptions (strong convexity and smoothness) on the objective functions, our theoretical analysis proves that the proposed algorithm can linearly seek the exact optimal solution when the upper bound of the uncoordinated step-sizes is smaller than a certain constant. We also conduct a clear estimate of the rate. Finally, a simple numerical example on distributed economic dispatch problem in power system is provided to illustrate the effectiveness of our event-triggered based optimization algorithm and validate the correctness of the analysis process.