Economic Load Dispatch Optimization Research Articles

Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System

Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively.

Optimal power flow-based approach for grid dispatch problems through Rao algorithms

In order to fulfill the load demand and operational restrictions, the grid dispatch problem's optimization goal is to reduce fuel cost through variation of active and reactive power of the generators within fixed limits and to uplift the security of the power plants during voltage instability through proper allocation of static voltage compensator to ensure minimum power loss. This paper proposes a collaborative optimization of economic load dispatch and reactive power dispatch using three metaphor-less algorithms called Rao algorithms, where the current solution will interact with the finest and inferior solutions in the population, as well as a randomly selected solution. These strategies are free from any specific control parameters. These algorithms are formulated on the IEEE 30 bus system. The outcomes of the suggested techniques are compared with those of existing optimization approaches such as Bat algorithm (BOA), Modified Harmonic search algorithm (MSG-HS), Root tree optimization (RTO), Adaptive Gravitational search optimization (AGSO), Whale optimization algorithm (WOA), Fruit fly optimization (MOFOA), Crow search algorithm (CSA), Quasi-Oppositional Teaching learning based optimization (QOTLBO), and Jaya algorithm (JA) which demonstrates that the proposed strategies have great convergence characteristics and are the most systematic way to addressing these complicated power system issues. They are unable to converge on a local optimal solution prematurely while maintaining a high level of precision.

Optimal Economic and Emission Dispatch of Photovoltaic Integrated Power System Using Firefly Algorithm

The main purpose of Economic Load Dispatch (ELD) is to determine the optimal output of generating units to meet the power demand at lowest possible cost and subjected to the operational constraints. Various ELD optimization methods have been developed in order to deal with the challenges of continuous and sustainable power at optimal cost. The deficiency of fossil fuel reserve and rapid increase of fuel prices to generate electricity have encouraged the use of Renewable Energy (RE). Furthermore, the concerns over environmental pollution also become a factor to incorporate the RE and fossil fuel in generating electricity. This paper proposes the Firefly Algorithm (FA) to solve Economic and Emission Load Dispatch (EELD) problems by considering the photovoltaic integration power systems. The FA algorithm is used to determine the optimal cost and emission level of power generation. The test case considered in this paper is Static Combined Economic and Emission Dispatch (SCEED) that been simulated for each hour. The test system with 6 units of thermal generator and 13 units of PV generator are used to optimize SCEED problem by using FA. The Weight Sum Method (WSM) approach is used to determine the best compromise solution among the cost and emission. It found that FA can provide the fast convergence in finding the global minima value. Itcan be concluded that FA can solve the problem of economic and emission dispatch accurately.

Greedy Sine-Cosine Non-Hierarchical Grey Wolf Optimizer for Solving Non-Convex Economic Load Dispatch Problems

Economic load dispatch (ELD) provides significant benefits to the operation of the power system. It appears to be a complex nonconvex optimization problem subject to several equal and unequal constraints. The greedy sine-cosine nonhierarchical gray wolf optimizer (G-SCNHGWO) is introduced in this study to solve complex nonconvex ELD optimization problems efficiently and robustly. The sine and cosine functions assist the search agents of the grey wolf optimizer (GWO) algorithm in avoiding trapping in a local optimum. In addition, the greedy nonhierarchical concept is integrated into GWO to enrich the optimization power of the conventional GWO algorithm. Simulations are implemented to validate the capability of the suggested algorithm in solving the different ELD problems. According to the results, the algorithm demonstrates very suitable performance compared to other state-of-the-art methods.

An Energy Management System for the Control of Battery Storage in a Grid-Connected Microgrid Using Mixed Integer Linear Programming

This paper proposes an energy management system (EMS) for battery storage systems in grid-connected microgrids. The battery charging/discharging power is determined such that the overall energy consumption cost is minimized, considering the variation in grid tariff, renewable power generation and load demand. The system is modeled as an economic load dispatch optimization problem over a 24 h horizon and solved using mixed integer linear programming (MILP). This formulation, therefore, requires knowledge of the expected renewable energy power production and load demand over the next 24 h. To achieve this, a long short-term memory (LSTM) network is proposed. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the EMS that benefits from using actual generation and demand data on the day. At each hour, the LSTM predicts generation and load data for the next 24 h, the dispatch problem is then solved and the battery charging or discharging command for only the first hour is applied in real-time. Real data are then used to update the LSTM input, and the process is repeated. Simulation results show that the proposed real-time strategy outperforms the offline optimization strategy, reducing the operating cost by 3.3%.

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.

Data-Driven Real-Time Pricing Strategy and Coordinated Optimization of Economic Load Dispatch in Electricity Market

Compared to the step tariff, the real-time pricing (RTP) could be more stimulated for household consumers to change their electricity consumption behaviors. It can reduce the reserve capacity, peak load, and of course the electricity bill, which could achieve the purpose of saving energy. This paper proposes a coordinated optimization algorithm and data-driven RTP strategy in electricity market. First, the electricity price is divided into two parts, basic electricity price and fluctuating price. When the electricity consumption is equal to the average daily electricity consumption, the price is defined as the basic electricity price, which is the clearing electricity price. The consumer electricity data are analyzed. A random forest algorithm is adopted to predict the load data. Optimal adjustment parameters are obtained and the load fluctuation and the fluctuation of the electricity price are further quantified. Secondly, the appliances are modeled. The operation priority is established based on the preferences of customers and the Monte Carlo method is used to form the power load curve. Then, the smart energy planning unit is proposed to optimize the appliances on/off time and running time of residential electrical appliances. An incentive mechanism is used to further standardize the temporary electricity consumption. An improved multiobjective particle swarm optimization (IMOPSO) algorithm is adopted, which adopts the linear weighted evaluation function method to maximize the consumer’s social welfare while minimizing the electricity bill. The simulation proves that the stability of the power grid is improved while obtaining the best power strategy.

Incentive-based demand response in grid-connected microgrid using quasi-opposed grey wolf optimizer

The paradigm shifts in the electrical industry from demand-driven generation to supply-driven generation due to the incorporation of renewable generating sources is a growing research field. Implementing demand response in present-day distribution schemes is anattractive approach often adopted by microgrid (MiG) operator.This paper incorporates an incentivebased demand response (IBDR) method in a grid-connected microgrid (MiG) comprising of conventional generators (CGs), wind turbines (WTs), and solar PV units. The main aim is to collectively minimize the fossil fuel cost of CGs, lower the transaction cost of portable power from the grid, and maximize theMiG operator's profitafter implementing demand response. This multi-objective problem combining optimal economic load dispatch of MiG with an efficient demand-side response is solved using a proposed Quasi-opposed Grey Wolf Optimizer (QOGWO) algorithm. The effect of the proposed algorithm on demand-side management (DSM) is analyzed for two cases, (i) varying the value of power interruptibility (ii) varying the maximum limit of curtained power. Performance of QOGWO is compared with original GWO and a variant of GWO, Intelligent Grey Wolf Optimizer (IGWO). Results show the superior global search capability and complex constrained handling capability of QOGWO.

Optimal Economic Load Dispatch with Prohibited Operating Zones Using Large to Small Area Technique

In practical power plant operation, the economic load dispatch problem is not straightforward. The objective function is generally differentiable (in a quadratic form). It can change to non-differentiable (non-smoothing) or discontinuous fuel cost curve (prohibited operating zones) in the actual power plant operation. The economic load dispatch problem (non-differentiable) solved by calculus methods based on differential (LaGrange method) will not be relevant again. The method currently being developed is based on artificially (for example, Genetic Algorithm), which does not depend on the form of the objective function. It is very relevant to be implemented. There are prohibited operating zones that will make the economic load dispatch problem more complicated to solve. This complicated problem will be solved more easily by area to smaller area technique, which is the simplest artificial method. In addition, this method can give good accuracy because all subareas are traceable and can work for a large scale based on a big candidate size. The simulation results for 15-unit generators with prohibited zones show that the proposed method is working well. The method has shown a small reduction in total cost compared to particle swarm optimization method, and the total cost has closed to the Lambda Iteration and Dynamic Programming methods.

Modified moth swarm algorithm for optimal economic load dispatch problem

In this study, optimal economic load dispatch problem (OELD) is resolved by a novel improved algorithm. The proposed modified moth swarm algorithm (MMSA), is developed by proposing two modifications on the classical moth swarm algorithm (MSA). The first modification applies an effective formula to replace an ineffective formula of the mutation technique. The second modification is to cancel the crossover technique. For proving the efficient improvements of the proposed method, different systems with discontinuous objective functions as well as complicated constraints are used. Experiment results on the investigated cases show that the proposed method can get less cost and achieve stable search ability than MSA. As compared to other previous methods, MMSA can archive equal or better results. From this view, it can give a conclusion that MMSA method can be valued as a useful method for OELD problem.

An Effective Method for Minimizing Electric Generation Costs of Thermal Systems with Complex Constraints and Large Scale

In this paper, an improved antlion optimization algorithm (IALO) was proposed to search for promising solutions for optimal economic load dispatch (ELD) problems to minimize electrical generation fuel costs in power systems with thermal units and to ensure all constraints are within operating ranges. IALO can be more effective than the original method, called the antlion optimization algorithm (ALO), because of the high performance of the applied modifications on the new solutions searching process. In order to evaluate the abilities of the IALO method, we completed many tests on thermal generating systems including 10, 15, 20, 30, 60, 80, and 90 units with different constraints and fuel-consuming characteristics. The results suggest that the offered method is superior to the ALO method with more stable search ability, faster convergence velocity, and shorter calculation times. Furthermore, the obtained results of the IALO method are much better than those of almost all the other methods used to solve problems for the same systems. As a result, IALO is suggested to be a highly effective method, and it can be applied to other problems in power systems instead of ALO, which has a lower performance.

Antlion optimization algorithm for optimal non-smooth economic load dispatch

This paper presents applications of Antlion optimization algorithm (ALO) for handling optimal economic load dispatch (OELD) problems. Electricity generation cost minimization by controlling power output of all available generating units is a major goal of the problem. ALO is a metaheuristic algorithm based on the hunting process of Antlions. The effect of ALO is investigated by solving a 10-unit system. Each studied case has different objective function and complex level of restraints. Three test cases are employed and arranged according to the complex level in which the first one only considers multi fuel sources while the second case is more complicated by taking valve point loading effects into account. And, the third case is the highest challenge to ALO since the valve effects together with ramp rate limits, prohibited operating zones and spinning reserve constraints are taken into consideration. The comparisons of the result obtained by ALO and other ones indicate the ALO algorithm is more potential than most methods on the solution, the stabilization, and the convergence velocity. Therefore, the ALO method is an effective and promising tool for systems with multi fuel sources and considering complicated constraints.

Grey Wolves Optimization for Economic Load Dispatch with Practical Constraints

This paper manifests the applicability of grey wolf’s optimization (GWO) for solving economic load dispatch (ELD) problems by considering practical constraints of power generation, but effects of valve-point loading are not considered. This recommended technique is a evolutionary strategy of upgrade with fundamental structure and single control parameter to handle the complex mathematic as well as complicated engineering problems. It is a groundbreaking technique for adequately investigating the exploration and exploitation to locate the optimal solution within a low calculation time. The six generating tests-system have been utilized to exhibit the proficiency of GWO to solve ELD problem. The outcomes are contrasted with a several methods of optimization to confirm the elite of GWO for tackling the problem of ELD.

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.

Ant Lion Optimizer for Optimum Economic Dispatch Considering Demand Response as a Visual Power Plant

Demand response is the reduction in power consumption of electric utility customers from their normal consumption to better match the demand for power with the supply. It comprises changes in consumption when the power system operators need it in order to balance the electrical grid. These changes are obtained by offering price incentives or any other way to encourage consumers to shift their demand peaks to higher energy production hours. The reduction in electric energy due to large consumers’ response can be considered as virtual power plants which can be added to the system capacity as a demand resource. In this paper, demand response is included in the optimal economic load dispatch problem as a demand resource. The demand response cost function is proposed in a similar way as the cost functions of conventional generation units. A new nature-inspired optimization algorithm called ant lion optimizer is used to calculate the optimum combination between both generations; conventional generators and demand response as a demand resource. The proposed approach takes into account two objectives; minimization of generation cost, and the emissions from the thermal power plants. The ant lion optimizer algorithm is coded and implemented in MATLAB environment. The proposed method is validated by applying it to two test systems; 6-bus system and IEEE-30 bus test system. The obtained results prove that the proposed method can significantly minimize both the operation and emissions costs of power systems while satisfying the specified constraints.

Optimization of Economic Load Dispatch for 2.5 MW Solar Power System

Economic load dispatch (ELD) is a complicated issue, when renewable energy sources are also considered with thermal power generation systems. It becomes challenging and difficult to find the optimum solution at lower fuel costs, in order for generated power to meet the active power load demand. This paper primarily focuses on the modeling of ELD for solar power plants synchronized with Thermal power plants. In this paper, the purposed optimization method used is Genetic algorithm (GA). According to which, all thermal generators in the system will supply output power at lowest fuel rate however satisfying the system limitations. Minimum fuel cost of a generator will be calculated by using generator characteristic curve for all generators present in the system. With the help of GA, achievable range for best solution is attained while restricting the output power of thermal generators. Proposed system priority to achieve is solar at first and generators on second such that generator or combination of generators resulting in lowest cost will be on second priority. Simulations on various test systems were performed including one with six thermal generators and the other with six thermal generators and solar power system. By using MATLAB the proposed problem is simulated.

New PSO Techniques for the Optimization of Economic Load Dispatch of Thermal Generated Power

Generation of electrical power at a thermal power plant at low cost is a great challenge. As the cost of fuel, transportation, and maintenance increased, the generation of power cost per unit also increases. Economic load dispatch analysis is one of the ways to use the all available generating units, generate the power in such a way so that demand can be fulfilled. It also helps us to maintain the different constraints. The latest PSO with strong capability of searching global solution proposed in the work. It can handle the economic load dispatch without affecting the constraints limits. The proposed new PSO has the capability to move the particles in the area of examining very fast. And evaluate the solution effectively in very short of the span. Work also extended and solved for ELD problem of the test data of 2, 3 and 6 generating unit systems using PSO, WIPSO, SOH-PSO and new PSO. The result with new PSO shows that the associated algorithm is effective and efficient compared with PSO techniques used in this work.

Optimal economic load dispatch based on wind energy and risk constrains through an intelligent algorithm

This article focus on optimal economic load dispatch based on an intelligent method of shark smell optimization (SSO). In this problem, the risk constrains has been considered which has root in uncertainity and unpredictable behavior of wind power. Regarding to increasing of this clean energy in power systems and un‐dispatchable behavior of wind power, its conditional value at risk index considered in this article which consists of loss from load and "spilling" wind energy connected with unpredictable imbalances among generation and load. This problem has been considered as an optimization problem based on SSO that evaluate the balance between cost and risk. This algorithm is based on distinct shark smell abilities for localizing the prey. In sharks' movement, the concentration of the odor is an important factor to guide the shark to the prey. In other words, the shark moves in the way with higher odor concentration. This characteristic is used in the proposed SSO algorithm to find the solution of an optimization problem. Effectiveness of the proposed method has been applied over 30‐bus power system in comparison with other techniques. © 2016 Wiley Periodicals, Inc. Complexity 21: 494–506, 2016

A new solution to the economical load dispatch of power plants and optimization using differential evolution

The optimization of economic load dispatch (ELD) is one of the oldest and most important tasks in power plant management. The objective of this paper was to analyze a new solution of the old problem of the ELD optimization by differential evolution (DE) including turning off the most inefficient generators. The criteria of incremental fuel costs and losses are used to determine the best parameters of active power of each ith generator unit, ensuring that the demand and total losses are equal to the total generated power but minimizing the total cost of fuel. Materials and methods have been developed to solve the ELD, including lambda iteration method, gradient method, Newton’s method and so on. The results found for this case study, with the application of DE, were outstanding having a reduction of 19.88 % in the total fuel cost, comparing to classical methods that distribute the generation of power among all generators, including the least efficient ones. This method improves not only the efficiency of generation but also of the power plant generation planning.

경제급전문제의 통합운영에 관한 경제적 이득 분석

This paper demonstrates that centralized economic load dispatch optimization is much more economical than independent optimization carried out by individual power generating companies. The algorithm applied here optimizes by balancing the generation power at the valve-point, then readjusting generation power by comparing incremental operating cost incurred by marginal increase in the generation power and decremental operating cost likewise incurred by marginal decrease in the generation power. Upon comparing 3 individual optimization cases of 10, 13, and 40 generators respectively with centralized optimization of 63 generators, centralized operation for economic load dispatch optimization has proven to maximize economic benefits by markedly reducing operation costs of individual optimization.