Dynamic Economic Load Dispatch Research Articles

A novel order characteristic load shifting policy for load factor improvement, peak reduction, and economical operation of distribution systems

Demand side management (DSM) and demand response programs (DRPs) are widely used economic strategies that insist customers shift or curtail loads to economize the generation cost of the distribution system. These policies often involve tedious calculation processes or optimization tools to calculate the apt amount of load to be shredded or shifted to get a new modified load profile. This paper proposes an order characterized load-shifting policy (OCLSP), which is very easy to calculate and yields a considerable decrease in the peak demand, eventually improving the distribution system's load factor, also known as the peak-to-average ratio (PAR). Unlike other DSM and DR strategies, the proposed approach requires no parameters except the forecasted load demand. The proposed OCLSP is implemented on ten distribution systems from the literature which consists of a 3-, 6-, 7-, 10-, 15- units system, one islanded microgrid (MG) system and four grid-connected MG systems. Initially, the load demand of these test systems is restructured using OCLSP, and thereafter, dynamic economic load dispatch (DELD) is performed to minimize the generation cost of the system. A latest circle search algorithm (CSA) is utilized as the optimization tool. Results show that the generation cost of all ten test systems has been decreased by a considerable amount when their load demand is restructured using the proposed OCLSP. The minimum percentage improvement in the PAR of the ten systems was 2 % and maximum improvement was 60 %. Likewise, the percentage decrement in the peak demand was within 2 % to 37.5 % for the ten test systems and the decrement in generation cost rose up to 16 %. All these parameters were in comparison with the base load demand. Numerical results also corroborate to the efficiency and robustness of proposed CSA.

Dynamic economic load dispatch in microgrid using hybrid moth-flame optimization algorithm

Dynamic economic load dispatch in microgrid using hybrid moth-flame optimization algorithm

Dynamic economic load dispatch problems in microgrid containing renewable energy sources based ontunicate swarm algorithm

Dynamic Economic Load Dispatch (DELD) is a significant problem in microgridincluding renewable energy sources (RESs). The aim of this paper is to minimize the overall cost of the system while ensuring that the power demands of the consumers are met. However, with the added complexity of RESs, conventional optimization techniques may not be able to provide optimal solutions. This is where metaheuristic algorithms come into play, which are optimization techniques inspired by natural phenomena such as biological, nature, and animal behavior. This studycovers a new bio-inspired algorithm called Tunicate Swarm Algorithm (TSA)applied for solving DELD in microgrid with considering the variable power output of RES such as wind and solar energy. The DELD problem should also incorporate various constraints such as power balance and generation limits. Four cases of DELD containing RESs are treated. The obtained resultsare compared to other methods.. The result shows that for the DELD problem, the TSA demonstrate the superiority in term of optimise thegeneration cost compared to other optimization techniques.

Dynamic Economic Dispatch Problem in Hybrid Wind Based Power Systems Using Quasi Oppositional Based Chaotic Honey Badger Algorithm

This article proposes a dynamic economic load dispatch (DELD) including wind power involvement process, which is capable to produce least possible generation cost of electrical systems by integrating large-scale wind power penetration. The primary component of hybridized power system comprises of conventional thermal generators and wind generators. To expedite the convergence flexibility and escape the clarifications from the confined optimal purpose, quasi-oppositional based learning (Q-OBL) is combined with HBA, which results in quasi oppositional chaotic honey badger algorithm (QOHBA). Moreover, chaotic strategy is integrated with QOHBA to boost the convergence speed as well as enhance the performance of the basic honey badger algorithm (HBA). The proposed QOCHBA is suggested to explain wind-based shared DELD problem to reduce thermal-wind generation energy cost. The superiority and expediency of the prospective technique is proved through its operation in two benchmark investigation systems. We use probability density functions (PDF) for wind energy (WE) forecasting to assessment dynamic working reserve necessities, based on the readjust of uncertainty in the prediction. To establish the efficacy of the recommended QOCHBA method in explaining non-linear, non-convex, and constrained DELD problem, the suggested structures are implemented on benchmark of 10-unit in addition to 15-unit test methods.

A Cost Effective Solution to Dynamic Economic Load Dispatch Problem Using Improved Chimp Optimizer

The electricity sector has encountered several economic challenges in recent years. Increasing the expense of fossil fuels and environmental legislation such as the Kyoto Protocol and the Low Carbon Transition Plan have compelled governments to use renewable energy sources (RESs) more widely. In the proposed research, the dynamic economic load dispatch problem has been solved using improved chimp optimizer algorithm. The test systems consisting of 6, 7 and 10-unit generators has been taken into consideration along with significant contribution of renewable energy sources for effective research studies. The test systems has been evaluated for different cases considering renewable energy sources and electric vehicles using proposed algorithms. Experimentally, it has been observed that proposed optimizer yields better results as compared to other recently proposed optimizers.

AEFA Based Optimal Dynamic Economic Load Dispatch Problem Considering Penetration of Renewable Energy Systems

In power system, maintaining the load generation balance is critical. This paper proposes an artificial electric field algorithm (AEFA) for dynamic economic load dispatch (DELD) in a power system with security constraints, taking into account renewable energy sources (RES).. The RES penetration and total savings are examined considering variation of underestimation and overestimation costs associated with RES. Further, the impact of increased RES penetration on thermal fuel and overall costs has been studied. The AEFA algorithm performs better than PSO and DE, in terms of performance. The result reveals that when RES penetration rises savings as well as net cost of RES rises. Numerically, at 65% penetration of RES, a savings of 28.19% is achieved in the generator fuel cost and the RES penetration leads to a reduction in the thermal unit’s fuel cost.

A Flexibility Based Approach on Wind/Load Curtailment Reduction in Presence of BESS

Nowadays, renewables are the first choice option for a modern power system generation scenario. It is due to their high attraction, especially environmental attraction, cost aspects and also availability in almost all over the world. Wind and solar sources are now competitive with conventional sources and command a high percentage of investments in renewable power. The main challenge of using these cheap and clean energies is their output power uncertainty, and their variability may lead to wind/solar power curtailment, or load shedding caused by insufficient spinning and fast reserve. Energy storage systems integrated with renewable energies are a common solution for this challenge. However, they impose extra cost to planning, and operation costs need a suitable economic study for the best location and size of these systems. In this paper, a flexibility based approach is used to show the role of Battery Energy Storage System (BESS) in the wind/load curtailment reduction. This approach can lead to a suitable economic routine to determine BESS size based on economic trade-off between BESS fixed, variable costs and wind/load curtailment costs. First, the BESS flexibility index is introduced and the suitable State of Charge (SoC) control is presented to use for Dynamic Economic Load Dispatch (DELD) solution based on the wind/load curtailment reduction. The simulation results show the efficient dependency between system flexibility improved by BESS integration, and the wind/load curtailment reduction.

A new flexibility based probabilistic economic load dispatch solution incorporating wind power

Nowadays, by the growing in renewable energies in the power system, the uncertainty of power output related to these resources increases. This needs to improve power system flexibility to reduce the risk of load shedding or renewable curtailment due to the mentioned challenge. So, nowadays power system flexibility is an important characteristic especially in power system operation which should be evaluated continuously and maintained in the desired value. In operation horizon time such as operational planning or real time operation the system generation flexibility should be monitored, and sufficient system flexibility should be provided to avoid unacceptable generation/load unbalance cause unwanted renewable curtailment or load shedding.This paper presents a new flexibility based risk limiting dynamic economic load dispatch solution incorporating wind power. In the proposed method the generation system flexibility index, introduced by the authors is converted to economic value and included in the total operation cost function. Then by the suitable economic trade-off between system flexibility level and risk of load shedding and wind power curtailment, the best reduction in these two unwanted risks are obtained. Increasing the system generation flexibility comes from the generation rearrangement of thermal generation units imposes more cost to the generation cost but is balanced by reduction in unwanted renewable curtailment or load shedding.

A multi-objective multi-verse optimization algorithm for dynamic load dispatch problems

In power system operation, optimal economic dispatch is imposed by the costs of increasing power generation, the increasing demand for electrical energy and the scarcity of energy resources. By satisfying all constraints, the most important thing is the economical load distribution in order to enable the generators used in the system to generate optimal power. The non-smooth cost function and emission with nonlinear constraints are the practical economic load dispatch issues that create a challenge that is effectively reduced. This paper presents a multi-objective multi-verse optimization scheme to minimize the dynamic economic load dispatch issue using valve-point effects. Along with all other necessary constraints, this algorithm preserves the ramp of unit required rate constraint. However, it maintains these limitations via the transaction duration to the next time horizon to eliminate the power system operation’s discontinuity, not only for its time horizon. The objective of this method is by satisfying various operational constraints and the power generator has the load requirement along with the minimization of cost. The proposed algorithm is tested on two test systems by varying the generating units as 40, 80, and 160. Simulation results are performed under the MATLAB environment and, the acquired results are compared with many existing algorithms in terms of fuel cost, emission cost, and robustness. The proposed scheme is very encouraging and proves the effectiveness of solving various dynamic economical load dispatch problems depending on the numerical outcomes.

Economic dispatch in grid-connected and heat network-connected CHP microgrids with storage systems and responsive loads considering reliability and uncertainties

Combined heat and power microgrids (CHP MGs) are energy efficient solutions for simultaneous supply of electric and thermal demand in a neighborhood and play a very important role in sustainable cities and societies MGs have energy management systems (EMSs) that aim to minimise their operation cost and include two modules; unit commitment module that determines commitment status of generating units and schedule of storage systems and dynamic economic load dispatch (DELD) module that determines the schedule of committed units and storage systems In this research, DELD in grid-connected and heat network-connected MGs with CHP units, boilers, microturbines, photovoltaic (PV) and wind units, electric and thermal storage systems, adjustable electric and thermal demand is formulated as a mixed-integer linear problem, while the reliability of the MG as well as uncertainties of PV and wind power, electric and thermal demand have been considered Monte Carlo Simulation and stochastic method is used to model the uncertainties In order to have a convex model in which the achievement of the global optimum is guaranteed, the non-convex feasible operating region (FOR) of CHP units is transformed into two convex feasible operating regions Moreover, effect of grid and heat network on MG operation and the sensitivity of MG operation cost to electricity and heat price are investigated The results show the effect of reliability and uncertainties on dispatch of resources and MG operation cost The results show that for the studied MG, with consideration of reliability, the operation cost increases by 14%, while.89, 10% of the cost is for Expected energy not supplied (EENS) cost and the remaining 90% is for other MG costs.

Power Distribution Optimization Based on Demand Respond with Improved Multi-Objective Algorithm in Power System Planning

In this article, a novel dynamic economic load dispatch with emission based on a multi-objective model (MODEED) considering demand side management (DSM) is presented. Moreover, the investigation and evaluation of impacts of DSM for the next day are considered. In other words, the aim of economical load dispatch is the suitable and optimized planning for all power units considering different linear and non-linear constrains for power system and generators. In this model, different constrains such as losses of transformation network, impacts of valve-point, ramp-up and ramp-down, the balance of production and demand, the prohibited areas, and the limitations of production are considered as an optimization problem. The proposed model is solved by a novel modified multi-objective artificial bee colony algorithm (MOABC). In order to analyze the effects of DSM on the supply side, the proposed MODEED is evaluated on different scenarios with or without DSM. Indeed, the proposed MOABC algorithm tries to find an optimal solution for the existence function by assistance of crowding distance and Pareto theory. Crowding distance is a suitable criterion to estimate Pareto solutions. The proposed model is carried out on a six-unit test system, and the obtained numerical analyses are compared with the obtained results of other optimization methods. The obtained results of simulations that have been provided in the last section demonstrate the higher efficiency of the proposed optimization algorithm based on Pareto criterion. The main benefits of this algorithm are its fast convergence and searching based on circle movement. In addition, it is obvious from the obtained results that the proposed MODEED with DSM can present benefits for all consumers and generation companies.

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.

A Review Paper on Economic Load Dispatch Problem

Efficient methods for solving economic load dispatch (ELD) problems have been provided to build effective multi-objective evolutionary algorithms (MOEAs). ELD is a dynamic bi-objective optimization problem that is strictly limited. The MOEA applications to solve the ELD problems have been published in various publications since the 1990s. This paper explores the state of the art of this direction-related research. It addresses subjects such as standard MOEAs, traditional ELD problems, dynamic ELD problems, wind power integration ELD problems, electric car integration ELD problems and micro-grid ELD problems. Furthermore, several potential directions are also provided for future study.

Dynamic economic dispatch problem in hybrid wind based power systems using oppositional based chaotic grasshopper optimization algorithm

This article introduces a relatively current method named grasshopper optimization algorithm (GOA) for explaining power system based dynamic economic load dispatch (DELD) problem. However, like other optimization approaches, GOA suffers from premature convergence and a slow convergence rate. Thus, to boost the convergence mobility of GOA, oppositional based learning (OBL) is merged with the GOA approach. Moreover, another improvization, namely, chaotic concept, is also integrated with the GOA to improve its solution quality. The suggested oppositional based chaotic grasshopper optimization algorithm (OCGOA) method is applied to handle the DELD problem in the most efficient manner. Among the renewable energies, intermittent wind energy (WE) is the most sustainable one and remains technically and economically advantageous for electrical energy generation. The thermal and wind based power systems are hybridized, so that power generation is distributed among the operating units in an equivalent manner such that overall cost and loss part are optimized until all practical constraints are fulfilled. The production cost of the unpredictable wind generation power is further incorporated in the operational cost by employing a probability density function (PDF) formula. The precision and performance of the suggested GOA and OCGOA approaches are validated on 6-unit and 10-unit DELD systems for conventional and wind based energy systems. The efficacy and performance of the suggested OCGOA is judged by comparing it with conventional GOA and other presently developed meta-heuristic optimization techniques found in the literature.

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.

Enhanced emperor penguin optimization algorithm for dynamic economic dispatch with renewable energy sources and microgrid

In this paper, an enhanced version of the emperor penguin optimization algorithm is proposed for solving dynamic economic dispatch (DED) problem incorporating renewable energy sources and microgrid. Dynamic economic load dispatch optimally shares the power on an hourly basis for a day among the committed generating units to satisfy the feasible load demand. Emission of pollutants from the combustion fossil fuel and gradual depletion of fossil fuel encourages the usage of renewable energy sources. Implementation of renewable energy sources with the reinforcement of green energy transforms the fossil fuel-based plant into a hybrid generating plant. The increase in power production with the increase in electricity demand implicates challenges for economical operation. The proposed algorithm is applied to the DED problem for fossil fuel based and renewable energy system to find economic schedule of generated power among the committed generating units. The proposed optimization algorithm is inspired by the huddling behavior of the emperor penguin. The exploration strategy is enhanced by adapting oppositional based learning. Chaotic mapping is used to maintain a proper balance between exploration and exploitation in the entire search space, which minimizes the cost of generation in the power system.

A novel disruption based symbiotic organisms search to solve economic dispatch

Economic dispatch (ED) is one of the vital prospects in the energy management system for determining the optimal power generation distribution among several committed power generating units. The objective of ED is to generate the electric power to meet the load demand by minimizing the total operating cost subjected to various equality and inequality constraints. This ED problem is generally solved using conventional techniques such as a gradient method by assuming the objective function as linear and convex. However, in general, the ED problem is constrained, non-convex, large scale, multimodal and highly nonlinear optimization problem. In order to overcome this drawback, various heuristic techniques have been introduced in the literature. Symbiotic organism search (SOS) is one of the emerging nature-inspired meta-heuristic algorithms. Due to simple in implementation and free from algorithm-specific control parameters, the SOS algorithm has gained a reputation among the researchers. The SOS technique mimics the biological interaction between two distinct species to survive and proliferate in the ecosystem. To evade the suboptimal solution and to enhance the exploration and exploitation of the SOS algorithm, the disruption operator is integrated with the basic SOS to form a novel disruption based SOS (DSOS) algorithm. The disruption strategy is originated from astrophysics and has the ability to shift between exploration and exploitation during the process of finding an optimum solution. To illustrate the effectiveness of the proposed method, it is applied on various test systems such as 3-unit, 5-unit, 6-unit, 13-unit, 20-unit, 38-unit, and 110-unit systems to solve different ED problems, namely, without transmission losses (TL), with transmission losses, by simultaneously considering valve point loading (VPL) effect along with TL, by simultaneously considering the effect of ramp rate limits (RRLs), prohibited operating zones (POZs) and TLs, and dynamic economic load dispatch by considering RRLs, POZs, VPL, and TL. The comparison of DSOS based simulation results with other methods reveal the efficiency and robustness of the proposed method to provide minimum total generation cost in all the cases with better convergence rate and less computational time.

Dynamic environmental‐economic load dispatch in grid‐connected microgrids with demand response programs considering the uncertainties of demand, renewable generation and market price

AbstractMicrogrids (MGs) as a key building block of smart grids have been emerged to address the proliferation of distributed energy resources. In grid‐connected MGs, dynamic economic load dispatch (DELD) module determines optimal schedule of distributed energy resources and adjustable loads and power to be exchanged with upstream grid, while all operational constraints of the MG are respected. DELD in MGs represents a constrained optimization problem with uncertain input data, as the forecasts of demand, renewable generation and market price are uncertain. In this research, particle swarm optimization (PSO) as a bio‐inspired optimization algorithm is used to solve DELD in grid‐connected MGs, while demand response program is integrated into MG and the uncertainties of demand, renewable power generation and market price are dealt with two‐point estimate method (TPEM). Load curtailment as a demand response program is used for reducing operation cost of microgrids. The performance of PSO is compared with two optimization algorithms including grey wolf optimization and backtracking search algorithm. As per the results, at times with low grid power price, microgrid imports power from upstream grid and at times with high power price it exports power to the upstream grid. The results show that the integration of demand response has significantly reduced the operation cost of the microgrid. The effect of change in maximum curtailable power on the operation cost of the MG has been investigated.

SOLVING WIND INTEGRATED DYNAMIC ECONOMIC LOAD DISPATCH USING DYNAMIC NEIGHBORHOOD BASED DIFFERENTIAL EVOLUTION

Economically satisfying the power demand of various consumers in dynamic mode becomes a prime challenge nowadays. Unbalanced load variation makes the power demand and power loss vibrant which leads to the rough load curve. To deal with differential power requirement, dynamic economic (DELD) load dispatch is witnessed as major one in consideration with unauthorized operating zone, economic load dispatch in valve pointed loading, etc. On considering the surplus power demand it is mandatory to find an alternate way to satisfy it economically, hence renewable energy support is crucial. The discontinuous power generation of wind energy with respect to the availability of wind source makes the DELD problem non smooth and non convex in practical. Hence an evolutionary computational technique is prominent to find a better solution. In proposed topology, Neighborhood based Differential Evolution Algorithm has been evaluated for resolving the wind integrated DELD problem including various constraints. To check out values attained from proposed method, 15 unit and 10 unit system test data is optimized for various power demand including the wind support and evaluated with the results from the literature.

Dynamic Economic Load Dispatch of CHP Microgrid using Improved Particle Swarm Optimization

For the sustainable development and various industrial applications,Combined Heat - Power system offers advantages such as saving of energy, economic as well as environment protection benefits. Paper deal with dynamic economic load dispatch of C-H-P,Micro-Grid (Wind- Energy Conversion system, P-V array), Fuel Cell, Boiler(Waste- Heat), thermal and electrical loads. For this purpose, a nonlinear optimal CHP micro grid model has been built and simulated for the economic function of present power generating resources and to generate the twenty four(24) work schedule with forecasted condition of wind, solar, heat and electrical demand for next 24 hours. The methodology used for finding the solution of model is improved particle swarm optimization. Model has been tested with and without peak valley pricing. Results indicate that cost of the system is effectively reduced with peak valley pricing. The simulated results of the system with improved particle swarm optimization (IPSO) and PSO techniques have been shown and compared. The simulation results indicate that the improved PSO providesimprovised solution as compared to PSO.