Dynamic Economic Emission Dispatch Research Articles

An Efficient Multi-objective Approach Based on Golden Jackal Search for Dynamic Economic Emission Dispatch

An Efficient Multi-objective Approach Based on Golden Jackal Search for Dynamic Economic Emission Dispatch

An improved NSGA‐II for the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles and home‐distributed photovoltaic generation

AbstractThis paper investigates four energy utilization scenarios with or without home‐distributed photovoltaic generation (HDPG) to reduce the generation cost and pollutant emission of the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles (DEED‐PEV). The first scenario considers valley filling for the charging of PEVs. The second scenario combines valley filling and peak shaving for the charging and discharging of PEVs. The third scenario adds peak shaving of HDPG to the first scenario, followed by the peak shaving with the discharging of PEVs. The fourth scenario rearranges the distribution of photovoltaic (PV) power for the third scenario, and the PV power in the afternoon is stored by a photovoltaic energy storage system (PESS) and consumed in the evening. A universal procedure is designed for the valley filling and peak shaving of the four scenarios, which is beneficial for determining the filled and shaved loads with respect to certain time intervals. An NSGA‐II method based on a modified crossover and an elimination of individuals (NSGA‐II‐MCEI) is proposed for the multiobjective optimization of DEED‐PEV. The modified crossover can improve the convergence of NSGA‐II‐MCEI, and the elimination operator can maintain the evenness of the nondominated solutions. According to experimental results, scenario 4 achieves cost savings of 95386.62 $, 85636.87 $, and 6776.85 $, respectively, for Scenarios 1, 2, and 3, and it reaches emission reductions of 27617.64 kg, 17252.71 kg, and 220.98 kg, respectively, for scenarios 1, 2, and 3. Also, scenario 4 outperforms the other three scenarios for three weather conditions such as sunny day, cloudy day, and rainy day.

Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model

Dynamic economic emission dispatch problems are complex optimization tasks in power systems that aim to simultaneously minimize both fuel costs and pollutant emissions while satisfying various system constraints. Traditional methods often involve solving intricate nonlinear load flow equations or employing approximate loss formulas to account for transmission losses. These methods can be computationally expensive and may not accurately represent the actual transmission losses, affecting the overall optimization results. To address these limitations, this study proposes a novel approach that integrates transmission loss prediction into the dynamic economic emission dispatch (DEED) problem. A Random Forest machine learning model was offline-trained to predict transmission losses accurately, eliminating the need for repeated calculations during each iteration of the optimization process. This significantly reduced the computational burden of the algorithm and improved its efficiency. The proposed method utilizes a powerful multi-objective stochastic paint optimizer to solve the highly constrained and complex dynamic economic emission dispatch problem integrated with random forest-based loss prediction. A fuzzy membership-based approach was employed to determine the best compromise Pareto-optimal solution. The proposed algorithm integrated with loss prediction was validated on widely used five and ten-unit power systems with B-loss coefficients. The results obtained using the proposed algorithm were compared with seventeen algorithms available in the literature, demonstrating that the multi-objective stochastic paint optimizer (MOSPO) outperforms most existing algorithms. Notably, for the Institute of Electrical and Electronics Engineers (IEEE) thirty bus system, the proposed algorithm achieves yearly fuel cost savings of USD 37,339.5 and USD 3423.7 compared to the existing group search optimizer algorithm with multiple producers (GSOMP) and multi-objective multi-verse optimization (MOMVO) algorithms.

Equilibrium optimizer for multi-objective dynamic economic emission dispatch integration with plug-in electric vehicles and renewable sources

Equilibrium optimizer for multi-objective dynamic economic emission dispatch integration with plug-in electric vehicles and renewable sources

Integrating renewable energy sources and electric vehicles in dynamic economic emission dispatch: an oppositional-based equilibrium optimizer approach

ABSTRACT This article proposes a solution to the Dynamic Economic Emission Dispatch (DEED) problem, which incorporates wind, solar and plug-in electric vehicles (PEVs) into the optimization challenge. The new model, called Wind-Solar-Plug in Electric Vehicle (WSPEV) DEED, utilizes an optimization technique called the Oppositional-based Equilibrium Optimizer (OEO) method with the Weibull and Beta distributions to model wind and solar resources. The charging and discharging patterns of PEVs are also considered in the model. The proposed approach is evaluated through several scenarios involving Renewable Energy Sources (RESs) and PEVs, and the simulation results demonstrate the effectiveness of the proposed model in achieving a sustainable and cost-effective power system. The WSPEV DEED model provides a valuable solution to the DEED problem, which is crucial for successfully integrating RESs and PEVs into the power system for a sustainable future.

Hybrid renewable energy source optimization using black widow optimization techniques with uncertainty constraints

To encourage the growth and use of renewable energy such as PV Solar Energy, Wind Energy, India is speeding up the creation of a renewable portfolio standard. Scheduling practises for thermal power and other renewable energy producers in the electric power system will shift as a result of the adoption of the standard and accompanying marketable green credits. In this study, we develop a dynamic economic emission dispatch model for wind, solar, and hydro power under tradable green certificates, taking into account many objectives simultaneously. The drive to transition towards cleaner and more sustainable energy sources has underscored the significance of optimizing hybrid renewable energy systems. This study presents a novel approach that leverages the Black Widow Optimization (BWO) algorithm to address the challenges of optimizing such systems while accounting for uncertainty constraints. The core problem revolves around the effective integration and management of multiple renewable energy sources, including solar, wind, and hydro, within a framework that ensures robustness and adaptability. In the face of variable weather conditions and equipment reliability uncertainties, the BWO algorithm excels in balancing exploration and exploitation to yield optimal solutions. Through rigorous modelling and sensitivity analysis, this study provides quantitative insights into the system's performance, offering a clear understanding of the trade-offs between cost-effectiveness and reliability. Real-world case studies validate the practicality of the proposed approach, underlining its importance in achieving sustainable and resilient energy systems. This study's goal is to minimise the yearly cost of operating a hybrid wind-solar renewable energy system to meet a specified load. The objective was to minimise the yearly cost of the system by selecting the optimal level of system components to meet the required load in an efficient and cost-effective manner. Several computational strategies inspired by nature, such as Black Widow Optimization (BWO), were used to maximise fitness. The fitness function and the techniques used to create it where both coded in MATLAB.

A variegated GWO algorithm implementation in emerging power systems optimization problems

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm.

Decentralized Coordination of DERs for Dynamic Economic Emission Dispatch

This paper focuses on the dynamic economic emission dispatch (DEED) problem, to coordinate the distributed energy resources (DERs) in a power system and achieve economical and environmental operation. Distributed energy storages (ESs) are introduced into problem formulation in which charging/discharging efficiency is taken into account. By relaxing the nonconvexity induced by the charging/discharging model of ESs and network losses, we convert the non-convex DEED problem into its convex equivalency. Then, through a Lagrangian duality reformulation, an equivalent unconstrained consensus optimization model is established—a novel consensus-based decentralized algorithm, where the incremental cost is chosen as the consensus variable. At each iteration, only one primal variable requires sub-optimization, and it is completely locally updated. This is different from the well-known alternating direction method of multiplier (ADMM)-based algorithms where more than one subproblem needs to be solved at each iteration. The results of the comparative experiments also reflect the algorithm’s advantage in terms of computational efficiency. The simulation results validate the effectiveness of the proposed algorithm, achieving a balance between emissions and economic considerations.

The dynamic economic emission dispatch of the combined heat and power system integrated with a wind farm and a photovoltaic plant

Two renewable energies are included in the combined heat and power (CHP) system to optimize its energy configuration, and they are wind power generation and photovoltaic power generation, respectively. Furthermore, a global nondominated sorting genetic algorithm II (GNSGA-II) is proposed to confront the combined heat and power dynamic economic emission dispatch (CHPDEED) with renewable energies. GNSGA-II produces offspring individuals by a crossover with negative exponential distribution, and it is able to carry out global search in the decision space. GNSGA-II also assigns an adaptive weight to original crowding distance to give consideration to both crowding degree and evenness of each individual in the objective space, which is beneficial for improving the evenness of the Pareto set. In addition, a constraint handling approach is proposed to satisfy all constraints, such as power generation limits, heat generation limits, capacity limits of the CHP units, power balances, heat balances, ramp rate limits and spinning reserve requirements. Seven multi-objective evolutionary algorithms (MOEAs) are used to solve the four CHPDEED scenarios with or without renewable energies, and GNSGA-II outperforms the other six MOEAs. It does not only obtain larger hypervolumes and coverage rates, but also obtain relatively small spacings. For the four compromise solutions of GNSGA-II, the generation costs of Scenario 2, Scenario 3 and Scenario 4 are, respectively, 0.51%, 0.34% and 4.1% higher than that of Scenario 1. In the meantime, the pollutant emissions of Scenario 2, Scenario 3 and Scenario 4 are, respectively, 54.78%, 19.05% and 71.45% lower than that of Scenario 1.

A novel FFA‐GWO‐based optimization paradigm for solving a dynamic economic emission dispatch in power plants

AbstractThermal power plants are the primary components of the electrical energy production industry. These plants come with certain challenges such as the production of harmful gases causing environmental pollution. This adds to global warming issues that are dangerous for the earth's ecosystem. Another crucial aim is the least cost operation of these power plants. Combining both of these results in the formulation of the Dynamic Economic Emission Dispatch (DEED). This is a multi‐objective, constrained, dynamic, complex, and non‐linear optimization problem. Traditional methods are insufficient to undertake the complexities of DEED due to challenges such as high computational requirements and local optimal solutions. Hence, for obtaining an optimal or near‐optimal solution to such a complex problem, a metaheuristic technique is greatly needed. Metaheuristic techniques offer several benefits to solving complex problems such as being robust, efficient, and overall, less affected by an increase in problem size. In this work, a novel hybrid Firefly Optimization Algorithm (FFA)—Grey Wolf Optimization Algorithm (GWO) has been proposed to solve the DEED problem. The complexity of the DEED problem is undertaken by involving constraints like power limits, power balance, and transmission losses. FFA‐GWO was applied on 5, 10, and 15‐unit standard test systems to meet 24‐h load demands. The optimal costs obtained in 5, 10, and 15‐unit standard test systems are 32264$, 1394212.615$, and 465373.448$ respectively. The comparison of results shows that the proposed framework surpasses the other methodologies in terms of reduced fuel cost and emissions.

Performance enhancement of smart grid with demand side management program contemplating the effect of uncertainty of renewable energy sources

ABSTRACT In a day ahead electricity market, all candidates of the electricity market, i.e. electricity users, aggregator, and grid operator urge to grow individual profit, but it is quite challenging to assure profit for all the candidates at a time. In this work, a multi-objective problem is formulated by combining the concept of demand side management (DSM) and Dynamic Economic Emission Dispatch (DEED). The multi-objective DSM – DEED problem is optimized by class topper optimization algorithm. In addition, an energy management algorithm (EMA) is proposed for optimal power utilization from various energy sources and to match the load demand with generated energy in presence of uncertainties of RESs. To get an accurate model, a random forest regression-based machine learning approach is considered in this paper to predict load demand, wind, and solar power on an hourly basis for a span of 24 hours. The objective here is to optimally schedule load consumption and power generation patterns simultaneously for a day to improve load factor, minimize operational cost, and maximize the profit of all the candidates of the electricity market simultaneously. The simulation findings highlight the effects of the proposed EMA and modified DSM program on the smart grid’s economy and performance.

An improved differential evolution using self-adaptable cosine similarity for economic emission dispatch

As increasing demand of electric energy and deterioration of environment pollution in global, economic emission dispatch (EED) problem has become one of the most important problems in power systems. In order to improve the quality of life and further reduce emissions, plug-in electric vehicle (PEV) has become a popular transportation. But the frequent and random charging of PEVs will bring potential risk that undermines the stability of the power grid. Therefore, dynamic economic emission dispatch considering plug-in electric vehicles (DEED-PEV) as a new hot-spot issue has gradually become more and more important in the power system. In this paper, an improved differential evolution using self-adaptable cosine similarity (DE-SCS) is proposed for EED and DEED-PEV problems. First, a dynamic cosine similarity calculated replaces the F of the differential evolution algorithm (DE) to scale the perturbation by self-adaptability. Second, a result-driven selection operation for multiple mutation strategies is implemented in each iteration. Third, a modified environment selection changes the usual way to ranking in the vectors pool during the update on Pareto front. Last, an evaluation mechanism that involves two series of solution sets and three types of comparing approaches is conducted on our proposed algorithms in the experiment. DE-SCS improves the equilibrium between exploration and exploration of the DE. The cosine similarity makes the convergence more self-adaptive. The modified environment selection maximizes the worth of previous generation vectors and brings the randomness into the next iteration. DE-SCS matching various environmental selection are tested on twelve unconstrained multi-objectives problems, six EED cases and six DEED-PEV cases containing charging load, discharging power, valley filling, and peak shaving. Experimental results confirm that DE-SCS is capable of obtaining such excellent and feasible solutions that it has good potential to deal with EED and DEED-PEV problems.

Multi-county combined heat and power dynamic economic emission dispatch incorporating electric vehicle parking lot

Multi-county combined heat and power dynamic economic emission dispatch incorporating electric vehicle parking lot

Economic emission dispatch considering electric vehicles and wind power using enhanced multi-objective exchange market algorithm

Due to the environmental concerns and the high penetration of wind power (WP) and electric vehicles (EVs), reducing the generation costs and emissions, coordinating EV charging in the distribution networks, and addressing the WP uncertainty, are among the most challenging issues in power system control and operation. Hence, the multi-objective dynamic economic emission dispatch problem (MDEEDP) integrated with EVs and wind farms has become one of the hottest topics in this research area. In this paper, the MDEEDP in the presence of EVs, the random behavior of EV drivers, and WP uncertainty is solved using a new multi-objective evolutionary algorithm called the enhanced multi-objective exchange market algorithm. In the proposed algorithm, the novel point-to-point distance technique is introduced to find the Pareto front solutions set (PFSS) with a uniform and diverse distribution. Also, smart strategy management for charging and discharging EVs is proposed to smooth the load curve. The effectiveness of the proposed method and performance of the proposed smart strategy are investigated on two single-objective and multi-objective test systems, including EVs and wind farms, considering uncertainty and system constraints such as ramp rate limits. The simulation results show that the PFSS of the MDEEDP is well extracted by the proposed method while maintaining its uniformity and diversity. The presence of EVs and the application of the proposed smart strategy lead to a reduction of 2,926,497 ($) in the operation costs for test system 1, and, 38,694,698.28 ($) and 7,832,876.64 (kg), respectively in the operation costs and emissions for test system 2, assuming the same load curve throughout the year. In addition, the load factor in test systems 1, and 2 is improved by 11 and 19 percent, respectively.

A nondominated sorting genetic algorithm III with three crossover strategies for the combined heat and power dynamic economic emission dispatch with or without prohibited operating zones

This paper presents a nondominated sorting genetic algorithm III with three crossover strategies (NSGA-III-TCS) for the combined heat and power dynamic economic emission dispatch (CHPDEED) problems with or without prohibited operating zones. The first strategy updates the individuals of the first nondomination rank by a modified improvisation of the harmony search algorithm, which accelerates the information exchanges of the elite individuals. The second strategy regulates the individuals of the last nondomination rank according to a modified single-point crossover of the genetic algorithm, which strengthens the convergence of the population. The third strategy adjusts the remaining individuals in terms of a modified DE/rand-to-best/1 mutation of the differential evolution algorithm, and it guides these individuals to move towards the ones which have at least one minimum objective function value, which is beneficial for expanding the distribution range of the population. NSGA-III-TCS obtains the largest average hypervolumes and shows a desirable convergence for six CHPDEED problems. NSGA-III-TCS achieves relatively high coverage rates in most cases, indicating the dominance of the Pareto sets of NSGA-III-TCS over those of the other algorithms. Also, it has relatively low average spacings for all problems, suggesting the good evenness of the Pareto sets.

An approach to attain a balanced trade-off solution for dynamic economic emission dispatch problem on a microgrid system

An approach to attain a balanced trade-off solution for dynamic economic emission dispatch problem on a microgrid system

Multi-objective dynamic economic emission dispatch integration with renewable energy sources and plug-in electrical vehicle using equilibrium optimizer

Multi-objective dynamic economic emission dispatch integration with renewable energy sources and plug-in electrical vehicle using equilibrium optimizer

A Novel Quasi-Oppositional Learning-Based Chaos-Assisted Sine Cosine Algorithm for Hybrid Energy Integrated Dynamic Economic Emission

In the current era of industrialization, renewable energy (such as wind and solar energy) plays an effective role in power generation sector, and it helps to decrease the generation cost and the environmental pollution. In this work, hybrid source of energy (like solar and wind energy) has been integrated with the traditional dynamic economic emission dispatch (EED) (DEED) problem to formulate a new model, i.e., complicated constrained hybrid energy integrated DEED problem, which may be able to generate less polluted power than the traditional DEED problem. Here, the quasi-opposition learning (QOL) approach and chaotic dynamics have been introduced in a novel sine cosine algorithm (SCA) to enhance its convergence and diversity. Effectiveness of the presented solution of the dynamic thermal-wind-solar EED problem has been properly validated by solving three aspects, i.e., the cost, the emission and the combined cost-emission using QOL-based chaotic SCA. The use of solar and wind energy in the DEED problem reduces the generating costs by 8% and 20.84%, respectively, compared to traditional and wind-integrated DEED. The robustness of the proposed modified SCA has been presented by comparing the results with the results offered by SCA and other recently published algorithms.

Dynamic economic emission dispatch including electric vehicles’ demand management and vehicle to grid support considering physical constraints

Dynamic economic emission dispatch including electric vehicles’ demand management and vehicle to grid support considering physical constraints

A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns

Economic dispatch (ED) problems, especially in multi-area power networks, have been challenging concerns for power system operators for several decades. In this paper, we introduce a novel approach for solving the multiobjective multi-area dynamic ED (MADED) problem in the presence of practical constraints such as valve-point effect (VPE), prohibited operating zone (POZ), multi-fuel operation (MFO), and ramp rate (RR) limitations. Different objective functions including energy not supplied (ENS), generation costs, and emissions are investigated. The reliability objective, which has been less studied in economic dispatch area, distinguishes the proposed study from other studies. A compromise has been made from economic and reliability points of view. The MADED problem in the power system is inherently a complex and nonlinear problem, considering the operational constraint increments and the intricacy of the problem. Hence, the modified grasshopper optimization (MGO) algorithm based on a chaos mechanism is presented to prevent being trapped in local optima. The proposed method is tested on two systems including a 10 unit, 3-zone test system and a 40-unit 3-zone test system, and then, the outcomes are compared with those of other evolutionary techniques such as gray wolf optimization (GWO) and modified honey bee mating optimization (MHBMO). The simulation results demonstrate that the suggested strategy is successful in resolving both single-objective and multiobjective MADED problems.