Dynamic Economic Emission Dispatch Problem Research Articles

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.

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.

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.

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.

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.

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

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.

Sooty Tern Optimization Algorithm for Solving the Multi-Objective Dynamic Economic Emission Dispatch Problem

The sooty tern optimization algorithm (STOA) has been used in this study to solve and optimize the dynamic economic emission dispatch (DEED) problem. The main aim of the DEED model is to minimize total fuel cost and emission of pollutant gases from thermal generators for 24 hours. The various operating constraints like valve point loading effect, ramp rate limit, transmission losses, operating conditions, and power balance constraints have been considered in this study to get a closer practical system. The swarm intelligence-based STOA method has been inspired by the migration and attacking behaviors of sea bird sooty tern. The exploration and exploitation approach of the proposed algorithm help to get an optimum solution in less convergence time. The algorithm has been tested in 5 and 10 thermal generating units to verify the algorithm's performance. The results obtained by the proposed algorithm have been compared with results obtained by other recently developed algorithms.

MSSA-DEED: A Multi-Objective Salp Swarm Algorithm for Solving Dynamic Economic Emission Dispatch Problems

Due to the rising cost of fuel, increased demand for energy, and the stresses of environmental issues, dynamic economic emission dispatch (DEED), which is the most precise mode for actual dispatching conditions, has been a significant study topic in current years. In this article, the higher dimensional, deeply correlated, non-convex, and non-linear multi-objective DEED problem is designated, involving both the fuel costs and emissions objectives simultaneously. In addition, the valve point effect, transmission loss, as well as the ramping rate, are considered. The Salp Swarm Algorithm (SSA) is a well-established meta-heuristic that was inspired by the foraging behavior of salps in deep oceans and has proved to be beneficial in estimating the global optima for many optimization problems. The objective of this article is to evaluate the performance of the multi-objective Salp Swarm Algorithm (MSSA) for obtaining the optimal dispatching schemes. Furthermore, the fuzzy decision-making (FDM) approach is employed to achieve the best compromise solution (BCS). In order to confirm the efficacy of the MSSA, the IEEE 30-bus six-unit power system, standard 39-bus ten-unit New England power system, and IEEE 118-bus fourteen-unit power system were considered as three studied cases. The obtained results proved the strength and supremacy of the MSSA compared with two well-known algorithms, the multi-objective grasshopper optimization algorithm (MOGOA) and the multi-objective ant lion optimizer (MALO), and other reported methods. The BCS of the proposed MSSA for the six-unit power system was USD 25,727.57 and 5.94564 Ib, while the BCS was 2.520778 × USD 106 and 3.05994 × 105 lb for the ten-unit power system, and was 1.29200 × USD106 and 98.1415 Ib for the 14 generating units. Comparisons with the other well-known methods revealed the superiority of the proposed MSSA and confirmed its potential for solving other power systems’ multi-objective optimization problems.

Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem

A chance-constrained programming-based optimization model for the dynamic economic emission dispatch problem (DEED), consisting of both thermal units and wind turbines, is developed. In the proposed model, the probability of scheduled wind power (WP) is included in the set of problem-decision variables and it is determined based on the system spinning reserve and the system load at each hour of the horizon time. This new strategy avoids, on the one hand, the risk of insufficient WP at high system load demand and low spinning reserve and, on the other hand, the failure of the opportunity to properly exploit the WP at low power demand and high spinning reserve. The objective functions of the problem, which are the total production cost and emissions, are minimized using a new hybrid chaotic maps-based artificial bee colony (HCABC) under several operational constraints, such as generation capacity, system loss, ramp rate limits, and spinning reserve constraints. The effectiveness and feasibility of the suggested framework are validated on the 10-unit and 40-unit systems. Moreover, to test the robustness of the suggested HCABC algorithm, a comparative study is performed with various existing techniques.

Multiobjective multi verse optimization algorithm to solve dynamic economic emission dispatch problem with transmission loss prediction by an artificial neural network

To overcome the drawback of using only one B-loss coefficient for a widely varying demand pattern that occurs during the entire period of a dynamic economic emission dispatch model, this research work presents a new approach to integrating artificial neural network-based loss prediction into the dynamic economic emission dispatch model. The trained neural network will predict the transmission loss only once during each interval of the dispatch period. The novelty of the work lies in employing a trained neural network to replace the existing procedure of solving complex power flow equations or B-loss coefficients during every iteration. A potent multiobjective multiverse optimization algorithm along with an effective constraint handling mechanism is employed to solve the highly complicated dynamic economic emission dispatch model. A fuzzy membership-based approach is adopted to help the decision-maker select one optimal solution from the Pareto Optimal solutions during each hour of the dispatch period. The proposed algorithm is tested on four systems of varying complexity including a large-scale IEEE 118 bus power system with fifty-four thermal generators. The results obtained are compared with 16 state-of-the-art algorithms to prove the competitive behavior of the algorithm. For an IEEE 30 bus system, the proposed algorithm results in a saving of 92.92$ in fuel cost and a reduction of emission levels by 0.0375 tons per day. The total time taken by the algorithm is faster than the well-known non dominated sorted genetic algorithm II and multiobjective particle swarm optimization algorithm by 30 times and 7.6 times respectively.

Artificial Intelligence Based Demand Side Management Approach to Solve Non-Linear Dynamic Economic Emission Dispatch Problem

Artificial Intelligence Based Demand Side Management Approach to Solve Non-Linear Dynamic Economic Emission Dispatch Problem

Multiobjective Multi Verse Optimization Algorithm to Solve Dynamic Economic Emission Dispatch Problem with Transmission Loss Prediction by an Artificial Neural Network

Multiobjective Multi Verse Optimization Algorithm to Solve Dynamic Economic Emission Dispatch Problem with Transmission Loss Prediction by an Artificial Neural Network

A novel oppositional learning-based chaotic sine cosine algorithm for the dynamic thermal–wind economic dispatch problem

Wind power generation is efficiently utilized with traditional thermal power generation in the dynamic economic emission dispatch (DEED) problem to achieve the demand power of scheduled periods with minimum generation cost and emission of pollutants. This study uses a novel oppositional learning-based chaotic sine cosine algorithm (SCA) (OL-CSCA) to solve a new model of wind energy-based DEED, called dynamic thermal–wind economic emission dispatch (DTWEED). The concepts of oppositional learning and chaos are introduced into the SCA to strengthen its optimization performance. The effectiveness of the proposed model is tested by solving four cases (generation cost, spinning reserve based generation cost, emission and combined cost–emission) using OL-CSCA. The novelty of DTWEED is shown by comparing the results of different cases with the traditional DEED model. Comparison of the results of OL-CSCA for different cases of DEED and DTWEED with the results of SCA and other algorithms demonstrated its robustness and proficiency.

Improved tunicate swarm algorithm: Solving the dynamic economic emission dispatch problems

This study proposes improved tunicate swarm algorithm (ITSA) for solving and optimizing the dynamic economic emission dispatch (DEED) problem. The DEED optimization target is to reduce the fuel cost and pollutant emission of the power system. In addition, DEED is a complex optimization problem and contains multiple optimization goals. To strengthen the ability of the ITSA algorithm for solving DEED, the tent mapping is employed to generate initial population for improving the directionality in the optimization process. Meanwhile, the gray wolf optimizer is used to generate the global search vector for improving global exploration ability, and the Levy flight is introduced to expand the search range. Three test systems containing 5, 10 and 15 generator units are employed to verify the solving performance of ITSA. The test results show that the ITSA algorithm can provide a competitive scheduling plan for test systems containing different units. ITSA proposed algorithm gives the optimal economic and environmental dynamic dispatch scheme for achieving more precise dispatch strategy.

Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function

Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi-objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB® for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique.

Solving dynamic economic and emission dispatch in power system integrated electric vehicle and wind turbine using multi-objective virus colony search algorithm

Climate change and development of today’s power grids have led to unprecedented changes in the old power grid in terms of fossil fuel consumption level and environmental pollution. So, the ancillary power plants and services have found a good position in power systems. In this paper, renewable energy generators such as wind energy units and plug-in electric vehicles (PEVs) are being integrated in the multi-objective dynamic economic emission dispatch (DEED) problem during a day, which minimizes wind-thermal electrical energy cost and emissions obtained by fossil-fueled power generators at the same time. To obtain a real-world problem, consider several practical and nonlinear constraints such as valve-point effect, ramp-up/ramp-down, prohibited zones, and power generation boundaries. Since the proposed multi-objective problem consists of different variables and constrains, the classical mathematic methods cannot guarantee finding the best solution. Therefore, this paper suggested a new multi-objective virus colony search (MOVCS) based on non-dominated sorting theory and fuzzy decision making for finding the best solution among Pareto fronts. Also, PEVs are remarkable and stochastic power demands and, unlike conventional demands, four dissimilar charging scenarios are considered. The efficiency of the proposed method and model is investigated on some test systems with the presence of wind units and electric vehicles during the day and night. The results are evaluated based on appropriate performance criteria. The results obtained from the proposed algorithm are compared with other algorithms, which indicates the efficiency, accuracy, and speed of this algorithm in solving the proposed problem.

An agile and intelligent dynamic economic emission dispatcher based on multi-objective proximal policy optimization

The recent development in power systems claims the improvement of the efficiency to solve dynamic economic emission dispatch (DEED) problem. However, the popular pure optimization framework based on evolutionary algorithms only starts the compute-intensive optimization process after receiving the power demand, which causes significant delays To address this limitation, this research defines a novel dynamic economic emission dispatcher (DEEDer) problem and the dispatcher learning framework. It is different from previous research and the most current algorithms in the advantage of transferring the on-line compute-intensive optimization task to off-line. To solve the dispatcher, we model the dispatching process as a conditional deterministic Markov Decision Process (MDP) and propose the multi-objective proximal policy optimization (MOPPO). The Benchmark Test Set with 10,000 different dispatching tasks for 5-unit and 10-unit system is released to evaluate the generalization of the dispatcher. The experiment results indicate that the neural network dispatcher trained with MOPPO is hundreds to thousands of times faster than the state-of-the-art pure optimization algorithms. Meanwhile, the dispatcher not only has comparable performance as the state-of-the-art multi-objective optimization algorithms in standard dispatching task but also shows generalization on generating Pareto optimal solutions given any possible dispatching task. Following the DEEDer framework, the proposed method makes it possible to dispatch power in a more agile way as the timely response to the changing of power demand while still controlling economic and emission.