Dynamic Economic Emission Dispatch Model Research Articles

Multi-objective Approach for Dynamic Economic Emission Dispatch Problem Considering Power System Reliability and Transmission Loss Prediction Using Cascaded Forward Neural Network

This study addresses the significant problem of Dynamic Economic Emission Dispatch (DEED), a critical consideration in power systems from both economic and environmental protection viewpoints. Reliability stands as another vital facet, impacting maintenance and operation perspectives. The integration of Artificial Neural Network (ANN)-based transmission loss prediction into the DEED model is also essential to address specific limitations and enhance the overall performance of the dispatch process. Traditionally, the DEED model relies on a single B-loss coefficient to estimate transmission losses. While this approach simplifies calculations, it fails to account for the significant variations in demand that occur throughout the dispatch period and it leads to inaccuracies in loss prediction, especially in dynamic environments. Using a single coefficient, the model cannot adequately capture the complex, non-linear relationships between power generation, load, and transmission losses under different operating conditions. To overcome this limitation, this study introduces an ANN-based loss prediction method integrated into the DEED model and uses trained ANN to replace the process of finding B-loss coefficients during each dispatch period. This paper also introduces a strategy leveraging the multi-objective northern goshawk optimizer algorithm, characterized by a non-dominated sorting and crowding distance mechanism, to enhance DEED considerations incorporating reliability (DEEDR). This novel algorithm improves the solution space effectively, maintains high population diversity and enables an even distribution of individuals sharing the same rank in the objective space. The fundamental objective of this study is to balance fuel cost, emission, and system reliability in power system operations. Compared with a few existing multi-objective optimization algorithms, this study demonstrates superior performance in generating a series of non-dominated solutions. The experimental results highlight its competitive and potential as an efficient tool in the DEED and DEEDR problems, promising a synergistic coordination of economy, environmental protection, and system reliability benefits in power system management.

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.

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.

Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm

Dynamic economic emission dispatch (DEED) in combination with renewable energy has recently attracted much attention. However, when wind power is considered in DEED, due to its generation uncertainty, some additional costs will be introduced and the stability of the dispatch system will be affected. To address this problem, in this paper, the energy-storage characteristic of electric vehicles (EVs) is utilized to smooth the uncertainty of wind power and reduce its impact on the system. As a result, an interaction model between wind power and EV (IWEv) is proposed to effectively reduce the impact of wind power uncertainty. Further, a DEED model based on the IWEv system (DEEDIWEv) is proposed. For solving the complex model, a self-adaptive multiple-learning multi-objective harmony-search algorithm is proposed. Both elite-learning and experience-learning operators are introduced into the algorithm to enhance its learning ability. Meanwhile, a self-adaptive parameter adjustment mechanism is proposed to adaptively select the two operators to improve search efficiency. Experimental results demonstrate the effectiveness of the proposed model and the superiority of the proposed method in solving the DEEDIWEv model.

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.

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.

Impacts of carbon trading and wind power integration on carbon emission in the power dispatching process

Currently, many low-carbon approaches are being applied in the power dispatching process, such as dynamic economic emission dispatch (DEED), wind power integration and carbon trading. Furthermore, these approaches may influence the carbon emission from electricity. To investigate such joint influence, this paper firstly builds a stochastic DEED model in the forms of chance-constrained programming related to wind power uncertainties. After that, the established model is transformed to a deterministic form by discussing relevant statistical characteristics. Next, an auxiliary decision-making method is designed to draw the optimal dispatching solution upon real-time price of carbon trading. Finally, empirical analysis shows that: (1) the minimization of carbon emissions and fuel costs comes at the expense of the reliability of wind power integrated system, for reasons that the decrement rate of thermal power output is about 60.8% from higher reliability level to lower level, (2) the decrement rate of carbon emissions (0.01%–0.012%) is superior to the growth rate of fuel costs (0.0011%–0.0022%) as the carbon trading price increases, which proves that carbon emission reduction can be effectively realized by carbon trading rather than substantial increase in fuel costs, and (3) carbon trading is conducive to the improvement of carbon emission efficiency.

Optimization of power dispatching strategies integrating management attitudes with low carbon factors

With the rapid development of low-carbon electricity, wind power and carbon prices serving as low carbon factors have drawn significant attention. Owing to the wind power uncertainties, the carbon price fluctuations and the immeasurability of management attitudes towards these two factors, the traditional dynamic economic emission dispatch (DEED) models are no longer completely practicable for low-carbon power dispatching problem. Aiming at these difficulties, this paper establishes a stochastic DEED model concentrating on low carbon factors. After discussing the wind power’s statistical characteristics, a scenario-based deterministic form for the proposed model is set up. Furthermore, a sequence of nondominated sorting genetic algorithm-II (NSGA-II) program steps with constraints-handling techniques is designed to solve this model, and an auxiliary decision-making method is developed for the selection of optimal power dispatching strategies. According to carbon prices, wind power uncertainties and satisfaction degrees, the optimal power dispatching strategies integrating management attitudes with low carbon factors could be achieved through the proposed model and method. The simulation results finally prove that carbon emissions could be controlled by carbon trading without significantly increasing generation costs, and carbon trading will further contribute to improvement of energy efficiency.

Dynamic environmental economic dispatch of hybrid renewable energy systems based on tradable green certificates

China is accelerating the formulation of a renewable portfolio standard to promote the development and utilization of renewable energy. The implementation of the standard and corresponding tradable green certificates will change the scheduling procedures of thermal power and other renewable energy generators in the electric power system. This paper formulates a multi-objective dynamic economic emission dispatch model for wind-solar-hydro power under tradable green certificates. Weibull, Beta and Gumbel distributions are used to simulate the random behavior of wind, solar and hydro power, respectively, thus taking into account the overestimation and underestimation costs caused by the randomness of renewable energy. Moreover, a new multi-objective moth-flame optimization (MOMFO) based solution is proposed according to the characteristics of the dispatch model. The MOMFO is based on the external elite reservation and Pareto dominance. To improve the search capability, the strategy of average entropy initialization, Lévy flight and variable scale chaotic strategy are introduced. A heuristic dynamic relaxation method is proposed, which is used to handle the complex constraints of the model. Finally, the feasibility of the proposed scheduling model and the effectiveness of the algorithm are illustrated using examples of different scheduling situations based on IEEE 39-bus standard system.

Multiobjective dynamic economic emission dispatch using evolutionary algorithm based on decomposition

Due to the pressures of energy and environment problems, the dynamic economic emission dispatch (DEED), which is more in line with actual dispatching requirements, has become an important research issue in recent years. In this paper, the highly dimensional, strongly coupled, nonlinear and nonconvex multiobjective DEED model is established considering both the fuel costs and pollution emissions objectives. Furthermore, an improved multiobjective evolutionary algorithm based on decomposition with constraints handling (IMOEA/D‐CH) is proposed to obtain the optimal dispatching schemes. The IMOEA/D‐CH method first decomposes the DEED problem into a number of scalar optimization subproblems and then evolves them simultaneously using the neighborhood information. The real‐time heuristic constraints adjustment and modification methods and the adaptive threshold punishment mechanism are adopted in allusion to the complex constraints of the dispatching model. An evolutionary control strategy is also utilized to avoid excessive evolution of the algorithm toward a certain objective. In addition, the fuzzy decision‐making strategy is used to provide the decision maker with the optimal compromise solutions. To validate the effectiveness of the IMOEA/D‐CH method, the IEEE 30‐bus 6‐generator system and 10‐generator system and 118‐bus 14‐generator system are studied as test cases. The simulation results indicate the validity and superiority of the proposed method compared with the other reported methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm

The intermittency of wind power and the large-scale integration of electric vehicles (EVs) bring new challenges to the reliability and economy of power system dispatching. In this paper, a novel multi-objective dynamic economic emission dispatch (DEED) model is proposed considering the EVs and uncertainties of wind power. The total fuel cost and pollutant emission are considered as the optimization objectives, and the vehicle to grid (V2G) power and the conventional generator output power are set as the decision variables. The stochastic wind power is derived by Weibull probability distribution function. Under the premise of meeting the system energy and user’s travel demand, the charging and discharging behavior of the EVs are dynamically managed. Moreover, we propose a two-step dynamic constraint processing strategy for decision variables based on penalty function, and, on this basis, the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm is improved. The proposed model and approach are verified by the 10-generator system. The results demonstrate that the proposed DEED model and the improved MOEA/D algorithm are effective and reasonable.

Adaptive grid based multi-objective Cauchy differential evolution for stochastic dynamic economic emission dispatch with wind power uncertainty

Since wind power is integrated into the thermal power operation system, dynamic economic emission dispatch (DEED) has become a new challenge due to its uncertain characteristics. This paper proposes an adaptive grid based multi-objective Cauchy differential evolution (AGB-MOCDE) for solving stochastic DEED with wind power uncertainty. To properly deal with wind power uncertainty, some scenarios are generated to simulate those possible situations by dividing the uncertainty domain into different intervals, the probability of each interval can be calculated using the cumulative distribution function, and a stochastic DEED model can be formulated under different scenarios. For enhancing the optimization efficiency, Cauchy mutation operation is utilized to improve differential evolution by adjusting the population diversity during the population evolution process, and an adaptive grid is constructed for retaining diversity distribution of Pareto front. With consideration of large number of generated scenarios, the reduction mechanism is carried out to decrease the scenarios number with covariance relationships, which can greatly decrease the computational complexity. Moreover, the constraint-handling technique is also utilized to deal with the system load balance while considering transmission loss among thermal units and wind farms, all the constraint limits can be satisfied under the permitted accuracy. After the proposed method is simulated on three test systems, the obtained results reveal that in comparison with other alternatives, the proposed AGB-MOCDE can optimize the DEED problem while handling all constraint limits, and the optimal scheme of stochastic DEED can decrease the conservation of interval optimization, which can provide a more valuable optimal scheme for real-world applications.

Dispatching strategies for coordinating environmental awareness and risk perception in wind power integrated system

Wind power plays a significant role in economic and environmental operation of electric power system. Meanwhile, the variability and uncertainty characteristics of wind power generation bring technical and economical challenges for power system operation. In order to harmonize the relationship between environmental protection and risk management in power dispatching, this paper presents a stochastic dynamic economic emission dispatch model combining risk perception with environmental awareness of decision-makers by following the principle of chance-constrained programming. In this power dispatching model, the description of wind power uncertainty is derived from the probability statistic character of wind speed. Constraints-handling techniques as a heuristic strategy are embedded into non-dominated sorting genetic algorithm-II. In addition, more information is digested from the Pareto optimum solution set by cluster analysis and fuzzy set theory. The simulation results eventually demonstrate that the increase of the share of wind power output will bring higher risk, though it is beneficial for economic cost and environmental protection. Since different risk perception and environmental awareness can possibly lead to diverse non-dominated solutions, decision-makers may choose an appropriate dispatching strategy according to their specific risk perception and environmental awareness.

Distributed Model Predictive Control for Hybrid Energy Resource System With Large-Scale Decomposition Coordination Approach

Due to the large-scale and distributed characteristics of increasing renewable energy resources, dynamic economic emission dispatch (DEED) of hybrid energy resource system becomes more and more important in the power system operation. This paper proposes a distributed model predictive control (DMPC) method for hybrid energy resources system of dynamic economic optimal dispatch with large-scale decomposition coordination approach. First, the DEED model of hybrid energy resources is converted into predictive control model, which can provide rolling optimization mechanism for dealing with intermittent energy resources optimization. Second, predictive control model is decomposed into several subsystems with Lagrangian multipliers for coordinating those subsystems, which can greatly decrease the computational complexity. Third, due to the randomness or uncertainty of intermittent power generation, model predictive control can dynamically optimize random or uncertainty problem with rolling optimization mechanism. Furthermore, adaptive dynamic programming is utilized to solve those subsystem optimization problems, which can optimize the random or uncertain problem in real-time condition. In the optimization process, probability constraint is converted into deterministic constraint with its probability density function, and system load balance can be properly handled with coupled coarse-fine constraint-handling technique. According to the obtained results in the case studies, the proposed DMPC can optimize the DEED of hybrid energy resources well combining with the large-scale decomposition-coordination approach, while greatly decreasing the optimization complexity and computation time, which reveals that the proposed method can provide an alternative way for solving the DEED problem of hybrid energy resources.

Dynamic Economic Emission Dispatch Using Multi-Objective Hybrid Evolutionary Algorithm

Dynamic economic emission dispatch (DEED) is an important optimization task for power plants. The problem is a highly constrained multi-objective optimization problem involving conflicting objectives with both equality and inequality constraints. This paper introduces two objective functions of DEED model: the lowest generation cost and the smallest carbon emissions with power balance constraints, unit output constraints and unit ramp rate limits. Then the paper presents a multi-objective hybrid evolutionary algorithm (MHEA) to solve the DEED model. The MHEA is a hybrid optimization algorithm based on orthogonal initialization, improved differential operation with migration strategy, parameter adaptive control, multi-objective selection strategy and analytic hierarchy process based fuzzy technique (AFT). Numerical results of one test system demonstrate the capabilities of the proposed approach. Compared with other classical methods, the proposed approach gets better result.