Multi-objective Dynamic Economic Emission Dispatch Research Articles

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

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.

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

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.

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.

Multi-objective dynamic economic emission dispatch based on electric vehicles and wind power integrated system using differential evolution algorithm

Electric vehicles (EVs) are currently the most popular green transportation. Wind power has received widespread attention as a kind of renewable energy source. Their rapid development has brought new challenges for the power grid. However, the technology of vehicle to grid (V2G) can realize the peak shaving of the power grid, which is considered to be an effective method to promote the integration of EVs and wind power. Therefore, in this paper, a novel integrated framework of EVs and wind farms (WEV) is proposed to use the EVs charging and discharging to smooth the wind power penalty cost that caused by overestimated and underestimated of available wind power. While a new multi-objective dynamic economic emission dispatching model based on the WEV system (WE_DEED) is developed to consider both emission and total cost objectives. In order to solve the complex constrained WE_DEED problem, we propose a multi-objective differential evolution algorithm with the self-adaptive parameter operator and local search operator based on non-dominant sorting (SaMODE_LS). Moreover, an effective constraint handling method is applied to deal with the constraints in WE_DEED problems. Different test systems based on 10-unit are given to demonstrate the reasonability and efficiency of the proposed model and method.

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.

A combined dynamic economic emission dispatch and time of use demand response mathematical modelling framework

In this paper, we integrate a Demand Response (DR) program into the multi-objective dynamic economic emission dispatch (DEED) optimization problem. The resulting optimization problem is termed DR-DEED. The DR program is a time based program known as the Time of Use DR program. The DR program has been developed using the customers' Price Elasticity Matrices, which models the customer behavior under different conditions. An interactive control strategy between utility and consumers is proposed for the combined DR-DEED model, which determines the optimal power to be generated by minimizing fuel, emissions, and DR costs and also the optimal price. The customer in light of the utility's optimal price minimizes its electricity cost and optimally schedules power consumption. Obtained results indicate that DR programs are mutually beneficial to utility and consumers alike and can bring about desired demand reduction in the power system.

Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs

The dynamic economic emission dispatch (DEED) of electric power generation is a multi-objective mathematical optimization problem with two objective functions. The first objective is to minimize all the fuel costs of the generators in the power system, whilst the second objective seeks to minimize the emissions cost. Both objective functions are subject to constraints such as load demand constraint, ramp rate constraint, amongst other constraints. In this work, we integrate a game theory based demand response program into the DEED problem. The game theory based demand response program determines the optimal hourly incentive to be offered to customers who sign up for load curtailment. The game theory model has in built mechanisms to ensure that the incentive offered the customers is greater than the cost of interruption while simultaneously being beneficial to the utility. The combined DEED and game theoretic demand response model presented in this work, minimizes fuel and emissions costs and simultaneously determines the optimal incentive and load curtailment customers have to perform for maximal power system relief. The developed model is tested on two test systems with industrial customers and obtained results indicate the practical benefits of the proposed model.

Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties

This study protracts the multi-objective dynamic economic emission dispatch (DEED) problem by integrating wind power output of wind turbines. DEED simultaneously minimizes total electrical energy costs and emissions over a 24-h time span. In order to model the random nature of load demand and wind forecast errors, a scenario-based stochastic programming framework is presented. A scenario set is generated by a roulette wheel mechanism based on the probability distribution functions of these input uncertain variables. Therewith, the stochastic DEED (SDEED) problem is transformed into an equivalent deterministic scenario-based DEED. Thus, to solve the complicated nonlinear, non-smooth, and non-differentiable SDEED, an enhanced particle swarm optimization (PSO) algorithm is applied to obtain the best solution for the corresponding scenarios. In order to improve the quality of the solutions attained by PSO a self-adaptive probabilistic mutation strategy is used to escape from local minima. The proposed framework is successfully applied to four test systems with small, medium, and large-scale characteristics. In this regard, some metrics are used in order to characterize the effective performance of the solution method.