Environmental Dispatch Problem Research Articles

Robust economic–environmental dispatch problem for BESS and PV generators in MT-HVDC systems: A MI-MPC approach

The research addresses the energy management problem (EMP) in multi-terminal high-voltage direct current (MT-HVDC) systems using a mixed-integer model predictive control (MI-MPC). The MT-HVDC system consists of conventional thermal generation plants (fossil sources), renewable generation sources (solar photovoltaic plants), and battery energy storage systems (BESS). This combination creates a complex nonlinear EMP, as it introduces the power losses of the transmission networks through B-coefficients. The main contributions of this research can be summarized in three elements: (i) the formulation of a measurement- or simulation-based approach to determine B−coefficients in MT-HVDC systems for calculating power losses; (ii) the convexification of the EMP model through MI-MPC approaches, allowing to minimize economic and environmental objective functions; and (iii) the inclusion of uncertainties through the robust convex method. Numerical validations in the 11-bus grid, including the linear transportation model for the MT-HVDC system, confirm the multi-objective nature of the EMP when considering economic and environmental indices. In addition, the effect of uncertainties on generation and demand causes significant variations in the optimal Pareto front when compared to the deterministic scenario.

A hybrid evolutionary approach for multi-objective unit commitment problem in power systems

The operating strategy of the power system is changing from the minimum cost to the minimum cost with a minimum emission approach as a result of growing worries about global warming and environmental change brought on by the combustion of fossil fuels. The generating scheduling problem becomes a multi-objective one as a result of the economic and emission objectives' inherent conflict. In this study, a multi-objective whale-differential evolution-genetic algorithm (WODEGA) is employed to produce an optimal solution that illustrates a potential trade-off between the unit commitment (UC) problem's emission and cost objectives. The environmental and economic dispatch problem with system and unit constraints is solved using the multi-objective WODEGA method. Main goal of research is to explore the problem of unit commitment and sub-problem of unit commitment i.e. economic dispatch.

A constrained multi-objective evolutionary algorithm with clustering based weight vector adaptation

In constrained multi-objective optimization, balancing convergence, diversity, and feasibility of solutions in the population are challenging. Most existing approaches focus on convergence and feasibility but fail to enforce the diversity and uniformity of solutions in the approximated Pareto Front (PF). In decomposition-based approaches, the diversity and uniformity of the obtained solution set depend on the set of weight vectors employed, which are generally fixed and initialized uniformly. However, Constrained Multi-objective Optimization Problems (CMOPs) are characterized by discontinuous Pareto-optimal fronts due to the presence of constraints. Therefore, to maintain better diversity and uniformity in the population, the weight vector set employed in the framework should consider the characteristics of PF, which is not known in advance. However, during the evolution, the characteristics of PF can be estimated and the associated weight vectors can be appropriately distributed. Motivated by this issue, we propose a novel Constrained Multi-Objective Evolutionary Algorithm with a Clustering-based Weight vector Adaptation strategy called CMOEA-CWA. The proposed CMOEA-CWA uses the basic framework of CMOEA-DPMS. After exploring the search space, CMOEA-CWA reinitializes the weight vectors if there exist any discontinuities in the approximated pareto front (PF). In CMOEA-CWA, the PF is approximated using the feasible solutions stored in an archive and novel parameter-free clustering techniques. During the clustering process, if there exist any discontinuities in the approximated PF, one population will exploit the feasible regions with a re-initialized set of weight vectors while the other population will explore the feasible regions with uniformly distributed weight vectors. Empirical results obtained on four popular and widely used benchmarks when compared with nine latest state-of-the-art algorithms (ARMOEA, CCMO, TiGE-2, ToP, MOEA/D-DAE, PPS, CTAEA, MOEA/D-2WA and CMOEA-DPMS) suggest the superiority of the proposed algorithm, especially on 47 complicated constrained multi-objective problems. The proposed algorithm performs statistically better than or comparable (in terms of HV) to ARMOEA, CCMO, TiGE-2, ToP, MOEA/D-DAE, PPS, CTAEA, MOEA/D-2WA and CMOEA-DPMS in 91.48 %, 78.72 %, 100 %, 70.21 %, 85.11 %, 78.72 %, 91.49 % and 82.97 % of test instances, respectively. The performance of the proposed algorithm is also demonstrated on a real-world Economic Environmental Dispatch (EED) problem.

Multiobjective Demand Double-Layer Energy Consumption Optimization Strategy for Microgrid Based on Improved HPSOFA

In order to optimize the economy and environmental protection of microgrid, this paper establishes a demand response model based on comprehensive satisfaction, combines the advantages of the classical multiobjective particle swarm algorithm and multiobjective firefly algorithm, and proposes a hybrid particle swarm optimization and firefly algorithm (HPSOFA) to solve the joint economic and environmental dispatch problem of microgrid and improve the wind and light consumption capacity. The proposed improved algorithm introduces a random perturbation term and adaptive learning coefficients, and the algorithm is selected by the dominance relationship of individuals, which improves the diversity of populations and increases the possibility of the algorithm to solve the global optimum. The proposed algorithm is used to solve the test functions of different dimensions, and the results show that the Pareto front of HPSOFA has better distribution and accuracy, which verifies the effectiveness of the proposed algorithm. Simulation analysis is carried out using power data from a region of Liaoning Province, China, and experiments conclude that the operating cost and environmental cost of HPSOFA are significantly lower than other algorithms.

Optimisation of Economic and Environmental Dispatch of Power System with and without Renewable Energy Sources

Combined Economic and Environmental load dispatch is critical to the functioning of the power grid, and many models have been developed to address these issues using various techniques. Specially, soft computing methods have recently risen in popularity and have been used in a variety of popular and practical applications. The aim of this paper is to determine the benefits of applying Particle Swarm Optimization (PSO) to the Combined Economic and Environmental Dispatch (CEED) problem in particular. Here, an attempt has been made to find the minimum cost of generation of a system of thermal and solar power plants. The problem is multi-objective and is converted into a single objective function using weighted sum method. Analysis is also done with and without unit commitment using Priority List method and the results are compared. The analyses has been done in MATLAB tool using six thermal power generators and thirteen solar power plants.

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

This paper proposes a multi-objective teaching–learning studying-based algorithm (MTLSBA) to handle different objective frameworks for solving the large-scale Combined Heat and Power Economic Environmental Dispatch (CHPEED) problem. It aims at minimizing the fuel costs and emissions by managing the power-only, CHP and heat-only units. TLSBA is a modified version of TLBA to increase its global optimization performance by merging a new studying strategy. Based on this integrated tactic, every participant gathers knowledge from someone else randomly to improve his position. The position is specified as the vector of the design variables, which are the power and heat outputs from the power-only, CHP and heat-only units. TLSBA has been upgraded to include an extra Pareto archiving to capture and sustain the non-dominated responses. The objective characteristic is dynamically adapted by systematically modifying the shape of the applicable objective model. Likewise, a decision-making approach based on the fuzzy concept is used to select the most suitable CHPEED solution for large-scale dispatching of combined electrical power and heat energies. The proposed MTLSBA is assigned to multiple testing of 5-unit, 7-unit and 96-unit systems. It is contrasted with other reported techniques in the literature. According to numerical data, the suggested MTLSBA outperforms the others in terms of effectiveness and robustness indices. For the 5-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 0.6625% and 0.3677% and reduction in the emissions of 2.723% and 7.4669% compared to non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm (SPEA 2), respectively. For the 7-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 2.927% and 3.041% and reduction in the emissions of 40.156% and 40.050% compared to NSGA-II and SPEA 2, respectively.

Investigation on New Metaheuristic Algorithms for Solving Dynamic Combined Economic Environmental Dispatch Problems

In this paper, the dynamic combined economic environmental dispatch problems (DCEED) with variable real transmission losses are tackled using four metaheuristics techniques. Due to the consideration of the valve-point loading effects (VPE), DCEED have become a non-smooth and more complex optimization problem. The seagull optimization algorithm (SOA), crow search algorithm (CSA), tunicate swarm algorithm (TSA), and firefly algorithm (FFA), as both nature and biologic phenomena-based algorithms, are investigated to solve DCEED problems. Our proposed algorithms, SOA, TSA, and FFA, were evaluated and applied on the IEEE five-unit test system, and the effectiveness of the proposed CSA approach was applied on two-unit, five-unit, and ten-unit systems by considering VPE. We defined CSA for different objective functions, such as cost of production, emission, and CEED, by considering VPE. The obtained results reveal the efficiency and robustness of the CSA compared to SOA, TSA, FFA, and to other optimization algorithms reported recently in the literature. In addition, Matlab simulation results show the advantages of the proposed approaches for solving DCEED problems.

Distributed Optimal Economic Environmental Dispatch for Microgrids Over Time-Varying Directed Communication Graph

This paper investigates the combined economic environmental dispatch problem of Microgrids in a distributed manner. In order to optimize the competing agminated operation cost and environmental impact objectives simultaneously, a distributed method which combines the distributed consensus based algorithm with dynamic weights is proposed to assign the energy among generation units, energy storage units, and load units. By this method, each unit only needs to share its local information among neighbours, thus it offers better flexibility, robustness and privacy. Besides, different from existing distributed optimization algorithms, the proposed algorithm can solve the multi-objective problem over time-varying directed communication graph with a fixed step-size. Furthermore, it is proved that the proposed algorithm can converge to the optimal solution if the step-size does not exceed some upper bound. The simulation results show that the proposed method can simultaneously coordinate the two conflicting goals of the economic and environmental aspects of Microgrids, and obtain the entire Pareto front, effectively reducing operating costs and pollutant emissions. The advantage of convergence speed is also shown compared with some distributed algorithms.

Teaching-Learning-Based Optimization Algorithm for the Combined Dynamic Economic Environmental Dispatch Problem

The Dynamic Economic Environmental Dispatch Problem (DEEDP) is a major issue in power system control. It aims to find the optimum schedule of the power output of thermal units in order to meet the required load at the lowest cost and emission of harmful gases. Several constraints, such as generation limits, valve point loading effects, prohibited operating zones, and ramp rate limits, can be considered. In this paper, a method based on Teaching-Learning-Based Optimization (TLBO) is proposed for dealing with the DEEDP problem where all aforementioned constraints are considered. To investigate the effectiveness of the proposed method for solving this discontinuous and nonlinear problem, the ten-unit system under four cases is used. The obtained results are compared with those obtained by other metaheuristic techniques. The comparison of the simulation results shows that the proposed technique has good performance.

Environmental/Economic Dispatch Problem: Coulomb’s and Franklin’s Laws Based Optimization Algorithm

Conventionally, electric power plants mitigate the total fuel cost irrespective to the emissions generated. The need to safeguard the ecology resulted in many alternative tactics. This paper endeavours to solve the combined environmental and economic dispatch problem using a newly developed heuristic optimization technique called Coulomb’s and Franklin’s Laws Based Optimization algorithm. The envisaged algorithm is devised from the Coulomb’s and Franklin’s theories which comprise attraction/repulsion, probabilistic ionization and contact phases. A fuzzy approach is used to decide the best concessive solution from the Pareto-optimal set. In order to access the practicability and the effectiveness of the envisaged algorithm, it has been applied for 6, 10 and 40 units’ power systems, and compared with other state-of-the-art approaches in the literature. Results divulge the supremacy of the envisaged algorithm and authenticate its prospective to solve the environmental/economic dispatch problem.

Nonconvex Economic Environmental Load Dispatch Using Fuzzy Based Squirrel Search Algorithm

Dangerous emanation of gases and nano particles from fossil fuels during power generation taints the air leading to long lasting destruction to the environment. To handle this issue, wider research works in various points of view are conducted to reduce the fuel cost in addition to the harmful gases emission. The aforementioned power generation problem is named as economic environmental load dispatch problem. In this study, a novel fuzzy based squirrel search algorithm is presented to solve the non-convex economic environmental load dispatch problem. The valve point loading effects formulate the problem as a non-convex optimization issue. Since, the two objective functions (cost and emissions) are contradictory practices; a fuzzy based concessive technique is utilized to designate the best concessive solution from the non-dominated solution set. In order to substantiate the competence of the suggested framework, the simulations are executed on different case studies and compared with recently published techniques. Simulation results divulge that the fuzzy based squirrel search algorithm performs better than the other compared techniques to efficiently solve the economic environmental load dispatch problem.

Algorithm for Solving Economical and Environmental Dispatch Problems of Thermal Power Plants

This paper presents a modified tabu search algorithm which uses a real-valued solution vector and an adaptive mechanism for generating neighbors. The neighbors are produced by adding an adjusted coefficient at each iteration which enables us to find the most promising region of the search space. By consequent, a modified tabu search algorithm is applied to economic and environmental economic dispatch problems. The proposed method has achieved efficient and accurate solutions for power systems with 3, 6, 10, 20, 40, 80, 120, 160 and 240 units respectively. The results of the proposed method are compared with those of an improved Hopfield neural network approach, a fuzzy logic controlled genetic algorithm, an advanced engineered-conditioning genetic approach, and an advanced Hopfield neural network approach. It is shown that the proposed method is superior in providing optimal allocation of generation units which minimizes the total fuel cost. Simulation results show that the proposed method yields better solutions when compared to the alternate techniques.

Deterministic approach for solving multi-objective non-smooth Environmental and Economic dispatch problem

The Environmental and Economic Dispatch Problem with Valve-Point loading effect representation (EEDP-VP) is a multi-objective, nonconvex and non-differentiable optimization problem. Due to these difficulties, it has been solved in the literature mainly by heuristic approaches, while deterministic approaches are scarce. Therefore, the main objectives of this paper are to propose a deterministic approach for solving this problem and compare its solutions with the ones obtained by some heuristic and deterministic approaches. The deterministic approach proposed has the following features: the multi-objective nature of the problem is handled by the Progressive Bounded Constraints (PBC) strategy, while the modified logarithmic barrier function method is used to solve the subproblems resulting from the PBC strategy; a smoothing technique is used to handle non-differentiability issues, while the inertia correction strategy is used so that only descent directions are generated. The methodology is applied to five generation systems and the results show that the Pareto-curve is obtained more efficiently when compared to other heuristic and deterministic optimization approaches.

Coordinated consensus for smart grid economic environmental power dispatch with dynamic communication network

Combined economic environmental dispatch problem (CEEDP) is one of the greatest challenges of the future smart grids. It aims at reducing the total cost during the power production process considering the growing environmental impact due to the emission of gaseous pollutants of fossil fuels. This study develops a robust distributed algorithm based on consensus protocols in multi-agent systems, to solve the smart grid CEEDP with a practical communication network consisting of a dynamic communication network, randomly communication failure, transmission delay and noise in communication channels. The proposed algorithm is fully distributed and cooperative in such a way that it eliminates the need for a central energy-management unit, or a leader. The performance of the fully decentralised consensus protocol was evaluated on the IEEE 30-bus and the IEEE 118-bus test system. A comparison with previous consensus algorithms proves the supremacy of the proposed approach in terms of its robustness under dynamic communication network with randomly link failure.

A Modified Artificial Bee Colony Algorithm Application for Economic Environmental Dispatch

In conventional fossil-fuel power systems, the economic environmental dispatch (EED) problem is a major problem that optimally determines the output power of generating units in a way that cost of total production and emission level be minimized simultaneously, and at the same time all the constraints of units and system are satisfied properly. To solve EED problem which is a non-convex optimization problem, a modified artificial bee colony (MABC) algorithm is proposed in this paper. This algorithm by implementing weighted sum method is applied on two test systems, and eventually, obtained results are compared with other reported results. Comparison of results confirms superiority and efficiency of proposed method clearly.

Robust Variant of Artificial Bee Colony (JA-ABC4b) Algorithm

The simplicity and robustness of the artificial bee colony (ABC) algorithm has attracted the attention of optimisation researchers. Although ABC has fewer tuned parameters, making it an easy-to-use tool, it has shown better performance than other prominent optimisation algorithms such as differential evolution (DE), evolutionary algorithms (EA) and particle swarm optimisation (PSO) algorithms at solving optimisation problems. Despite these advantages, researchers have found that the standard ABC actually suffers from slow convergence speed on unimodal functions and is often trapped in local minima of multimodal functions. Most problematically, it does not balance the exploitation and exploration stages, leading to various inefficiencies in terms of capability. This paper presents a new ABC variant referred to as JA-ABC4b, which has been formulated to balance exploitation and exploration in order to boost optimisation performance. JA-ABC4b has been experimentally tested on 27 benchmark functions and economic environmental dispatch (EED) problems. The results have revealed a robust performance of JA-ABC4b in comparison to other existing ABC variants and other optimisation algorithms.

An economic load dispatch and multiple environmental dispatch problem solution with microgrids using interior search algorithm

Microgrid is a novel small-scale system of the centralized electricity for a small-scale community such as villages and commercial area. Microgrid consists of micro-sources like distribution generator, solar and wind units. A microgrid is consummate specific purposes like reliability, cost reduction, emission reduction, efficiency improvement, use of renewable sources and continuous energy source. In the microgrid, the Energy Management System is having a problem of Economic Load Dispatch (ELD) and Combined Economic Emission Dispatch (CEED) and it is optimized by meta-heuristic techniques. The key objective of this paper is to solve the Combined Economic Emission Dispatch (CEED) problem to obtain optimal system cost. The CEED is the procedure to scheduling the generating units within their bounds together with minimizing the fuel cost and emission values. The newly introduced Interior Search Algorithm (ISA) is applied for the solution of ELD and CEED problem. The minimization of total cost and total emission is obtained for four different scenarios like all sources included all sources without solar energy, all sources without wind energy and all sources without solar and wind energy. In both scenarios, the result shows the comparison of ISA with the Reduced Gradient Method (RGM), Ant Colony Optimization (ACO) technique and Cuckoo Search Algorithm (CSA) for the two different cases which are ELD without emission and CEED with emission. The results are calculated for different Power Demand of 24 h. The results obtained to ISA give comparatively better cost reduction as compared with RGM, ACO and CSA which shows the effectiveness of the given algorithm.

NEW DEVELOPMENT OF ANALYSIS TOOL FOR OPTIMIZING GENERATION COST WITH GAS EMISSION VIA AN ELECTROMAGNETISM LIKE ALGORITHM

This paper addresses the preliminary new development results of the evolutionary algorithm technique to optimize the formulated problems incorporating the generation cost with emission gas as objective function or constraints. The power generation cost with emission gas are a complex problem which also the major concerns in electric power generation systems in the Environmental or Economic Dispatch Problems (EDP). Thus, due to environmental concern the electrical utilities required to minimize the emission level while optimizing the thermal generating units at a minimum generating cost and hence, satisfying the load demand and the emissions. In this work the electromagnetism-Like algorithm (EML) has been employed for optimizing generation cost and emission constraints economic dispatch problem. The proposed decision analysis tool software in this work will optimize the generation cost with emission gas objective function. The best generation cost with emission gas solution are obtained from different fuel technology via the developed software.

Multi-Objective Unit Commitment With Renewable Energy Using GSA Algorithm

With increasing concerns of global warming and environmental change caused by the burning of fossil fuels, the operating paradigm of power system is shifting from minimum cost to minimum cost with minimum emission approach. Due to conflicting nature of economic and emission objectives, the generation scheduling becomes a multi-objective problem. In this paper, a multi-objective gravitational search algorithm (MOGSA) is used to provide Pareto optimal solutions which present the possible trade-off between the cost and emission objectives of unit commitment (UC) problem. The MOGSA technique is used for solving the environmental and economic dispatch problem considering hydro, pump storage plant, wind farm and solar farm, with and without ramp rates. The simulation results demonstrate the effectiveness of the proposed approach in solving multi-objective UC problem.

Security Constrained Economic Environmental Dispatch through Fuzzy Based Gravitational Search Algorithm

A robust and reliable algorithm called as fuzzy based gravitational search algorithm has been developed for solution of the security constrained economic and environmental load dispatch (EED) problems. A fuzzy maximizing decision approach using gravitational search algorithm (GSA) is applied to a six-unit thermal power test system to obtain the best compromising solution. The usefulness and quality of the proposed algorithm is demonstrated through its application to the test system in terms of convergence speed, solution time and minimum operating cost. The obtained results from the proposed algorithm confirm the effectiveness and potential of the promising proposed algorithm compared to conventional method, real coded genetic algorithm (GA), hybrid GA and PSO methods, performed in different central load dispatch centers to solve multi-objective generation dispatch problems.