Environmental Economic Dispatch Research Articles

A novel improved harbor seal whiskers algorithm for solving hybrid dynamic economic environmental dispatch considering uncertainty of renewable energy generation

The originality of this work is introducing a novel Improved Harbor Seal Whiskers Algorithm (IHSWA) as an innovative and improved optimizer for solving complex hybrid dynamic economic-environmental dispatch (HDEED) considering uncertainty of renewable energy generation (REG) and various system constraints. IHSWA is implemented by hybridizing opposition-based algorithm (OBA) and local search algorithm (LSA) approaches to enhance the algorithm's search efficiency and overcome the challenges of other counterpart approaches. IHSWA has an optimal balancing feature between searching modes (exploring and exploiting modes). This feature makes the proposed algorithms more advanced than their counterpart optimizing techniques. The HDEED is modeled using a non-convex and non-smooth objective function, considering various equality and inequality constraints such as ramp rate bounds (RRLs), valve-point effects (VPE), production limits (PL), spinning reserves (SRLs), prohibited operating zones (POZs), and power balance (PB). The proposed approach is verified on a hybrid modified IEEE 57-bus 7-unit power system incorporating solar and wind energy generation. The proposed approach is compared with various modern optimization techniques, such as the traditional Harbor Seal Whiskers Algorithm (HSWA), the Lemurs Optimization Algorithm (LOA), the Nutcracker Optimization Algorithm (NOA), and the Artificial Hummingbird Algorithm (AHA). The incorporation of REG sources produces a significant reduction in operative costs and a remarkable decline in emissions. The convergence rate of the proposed approach indicates that the novel hybrid algorithm converges rapidly towards the optimal solution, achieving the best global optimum solution. The ANOVA test and the Tukey test are used to analyze the results statistically. The IHSWA approach shows superior advanced features with a best fuel cost of 1640.5890 $ and a minimum std. value of 1708.4895. While for emission objective minimizing, IHSWA attained a minimum emission cost and std. value of 1080.1629 kg and 1777.0770, respectively. The statistical analysis of the results approves the stability, reliability, and consistency of the proposed strategy with significant convergence. The appreciated contribution of this work is introducing a novel hybrid approach for solving HDEED efficiently, incorporating REG, and considering various system constraints. IHSWA has advanced features over its counterpart approaches. The contribution holds specific importance for the economic development of countries. It creates the path for increased self-sufficiency by reducing their need for fossil fuels for energy production. The study emphasizes the transition from being reliant on fossil fuels to the development of sustainable urban economies.

Optimal economic environmental power dispatch by using artificial bee colony algorithm

<span>Today, most power plants worldwide use fossil fuels such as natural gas, coal, and oil as the primary resource for energy reproduction primarily. The new term for economic environmental power dispatch (EEPD) problems is on the minimum total cost of the generator and fossil fuel emissions to address atmosphere pollution. Thus, the significant objective functions are identified to minimize the cost of generation, most minor emission pollutants, and lowest system losses individually. As an alternative, an Artificial Bee Colony (ABC) swarming algorithm is applied to solve the EEPD problem separately in the power systems on both standard IEEE 26 bus system and IEEE 57 bus system using a MATLAB programming environment. The performance of the introduced algorithm is measured based on simple mathematical analysis such as a simple deviation and its percentage from the obtained results. From the mathematical measurement, the ABC algorithm showed an improvement on each identified single objective function as compared with the gradient approach of using the Newton Raphson method in a short computational time.</span>

A Review on Economic Dispatch of Power System Considering Atmospheric Pollutant Emissions

The environmental/economic dispatch (EED) of power systems addresses the environmental pollution problems caused by power generation at the operational level, offering macroscopic control without requiring additional construction and remediation costs, garnering widespread attention in recent years. This paper undertakes a comprehensive review of existing EED models, categorizing them according to the control of atmospheric pollutants into total air pollutant control (TAPC) and control considering the spatial and temporal diffusion (STD) of atmospheric pollutants. In addition, various methods employed to address the EED problems, as well as the current state of research on multi-area EED models, are presented. Finally, this paper analyzes and summarizes the literature on existing EED models, highlighting the deficiencies of the current work and future research directions. Through these explorations, the authors find that controlling the EED model by considering TAPC is more suitable for general macro planning, whereas the EED model considering the STD of air pollutant emissions enables more precise and effective control. Summarizing such models and techniques is conducive to developing dispatch plans adapted to local conditions, which is significantly beneficial for public welfare and government management, promoting sustainable and environmentally friendly power system dispatch methods.

Environmental and economic dispatching strategy for power system with the complementary combination of wind-solar-hydro-thermal-storage multiple sources

The linkage, coordination, and complementary cooperation of energy supply can improve the efficiency of transportation and utilization. At present, the level of new energy consumption needs to be improved, the coordination of the source network load storage link is insufficient, and the insufficient complementarity of various types of power sources in the power system. This article fully explores the differences and complementarities of various types of wind-solar-hydro-thermal-storage power sources, a hierarchical environmental and economic dispatch model for the power system has been established. Among them, the upper level model takes the flexible consumption of new energy as the optimization goal, the middle level model uses a combination of hydropower station and energy storage to minimizes the fluctuation variance and peak-to-valley difference of the net load curve, the lower level model aims to achieve optimal environmental and economic benefits of the power system, comprehensively considers the coal consumption cost, startup and shutdown cost, energy storage operation cost, and pollutant emissions of thermal power units, determines the startup and shutdown mode and output power of thermal power units. Finally, an improved IEEE 6-machine 30-node system is used as an example for simulation analysis, the results show that after applying the proposed hierarchical environmental and economic dispatch strategy of the power system, the fluctuation variance and the peak-to-valley difference of the net load curve have been reduced by 46.3% and 31.5%, respectively, and the environmental and economic benefits of the system is improved by 5.1% compared with the traditional economic dispatch strategy. It can meet the requirements of energy system cleaning and decarbonization while improving the operation economy, which verifies the effectiveness of the proposed environmental economic dispatch model.

A Semidefinite Programming Method Weighted Sum Based for Solving a Multi-area Emission (Environmental) Economic Dispatch Problem

This paper presents a semi-definite programming (SDP) optimisation approach for solving multi area, emission/economic dispatch (MAEED) problems in thermal power systems. The multi-objective problem was transformed into matrix form as an SDP relaxation problem and subsequently solved with a MATLAB programming suite. The system inequality and equality constraints were entered into a Self-Dual Minimisation (SeDuMi) parser. Simulations were tested on four (4) IEEE benchmark models to validate the efficiency of the proposed SDP method. However, in the generation of the Pareto-fr ont solutions, ideal minimum points were used in the determination of the maximum spread out of the Pareto solutions by the algorithm. This involves the use of a standard weighted sum method in generating the Pareto-optimal solution between two objective functions. Twenty-one (21) runs were carried out for the parameter value to explore the effect of control weight selection k1. A comparative study demonstrated the effectiveness of tie-line constraint and without tie-line constraint, respectively on the optimi sation model for the power dispatch problems solved by using the proposed SDP approach. The optimisation results for the minimum fuel cost using the SDP approach for the case when a tie-line constraint is included was US$ 2,151.7 /hr with minimum emission of 3.4687 Ton/hr. This is lower than US$ 2,162.2 /hr with minimum emission of 3.6662 Ton/hr obtained when the tie-line constraint is not included in the optimisation model on IEEE four-area, sixteen network. Additionally, the comparative results with all other methods reported in the literature for different test cases were also presented in this paper. Lower operational costs were obtained for both tie-lines and no tie-lines power transfer cases, showing that the SDP method performed well in accuracy compared to other methods. The SDP method was employed in this research to deal with the multi-area emission economic power dispatch problem due to its inherent advantages in achieving a global optimal solution and, this approach would also assist the power administrator to come up with an improved decision making in attaining an optimum power dispatch of power generation.

A chaotic Jaya algorithm for environmental economic dispatch incorporating wind and solar power

<abstract> <p>The integration of renewable energy resources (RESs) into the existing power grid is an effective approach to reducing harmful emission content. Environmental economic dispatch is one of the complex constrained optimization problems of power systems. These problems have become more complex as a result of integrating RESs, as the availability of solar and wind power is stochastic in nature. To obtain the solution of such types of complex constrained optimization problems, a robust optimization method is required. Literature shows that chaotic maps help to boost the search capability through improvisation in the exploration and exploitation phases of an algorithm; hence, they are able to provide superior solutions during optimization. Therefore, in this study, a new optimization technique was developed based on the Jaya algorithm called the chaotic Jaya algorithm. Here the main aim was to investigate the impact of RES integration into conventional thermal systems on total power generation cost and emissions released to the environment. The proposed approach was tested for two standard cases: (i) scheduling of a committed generating unit for a specific time and (ii) scheduling of a committed generating unit for a time period of 24 hours with 24 intervals of 1 hour each. The simulation results show that a tent map is the best-performing map for a sample problem under consideration, as it provides better results. Hence, it has been considered for detailed analysis.</p> </abstract>

Fuel-constrained joint heat and power dynamic economic environmental dispatch

The economical use of available fuel for producing electricity has been a very important challenge for power companies due to the continuously declining supply of fossil fuels. FCJHPDEED (fuel-constrained joint heat and power dynamic economic environmental dispatch) and JHPDEED (joint heat and power dynamic economic environmental dispatch) with DSM (demand-side management) integrating solar PV plants, WTGs (wind turbine generators), and PHS (pumped hydro storage) plants have been presented. Using SPEA 2 (strength Pareto evolutionary algorithm 2) and NSGA-II (non-dominated sorting genetic algorithm-II), FCJHPDEED and JHPDEED have been solved. It is seen that the results obtained without fuel constraints are more optimal than the results obtained with fuel constraints. The joint heat and power dynamic economic dispatch cost obtained with fuel constraints is approximately 2.14% more than the cost obtained without fuel constraints and joint heat and power dynamic emission dispatch, and the emission obtained with fuel constraints is approximately 6.7% more than the emission obtained without fuel constraints.

Application of the Slime Mould Algorithm on the Bi-Objective Environmental Economic Dispatch Problem

This paper investigates the performance of one of the latest metaheuristic swarm-based approaches called Slime Mould Algorithm (SMA). SMA is used here to solve the static bi-objective constrained Economic Emission Dispatch (EED) problem in the presence of renewable energy sources while considering the Valve-Point Effects (VPE). The SMA approach is applied to indicate the adequate optimal solutions for operating the committed thermal units under different operational constraints. The sought optimal solutions are the midpoint between cost saving and pollutant gas emission reduction. This study also examines the influence of integrating renewable energy sources (wind and solar) into the conventional power production network. SMA technique is applied on 3- and 6-unit IEEE test systems in several case studies. The simulation numerical results indicate that SMA has a high efficiency and a better performance compared to known state-of-the-art algorithms. The proposed approach was programmed and simulated in MATLAB.

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.

A novel approach to hybrid dynamic environmental-economic dispatch of multi-energy complementary virtual power plant considering renewable energy generation uncertainty and demand response

Renewable energy generation significantly reduces harmful emissions and promotes sustainable development. Still, its volatility leads to extensive grid connections, affecting electricity system security. Diversifying forms of energy consumption further expands the peak valley difference in electricity demand. This study aims to investigate the hybrid dynamic environmental-economic dispatch problem of multi-energy complementary virtual power plant, considering the renewable energy generation uncertainty and demand response to promote renewable energy integration and mitigate the electricity system supply-demand mismatch. It proposes a novel approach based on the sine cosine and multi-objective particle swarm optimization algorithm to reduce the economic and environmental costs of the multi-energy complementary virtual power plant. First, a hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant is established, considering climbing power, equality, and inequality constraints. Second, targeting the multi-objective, nonlinear, and high-dimension characteristics of the hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant, a sine cosine and multi-objective particle swarm optimization algorithm is proposed to optimize the particle position update method. Finally, simulation cases are constructed based on the development trend of the virtual power plant, setting various dispatching situations to validate the robustness of the proposed approach and determining a compromise solution by membership functions. The simulation results show that the lowest economic and environmental costs obtained by the sine cosine and multi-objective particle swarm optimization algorithm are at least 11.37 % and 2.79 % lower than those obtained by the NSGA-II algorithm and multi-objective particle swarm optimization algorithm when considering the renewable energy generation uncertainty and demand response. Therefore, the work contributes to decreasing the economic and environmental costs of multi-energy complementary virtual power plant and better enhancing the consumption ratio of renewable energy.

Environmental economic dispatch method of power system based on multiobjective artificial bee colony algorithm

Environmental economic dispatch method of power system based on multiobjective artificial bee colony algorithm

Multi-Agent Distributed Reinforcement Learning Algorithm for Free-Model Economic-Environmental Power and CHP Dispatch Problems

In conventional methods for Economic Dispatch Problem (EDP) and Economic-Environmental Dispatch Problem (EEDP), a full connection between the units and the control center is considered, while some faults in the communication system are possible in practical conditions. Hence, an intelligent and high-speed distributed method that is robust against disconnection is needed. In this paper, a Multi-Agent Distributed Reinforcement Learning (MADRL) algorithm based on consensus control for EDP and EEDP is presented. In this method, the incremental cost of units is optimized based on Lagrange method and the reinforcement learning algorithm. Thus, a performance index is defined for each agent in proposed algorithm to be independent of the unit model. The performance index of each agent is the sum of two terms, including i) the difference between the previous performance index value and local power and heat mismatch values and ii) the sum of the difference between the incremental cost of the unit with other neighboring units. In other words, optimizing the defined performance indexes of the agents eliminates the need for the system model. The MADRL method is tested on several grids and compared with other methods. The numerical results show an improvement in the algorithm speed and optimal point.

A multi-objective multi-verse optimizer algorithm to solve environmental and economic dispatch

The combustion and emission of coal have always been a concern. A class of multi-objective Environmental Economic Dispatch (EED) problems has been widely studied to reduce the pollution problem of fossil fuel power plants. In this study, a multi-objective Multi-Verse Optimization algorithm based on Gridded Knee Points and Plane Measurement technique (GKPPM-MVO) is proposed for the multi-objective EED problems. Knee points are usually considered as the most critical points in unbiased decision-making, while plane measurement can find the largest distant points in the population neighborhood. We apply the knee and maximum plane distance points in the local search phase. The original mechanism of parameter control in local search is replaced by using the two above points to exploit the pretty information and inherit it to the next generation. The algorithm is applied to various EED problems. The four algorithms, including MOMVO, NSGA-ii, MOABC, and MOEGO are also used to compare the performance of the algorithms thoroughly. Results show that the GKPPM-MVO algorithm has good convergence performance, high stability, and high uniformity of the Pareto Front.

Research on Optimal Operation of Regional Integrated Energy Systems in View of Demand Response and Improved Carbon Trading

In order to solve the difficulties of dispatching the regional integrated energy system (RIES) under the operating conditions of multi-energy complementary mechanisms, as well as to achieve the purpose of economic operation and low carbon operation of the system, an optimal dispatching model of RIES, including demand response (DR) and an improved carbon trading mechanism (ICTM), is proposed. Firstly, a demand response model is established, the cooling, thermal, electricity, and gas load models under demand response are built, and then an improved customer satisfaction model is proposed based on the four demand response load models. In addition, since EV trips fit a normal distribution, the charging load of EVs is predicted using a Monte Carlo method and incorporated into RIES as a demand-side load; moreover, for EVs, an improved genetic algorithm is used to optimize EV charging, aiming to reduce the peak-to-valley difference; secondly, carbon emission quotas are provided for systems and EVs based on the baseline method and gratuitous allocation, and a carbon trading model is constructed based on carbon quotas and actual A carbon trading model for the system and EV is constructed based on the carbon allowances and actual carbon emissions; finally, four operation scenarios are set up in this paper, and the unit output scheme is developed with the objective of achieving the lowest total system operation cost and lowest carbon emissions. The four typical scenarios are solved using the MATLAB/CPLEX solver and compared for analysis. The simulation results show that an improved genetic algorithm for optimizing the ordered charging method of electric vehicle charging reduces the peak valley difference by 23.06%, and the total operation cost and carbon transaction cost are reduced by 16.13% and 83.10%, respectively, which can provide a reference for the environmental protection and economic dispatch of RIES.

Research on the environment & economic indicators for electricity trading

In this paper, the environmental and economic indicators based on the reactive power pricing are proposed so as to formulate the novel environmental economic power dispatch (NEED) problems for electricity trading. Here, a new way of calculating total cost is proposed based on the reactive power pricing instead of the pure fuel cost, which leads to the NEED problems. In addition, an improved tunicate swarm algorithm (ITSA) is proposed to deal with the NEED problems. Different from the original tunicate swarm algorithm (TSA), adaptive weight parameter and mutation process are used to improve the disadvantages of TSA which converges too fast and easily falls into local optimum. In combination with the Pareto dominance rule, the ITSA can be more effective in dealing with the NEED problems. In order to examine the performance of the ITSA, simulation tests of ITSA, TSA, and particle swarm optimization (PSO) are conducted in IEEE30 node system. During the experimental process, optimized objective functions include total cost considering reactive cost, pollution emission and power loss. The final comparison results show that the ITSA is able to get better distributed Pareto front (PF) and superior best compromise solution (BCS), which proves the high efficiency of the ITSA in dealing with the NEED problems.

Taylor Series Approach to Grey Multiobjective Integer linear Fractional Programming Problem and A case study for environmental economic energy dispatch

Taylor Series Approach to Grey Multiobjective Integer linear Fractional Programming Problem and A case study for environmental economic energy dispatch

Economic environmental dispatch of combined wind and thermal power system

For successful operation of a power generating station, Economic Emission Dispatch (EED) of a thermal and wind power combination becomes a serious concern as it commits the distribution of production among all the generating units which are on line so that the cost, level of emission including NOx, SO2 both are magnified concurrently satisfying each of the experimental constraints. This is an exceptionally restricted multi-objective optimizing case which has conflicting objectives having both primary as well as secondary constraints. In this paper, an Oppositional Base Differential Evolution (OBDE)is suggested to take care of EED matter. The results of simulation here are obtained by realizing the proposed scheme taking two test cases for two systems and then evaluating it against Non dominated Sorting Genetic Algorithm-II (NSGA-II).

Environmental Economical Dispatching of Electric–Gas Integrated Energy System Considering Hydrogen Compressed-Natural Gas

As a high-quality secondary energy, hydrogen energy has great potential in energy storage and utilization. The development of power-to-hydrogen (P2H) technology has alleviated the problem of wind curtailment and improved the coupling between the power grid and the natural gas grid. Under the premise of ensuring safety, using P2H technology to mix the produced hydrogen into the natural gas network for long-distance transmission and power generation can not only promote the development of hydrogen energy but also reduce carbon emissions. This paper presents a new model for incorporating hydrogen into natural gas pipelines. To minimize the sum of wind curtailment cost, operation cost and carbon emission cost, an electric–gas integrated energy system (EGIES) model of hydrogen-compressed natural gas (HCNG) containing P2H for power generation is constructed. Aiming at the problem of global warming caused by a lot of abandoned wind and carbon emissions, the economy and environmental protection of the system model are analyzed. The results show that the model of EGIES considering HCNG can not only absorb excess wind power but also reduce carbon emission costs and system costs, which can reduce the total cost of the environmental economic dispatch of the EGIES by about 34.1%. In the context of the EGIES, the proposal of this model is of great significance to the economical and environmentally friendly operation of the system.

Dynamic Economic Environmental Dispatch of Power System Using An Improved Whale Algorithm

With the global energy crisis and environmental problems becoming increasingly serious, the construction of a new power system containing multiple renewable energy sources for power generation has become a current research direction. In this study, a dynamic economic environmental dispatch model for power systems containing wind and thermal power generation is proposed to promote renewable energy consumption while ensuring system power balance. Firstly, a comprehensive objective function that integrates system operating costs and pollutant emission targets is established, and system active balance constraints and unit output constraints are considered. Secondly, an improved whale optimization algorithm is proposed to deal with the solution problem of the dynamic economic environmental dispatch model by introducing circle chaos mapping, dynamic sine cosine factor, and polynomial variation strategy. Finally, the optimization capability of the proposed model is verified by running a unit test system. The proposed model promotes the consumption and utilization of wind and photovoltaic resources and positively impacts achieving carbon peaking and carbon neutrality.

Fast yet balanced trade-offs for multi-timescale multi-objective economic-environmental dispatch under varying conflicts

The conflicts between generation cost and the power generation-induced effects on the environment are always considered to be constant. However, the conflicts can vary with weather conditions and have typical daily fluctuations if the air pollutant dispersion process is considered when minimizing the generation-induced air pollution. Although understanding the conflicting situation by a Pareto front (PF) can contribute to a better economic-environmental balance, the PF calculation is inherently time-consuming and a threat to fast computation, especially when the time granularity is constantly increasing in today’s multi-timescale-based power operation. This paper proposes a novel multi-timescale multi-objective economic-environmental dispatch (MTMO-EED) method to solve the “fast computation or good balance” dilemma from the following two aspects: On the modeling side, we establish a new MTMO-EED framework with intra-day offline–online coordination (IOOC), where the intra-day PF is calculated offline while only a simple single-objective optimization is needed online. We quantify the error bounds of the deviations between the offline estimated PF and the accurate PF induced by offline forecast errors, and then provide explicit operational conditions under which the offline PF can closely approximate the accurate online PF despite the offline forecast errors; On the solution method side, we propose a new multi-objective algorithm, termed basis changing boundary intersection (BCBI), to improve the PF computational efficiency at the day-ahead stage and the intra-day offline stage. Specifically, mathematical properties of the MTMO-EED’s PFs are exploited in the BCBI. Case studies are conducted on a modified IEEE 39-bus system, which validate the proposed method can achieve fast yet balanced economic-environmental trade-offs at both the day-ahead and intra-day stages.