Economic Operation Cost Research Articles

An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid

AbstractDue to the energy storage lifetime effects of the power allocation, there is a large space to improve the economy of the electric‐hydrogen hybrid DC microgrid. This paper provides an optimal control method based on the mutual learn salp swarm algorithm (MLSSA) in real‐time, which aims to enhance the economy and extend the system's service life. In order to realize the economic operation, operation cost and degradation cost of battery and hydrogen system are considered as the objective function first. Then, salp swarm algorithm based on mutual learn strategy is introduced to obtain optimal economy power allocation results in real‐time with higher convergence speed and increased accuracy. In addition, the proposed method also maintains the battery state of charge (SOC) and state of hydrogen charge (SOHC) within a proper range to guarantee the stable operation of the system. Finally, the results including power results, cost analysis and degradation rate analysis of the MATLAB/Simulink show that the proposed method is more economically beneficial than the non‐considering degradation cost strategy.

FACTS placement for reactive power planning with weak node constraints using an improved symbiotic search algorithm

In this paper, an economically feasible and reliable operation of the IEEE 57 bus system for Optimal Power Flow (OPF) is proposed. The Improved Symbiotic Organisms Search (ISOS) algorithm is proposed for effective reactive power planning as an OPF issue. Further, the optimal position of Flexible AC Transmission Systems (FACTS) is taken into consideration, by including the existing system controlling variables like reactive power generators output, transformer tapping and capacitors connected at shunt. The objective of the work is two-fold; i.e., to reduce the energy loss and to enhance the voltage profile within the prescribed limit by ensuring the economic operation and investment cost of FACTS in the system. In this work, two FACTS devices like Static Var Compensator (SVC) and Thyristor-Controlled Series Controller (TCSC) have been taken into consideration. Voltage sensitivity indicator and reactive power flow are two tools that are utilised in order to locate weak nodes for the implementation of FACTS. Finally, the performance of the ISOS algorithm is compared with that of three other state- of-the -art optimization techniques, such as, Symbiotic Organisms Search (SOS), Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO). A Non-parametric statistical analysis is also performed to investigate the dominance of the ISOS algorithm over others.

Collaborative and effective scheduling of integrated energy systems with consideration of carbon restrictions

AbstractTo address the balancing challenge of real‐time power demand and supply in power systems caused by unpredictable output fluctuations of renewable energies, collaborative and effective scheduling of integrated energy systems is proposed with consideration for carbon restrictions. The model incorporates various energy resources, including cold, heat, electricity, and gas, and overcomes the barriers among different levels of heterogeneous energy grid systems. This makes it possible to coordinate the vertical complementarity of source‐grid‐load‐storage and the horizontal complementarity of multi‐energy while taking into account investment costs and carbon trading to achieve the economic operation of the system. This analysis shows that the proposed model can dissipate the excess of new energy generation from the grid. and achieve a balance between supply and demand, while effectively reducing the economic operation cost of the whole system.

Multi-energy optimization under the water-energy-carbon nexus in a typical iron and steel plant

The multi-energy system in iron and steel plants has strong coupling characteristic, which makes the energy distribution complex and difficult in energy management. Additionally, the steel works is a manufacture process with high-carbon emissions, and it is vital to assess the environmental stress from the sights of nexus between energy, water and carbon emission. In this study, a multi-energy and multi-objective mixed integer non-linear programming (MINLP) model is established to optimize the energy system under the water-energy-carbon nexus in iron and steel plant. And the multi-energy is considered the couple of gas-steam-electricity. The multi-objective functions include the economic performance, the CO2 emissions, the water generation, and the global energy efficiency. Due to the contradictory of the objective functions, the utopia tracking approach is used to normalize and assess the functions. To fit the actual situation of the equipment, this paper made a fitting curve for the energy efficiency of the equipment. The minimum economic operation cost results show that the studied iron and steel plant has great potential in reducing economic cost (7.6 %), emission mitigation (4.7 %), system water production improvement (807 t) and global energy efficiency (54.48 %). Besides, the research will help the iron and steel plant to reach the carbon peak and carbon neutrality target.

Double-layer Optimization Study of Virtual Power Plants with Source-load-storage

In order to effectively solve the supply-demand imbalance of the grid caused by intermittent new energy penetration and improve the scheduling flexibility of virtual power plants, a two-layer optimization model of virtual power plants with source-load-storage is proposed. The upper layer of the model is designed to reduce the forecast error of wind power output and improve the revenue of the virtual power plant. On the basis of the maximum wind power consumption, distributed energy storage and fuel cells are used to coordinate and compensate for the deviation between the planned and actual output of renewable energy. The active output of the upper layer is transferred to the lower layer. The lower layer of the model is solved by using the improved linear decreasing inertia weight particle swarm algorithm for the output of the gas turbine unit. The analysis results show that the model can absorb the excess new energy generation in the grid, achieve the balance of supply and demand regulation in the grid, and effectively reduce the economic operation cost of the system.

Optimal scheduling of building integrated energy systems using an improved ant lion algorithm

Abstract In order to deal with the increasingly severe energy shortage problem, to find new energy solutions and to alleviate the dual pressure of domestic energy and environment, building a new type of multi-energy complementary power system has become one of the hot issues in recent years. This paper presents an optimal scheduling model of building integrated energy system (BIES) based on the comprehensive utilization of electricity, heat and gas. Based on the traditional combined cooling, heating, and power (CCHP) system, this model adds wind power and photoelectric renewable energy power generation systems, ground source heat pump system and energy storage system. Then, taking the environmental cost and system operation and maintenance cost as the objective function, it is solved by using the improved multi-objective ant lion algorithm based on differential evolution. Finally, a building in Northeast China is taken as an example to verify the simulation. The simulation results show that the optimal scheduling model of building integrated energy system proposed in this paper can promote the consumption of renewable energy such as wind power and photoelectric, realize the peak cutting and valley filling of power load, effectively reduce the economic operation cost of the system, reduce carbon emissions and effectively solve the problem of environmental pollution to a certain extent.

Design and thermoeconomic analysis of a solar parabolic trough – ORC – Biomass cooling plant for a commercial center

Hybrid renewable polygeneration systems are regarded as key solutions for the sustainable energy supply of buildings. While solar heating and cooling comprises a wide range of technologies, there has been limited research on the combined production of power and cooling only, with little or no heat demand. This study designs and analyzes a hybrid solar–biomass ORC-based polygeneration system from energy, economic, and environmental viewpoints. The polygeneration system is designed to cover the electricity and cooling demands of a commercial center located in Zaragoza, Spain. A parabolic trough collector field coupled with thermal energy storage, and an auxiliary biomass boiler supply heat to an Organic Rankine Cycle (ORC), which generates electricity to cover electrical demands and to produce cooling in mechanical chillers. The biomass boiler supports the solar thermal production to ensure a stable and reliable heat supply to the ORC. The system is connected to the electric grid, so that electricity purchases and sales are possible. The equipment sizing is performed with the goal of achieving a high renewable fraction in the total electricity consumed by the commercial center. The analysis of the proposed plant includes the hourly operation throughout the year, complemented by an economic assessment, considering investment and operation costs, and an estimate of the environmental benefits of the plant. Also, a thermoeconomic analysis is developed to determine the cost formation process of the internal flows and final products of the plant. The unit cost of each flow is broken down into investment and operation cost components. Sensitivity analyses of the investment cost, interest rate, biomass price, and electricity selling price discount are made. The results show that, in economic terms, the system is not presently viable, since the cost of the electricity produced (279.07 €/MWh) is much higher than the electricity purchase price (126.70 €/MWh). In environmental terms, the system is capable of displacing 96.1% of the CO2 emissions and 85.6% of non-renewable primary energy consumption relative to a conventional system consuming grid electricity only.

Estimation of Operation Cost of Residential Multiple Energy System Considering Uncertainty of Loads and Renewable Energies

This paper introduces a new method of estimating the variation interval and the expected value of optimal economic operation cost for the energy hub (EH) model considering load and renewable energy uncertainties in the residential district. Firstly, renewable energy and load uncertainties are defined as uncertain sets of historical data. Due to dispatch factors, the traditional EH model is non-linear and difficult to calculate and analyze. In this paper, a multi-step modeling method is developed for the breakdown of a complex EH model (CEH) into several simple EH (SEH) models and variable substitutions in each SEH, to eliminate the non-linearity of CEH. The upper and lower bound of the operating costs are then obtained based on the duality theorem and the expected value is calculated by expected value model. Finally, the case study in Beijing residential district is carried out to confirm the efficiency of the proposed method.

Dynamic economic emission dispatch of thermal-wind-solar system with constriction factor-based particle swarm optimization algorithm

PurposeDynamic economic and emission dispatch (DEED) aims to optimally set the active power generation with constraints in a power system, which should target minimum operation cost and at the same time minimize the pollution in terms of emission when the load dynamically changes hour to hour. The purpose of this study is to achieve optimal economic and emission dispatch of an electrical system with a renewable generation mix, consisting of 3-unit thermal, 2-unit wind and 2-unit solar generators for dynamic load variation in a day. An improved version of a simple, easy to understand and popular optimization algorithm particle swarm optimization (PSO) referred to as a constriction factor-based particle swarm optimization (CFBPSO) algorithm is deployed to get optimal solution as compared to PSO, modified PSO and red deer algorithm (RDA).Design/methodology/approachDifferent model with and without wind and solar power generating systems; with valve point effect is analyzed. The thermal generating system (TGs) are the major green house gaseous emission producers on earth. To take up this ecological issue in addition to economic operation cost, the wind and solar energy sources are integrated with the thermal system in a phased manner for electrical power generation and optimized for dynamic load variation. This DEED being a multi-objective optimization (MO) has contradictory objectives of fuel cost and emission. To get the finest combination of the two objectives and to get a non-dominated solution the fuzzy decision-making (FDM) method is used herein, the MO problem is solved by a single objective function, including min-max price penalty factor on emission in the total cost to treat as cost. Further, the weight factor accumulation (WFA) technique normalizes the pair of objectives into a single objective by giving each objective a weightage. The weightage is decided by the FDM approach in a systematic manner from a set of non-dominated solutions. Here, the CFBPSO algorithm is applied to lessen the total generation cost and emission of the thermal power meeting the load dynamically.FindingsThe efficacy of the contribution of stochastic wind and solar power generation with the TGs in the dropping of net fuel cost and emission in a day for dynamic load vis-à-vis the case with TGs is established.Research limitations/implicationsCost and emission are conflicting objectives and can be handled carefully by weight factors and penalty factors to find out the best solution.Practical implicationsThe proposed methodology and its strategy are very useful for thermal power plants incorporating diverse sources of generations. As the execution time is very less, practical implementation can be possible.Social implicationsAs the cheaper generation schedule is obtained with respect to time, cost and emission are minimized, a huge revenue can be saved over the passage of time, and therefore it has a societal impact.Originality/valueIn this work, the WFA with the FDM method is used to facilitate CFBPSO to decipher this DEED multi-objective problem. The results reveal the competence of the projected proposal to satisfy the dynamic load demand and to diminish the combined cost in contrast to the PSO algorithm, modified PSO algorithm and a newly developed meta-heuristic algorithm RDA in a similar system.

A Method of Operating State Estimation of Pumped Storage Power Station Based on Load Peak-Valley-Normal Prediction

In order to make the power system absorb more renewable energy and improve the flexibility of power grid operation, one of the effective methods is to configure pumped storage power station in power system. In order to reduce the frequent start-up and shutdown of pumped storage power station, “A Method of Operating State Estimation of Pumped Storage Power Station Based on Load Peak-Valley-Normal Prediction” is proposed in this paper. This method is based on FCM clustering algorithm classifies peak-valley-normal loads at one time of power grid, evaluates the operation state of pumped storage power station on this basis, and finally applies it to the operation cost optimization model of wind-solar-fire-storage system to evaluate the renewable energy absorption capacity and economic operation cost. The simulation results prove that this method can reduce the start-up and shutdown times of pumped storage power station and is of positive significance for maintaining long-term and stable operation of pumped storage power station.

Removal of High Levels of Cyanide and COD from Cassava Industrial Wastewater by a Fixed-Film Sequencing Batch Reactor

The fixed-film sequencing batch reactor, or F-SBR, was developed to treat high organic compound levels and toxic cyanide concentrations in cassava wastewater. The performance of the F-SBR was compared with that of a conventional sequencing batch reactor, or SBR, that was operated with organic compound contents of 16,266.67–26,666 mg COD/L and 132.92–252.66 mg CN−/L. The cyanide and chemical oxygen demand removal efficiencies of the conventional SBR system were 42.61% and 36.83%, respectively, while those of the F-SBR were 77.95% and 74.43%, respectively; the cyanide removal efficiency reached 95.45% when the hydraulic retention time was increased to 5 days, and the F-SBR was very effective for the complete removal of cyanide when the hydraulic retention time was increased to 10 days. This effectiveness was similar to the effectiveness of chemical oxygen demand removal, which reached 40–78% efficiency with the F-SBR system. These results showed that the immobilization of cyanide-degrading bacteria such as Agrobacterium tumefaciens SUTS 1 and Pseudomonas monteilii SUTS 2 carried out with a polypropylene ring in a fixed-film aerobic system enhanced the performance of the reactor and can be successfully applied for cyanide and chemical oxygen demand removal from industrial wastewater with high cyanide and chemical oxygen demand concentrations. This study may provide a promising alternative technique that reduces economic operation costs in solving wastewater contamination problems.

Oil Consumption in 4WD Farm Tractors Used in Forestry Operations

Farm tractors are still widely used in many forestry operations. Predicting fuel and lubricant costs is difficult because their consumption depends on a number of factors such as hours worked and operations performed. Fuel and lubricant consumption is important since it can have an impact at both the economic and environmental level. Many fuel models have been studied in the last decades, but few studies have focused on oil consumption. The ASABE (American Society of Agricultural and Biological Engineers) Standard suggested a model for predicting engine oil consumption of farm tractors of the 1980s, which are potentially different from modern tractor engines. In addition, the recent widespread application of semi- and full-power-shift and continuous variable transmissions and the high number of hydraulic applications increased the amount of lubrication oil for transmission and hydraulic systems.For these reasons, we analysed 133 4WD recent model farm tractors used in forest operations with the aim to study:Þ engine, transmission and hydraulic system oil capacitiesÞ engine oil change intervals as recommended by the manufacturers.A new equation for engine oil consumption, as a function of the rated engine power, was first used and statistically analysed. It was similar to the equation developed by other authors (with a mean difference of 28%, decreasing to 11% at the highest engine power), but well below the ASABE model (with an average engine oil consumption three times higher). Another equation of total oil consumption related to the rated engine power was then studied and compared with a recent study. The results showed an average difference of 18%, decreasing to 8% at the highest engine power. The differences, due to a different machine dataset (only 4WD farm tractors that can be used for forestry operations were analysed) are, however, minimal also in the engine oil consumption model if compared with the oldest ones: a new proposal is therefore necessary, with new and affordable models for correctly evaluating economic and environmental forestry operation costs when using farm tractors.

Operational optimization of wastewater reuse integrated energy system

With the rush in global energy demand and the popularization of renewable energy utilization, the integrated energy system has been rapidly developed, and there have been diverse comprehensive energy resources, which includes wind energy, solar energy, sewage etc. In order to improve the comprehensive energy utilization efficiency of energy systems with sewage resources, this paper proposes an integrated energy system of wastewater reuse and an optimal operation model of it. Firstly, this paper proposes an integrated energy system operation control strategy based on sewage source heat pump and biogas trigeneration energy production. Secondly, the genetic algorithm model with the objective function of comprehensive energy utilization efficiency and economic operation cost is established. Finally, this paper adopts a sewage treatment system area as the simulation target to carry out the simulation analysis of wastewater reuse integrated energy system (WRIES) operation, and constructs an operation optimization scheduling scheme. Simulation results show that compared with no optimization, the operating cost of the system can be reduced more than 9%, and the renewable energy utilization rate can be increased more than 5%. After the thermoelectric ratio adjustment, the results of system operation optimization can be further improved.

Load shedding optimization for economic operation cost in a microgrid

Distributed generation and demand-side participation have been widely deployed for secure, reliable and economic power distribution networks. Microgrids have been merged in power systems to meet this increase in distributed generation and to provide more control on the massive demand expansion. This paper presents an optimization model for scheduling and operating a microgrid considering the participation of the end-user customers in the electricity market. Mixed-integer programming is used in the proposed model to procure the minimum operation cost of the microgrid and to apply load shedding (LS) optimization on the local responsive loads. The objective function is described by piecewise linear functions. The proposed model allows the implementation of high number of constraints related to generation units, power exchange with the main grid, energy balance and LS. The operation decisions are based on binary variables that represent the status of the generation units, grid-connection and the responsive loads. Furthermore, the proposed model demonstrates the relationship between pick-up/drop-off rates of the responsive loads and the hourly operation cost of the microgrid. Example studies have illustrated the performance of the optimization model in finding the minimum operation cost with multiple technical and economic constraints.

Research on Multi-Time Scale Optimization Strategy of Cold-Thermal-Electric Integrated Energy System Considering Feasible Interval of System Load Rate

The integrated energy system coupling multi-type energy production terminal to realize multi-energy complementarity and energy ladder utilization is of great significance to alleviate the existing energy production crisis and reduce environmental pollution. In this paper, the topology of the cold-thermal-electricity integrated energy system is built, and the decoupling method is adopted to analyze the feasible interval of load rate under the strong coupling condition, so as to ensure the “source-load” power balance of the system. Establishing a multi-objective optimization function with the lowest system economic operation and pollution gas emission, considering the attribute differences and energy scheduling characteristics of different energy sources of cold, heat and electricity, and adopting different time scales to optimize the operation of the three energy sources of cold, heat and electricity, wherein the operation periods of electric energy, heat energy and cold energy are respectively 15 min, 30 min and 1 h; The multi-objective problem is solved by standard linear weighting method. Finally, the mixed integer nonlinear programming model is calculated by LINGO solver. In the numerical simulation, the hotel summer front load parameters of Zhangjiakou, China are selected for simulation and compared with a single time scale system. The simulation results show that the multi-time scale system reduces the economic operation cost by 15.6% and the pollution gas emission by 22.3% compared with the single time scale system, it also has a wider feasible range of load rate, flexible time allocation, and complementary energy selection.

Optimization models for operation of a steam power system in integrated iron and steel works

ABSTRACT The steam power system (SPS) is one of the most significant contributors to the energy consumption and pollutant emissions in integrated iron and steel works. In this study, a mixed-integer nonlinear programming (MINLP) model is developed to solve the problem of surplus byproduct gas utilization and reduce the economic operation cost (EOC) of the system. Fluctuations of surplus byproduct gas, dynamic demand of steam and electricity, time-of-use power price, and production safety constraints are also considered. The models have been verified by actual production data of the iron and steel works. Based on the MINLP, the results show that the reasonable use of heat value constraint will improve the utilization rate of the BFG and reduce the EOC, and the effect of equipment maintenance is discussed. The reasonable use of the heat value constraint is also discussed in the MINLP, which is helpful to improve the utilization rate of BFG and reduce the cost of economic operation.

A double-use device based on bubble pump and solar energy

In order to solve the problem of high power consumption of traditional household air conditioning and meet the requirements of energy saving and economic operation cost, how to make full use of renewable energy and bubble pump technology has been widely concerned in the world. This device creatively combines the solar energy, the bubble pump and the air conditioning system. This device reduces the consumption of electric energy, and has obvious significance of energy saving and emission reduction.

OPTIMIZATION OF THE EXTRACTION OF FREE FATTY ACIDS APPLIED TO BIODIESEL PRODUCTION

Abstract The liquid-liquid extraction of free fatty acids (FFA) from residual oils and fats for biodiesel production, employing methanol as the solvent, has been optimized using process simulation and response surface methodology. The parameters investigated were temperature, number of stages and solvent-to-feed ratio (S/F). Responses evaluated were FFA mass fraction in the oil-rich phase (wFFAB) and total cost, using yellow and brown greases as the raw materials. Quadratic and linear models were fitted for wFFAB and cost responses, respectively. The optimal conditions satisfying technical (wFFAB ≤ 0.5%) and economic (minimum cost, including capital and operation costs, except for raw material cost) criteria were 321 K, 6 stages, S/F = 1.27, wFFAB = 0.41%, cost = $84.93/ton (yellow grease), and 318 K, 6 stages, S/F = 1.32, wFFAB = 0.49%, cost = $102.89/ton (brown grease).

Fuzzy multi-criteria decision-making method for technology selection for emissions reduction from shipping under uncertainties

This paper describes a methodology for the application of Multi-Criteria Decision-Making (MCDM) on the technology selection for emissions reduction from shipping under uncertainties and incomplete information. Nine criteria in four aspects, including technological (maturity), economic (capital cost and operation cost), environmental (effects of SOx, NOx, GHG, and PM reduction), and social–political aspects (government support and social acceptability), were used for the sustainability assessment. The study aims at developing the methodology for technology selection for emissions reduction from shipping by combining Fuzzy Analytic Hierarchy Process (AHP) and VIKOR. Fuzzy AHP was used to determine the weights of the evaluation criteria and the relative performance of the alternatives with respect to each evaluation criterion, and VIKOR was used to prioritize the alternative technologies. The two methods of VIKOR and the traditional AHP were combined to validate the proposed methodology. Three alternative technologies of low sulphur fuel, scrubber and LNG were studied using the proposed model, and the results indicate that the proposed methodology is capable of assisting the decision-makers to select the most sustainable technology for emissions reduction from shipping under uncertainties and incomplete information.

Application of Neural Networks to Secure Scenario Based OPF of Power Systems Considering Transient Stability Criterion

In this paper, a scenario based optimal power flow (OPF) is presented considering economic (operation cost minimization) and security objective functions. Security objective functions include both reliability and system transient stability improvement. Energy not supplied (ENS) cost is considered as the criterion for system reliability and critical clearing time (CCT) is considered as the criterion for power system dynamic stability. In order to reduce the computational burden of the proposed method, off-line training of neural network is used to determine CCT based on the system operating point. For this purpose, CCT parameter is calculated in Dig silent Software environment for various operating points of system and a data set is obtained to train neural network. In the proposed method, it is tried to improve dynamic stability of system, as well as decreasing the operation cost in post contingency state through optimal load shedding and generation rescheduling. Genetic algorithm (GA) is used as the optimization tool. The proposed framework is tested on IEEE 39-bus test system and results show efficiency of the proposed method.