Minimum Operating Cost Research Articles

Energy, exergy and economical analysis of N2O based cascade refrigeration system for ultralow temperature cooling applications using different eco-friendly refrigerants in high temperature cycle

Implementing the Montreal Protocol has limited the use of conventional refrigerants and has led industries to find optimal or economical eco-friendly refrigerants to reduce environmental hazards. The present study analyzes an N2O-based cascade refrigeration system for achieving ultralow cooling temperatures using different eco-friendly refrigerants with low GWP and ODP in a high-temperature cycle. 3E (Energy, Exergy, and Economy) analysis of a cascade refrigeration system using eco-friendly refrigerants such as R290, R1270, RE170, R600, R600a, HFE7100, and HFE7000 in the upper cycle and N2O in the lower cycle was conducted. The R744A/R600 pair performed best among the other refrigerant pairs due to its lower discharge pressure and compression ratio. The refrigerant pair R744A/R600 results in a higher cascade cycle COP of approximately 0.87, with 48 % exergetic efficiency, while the refrigerant pair R41/HFE7100 has the lowest COP (0.68) and lowest exergetic efficiency (42 %). The minimal operational cost varies from 73006 to 76907 USD per annum for refrigerant pair R744A/R600, while for the other pairs, the cost varies from 98625 USD to 114674 USD. The R744A/R600 refrigerant pair provides COP values that are 1.38–5.38 % greater than the others, with a 1.01–3.45 % variation in exergy efficiency.

A novel two-stage multi-objective dispatch model for a distributed hybrid CCHP system considering source-load fluctuations mitigation

Small-scale distributed energy systems with combined cooling, heating, and power (DES-CCHP) production have attracted international interest. However, fluctuating loads and renewable energies continuously disturb the real-time operation of DES-CCHP and even the connected grid, hindering the broad application of grid-connected DES-CCHP. In this paper, a novel model predictive control (MPC) based two-stage strategy is developed for DES-CCHP, with multiple timescales consideration. In the first stage, a multi-objective MPC is built with inheritance and fusion mechanisms of multi-step scheduling instructions, simultaneously minimizing operation costs and power fluctuations. The second stage performs intelligent decision-making on the results of the first stage, which allows appropriate state-switching instructions to ensure economy. Meanwhile, the decision-making rejects the unnecessary one and triggers a second-round flexibility-based interventional optimization to revise scheduling planning. The case studies show that compared to dispatch without MPC, the economic single-objective MPC, single-stage multi-objective MPC (only applies the first-stage models), and two-stage multi-objective MPC save costs by 5.31 %, 4.80 %, and 4.57 %, respectively. As for fluctuations mitigation, the single-stage and two-stage models significantly smooth power fluctuations by 51.66 % and 69.93 %, respectively. The proposed strategy operates DES-CCHP economically, stably, and grid-friendly under a rugged operating environment.

Optimal location of base stations for cellular mobile network considering changes in users locations

In the past few decades, the demand for cellular networks has grown tremendously due to the growing demand for data. However, the vast majority of the users are dynamic since they use their smart phones and tablets to connect to these networks when they move to one location to another to attend sport games or other events for example. The location of these events might not cover the large demand. In this paper, we address the classical problem of locating base stations for a mobile cellular network to serve mobile users in a given geographical area considering the users' movements within the network. We developed a mixed integer programming model to provide the optimal location of base stations at different time periods with the network's minimum total cost (i.e., installation and operation of base stations). This paper considers the COST-231-Walfisch-Ikegami propagation model for the path loss calculation with a non-line-of-site situation. To demonstrate the capability of the proposed model, it is experimented with a real-world problem on the Red Sea coast of Saudi Arabia. The model was capable of finding the optimal base station locations with minimum installation and operational costs considering the capacity and quality of service constraints.

Multi time scale optimal dispatch of distribution network considering load demand response

Considering the uncertainty of wind power output and load as well as the differences in the operating characteristics of various adjustable resources over different time scales, a multitime scale optimal dispatching model based on stochastic optimization and distributed robust optimization is designed for the distribution network. Firstly, the demand response of the user load side is divided into price-based load and incentive-based load, and price elasticity coefficients are introduced to describe it. Then, with the minimum operation cost of the distribution network, the loss cost, and the penalty cost of abandoned wind as the objective function, various active and reactive regulation elements as well as the system power flow formulas are taken as constraints. In the day-ahead and real-time stages, a stochastic optimization method based on scenarios and opportunity constraints is introduced. In the intraday stage, a self-regressive sliding window prediction model is used to predict wind power and load processing, and a Wasserstein distance-based prediction error uncertainty set is established to establish an intraday distributed robust rolling optimization model. The improved IEEE 33-node case is used for simulation verification, and the results show that the model can effectively reduce system costs and improve system operation stability.

Optimization of Integrated Energy System Considering Electricity and Hydrogen Coordination in the Context of Carbon Trading

In order to improve the consumption of renewable energy and reduce the carbon emissions of integrated energy systems (IESs), this paper proposes an optimal operation strategy for an integrated energy system considering the coordination of electricity and hydrogen in the context of carbon trading. The strategy makes full use of the traditional power-to-gas hydrogen production process and establishes a coupling model comprising cogeneration and carbon capture equipment, an electrolytic cell, a methane reactor, and a hydrogen fuel cell. Taking a minimum daily operating cost and minimal carbon emissions from the system as objective functions, a mixed-integer nonlinear optimal scheduling model is established. This paper designs examples based on MATLAB R2021b and uses the GUROBI solver to solve them. The results show that compared with the traditional two-stage operation process, the optimization method can reduce the daily operation cost of an IES by 26.01% and its carbon emissions by 90.32%. The results show that the operation mode of electro-hydrogen synergy can significantly reduce the carbon emissions of the system and realize a two-way flow of electro-hydrogen energy. At the same time, the addition of carbon capture equipment and the realization of carbon recycling prove the scheduling strategy’s ability to achieve a low-carbon economy of the scheduling strategy.

Distributed robust optimal configuration of multi-microgrid interconnected systems based on multi-objective bee colony algorithm

The distributed robust optimal allocation method for multi-microgrid interconnected systems usually involves a large number of variables and constraints, and the computational complexity is high in practical applications, which makes it difficult to solve the problem. Therefore, a distributed robust optimal allocation method for multi-microgrid interconnection systems based on multi-objective swarm algorithm is proposed. A distributed robust optimization configuration constraint index model for multi-microgrid interconnection system is established. Considering the influence of energy storage technology operation characteristics on its service life, a micro-grid hybrid energy storage capacity optimization configuration model with the minimum annual comprehensive energy storage cost as the objective function is established with charge and discharge power and residual power as the constraint conditions. The multi-objective swarm algorithm is used to realize the optimization model of distributed robust configuration microgrid interconnection system. By determining the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points, the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points are determined. The hybrid energy storage configuration model of multi-microgrid interconnection system is established with the minimum alternative operating cost as the objective function, so as to realize the distributed robust optimal configuration of multi-microgrid interconnection system. The simulation results show that the distributed configuration of multi-microgrid interconnection system with the proposed method has good robustness and strong optimization control ability.

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable nature of renewable energy in distributed power distribution networks, as well as to improve the economic and operational stability of distribution systems, this paper proposes the establishment of an active distribution network capable of accommodating renewable energy. The objective is to enhance the efficiency of new energy utilization. This study investigates optimal scheduling models for energy storage technologies and economic-operation dispatching techniques in distributed power distribution networks. Additionally, it develops a comprehensive demand response model, with real-time pricing and incentive policies aiming to minimize load peak–valley differentials. The control mechanism incorporates time-of-use pricing and integrates a chaos particle swarm algorithm for a holistic approach to solution finding. By coordinating and optimizing the control of distributed power sources, energy storage systems, and flexible loads, the active distribution network achieves minimal operational costs while meeting demand-side power requirements, striving to smooth out load curves as much as possible. Case studies demonstrate significant enhancements during off-peak periods, with an approximately 60% increase in the load power overall elevation of load factors during regular periods, as well as a reduction in grid loads during evening peak hours, with a maximum decrease of nearly 65 kW. This approach mitigates grid operational pressures and user expense, effectively enhancing the stability and economic efficiency in distribution network operations.

Enhanced reaction extraction distillation via multi-objective optimization and 4E analysis for sustainable recovery of organic compounds from wastewater

Resource utilization and sustainable treatment of wastewater, which can achieve efficient energy utilization and environmental protection, have received hot social attention. Tetrahydrofuran and methanol are the two most common substances in pharmaceutical wastewater. It is very necessary to explore the wastewater treatment process containing these two substances. In this study, extractive distillation and reactive distillation were proposed to effectively separate the tetrahydrofuran-methanol-water component of pharmaceutical wastewater. To further reduce energy consumption, two strengthening processes, double-column reactive-extractive distillation and reactive-extractive dividing wall column distillation were designed. Taking the minimum operating and equipment cost as the objective function, the processes were optimized by using the multi-objective genetic optimization algorithm to obtain the best process parameters. Four processes were evaluated in terms of total annual cost, energy consumption, gas emissions and thermodynamic efficiency. Compared to the traditional three-column extractive distillation process, the above-mentioned four aspects of the reactive-extractive dividing wall column process was reduced by 40.63%, 43.13%, 43.14% and 17.71%, respectively. The reactive-extractive dividing wall column distillation process is of great significance for the efficient purification of valuable organic matter in wastewater and environmental protection.

Multiparameter optimization of the acid gas removal process using self-heat recuperation technology in IGCC system based on genetic algorithm

The acid gas removal (AGR) process in the integrated gasification combined cycle (IGCC) system consumes a large amount of heat and reduces the generation efficiency. Process modification of self-heat recuperation (SHR) technology based on vapor compression heat pumps is expected to reduce the energy consumption of the AGR process. On this basis, this paper conducted a comprehensive and parametric analysis of the AGR process integrated with a vapor compression heat pump. Wherein, two key parameters, the stripper pressure (P1) and the heat pump heat exchanger inlet pressure (P2), were analyzed in detail to understand the energy consumption characteristics of each module of the system. By establishing the optimization objective functions of minimizing system operating cost and exergy loss respectively, the system multi-parameter optimization was conducted based on the genetic algorithm by coding and solving in HYSYS and MATLAB. The corresponding parameter variables for the optimal cases of the two optimization objectives are also comparatively derived. The results showed that P1 mainly changes the SHR capacity of the system by changing the allowable flow rate of the SHR mass under different working conditions, while P2 mainly affects the SHR capacity of the system by changing the heat of the fraction at the top of the stripper. The minimum operating cost of the system decreased to 0.0655 $/kgH2S, which is 1.50 % lower compared to the original case. The minimum exergy loss of the system is reduced from 7788.89 kW to 7398.59 MW, which is about 5.01 % lower than that of the original case.

Collaborative hierarchical scheduling model of interconnected multi-microgrid and ADN considering DR with different strategies

The demand response (DR) resources of multiple entities have different operation strategies, which makes it difficult to realize the co-optimization. This paper proposes a cooperative hierarchical scheduling optimization model of interconnected multi-microgrid and an active distribution network (ADN) considering DR with different operation strategies. First, the collaborative hierarchical scheduling framework is proposed and the interaction characteristics and cooperative scheduling mode of multi-interconnected microgrid and ADN are analyzed. Second, the two-layer cooperative hierarchical scheduling model is established, considering DR with different strategies. The upper layer model takes the minimum operating cost of the ADN as the objective to optimize the trading tariff between the ADN and microgrids, and its own DR, while the lower layer model takes the operating cost of multi-microgrid as the objective to optimize the purchasing and selling of electrical energy for the ADN, the DR, and the interaction power. Finally, the case studies with three microgrids and an ADN are used to demonstrate the validity and effectiveness of the proposed model.

A Communication-Free Economic Dispatch Control Scheme of Cascaded-Type Microgrids for Operating Cost Minimization

A Communication-Free Economic Dispatch Control Scheme of Cascaded-Type Microgrids for Operating Cost Minimization

Zonotope approximation based flexible cluster division method for load‐side resource scheduling

AbstractIn view of the insufficient flexibility and adjustment capabilities caused by the high proportion of renewable energy connected to the distribution network and the limitations of the source‐side adjustment method, a load‐side resource flexibility cluster partitioning method based on zonotope approximation is proposed to cope with the flexibility adjustment requirements brought by uncertain resources. First, the constraint space of load‐side resources such as energy storage, multi‐source mobile energy storage, electric vehicles, and temperature‐controlled loads is described as a convex polyhedral form general model based on the virtual battery framework, and the zonotope approximation is used to integrate its constraint space with the Minkowski sum. Secondly, modularity and flexibility supply and demand balance index are proposed to perform flexibility clustering of load‐side resources. Then, the minimum comprehensive operating cost of the cluster aggregator is set as the optimization goal, the cluster constraint space is equivalent to the cost function, and the optimal charging and discharging power and flexibility adjustment capacity of the load‐side resources are obtained. Finally, the method proposed here is verified through numerical example simulation to effectively balance aggregation accuracy and calculation speed.

Precision agriculture with AI-based responsive monitoring algorithm

Precision Agriculture (PA) is a relatively new farming approach, applying science and technology to enhance cost-effectiveness and improve food security by optimizing agricultural practices through the treatment of each crop individually. To support the new practice, an AI-based, responsive monitoring algorithm, called the Dynamic-Adaptive Search algorithm, has been developed to minimize operation costs with the benefit of acquiring new and timely information. Three modules of the algorithm are 1) Module for image processing based on AI, 2) Module for error-responsive search expansion, and 3) Module for estimating stress propagation. Computational experiments have demonstrated that the newly developed algorithm outperforms other alternatives, yielding significantly higher system performance and system gain, compared to other algorithms. The sensitivity analysis confirms the algorithm's ability to deliver within ± 10% of the theoretical optimal value, resulting in economic benefits under varying conditions. The algorithm's applications can be extended to other decision-making situations involving cost-benefit tradeoffs of acquiring more data.

Bi-Layer Planning of Integrated Energy System by Incorporating Power-to-Gas and Ground Source Heat Pump for Curtailed Wind Power and Economic Cost Reduction

The popularization of renewable energy is limited by wasteful problems such as curtailed wind power and high economic costs. To tackle these problems, we propose a bi-layer optimal planning model with the integration of power to gas and a ground source heat pump for the existing integrated energy system. Firstly, the inner layer optimizes the daily dispatch of the system, with the minimum daily operation cost including the penalty cost of curtailed wind power. Then, the enumeration method of outer-layer optimization determines the device capacity of various schemes. After that, optimal planning can be achieved with the minimum daily comprehensive cost. The result of this example shows that the improved system can reduce curtailed wind power and system costs, thus improving the overall economy. Finally, the influences of algorithms and gas prices on planning optimization are studied.

Hierarchical Energy Management of DC Microgrid with Photovoltaic Power Generation and Energy Storage for 5G Base Station

For 5G base stations equipped with multiple energy sources, such as energy storage systems (ESSs) and photovoltaic (PV) power generation, energy management is crucial, directly influencing the operational cost. Hence, aiming at increasing the utilization rate of PV power generation and improving the lifetime of the battery, thereby reducing the operating cost of the base station, a hierarchical energy management strategy based on the improved dung beetle optimization (IDBO) algorithm is proposed in this paper. The first control layer provides bus voltage control to each power module. In the second control layer, a dynamic balance control strategy calculates the power of the ESSs using the proportional–integral (PI) controller and distributes power based on the state of charge (SOC) and virtual resistance. The third control layer uses the IDBO algorithm to solve the DC microgrid’s optimization model in order to achieve the minimum daily operational cost goal. Simulation results demonstrate that the proposed IDBO algorithm reduces the daily cost in both scenarios by about 14.64% and 9.49% compared to the baseline method. Finally, the feasibility and effectiveness of the proposed hierarchical energy management strategy are verified through experimental results.

Low-Carbon Economic Dispatch of Integrated Energy Systems in Industrial Parks Considering Comprehensive Demand Response and Multi-Hydrogen Supply

To address the increasing hydrogen demand and carbon emissions of industrial parks, this paper proposes an integrated energy system dispatch strategy considering multi-hydrogen supply and comprehensive demand response. This model adopts power-to-gas technology to produce green hydrogen, replacing a portion of gray hydrogen and incorporates a carbon capture system to effectively reduce the overall carbon emissions of the industrial park. Meanwhile, incentive-based and price-based demand response strategies are implemented to optimize the load curve. A scheduling model is established targeting the minimization of procurement, operation, carbon emission, and wind curtailment costs. The case study of a northern industrial park in China demonstrates that the joint supply of green and gray hydrogen reduces carbon emissions by 40.98% and costs by 17.93% compared to solely using gray hydrogen. The proposed approach successfully coordinates the economic and environmental performance of the integrated energy system. This study provides an effective scheduling strategy for industrial parks to accommodate high shares of renewables while meeting hydrogen needs and carbon reduction targets.

Design and Technoeconomic Analysis of Fischer–Tropsch Fixed‐Bed Synthesis with Cobalt‐Based Catalysts

A multitubular Fischer–Tropsch (FT) reactor with gas recycle and a cobalt‐based catalyst is simulated by a 2D fixed‐bed model that considers the complex kinetics, all relevant mass and heat transfer aspects, and also the influence of the changing molar flow rate by the FT reaction, of pressure drop, and of formation of steam, which decreases the reaction rate of FT synthesis. Optimal values of gas velocity and single‐tube diameter and length are determined by a technoeconomic analysis. The comparison of the data of the FT reactor modeled in this work with data of commercial reactors indicates that the used model reflects reality well and is reliable. A superficial gas velocity of 0.5 m s−1, a tube diameter of 3 cm, and a length in a range of 12–20 m are appropriate for a high production rate of C2+‐hydrocarbons with minimal operating costs of the FT unit, even for a large range of the electricity price from 0.06 to 0.3 € kWh−1.

Optimization of solar-air source heat pump heating system with phase change heat storage

The optimization of heating system has always been of much concern, among which the annual life cycle cost (ALCC) was widely used as an optimization method, while the non-guaranteed hours were often overlooked. This study proposed the elasticity-economy (E-E) objective function as an optimizing indicator, and demonstrated it taking a solar assisted heat pump heating system with phase change material (SC-ASHP-PCM) as an example. Firstly, the single variation impact was analyzed. After optimizing the thermal performance of sub-systems, the proportion of heat supplied by solar collector (SC) and phase change material (PCM) sub-systems increased by 5.4 % and 3.4 %, respectively. Subsequently, the Hooke-Jeeves algorithm was developed to optimize multi-factors, hence minimizing the E-E objective function. The optimal solution was achieved when the non-guaranteed rate was 5 % and the penalty factor was 150 CNY/h. Compared with the baseline situation, the system energy consumption (156.8 × 103 kW‧h) was reduced by 30 %, and the E-E objective function was reduced by 24 %. Furthermore, the minimum operating cost was 18.3 CNY/m2, saving up to 46 % compared to traditional heating systems. The method is generalized to obtain the optimal energy saving potential under different non-guaranteed hours, which provides guidance for the heating systems' design and operation.

Penertiban Juru Parkir Liar di Kecamatan Tampan Kota Pekanbaru

Parking attendants without permits in parking operations are one of the many legal problems faced by motorists in Pekanbaru. When compared with other sub- districts in Pekanbaru City, Tampan Sub-district has the largest population, reaching 25.27% of the city's total population. And there is data showing that there are still a large number of illegal (illegal) parking attendants. So the aim of this research is to find out how to control illegal parking attendants in Tampan District, Pekanbaru City and what are the obstacles to controlling illegal parking attendants in Tampan District, Pekanbaru City. data collection in the form of interviews, observation and documentation. The type of research used in this research is qualitative and the data analysis techniques used are data collection, data reduction, data presentation and drawing conclusions. In the implementation of controlling illegal parking attendants in the city of Pekanbaru, it is still not optimal and there are still many illegal parking spots that occur. Inhibiting factors in controlling illegal parking in Tampan District, Pekanbaru City are caused by minimal operational costs, lack of personnel in controlling parking, lack of public awareness and compliance.

Optimizing smart microgrid performance: Integrating solar generation and static VAR compensator for EV charging impact, emphasizing SCOPE index

The rapid global increase in electric vehicle (EV) usage, driven by its low CO2 emissions, uncomplicated maintenance, and minimal operating costs, has prompted extensive research in the field of electric vehicle charging station (EVCS). The integration of EVCS into the current distribution grid poses challenges due to potential power losses and voltage variations beyond acceptable limits. This complexity is heightened by the growing penetration of randomly dispersed solar-based distributed generation (SDG) and battery energy storage system (BESS). To address these challenges, distribution static VAR compensator (DSVC) systems have been introduced, offering benefits such as enhanced power transfer capacity, improved voltage regulation, and increased system security without requiring extensive infrastructure upgrades. This study offers SCOPE, a novel multi-objective framework that unifies the optimization goals of minimizing real power loss, lowering bus voltage variation, maximizing system voltage stability, minimizing system operating costs, and mitigating CO2 emissions. The EVCS problem is optimized within this multi-objective framework utilizing an improved bald eagle search algorithm (IBESA). The proposed model accounts for vehicle to grid (V2G) capabilities and the actual driving patterns of users over a 24-h horizon. The formulation of a smart microgrid (SMG) structure is based on modifying the standard IEEE 33-bus test radial distribution network (RDN), comprising three interconnected SMGs serving residential, commercial, and industrial users. The optimization approach based on IBESA is utilized to optimize both the siting and capacity of EVCS as well as renewable energy sources (RESs). The findings show that SDG and DSVC are effective at lowering the SCOPE index, highlighting the advantages of the suggested approach.