Synergistic Optimization Model of Energy Storage and BF Gas Power Generation for Steel Enterprises Considering Power Spot Price and Maximum Demand

Traditional research on sintering burdening focuses on adjusting proportion of different ore and other raw materials to optimize certain objective functions that are defined in term of material costs. With regard to environmental concerns, energy consumption becomes another critical issue in sintering burdening. A collaborative optimization model is proposed to integrate the energy performance and physical index into the optimization of sintering burdening in this paper. Firstly, the collaborative optimization problem in sintering burdening is generally described, and then the association models between sintering raw material proportion, production parameters and energy consumption, drum index are established using data-based methods. Meanwhile, the future production parameters are predicted by GM(1,1) method. After that, objective functions and constraints of the collaborative optimization model are formulated based on the association models. After model reduction, two case studies derived from a large-scale iron and steel enterprise are investigated. The results show that the association models can describe the relationship effectively. Meanwhile, Energy performance was optimized with production constraints satisfied, and there is a trade-off between cost and energy consumption.

For large electricity users, such as smelting plants, their electric loads cannot exceed a concerted limit in production. Traditional single-furnace optimization methods aim to satisfy the electric demand of a furnace to improve its production, and hence cannot consider the maximum demand constraint in a smelting plant. Maximum demand (MD) control is often utilized to keep the total electric demand within the limit via shedding the electric loads of some furnaces once the demand approaches the limit. However, the control method will enlarge the fluctuation of electric loads, which does harm to the production and causes a decline in energy-efficiency. In this paper, we propose a multifurnace optimization strategy to improve the production targets of a whole plant instead of a single furnace. In the strategy, an offline multiobjective load scheduling is first performed to assign electric loads for furnaces in each sampling period, taking into account of the MD constraint and production constraints. A multiobjective particle swarm optimization algorithm, combined with population initialization and constraint-handing strategies, is proposed to search for the Pareto optimal set of the scheduling problem, from which decision-makers can select one solution as the load scheduling program. A double closed-loop control mechanism is used to change the scheduled load into detailed load setpoints of furnaces and keep the actual loads up with the load setpoints. In the outer loop, the detailed load setpoints of furnaces are dynamically adjusted based on the deviation of actual loads from the scheduled loads. Thereafter, the desired setpoints are sent to the automatic control mechanism of each furnace, which is in the inner loop and responsible to keep the actual load up with the setpoint via a proportional-integral-derivative (PID) controller. The case study on a typical magnesia-smelting plant shows that the proposed multifurnace optimization strategy can achieve an increase of about 12.29% in the production output, an improvement of about 0.46% of the magnesia in the product, and a slight reduction of 2.35% in electricity cost over the results of MD control. Note to Practitioners - For large electricity users, such as smelting plants, they are subjected to the maximum electric demand constraint. The maximum demand control device is widely adopted to solve the problem, but it will cause a decline in production output and energy-efficiency. This paper was motivated by improving the multiple production targets (i.e, the total production output, the product quality, and the total electricity cost) of a plant via load scheduling and control. In contrast to the maximum demand control, the load scheduling and control approach is a beforehand strategy that can optimize the operation. A case study on a magnesia-smelting plant shows that the proposed approach performs better than the maximum demand control technique.

In order to achieve the goals of “emission peak” and “carbon neutrality”, this paper proposes a collaborative optimization method of renewable energy and energy storage capacity for the construction of carbon-free county distribution networks, considering the complementary characteristics of wind and solar energy. Considering the uncertainty of renewable energy output, a multi-scenario collaborative stochastic optimization model of renewable energy and energy storage planning is established with the zero-carbon vision of county distribution network. Aiming at maximizing the net benefit of the wind-solar-storage configuration in a zero-carbon energy supply county system, the model optimizes the proportion structure of wind and solar energy sources as well as the corresponding energy storage capacity, taking the zero-carbon emission constraints into account. For solving the problem, the collaborative optimization model is converted into a mixed-integer linear programming model. Focusing on a county distribution grid in northern China, the impact of energy storage configuration and cascade utilization on the capacity configuration of various resources and the economic performance of the investment scheme in the zero-carbon wind-solar-storage energy supply system are analyzed respectively in case studies.

Facing the fierce competition in the domestic and abroad, it is a common task for all the steel enterprise to search for their way to survive. Steel industry is highly dependent on resources, and high energy consumption, high emissions. Improvement of energy efficiency is one way to get rid of the predicament and make Chinese steel industry from large to strong. This also meets the international trends of the energy conservation and emission reduction, green low-carbon, circular economy. Matter flow in iron and steel enterprise includes material flow, energy flow and emission flow. Energy flow drives the material flow in steelmaking process and brings out the emission flow at the same time (forming environmental problems). It is clear that energy flow plays an important intermediary role in the material flow. Energy management includes energy production, energy transfer and conversion, rational use of energy, which core indicator is energy efficiency. The energy efficiency of iron and steel enterprise is highly related to material flow structure and enterprise benefit. In this paper, by analyzing the energy efficiency of iron and steel enterprises, the specific direction and path of improving energy efficiency of iron and steel enterprises are put forward, including strengthen the coordination of energy flow material and flow emission flow, and enhanced waste heat energy utilization.

Given the difficulty in determining the parameters of the compressive strength prediction model of self-compacting concrete and the low prediction accuracy, this study focuses on the applicability of the relevance vector machine (RVM) model constructed using various optimization techniques in predicting the strength of self-compacting concrete. The principal component analysis (PCA) is first used to reduce the dimension of the influencing factors. Then, the particle swarm optimization algorithm (PSO) is introduced into the RVM to establish a PCA–PSO–RVM collaborative optimization model, which is compared with the traditional regression model through various statistical indicators and error analysis. The results show that the collaborative optimization model prediction based on PCA–PSO–RVM performs outstandingly in all performance indicators. In the test set, the R 2 of the collaborative optimization model is 0.978, MAE is 0.123, MSE is 0.021, and RMSE is 0.150. The evaluation of quantitative indicators verifies that the collaborative optimization model is feasible and advanced in predicting the strength of self-compacting concrete. This study also provides a reference for the research on durability, rheological properties, and other material predictions of self-compacting concrete.

A collaborative optimization model is proposed to integrate the energy performance to the optimization of sintering burden. Firstly, the collaborative optimization problem in sintering burden is described, and then the association model between sinter raw material proportion, production parameter and energy consumption, drum index is provided using data-based methods. Furthermore, objective functions and constrains of the collaborative optimization model are generally formulated based on the association models. During the phase of model reduction and solving, linearization is conducted to the model, and production parameters are fixed to some typical value to investigate the model performance under these specific conditions. Case study derived from a large-scale iron and steel enterprise shows that the association models can demonstrate the relationship effectively. Meanwhile, Energy performance was optimized with the production performance satisfied.

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises.

High-performance tooth flank collaborative optimization model for spiral bevel and hypoid gears

The collaborative development of conventional buses and urban metro has become an important research topic for the priority development of urban public transport. The topic of collaborative optimization of feeder bus route design and operation is studied in this study. The objective function is to minimize the total travel time of passengers and the operation cost of feeder buses. The improved particle swarm optimization (PSO) algorithm is used to solve the collaborative optimization model, and the effectiveness of the model and algorithm is verified through the case study. The research shows that it is feasible in model construction and algorithm to carry out collaborative optimization of feeder bus route design and operation. Compared with the multiple-to-one (M to 1) mode, the multiple-to-multiple (M to M) mode can better satisfy the needs of passengers from different places of departure and destinations to achieve a more reasonable and realistic goal. The case study is based on two metro stations and 16 feeder bus stops on Fuzhou Metro line 2 to obtain two bus routes and a corresponding operation scheme. Under the same topology road network, the operation time of the improved PSO algorithm is much shorter than the DFS algorithm, the total cost error of the feeder bus is 0.04%, and the departure frequency error is 4.6%, which is within the reasonable error range. Therefore, the collaborative optimization model proposed in this study is feasible and effective in optimizing the feeder bus routes and operation.

Fully harnessing the inherent flexible adjustment potential of steel enterprises and fostering their interaction with the power grid is a crucial pathway to advancing green transformation. However, traditional research usually takes reducing energy consumption as the optimization goal, which limits the adjustment response capability, or ignores the storage and conversion constraints of secondary energy sources such as gas, steam, and electricity, making it difficult to fully explore and reasonably utilize the potential of multi-energy coordination. This study considers the production constraints of the surplus energy recovery and utilization system, establishes a collaborative scheduling model for a gas–steam–power system (GSPS) in an iron and steel enterprise, and proposes a demand response strategy that considers internal production constraints. Considering the time-of-use (TOU) tariff, iron and steel enterprises achieve a dynamic optimization adjustment range of electricity demand response through the conversion and storage process of gas, steam, and power. The adjustment capability of the GSPS reaches 26.94% of the initial electricity load, while reducing the total system energy cost by 2.24%. There is vast development potential of iron and steel enterprises participating in electricity demand response for promoting cost reduction and efficiency improvement, as well as enhancing the power grid flexibility.

A Risk-Averse Hybrid Approach for Optimal Participation of Power-to-Hydrogen Technology-Based Multi-Energy Microgrid in Multi-Energy Markets

In the context of frequent public emergencies, emergency logistics distribution is particularly critical, and because of the unique advantages of unmanned aerial vehicles (UAVs), the model of coordinated delivery of vehicles and UAVs is gradually becoming an essential form of emergency logistics distribution. However, the omission of start-up costs prevents the cost of UAV battery replacement and the sorting, assembly and verification of packages from being factored into the total cost. Furthermore, most existing models focus on route optimization and delivery cost, which cannot fully reflect the customer’s desire for service satisfaction under emergency conditions. It is necessary to convert the unsatisfactory degree of time window into a penalty cost rather than a model constraint. Additionally, there is a lack of analysis on the mutual waiting cost between vehicles and UAVs when one of them is performing delivery tasks. Considering the effects of the time window, customer demand, maximum load capacity, and duration of distribution benefits, we propose a collaborative delivery path optimization model for vehicles and UAVs to minimize the total distribution cost. A genetic algorithm is used to obtain the model solution under the constraints of distribution subloops, distribution order, and take-off and landing nodes. To assess the efficacy of the vehicle and UAV collaborative delivery path optimization model, this paper employs a county-level district in Xi’an city as a pilot area for an emergency delivery. Compared with the vehicle-alone delivery model, the UAV-alone delivery model and vehicle-UAV collaborative delivery model, this model can significantly reduce the utilization of distribution vehicles while also significantly lowering the start-up cost, waiting cost and penalty cost. Thus, the model can effectively improve delivery timeliness and customer satisfaction. The total cost of this model is 39.2% less than that of the vehicle-alone delivery model and 16.5% less than that of the UAV-alone delivery model. Although its delivery cost is slightly higher than the vehicle-UAV collaborative delivery model, the reduction in the start-up cost and penalty cost decrease the overall cost of distribution by 11.8%. This suggests that to cut costs of all sizes and conserve half of the resources used by vehicles, employing the vehicle-UAV collaborative delivery model for emergency distribution is preferable. Moreover, the model integrating the start-up cost, penalty cost, waiting cost, etc., can more effectively express the requirements of timeliness for UAV delivery under emergency conditions.

The power industry’s participation in carbon trading and green certificate trading is an effective market-based approach to solve the negative externalities of power production. In this paper, the Virtual power plant (VPP) is taken as the aggregator to coordinate and optimize the carbon trading and green certificate trading between the power purchasing end and the power selling end, so as to achieve the goal of maximizing the comprehensive benefits of the VPP. Firstly, the operation model of VPP aggregating various types of distributed energy and different users participating in green certificate market and carbon trading market is analyzed; Secondly, a two-level collaborative optimization model of VPP participating in power purchase and sale transaction and green certificate transaction is constructed. On the one hand, the cost of power purchase and green certificate acquisition is minimized by combining various types of power generation resources at the power purchase end. On the other hand, the power purchased is distributed among various types of users at the power sale end, so as to maximize the power sale income and green certificate sales income. On this basis, the VPP as a whole participates in the electric energy market, carbon trading market and green certificate trading market to maximize the comprehensive income. Finally, a VPP is taken as an example to verify the economy and effectiveness of the proposed model in this paper.

Under the general policy of the national energy-saving emission reduction and sustainable development, the domestic iron and steel enterprises surge in the by-product gas power generation project. There exist many dangerous and harmful factors in by-product steel gas power generation process and which easily caused casualties and the pollution of the environment. The study on risk analysis and evaluation are still relatively dearth about the by-product gas generating process of domestic steel enterprises. The boiler system on a Combined Cycle Power Plant was analyzed and evaluation by ICI/MOND fire and explosion toxicity index method and the method of fault tree analysis, which combined with the actual situation of steel plant Combined Cycle Power Plant. The results show that the combustion system is more dangerous, and the hazard index levels are reduced to lower level after safety compensatory measures except the unit toxicity index higher. The shielding device or gas alarm failure was the main cause of gas poisoning. According to the analysis some feasible measures was put forward. The study has positive guiding significance for risk management and safety administration decision of the Combined Cycle Power Plant.

Enterprise benefit game model of collaborative supply chain in logistics industry park