Optimal Operation Strategy Research Articles

An Optimal Operation Strategy of Regenerative Electric Heating Considering the Difference in User Thermal Comfort

Regenerative electric heating has gradually become one of the main forms of winter heating with the promotion of “coal to electricity” project. By fully exploiting its regulating capacity, it can effectively achieve a win–win situation of “peak shaving and valley filling” on the grid side and “demand response” on the customer side. In order to meet the different heating demands of users, a regenerative electric heating optimization and control strategy is proposed, taking into account the difference in users’ thermal comfort. Firstly, the reasons for the difference in user thermal comfort are analyzed, and the differentiated preference factors are calculated based on the maximum likelihood estimation method to design differentiated heating schemes. Then, a dynamic optimization and control model for regenerative electric heating with comfort and economic evaluation indicators is established and solved by using quantum genetic algorithm. Finally, a numerical example is used for simulation analysis. The research results show that the strategy proposed in this paper can take into account the comfort of customers and the economy of peaking and low load shifting, so that the operation of regenerative electric heating can respond to the different needs of different customer groups, and realize flexible adjustment at any time of the day.

Multi-objective optimization of a hybrid energy system integrated with solar-wind-PEMFC and energy storage

The move towards achieving carbon neutrality has sparked interest in combining multiple energy sources to promote renewable penetration. This paper presents a proposition for a hybrid energy system that integrates solar, wind, electrolyzer, hydrogen storage, Proton Exchange Membrane Fuel Cell (PEMFC) and thermal storage to meet the electrical and heating demands of a student dormitory in Shanghai. The proposed system is optimized to simultaneously account for multiple objectives, including economy, environmental benefits, and grid interaction, measured by Equivalent Annual Cost (EAC) for the life cycle of 20 years, Primary Energy Saving Ratio (PESR) of the heating system and Grid Interaction Level (GIL) of the electrical system. The effectiveness of the optimization results from NSGA-II is verified and compared with MOPSO to determine the optimal installation configuration and operation strategies. The results highlight the significance of energy storage in enabling greater renewable integration and the potential of hydrogen to play a vital role in the transition to a low-carbon economy. The optimal design of the proposed hybrid system can meet the power and heat demand of a student dormitory with a floor area of 2679m2. The Pareto-optimal solutions of PESR and GIL for NSGA-II fall within the range of (89 %, 104 %) and (70 %, 88 %), respectively. A significant number of Pareto-optimal solutions cluster around an EAC of approximately 160 k RMB. The optimization by MOPSO exhibited the similar results. Additionally, the sensitivity analysis provides insights into the sensitivity of objectives to changes in optimal design parameters, facilitating the design and optimization of similar hybrid energy systems integrated with a closed loop for hydrogen production and utilization in the future.

Integrated effects of meteorological factors, edaphic moisture, evapotranspiration, and leaf area index on the net primary productivity of Winter wheat − Summer maize rotation system

Net primary productivity (NPP) assumes a pivotal role in the realm of plant growth, organic matter production, and carbon cycling. Nevertheless, the mechanism underlying the integrated effects of meteorological factors, soil water content (SWC), evapotranspiration (ET), and leaf area index (LAI) on the NPP in agricultural systems remains unclear. Elucidating the evolution pattern of NPP in agricultural system and its responding mechanism to meteorological factors, SWC, ET, and LAI hold pronounced significant for sustainable agricultural operation, agroecological maintenance, and climate-adaptive strategy optimization. This study focused on the winter wheat - summer maize rotation, a predominant agricultural system, in the North China Plain (NCP). Spatiotemporal pattern of NPP was clarified using geostatistical technique, Mann-Kendall test, Sen's Slope, and Rescaled Range analysis. The mechanism underlying the integrated response of NPP to different meteorological factors, SWC, ET, and LAI was revealed through spatiotemporal coupling correlation analysis, Random Forest, and Structural Equation Modeling. The results demonstrated a widespread annual increase in NPP across the majority of rotation areas from 2000 to 2019, with a prominent geospatial heterogeneity. Meteorological factors, SWC, ET, and LAI collectively explained 70.27% of NPP variability. Of which, ET, FAPAR, P, LAI, Tmax, Tmin, and the SWC in 40–100 cm were predominant factors affecting NPP, explaining 63.59% of its variability. These factors exerted multidimensional effects on NPP, instead of a unilateral induction. Despite the limited direct effect, precipitation indirectly improved NPP by significantly increasing the SWC in 40–100 cm soil horizon. The latter not only directly enhanced NPP, but also indirectly promoted NPP by facilitating ET. In context of drought and water deficit, maintaining adequate soil moisture in the 40–100 cm layer through irrigation emerges as a potential strategy to ensure sustainable productivity. ET exerted the greatest comprehensive effect and the largest direct promotion on NPP. LAI, FAPAR, and SWC induced NPP variation indirectly through affecting ET. These consistently underscored the major relationship between productivity and water use. Future intensive agriculture in the NCP should prioritize the trade-off between carbon sequestration and water loss at plant physiological level. Improving the carbon sequestration per unit of water consumption through optimizing the photosynthesis-transpiration mechanism is an imperative climate-adaptative pathway for achieving sustainable food provision and mitigating water crisis.

Planning of Optimal Operation Scheme for Ventilated Tunnel Fans Based on Cable Conductor Temperature

With the increasing construction of underground power cable tunnels for energy transmission, the fans in tunnels, used for ventilation, consume great deal of electric energy. Due to the lack of scientific planning, a large number of energy is unnecessarily consumed. This paper proposes an optimal planning method for fan operation based on cable conductor temperature. The improved flow field parameters are used to calculate the cable temperature, which is validated by finite element method (FEM). The conductor temperature is then taken as the constraint condition for fan operation planning. Based on the load forecasting, a variety of fan operation schemes are obtained. The improved particle swarm optimization (PSO) algorithm is implemented to obtain the optimal operation scheme such that the cable conductor temperature does not exceed the allowable value and the fan power consumption is minimum. The energy consumption of the fan after planning is only 18.1% of the original one in the best case for the studied scenario. To solve the load forecasting error problem, this paper adopts model predictive control (MPC) to replan the optimal operation scheme of the fan dynamically. The results show that the optimal planning scheme can always be guaranteed.

Optimal Operation Strategy of Combined Heat and Power System Considering Demand Response and Household Thermal Inertia

With the development of energy Internet and renewable energy, the combined heat and power micro-energy grid (CHPMEG) has become an important carrier for the use of clean energy. In order to improve the living comfort of households and the utilization efficiency of wind power in winter, this paper proposes a coordinated optimal operation strategy of CHPMEG considering the load demand side response and the thermal inertia of the user side. First, the load demand side response is introduced into the optimal operation of CHPMEG, and a load demand response model is proposed based on reward mechanism. Then, the household thermal inertia is taken into account in the system operation, and a heat load model considering thermal inertia is established to describe the corresponding characteristics more exactly. On this basis, an optimization model is further developed and solved to minimize the operation cost of the system. Finally, the case simulations demonstrate the effectiveness of the optimal operation strategy of CHPMEG proposed in this paper.

Research on the bi-layer low carbon optimization strategy of integrated energy system based on Stackelberg master slave game

With increasing reforms related to integrated energy systems (IESs), each energy subsystem, as a participant based on bounded rationality, significantly influences the optimal scheduling of the entire IES through mutual learning and imitation. A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives. This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation. The studied IES includes cogeneration, power-to-gas, and carbon capture systems. Based on the Stackelberg master-slave game theory, sellers are used as leaders in the upper layer to set the prices of electricity and heat, while energy producers, energy storage providers, and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system. An IES bilayer optimization model based on the Stackelberg master-slave game was developed. Finally, the Karush-Kuhn-Tucker (KKT) condition and linear relaxation technology are used to convert the bilayer game model to a single layer. CPLEX, which is a mathematical program solver, is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system. As an experimental demonstration, we simulated an IES coupled with an IEEE 39-node electrical grid system, a six-node heat network system, and a six-node gas network system. The simulation results confirm the effectiveness and feasibility of the proposed model.

Energy Saving Optimal Operation Strategy of Freight Trains under Complex Scenarios

Background: For the optimization of energy-saving driving of freight trains in complex operating environments, the use of reasonable train maneuvering methods can largely reduce the energy consumption of train traction. Recent patents on energy-efficient maneuvering strategies for complex scenarios of freight trains have been researched. Objective: Using the receding horizon algorithm and the improved NSGA-II algorithm to solve the target speed curve of freight trains to cope with the complex and changing operating environment, and to explore the recent patents of energy-saving maneuvering strategies for freight trains and methods. Methods: The recent patents of energy-efficient maneuvering strategies for freight trains in complex scenarios are investigated in this research. A multi-objective optimization model for freight train maneuvering with electrical phasing was developed with the objectives of reducing the traction energy consumption and running time of the train. A method for determining the optimal operating conditions of freight trains under complex line conditions is proposed. The offline optimization of the target speed curve under the electrical phasing constraints of freight trains and the online adjustment under the temporary speed restriction (TSR) are achieved by using the RH-INSGA-II (receding horizon-improved NSGA- II) algorithm. Results: Combined with an actual freight railroad line data as an example, simulation experiments were conducted and verified with HXD1 electric locomotive hauling 50 C80 wagons. Conclusion: The speed curve considering the split-phase constraint can effectively reduce the traction energy consumption. The electrical split-phase constraint affects the whole speed optimization process, not only the speed curve at the split-phase zone. Although the traction energy consumption is increased with the addition of the TSR on the line, the RH-INSGA-II algorithm dynamically changes the sequence of optimal train maneuvering conditions according to the planned train running time in order to avoid further amplification of the late train time.

Optimal Scheme of Remote Backup Automatic Switch Applied to Renewable Energy Grids

The support of renewable energy to the bus voltage during the grid fault period will cause the failure action of the backup automatic switch (BPAS). Moreover, the improper coordination of renewable energy and source supply lines in the process of BPAS operation will lead to the asynchronous close of a circuit breaker, which seriously threatens the stable operation of the power system. To solve the above problems, an optimal operation strategy applied to the remote BPAS is proposed in this paper. Firstly, the operation logic and characteristics of internal BPAS and remote BPAS are analyzed. Secondly, the mechanism of failure action and asynchronous close of a circuit breaker in regional power grids with renewable energy sources are analyzed. Then, to improve the speed and reliability of the BPAS, a strategy of remote BPAS operation suitable is proposed. To avoid the problem of failure action, by monitoring the low-frequency condition of the regional power grid, the strategy accelerates the bus voltage loss to shorten the operation time of the BPAS. Moreover, the operation conditions of BPAS are optimized to avoid the asynchronous close of the circuit breaker between renewable energy and source supply lines. Finally, the experimental results in the 110 kV transformer substation verify that the improved remote BPAS strategy can effectively improve the reliability and quickness of the grid connecting to renewable energy.

Blockchain-Enabled Supply Chain Internal and External Finance Model

This study applies Stackelberg game theory to analyze and compare optimal operational strategies in four supply chain finance scenarios: traditional trade financing (TI), trade financing through the blockchain platform (BI), traditional external financing (TE), and external financing through the blockchain platform (BE). The main findings are as follows: First, the adoption of the blockchain platform reduces the interest rate threshold, making external financing more advantageous for retailers with higher capital constraint. Further, financing through the blockchain platform leads to higher wholesale prices, retail prices, and order quantities compared to traditional financing scenarios. Second, internal trade financing and the use of blockchain technology are preferred over external bank financing. However, conducting external bank financing through the blockchain platform yields greater profit growth for manufacturers and retailers. Accessing the blockchain platform is the optimal strategy for retailers and banks, leading to a favorable “multi-win” situation when the manufacturer’s platform fees are reasonable. Third, the manufacturer’s risk guarantee ratio plays a crucial role in determining the choice of financing mode, particularly when the retailer faces the risk of debt default. This study contributes to the literature by quantifying the impacts of blockchain technology deployment for three aspects that have been overlooked in previous studies: the set-up cost and access fee of the blockchain platform, the service level provided by the platform, and the demand increase resulting from blockchain technology adoption.

Optimal Tunnel Dryer Operating Strategies for the Production of Ceramic Roof Tiles under Tight Quality Considerations

Optimal Tunnel Dryer Operating Strategies for the Production of Ceramic Roof Tiles under Tight Quality Considerations

Solar-Assisted Monetization of Municipal Solid Waste

Municipal solid waste (MSW) is a significant resource, especially for biomass-based monetization. In addition to its economic potential, it can also provide an effective pathway for decarbonization in the energy and chemical sectors. In this work, MSW monetization is coupled with the solar-assisted generation of “green” hydrogen and oxygen via electrolysis. The dual utilization of electrolysis-produced hydrogen and oxygen offers several advantages, including the reduction in the carbon footprint, the tunability of the characteristics of synthesis gas (syngas) to conform to the values needed for the manufacture of various chemicals, and the reduction in the overall cost compared to systems focusing on hydrogen generation only. A superstructure is developed to represent the configurations of interest and serve as a basis for formulating an optimization program that can be solved to identify the optimal design and operating strategies. A multi-period optimization formulation is developed to identify the maximum profit subject to the various modeling equations and constraints. The environmental considerations are addressed using the ɛ-constraint method by iteratively varying carbon footprint cuts. A case study is solved for the City of Jeddah, and the results assess the tradeoffs of various design and operating strategies, their impact on profitability, and their environmental impact.

Component model calibration using typical AHU data for improved prediction of daily heat source energy consumption

Model predictive control (MPC) has the potential to reduce energy consumption during building operations by determining an optimal operating strategy in advance. However, the data acquired from operational buildings are insufficient to fit the models, leading to difficulties in calibrating the entire target system. Therefore, this study evaluates the impact of local calibration on the performance of heat source energy consumption prediction models using only AHU data typically collected in an operational building. This load-side calibrated model showed an accuracy of within 30% of the CVRMSE. Daily heat source consumption was predicted using the proposed model, which demonstrated superior explanatory power (R2 ≈ 0.95) compared to the initial uncalibrated model. Therefore, such a locally calibrated model developed with insufficient data has the potential to be used for MPC without installing additional sensors.

Performance Evaluation Method of Day-Ahead Load Prediction Models in a District Heating and Cooling System: A Case Study

Many researchers are devoted to improving the prediction accuracy of daily load profiles, so as to optimize day-ahead operation strategies to achieve the most efficient operation of district heating and cooling (DHC) systems; however, studies on load prediction and operation strategy optimization are generally isolated, which leaves the following question: what day-head load prediction performance should be paid attention to in the operation optimization of DHC systems? In order to explain this issue, and taking an actual DHC system as a case study, this paper proposes an evaluation method for the prediction of daily cooling load profiles by considering the impact of inaccurate prediction on the operation of a DHC system. The evaluation results show the following: (1) When prediction models for daily load profiles are developed, the prediction accuracy of the daily mean load should be emphasized, and there is no need to painstakingly increase the accuracy of load profile shapes. (2) CV and RMSE are the most suitable deviation measures (compared to others, e.g., MAPE, MAE, etc.) for the evaluation of load prediction models. A prediction model with 27.8% deviation (CV) only causes a 3.74% deviation in operation costs; thus, the prediction performance is enough to meet the engineering requirements for the DHC system in this paper.

Operating performance characteristics and analysis of a high temperature fuel cell system integrated with a methanol steam reformer

A high temperature proton exchange membrane fuel cell system integrated with a methanol steam reformer is developed in this study. A system model considering thermal-coupled balance-of-plant components and several temperature constraints is established. The hydrogen production performance of the reformer is evaluated and experimentally validated. A high methanol conversion rate above 90.1% with a low CO content below 1.5% are obtained with the reformer temperature of 573 K at a steam to carbon ratio of 1.3. Besides, CO poisoning effect on the stack is inhibited with an increase in the operating temperature. The fuel cell voltage increases 35% with variation of the operating temperature from 160 °C to 180 °C at the current density of 0.4 A/cm2 and CO molar ratio of 1%. Furthermore, model-based analysis is conducted to investigate impacts of various input parameters on system operating performance. The proposed system presents a maximum electrical efficiency of 37.8%, and increasing the current density and decreasing air excess ratio are favorable for improving the net power output, but also increasing the heat duties of components and resulting in a reduced safety operating region. Finally, an optimal operating strategy with maximum system efficiency is proposed for variable output powers ranging from 15 to 55 W.

Optimization of integrated energy systems considering seasonal thermal energy storage

Integrated energy system are an effective way to achieve sustainable energy development and build a low-carbon society. However, the combination of different units needs further investigation to determine the optimum configuration and operating strategy. To address the problem of large differences in user loads and renewable energy sources between seasons, a regionally integrated energy system, including the seasonal thermal energy storage system, is established. The seasonal thermal energy storage system is coupled with heat pumps and solar collectors. We optimize the planning and scheduling of each device in the integrated energy system on a planning horizon of one year. The results show that the integrated energy system with a ground source heat pump and seasonal thermal energy storage device can effectively reduce the cost of the operation planning by 9.1 %. The cost of purchased energy and carbon emissions have also been reduced by 23.4 % and 12.6 %, respectively. In addition, the study also found that the available area of solar radiation in a region influences the optimal configuration of the system.

A multi-timescale optimal operation strategy for an integrated energy system considering integrated demand response and equipment response time

The response potential of demand-side resources is becoming increasingly significant in integrated energy system (IES) operations. In addition, to ensure the effective participation of system devices, their actual responsiveness at different timescales should be considered. Based on these considerations, this paper proposes an IES multi-timescale operation optimization strategy that incorporates multiple forms of integrated demand response (IDR) and considers the response characteristics of the equipment. First, the multi-timescale characteristics of IDR are analyzed. Moreover, a multi-timescale operation model of IES that comprises day-ahead, intraday, and real-time stages is further established. In the day-ahead dispatch, a low-carbon economic scheduling model is developed by considering the shifting demand response (DR) and the cost of carbon emissions. In the intraday scheduling, noting that cooling and heat energy transmission possess slow dynamic characteristics, a rolling optimization model for cooling/heating coupled equipment considering load shedding and substituting DR is established. In real-time scheduling, the output of electric/gas coupled equipment is adjusted. Finally, an industrial park-type IES in northern China was selected as an example for a case study. The results show that (1) the IDR multi-timescale response strategy can exploit different types of demand-side flexibility resources. After implementing the shifting DR, the peak-to-valley difference of the electric load curve was reduced by 20%, and the total system cost was reduced by 2.3%. After implementing load shedding, the maximum load differences per unit period of the electric, heat, and cooling load curves decreased by 18.7%, 40.0%, and 68.9%, respectively. (2) By refining the timescale of IES optimization, the proposed model can effectively ensure the energy supply and demand balance of the system under different load scenarios and reduce the system operation cost. After applying the model to simulation in three typical days (transition season, summer, and winter), the penalty costs of lost loads reduce by ¥3650, ¥3807, and ¥3599, respectively, and the total system costs decrease by 17.4%, 16.1%, and 16.2%, respectively.

A day-ahead optimal operation strategy for integrated energy systems in multi-public buildings based on cooperative game

A day-ahead optimal operation strategy for integrated energy systems in multi-public buildings based on cooperative game

Low-carbon optimal operation strategy of multi-park integrated energy system considering multi-energy sharing trading mechanism and asymmetric Nash bargaining

In the current energy transactions of multi-park integrated energy system (M-PIES), aiming at the problems that the interaction between the supply side and the demand side is not fully considered, the sharing potential of multiple energy sources is not fully tapped, the benefit distribution mechanism considering the contribution of the load aggregator (LA) is lacking, and with the all-round development of the carbon trading market (CTM), the existing ladder-type carbon trading (LCT) mechanism has insufficient capacity to limit CO2 discharges of M-PIES, etc., this paper puts forward a low-carbon optimal operation strategy of M-PIES considering the multi-energy sharing trading mechanism (MSTM) and asymmetric Nash bargaining (ANB). Firstly, the interaction between supply and demand (IS&D) is fully considered, and the benefit maximization model of PIES operator-LA multi-agent alliance considering MSTM and improved ladder-type carbon trading mechanism (ILCTM) is established by using Nash negotiation theory (NNT). Then, according to the different bargaining capabilities of each agent, a profit distribution model based on ANB is proposed, and it is solved by alternating direction method of multipliers (ADMM). Finally, through the comparison of different cases, it is proved that the proposed method can further reduce CO2 discharges of M-PIES, improve the benefits of each agent and alliance, and realize the fair and reasonable distribution of energy sharing (ES) benefits.

Optimal energy storage system control using a Markovian degradation model—Reinforcement learning approach

The degradation property of an energy storage system (ESS) has a decisive impact on the economic benefits of ESS operation. However, existing degradation models either do not fully reflect the effects of battery usage or require a full operation history of the battery. This study proposes an accurate Markovian model for battery degradation that reflects battery usage and requires only the current state. We demonstrate the use of the model by establishing a Markov decision process based on the proposed Markovian degradation model and solving it with deep reinforcement learning algorithms. Our intelligent agent, which aims to minimize the sum of electricity cost and degradation cost, generates cost savings of 5%–29% compared to baseline strategies. This framework offers an optimal ESS operation strategy considering battery degradation in a tractable and practical way.

Performance analysis and multi-objective optimization of solar assisted ground-source heat pump heating system

ABSTRACT Heating through a solar assisted ground-source heat pump system is an efficient method, and optimizing its parameters can significantly improve its energy efficiency. However, the research on the impact of the start-stop temperature difference of the heat collection cycle on the system is currently limited. To address this issue, this study aims to develop a SAGSHP system model located in Tianjin using TRNSYS software, investigate the effect of parameters such as the start-stop temperature difference on the system performance, and guide the optimization process. The optimization parameters include the collector’s area, the water tank’s volume, and temperature difference during the heat collection cycle. The NSGA-II algorithm is applied to perform multi-objective optimization to enhance the system’s energy efficiency and economy. Furthermore, a sensitivity analysis is conducted to study each variable’s impact on the optimization results. The variation in temperature between the start and stop of the collection cycle has a significant impact on the system’s performance, as revealed by the study. The optimal operation scheme lies within the boundary range of parameter settings. The optimization leads to a decrease of 8.63% in the system’s annual operating cost and a reduction of 1555 kWh in the annual power consumption. The relationship between the area of the collector and the temperature difference during heat collection startup is expressed as 0.034°C/m2, and the ratio to the tank volume is 1.33°C/m3. The degree of influence of optimization parameters on optimization results is ranked as V i>A i>T s>T e.