Scheduling Model Research Articles

Research on day-ahead and intraday scheduling strategy of distribution network based on dynamic partitioning

With the massive participation of new energy in the operation of distribution network (DN), the uncertainties arising from both load and renewable generation have brought challenges to its secure and reliable operation. In this paper, a model of day-ahead and intraday optimal scheduling based on dynamic partitioning (DP) of DN is constructed to realize the refinement of operation in both temporal and spatial dimensions, as well as to improve the reliability of system operation. For the strong fluctuation characteristics of the source-load in DN, the DP is achieved under the premise that the partition structure in each period is tight and the internal power supply has the ability to balance the power fluctuations. In order to improve the regional autonomy of DN, a day-ahead multi-objective optimal scheduling model is constructed based on the DP with the optimization objectives of weak inter-partition power coupling, lowest deficiency rate of flexibility, and lowest operating cost. And a scheme for articulation is proposed to ensure the effective guidance of day-ahead for intraday. Then the intraday dispatch model is constructed by shifting the focus to the incorporate characteristics of DP and quickly smooth the net load fluctuations. Finally, the performance and reliability of the proposed models are verified via case studies.

Wireless sensor network security management based on virtual resource scheduling model

ABSTRACT For lofting the performance of wireless sensor networks, a lightweight gradient elevator algorithm model is improved using a virtual resource scheduling model and sparrow search algorithm particle swarm optimization algorithm. A new network intrusion detection method (SSAPSO-LightGBM) has been proposed to improve detection accuracy and efficiency. Then, the data security transmission level and user historical priority resource scheduling model (SDT-HRU-VRSM) are used to schedule resources to ensure network resource revenue. The results show that the SSAPSO LightGBM algorithm has a detection accuracy of 99.61% for Normal, 98.42% for R2L, 97.03% for U2R, 96.01% for Probe, and 98.41% for DoS. The SDT-HRU-VRSM model not only meets the needs of secure data transmission, but also takes into account the stickiness of increasing high-quality users. And according to security requirements, the number of virtual resource scheduling is increased, reducing the number of service network slicing instances and improving security. This model can effectively increase the attack cost of malicious users and increase network revenue, which is crucial for ensuring the security and efficient operation of wireless sensor networks.

An improved channel scheduling model for wireless access point in high density traffic using Cognitive Radio.An improved channel scheduling model for wireless access point in high density traffic using Cognitive Radio

An improved channel scheduling model for wireless access point in high density traffic using Cognitive Radio.An improved channel scheduling model for wireless access point in high density traffic using Cognitive Radio

Improved scheduling model for a multistage biopharmaceutical process in a multiproduct facility

This study critically evaluates deterministic and stochastic/evolutionary strategies for long-term scheduling of multiproduct facilities in batch and continuous biopharmaceutical manufacturing, as reported in existing literature using discrete-time and continuous-time models. It identifies key limitations in literature model/algorithm, including (i) real-time storage violation, (ii) real-time violation of shelf-life due to its calculation over a single event, (iii) minimum campaign length calculated over single events leading to suboptimal use of resources, and (iv) early product deliveries. To overcome these limitations, a deterministic mathematical model is proposed extending the existing unit-specific event-based model(s) with additional features including (i) modified material balances, (ii) new minimum campaign length constraints captured over multiple events, (iii) new shelf-life constraints captured over multiple events, (iv) new setup constraints based on product sequencing, (v) updated tightening constraints, (vi) updated sales and penalty constraints for late deliveries, (vii) updated bounds, and (viii) updated objective function. These improved features lead to better modeling in terms of handling intermediate and final product storage without real-time violations of storage and shelf-life, handling of initial setup in production tasks, and timely delivery of final products. The proposed improved model yields up to 36 % increase in profit when applied on four literature examples.

Two-stage optimization model for day-ahead scheduling of electricity- heat microgrids with solid electric thermal storage considering heat flexibility

Traditional electricity-heat microgrid (EHM) is limited in flexibility due to real-time balancing between the supply and demand of electric and heat system. The heat flexibility (HF) can be released by thermal inertia and heat storage characteristics of district heating systems (DHS) and heat storage units, and it can be used to increase the electric power flexibility (EF) of EHM. In this paper, the quantitative relationship between HF and EF is investigated, and a two-stage optimization model for day-ahead scheduling of EHM with solid electric thermal storage (SETS) is established. Firstly, the electric-heat flexibility conversion coefficient (EHFC) is developed to decide the allocation of HF among different electricity-heat coupling units, such as CHP and SETS, and EF supply model of EHM based on HF is built. Secondly, a two-stage optimization model is proposed for day-ahead scheduling of EHM to improve EF and economy of EHM by optimizing the heating power and HF's allocation. Thirdly, the equivalent energy storage of the DHS is quantitatively analyzed, providing a reference for planning and operating of EHM's flexibility resource. Finally, the case study shows that by considering HF and deploying SETS, the total cost can be reduced by 18.8 % and 22.6 %, respectively.

Balancing Possibilist-probabilistic risk assessment for smart energy hubs: Enabling secure peer-to-peer energy sharing with CCUS technology and cyber-security

This paper introduces the Possibilistic-Probabilistic Risk-based Smart Energy Hub (PPR-SEH) scheduling model, addressing cybersecurity challenges in the transition to advanced energy systems characterized by decarbonization, decentralization, and digitalization. The model integrates cutting-edge technologies like Carbon Capture Utilization and Storage (CCUS) and demand response programs. It addresses uncertainties from renewable energy sources, demand variability, fuel supply, and energy price fluctuations using a Z-number-based approach. Covering various energy types—electricity, heat, cooling, gas, and water—the PPR-SEH model offers a comprehensive energy management solution. Employing a mixed-integer linear programming (MILP) framework optimized with the CPLEX solver in GAMS software, it uses an epsilon constraint method and a fuzzy satisfying approach for solution selection. The model also evaluates the economic and environmental impacts of peer-to-peer energy-sharing markets linked with carbon emission trading, enhancing the efficiency and sustainability of energy distribution. The findings reveal that incorporating robust cybersecurity measures and integrating demand response and CCUS significantly influence operational efficiency and sustainability, evidenced by an increase in costs and pollution by approximately 11.43 % and 1.8 %, respectively, compared to deterministic approaches. This study underscores the importance of robust planning and the beneficial impact of a carbon system on load curve management and economic returns in smart energy systems.

Privacy-preserving multi-level co-regulation of VPPs via hierarchical safe deep reinforcement learning

Large amounts of distributed energy sources have brought challenges in terms of the safe and stable operation of the power grid. As a key technology between user-side energy resources and the distribution network (DN), how to realize the online coordinated scheduling of VPP and DN as well as the real-time response strategy of distributed equipment (DE) within VPP is the focus of this study. Thus, the hierarchical deep reinforcement learning (DRL) Hierarchical-TD3 algorithm is designed based on the unified modeling of adjustable space to achieve the real-time economic scheduling of VPPs. The upper layer DN considers the network security constraints and solves the economic scheduling model of VPPs based on the single-agent TD3 algorithm. Based on the scheduling instructions from the upper layer, the lower layer VPPs consider the requirements of privacy protection and control autonomy and realize real-time response of the DE within VPP via multi-agent MATD3 algorithm. Numerical results in the modified 33-nodes system show that the proposed Hierarchical-TD3 algorithm can achieve privacy protection and the coordinated scheduling of VPP and DE to reduce the operating cost. It differs from the optimal value by only 1.46% but can achieve online decision-making on the millisecond scale. Compared with the traditional centralized and decentralized DRL algorithms, the total cost is reduced by 10.15% and 5.52% respectively. Compared with the traditional soft-constraint method, there is no constraint violation during the training and testing phases. Finally, the actual 116-nodes testing system validates the scalability of the proposed Hierarchical-TD3 algorithm.

Hierarchical distributed optimal scheduling decision-making method for district integrated energy system based on analysis target cascade

The district integrated energy system has the characteristics of multi-layer and multi-subjects. Aiming at the problems of data privacy, conflict of interest, and mismatch of interaction power among these multi-subjects, a hierarchical distributed optimal scheduling decision-making method for district integrated energy system based on analysis target cascade is proposed. Firstly, the operational communication architecture of the district integrated energy system based on the cloud-pipe-edge-end is constructed to solve the problems of data transmission, data computation, and data privacy. Secondly, a two-layer distributed scheduling model of district integrated energy system considering waste heat recovery is established based on a laddered carbon trading mechanism. At the upper level, an optimal scheduling model targeting low-carbon and economic performance is developed by considering the participation of district integrated energy system in demand response and the purchase and sale of electricity. At the lower level, an optimal scheduling model considering waste heat recovery for subsystem energy-coupled equipment is developed. Then, the analytical target cascade is used to realize the decoupling of the two-layer model, and the efficient solution is achieved in the process of multiple iterations. Finally, by analyzing the arithmetic examples, it is verified that the proposed method can improve the economy of system operation by 4.38% and the absorption capacity of wind power by 9.89%, while protecting the privacy and interest claims of multiple parties.

Medium-term interval optimal scheduling of terraced hydropower plants considering carbon trading scenarios

Given the escalating issue of greenhouse gas emissions, the Certified Emission Reduction (CER) mechanism has been further developed, which provides additional revenues and improves the efficiency of renewable energy projects including terraced hydropower plants. However, limited literature integrates the role of the CER scheme into the optimal scheduling of the terraced hydropower plants. To establish a low-carbon economical operation strategy for terraced hydropower plants considering CER, we first specify the CER calculation method for hydropower plants using emissions baseline analysis. Then, a CER-embedded optimal scheduling model for terraced hydropower plants is established. Therefore, the benefits of CER are combined to the consideration of the overall benefits of hydropower plants, which guides the hydropower plant generation operation strategy. Ultimately, we focus on five typical operating scenarios for terraced hydropower where generation benefits, different CER prices, power subsidies for the summer peak load period, and extreme weather factors with dry water are respectively considered. Further, a comparative analysis of individual factor impacts is conducted in a pairwise manner. Results demonstrate that the proposed model can improve the comprehensive efficiency of the terraced hydropower plants by 18.45 % while the water discarded can be reduced by about 0.413 million m³.

Collaborative optimization of intersection signals and speed guidance for buses run on overlapping route segments under connected environment

AbstractIn order to reduce bus bunching in overlapping route segments and improve the efficiency of bus operation, a dynamic scheduling model is proposed to adjust bus operation states by adopting a cooperative strategy involving multi‐line bus timetable optimization, arterial signal control, and speed guidance. Based on mixed integer linear programming, an arterial signal coordination model with autonomous public transport vehicles (APTVs) dedicated lanes is developed, which enables APTVs to pass through intersections without stopping under conditions that almost have no effect on regular vehicles (RVs). Based on this, a speed guidance strategy of APTVs under connected environment is proposed. After guiding APTVs into the overlapping route segments at a reasonable interval, the optimization goal of maintaining the independent running headway of each bus line to the maximum extent is realized. The simulation verification based on three actual overlapping lines in Hangzhou shows that only the combination of signal coordination considering the characteristics of APTVs and speed guidance can realize the full benefits of bus operation based on dedicated APTVs lane.

Optimized the Microgrid Scheduling with Ice-Storage Air-Conditioning for New Energy Consumption

In the face of the stochastic, fluctuating, and intermittent nature of the new energy output, which brings significant challenges to the safe and stable operation of the power system, it is proposed to use the ice-storage air-conditioning to participate in the microgrid optimal scheduling to improve wind and light dissipation. This paper constructs an optimal scheduling model for the ice-storage air-conditioning to participate in the microgrid, analyzes the regulation advantages of the ice-storage air-conditioning’s cold storage and cold release process and its participation in the scheduling process; secondly, based on the scenario method, the scenario modeling of the microgrid distributed wind and light output uncertainty and cold load uncertainty is carried out; lastly, an optimal scheduling model is constructed to minimize the operation and maintenance cost of each unit and the storage cost of the ice-storage air-conditioning, the highest rate of clean energy generation and the lowest cost of electricity consumption of the air-conditioning users as the objective function. The example simulations show that the proposed optimal scheduling model can promote the new energy consumption rate of the microgrid, proving that the ice-storage air-conditioning is more economical compared with ordinary air-conditioning and that the operating cost within the optimized microgrid is reduced by about 10.5%. The cost of the air-conditioning users’ electricity consumption has been reduced by about 11.7% after responding to the regulation.

Multi-strategy reference vector guided evolutionary algorithm and its application in multi-objective optimal scheduling of microgrid systems containing electric vehicles

To solve the optimization scheduling problem of microgrids involving Electric Vehicles (EVs), in this paper, the integration of EVs into the microgrid is considered in the microgrid system composed of wind turbine, photovoltaic turbine, micro gas turbine, fuel cell, and storage battery, and the economic dispatch model of the microgrid system containing EVs is established, with the minimization of the operation cost and pollutant treatment cost as the optimization objective. Meanwhile, Chebyshev mapping is introduced into the reference vector guided evolutionary algorithm (RVEA) to initialize the distribution of the population, and then an improved angle-penalized distance (APD) method is used to balance the convergence and diversity of the algorithm. The improved RVEA algorithm is applied to the scheduling model proposed in this paper, Different operational scheduling scenarios are investigated, scheduling results with different optimization objectives are discussed. The results show that the multi-strategy RVEA algorithm proposed in this paper can improve the economical and environmentally friendly protection of the microgrid system, and promote the optimal operation of the microgrid.

Research on deep peaking cost allocation mechanism considering peaking demand subject and thermal power unit

The current peak-shaving auxiliary service cost allocation mechanism balances the revenue and expenditure of the power plant side, the peak-valley difference of the power grid is mainly caused by the peak-valley characteristics of the load and renewable energy. It is not reasonable that not reasonable the peak-shaving cost allocation mechanism does not consider user allocation. Therefore, this paper establishes a peak-shaving cost allocation mechanism that considers the peak-shaving demand subject, this article incorporates renewable energy power plants and loads into the peak-shaving demand subject. Firstly, this paper constructs an alternative scenario without peak-shaving demand through the mean line of load and available energy output and establishes an optimal scheduling model with deep peak-shaving and pumped storage power stations, this article takes the difference of system peak-shaving cost between two scenarios with and without peak-shaving demand as the marginal peak-shaving cost. Then, the Shapley value method in cooperative game is used to establish the peak-shaving cost allocation mechanism considering the main body of peak-shaving demand. In addition, It is used that the equal-kWh following load method constructed the equivalent output curve of renewable energy, following load method improves the existing allocation mechanism by comprehensive similarity. Finally, this paper analyzes and compares three different peaking cost allocation mechanisms. The example analysis shows that the peaking cost allocation mechanism considering peaking demand subjects. The example can reflect the peaking cost caused by different subjects and reduce the peaking pressure of thermal power units.

Intelligent Energy System and Power Metering Optimization Based on the Energy Plan Model

With the national carbon neutrality and carbon peaking policies proposed one after another, this paper proposes intelligent Energy system design and power metering based on the Energy Plan model. In this paper, the Energy PLAN model is proposed as the basic model for energy development planning during the 14th Five-Year Plan period, and the simulation calculation is carried out for the energy system in 2020. The research shows that there is a large gap between the peak and valley of power load, the peak time of power supply and demand does not match, and the response-ability of the energy supply department is poor. Thus, the problems to be solved in the development of energy are determined. In addition, the accuracy of the model is further verified by comparing the actual data with the simulation results of Energy PLAN, and the direction of energy structure reform is determined. By coupling the NSGA-II (Non-dominated Sorting Genetic Algorithms-II) algorithm with the development model, a dual-objective multi-time scale scheduling model is established, which aims at the minimum operating cost and the best energy and environmental benefit coefficient. Secondly, the proposed model is solved using the NSGA-II algorithm. In order to ensure the diversity of solutions and promote the Pareto front approach to the ideal Pareto front, the fuzzy dominance method is used to perform a fast non-dominated sorting of the population, and the maximum satisfaction method is used to select the Pareto optimal compromise solution. Example analysis shows that when the power demand level is L*-L*-L*, the power input under “H-M”, “M-L” and “H-L” scenarios is 106.02 × 103, 102.73 × 103 and 94.2 × 103 GWh, respectively. However, when the power demand level is H*-L*-L*, the power input is always 142.64 × 103 GWh. With the decrease in the transmission rate, the measurement data obtained by the user’s electric acquisition platform becomes less and less, and the performance of state estimation becomes worse. When λ=50%, the proposed model still maintains a high estimation accuracy.

Distributionally robust optimal dispatching method for integrated energy system with concentrating solar power plant

Concentrating solar power (CSP) plants have significant potential to complement the growing wind energy in power scheduling. This study examines an integrated energy system (IES) that incorporates a wind turbine (WT), CSP, and combined heat and power (CHP) to promote the utilization of renewable energy (RE), reduce fluctuations caused by uncertainty, and enhance the economic viability of the system. We propose a distributionally robust optimization (DRO) model for IES scheduling that considers the uncertainty of wind power by using an ambiguity set defined by the Wasserstein metric. Before the occurrence of uncertainties, the system determines the initial dispatching scheme based on the forecast data. In the second stage, the system aims to minimize the adjustment cost expectation under the worst distribution of the ambiguity set and adjusts the flexible resources in real time to offset the fluctuations caused by forecasting errors. The proposed DRO model is transformed into a conventional two-stage robust problem using strong dual theory and KKT conditions, and then solved with a modified column-and-constraint generation (C&CG) algorithm. The results of case studies show that the CSP plant enhances the system's flexibility and controllability through thermal energy storage (TES).

Research on a multiobjective cooperative operation scheduling method for agricultural machinery across regions with time windows

This article aims to solve the problems of high cost, low efficiency, and task delay caused by the lack of scientific scheduling strategies during the agricultural harvest season. Considering the constraints of operation time windows, a multimachine multiobjective cross-regional collaborative operation scheduling model is constructed with the goal of minimizing the transfer distance of agricultural machinery and nonoperational scheduling costs. A multiobjective genetic algorithm (HTSMOGA) based on a hybrid time window priority rule and a tabu search strategy is designed. The initial population is generated by utilizing time window priority rules, genes with earlier operation start times are preferentially retained, and a mixed search strategy is introduced to avoid local optimal solutions. Experiments were conducted among 24 farms in a region of Hebei Province, and the results showed that, compared with other algorithms, the HTSMOGA achieved an average reduction of 17.18 % in the transfer distance of agricultural machine operations and 19.36 % in the nonoperational waiting time. Therefore, this study provides a rational and feasible solution for the cross-regional operation scheduling of agricultural machinery cooperatives and provides theoretical support for ensuring the timely completion of harvesting tasks and the cost savings and efficiency of agricultural machinery operations.

Novel integer L-shaped method for parallel machine scheduling problem under uncertain sequence-dependent setups

We study scheduling problems on unrelated parallel machines with uncertainty in job processing and sequence-dependent setup times. We first formulate this problem as a two-stage stochastic program using a scenario-based approach, in which the first-stage decisions involve job-machine assignments, and the second-stage deals with job sequencing. We then propose an innovative modified integer L-shaped method, uniquely designed to decompose the primary problem into independent subproblems at both the scenario and machine levels. We have augmented this method with a specialized integer optimality cut, which is further strengthened using a sequential lifting procedure. To broaden the applicability of our model and solution, we have adapted it to a distributionally robust optimization (DRO) framework under Wasserstein ambiguity. This process involves reformulating the DRO model into a mixed-integer linear program (MILP) using conic duality theory. Additionally, we integrate a distribution separation program to define the worst-case probability distribution during each iteration of the proposed integer L-shaped method, enhancing the computational speed for solving the DRO model. We conducted a series of numerical experiments to illustrate the effectiveness and scalability of the proposed solution methodology, which demonstrated highly promising results. Our innovative decomposition algorithms showed superior performance, achieving solution times that are significantly faster (up to two orders of magnitude) compared to the traditional integer L-shaped method for the stochastic scheduling model and the direct MILP reformulation of the DRO model. These results were obtained for problem instances solved to the exactness within the time limit. Moreover, the solution derived from the DRO model exhibited greater robustness in out-of-sample analysis with simulated data compared to the solution obtained from the stochastic model.

365‐day sectional work zone schedule optimization for road networks considering economies of scale and user cost

AbstractThis study proposes a methodology for deriving the optimal work zone schedule for the annual routine maintenance planning in an infrastructure asset management system considering the (i) economies of scale in work zone costs due to work zone synchronization and (ii) user costs across the road network with traffic assignments. A key aspect of the proposed methodology is the ability to derive in detail optimal work zone schedules of realistic‐scale road networks in 100 m sections for 365 days, which is beneficial in practice. To this end, an optimization model of the work zone schedule is newly formulated as a mixed‐integer programming (MIP) problem, and a novel bilevel solution method for the model utilizing the conventional solver Gurobi Optimizer with MIP algorithms such as root relaxation, dual simplex, barrier methods, and cutting planes is proposed. In application examples, the proposed methodology is applied to two real‐world road networks, which confirms that the optimal work zone schedule can be obtained in 313.7 s for a network with 2640, 100 m, sections and in 168,180 s (46 h and 43 min) for a network with 5038, 100 m, sections.

Economic Optimal Scheduling of Integrated Energy System Considering Wind–Solar Uncertainty and Power to Gas and Carbon Capture and Storage

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system’s operating cost, and the introduction of P2G-CCS can effectively help the park’s integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system’s operating costs and improve the economic benefits of the park.

Optimal Scheduling of a Cascade Hydropower Energy Storage System for Solar and Wind Energy Accommodation

The massive grid integration of renewable energy necessitates frequent and rapid response of hydropower output, which has brought enormous challenges to the hydropower operation and new opportunities for hydropower development. To investigate feasible solutions for complementary systems to cope with the energy transition in the context of the constantly changing role of the hydropower plant and the rapid evolution of wind and solar power, the short-term coordinated scheduling model is developed for the wind–solar–hydro hybrid pumped storage (WSHPS) system with peak shaving operation. The effects of different reservoir inflow conditions, different wind and solar power forecast output, and installed capacity of pumping station on the performance of WSHPS system are analyzed. The results show that compared with the wind–solar–hydro hybrid (WSH) system, the total power generation of the WSHPS system in the dry, normal, and wet year increased by 10.69%, 11.40%, and 11.27% respectively. The solar curtailment decreased by 68.97%, 61.61%, and 48.43%, respectively, and the wind curtailment decreased by 76.14%, 58.48%, and 50.91%, respectively. The high proportion of wind and solar energy connected to the grid in summer leads to large net load fluctuations and serious energy curtailment. The increase in the installed capacity of the pumping station will promote the consumption of wind and solar energy in the WSHPS system. The model proposed in this paper can improve the operational flexibility of hydropower station and promote the consumption of wind and solar energy, which provides a reference for the research of cascade hydropower energy storage system.