Economic Scheduling Research Articles

Two-Stage Locating and Capacity Optimization Model for the Ultra-High-Voltage DC Receiving End Considering Carbon Emission Trading and Renewable Energy Time-Series Output Reconstruction

With the load center’s continuous expansion and development of the AC power grid’s scale and construction, the recipient grid under the multi-feed DC environment is facing severe challenges of DC commutation failure and bipolar blocking due to the high strength of AC-DC coupling and the low level of system inertia, which brings many complexities and uncertainties to economic scheduling. In addition, the large-scale grid integration of wind power, photovoltaic, and other intermittent energy sources makes the ultra-high-voltage (UHV) DC channel operation state randomized. The deterministic scenario-based timing power simulation is no longer suitable for the current complex and changeable grid operation state. In this paper, we first start with the description and analysis of the uncertainty in renewable energy (RE) sources, such as wind and solar, and reconstruct the time-sequence power model by using the stochastic differential equation model. Then, a carbon emission trading cost (CET) model is constructed based on the CET mechanism, and the two-stage locating and capacity optimization model for the UHV DC receiving end is proposed under the constraint of dispatch safety and stability. Among them, the first stage starts with the objective of maximizing the carrying capacity of the UHV DC receiving end grid; the second stage checks its dynamic safety under the basic and fault modes according to the results of the first stage and corrects the drop point and capacity of the UHV DC line with the objective of achieving safe and stable UHV DC operation at the lowest economic investment. In addition, the two-stage model innovatively proposes UHV DC relative inertia constraints, peak adjustment margin constraints, transient voltage support constraints under commutation failure conditions, and frequency support constraints under a DC blocking state. In addition, to address the problem that the probabilistic constraints of the scheduling model are difficult to solve, the discrete step-size transformation and convolution sequence operation methods are proposed to transform the chance-constrained planning into mixed-integer linear planning for solving. Finally, the proposed model is validated with a UHV DC channel in 2023, and the results confirm the feasibility and effectiveness of the model.

Captura del pulpo en Campeche desde una perspectiva socioeconómica

he study was carried out in San Francisco de Campeche to analyze the Octopus maya fishery. Specialized literature was review followed by surveys with open and closed questions to local fishermen at the “7 de Agosto” pier. Data on economic characteristics, fishing methods and schedules were collected. The responses were analyzed and octopus fishing data of the years 2019-2022 were compared by downloading information from CONAPESCA. Results revealed that 100% of the fishermen surveyed fish Octopus maya, 44% are affiliated to cooperatives, 67% do not carry out other additional economic activities, and 45% rent boats. Patterns were observed in fishing times and months. CONAPESCA data analysis showed variations in octopus captures over the years. Regarding the characterization, it was found that 44% are affiliated with a fishing cooperative, and 67% do not engage in economic activities other than fishing. Additionally, 45% rent their boats, meaning they are not owners. Typically, there are 2 people on the boat, although one interviewee mentioned carrying up to 6 crew members. Furthermore, fuel usage is a crucial factor for fishermen to carry out their activities, with the required amount ranging from 40 to 80 liters. Finally, the distance covered by the boats can reach up to 50 nautical miles. Keywords: Octopus maya, overexploitation, artisanal fishing.

A Novel Day-Ahead Optimization-Oriented Low-Carbon Economic Scheduling Scheme for Integrated Energy Systems

As the global energy structure undergoes transformation and the low-carbon development process continues to advance, integrated energy systems have progressively emerged as crucial technical support for achieving sustainable development. In this paper, a joint-day optimal scheduling model is put forward considering the existence of dispatchable resources in community integrated energy systems (CIES). The aim is to cut down the system operation cost and enhance energy utilization efficiency. This model is founded on the concept of energy hubs and combines the shiftable, transferable, and reducible characteristics of demand-side flexible loads. It includes gas turbine power generation systems, energy storage, as well as wind and solar renewable resources. System operation cost and carbon trading cost are comprehensively taken into account, and ultimately, the CIES low-carbon economic dispatch model with the lowest total cost as the optimization objective is established. The Yalmip toolbox and Cplex solver are employed to solve the model. The optimization results of flexible electric and thermal loads participating in dispatching under different scenarios are analyzed through simulation. The economic benefits of electric and thermal independent dispatching are compared and analyzed, and the economic benefits of electric and thermal coupled dispatching are verified. The study reveals that the rational scheduling of user-side flexible loads can notably reduce operating costs, lower the load peak-to-valley difference and carbon emissions, and boost the comprehensive economic and environmental benefits of the system.

Research on cargo volume prediction and personnel scheduling optimization of sorting center based on BP neural network and improved genetic algorithm

With the development of Internet technology, e-commerce has become the primary choice for consumers to shop, and the huge order transaction volume has generated logistics problems. As an important part of the logistics network, the sorting platform is essential to improve its management efficiency. To enhance the efficiency of the sorting center, the topological relationship diagram is used to show the logistics network, and the cargo volume of the sorting center in the next 30 days and every hour is predicted based on the BP neural network model. Based on the mixed integer linear programming model and the improved genetic algorithm SCAGA model, the total man-days are minimized to ensure that the cargo volume is processed. At the same time, to ensure that the actual hourly efficiency is as balanced as possible, the optimization problem of personnel scheduling is studied. To maintain the efficient operation of the logistics industry, the accurate prediction of the number of goods and the reasonable scheduling of personnel, to provide a set of efficient and economical personnel scheduling programs for the logistics company, thus improving the overall operational efficiency and service quality.

Low carbon operation and evaluation methods for integrated energy system counting EVs with whole life cycle considering demand response

The integrated energy system has gradually become an effective way of energy saving and emission reduction with the proposal of “dual carbon" target and the continuous promotion of low-carbon development. However, most of the existing studies on demand-side flexible loads only consider flexible power loads, and there are fewer studies on the controllability of thermal loads. This paper proposes a low-carbon and economic optimal scheduling model and evaluation method for integrated energy system considering demand-side response. With the goal of minimizing the total operating cost of the integrated energy system, and taking into account the transferable and curtailing characteristics of the electric and thermal flexible loads, we establish an optimal scheduling model of the integrated energy system considering the user-side flexible loads, and comprehensively evaluate the performance of the system in terms of economic performance, reliability, and environmental performance. The performance of the system is evaluated in terms of economic performance, reliability and environmental performance. The role of flexible loads in improving the economy of the combined supply system is investigated with examples, and the rationality and effectiveness of the study are verified through comparative analysis of different scenarios. The results show that the total cost of the system is reduced by 18.04 %, 9.1 %, 3.35 % and 7.03 % when carbon trading cost and demand-side flexible electric-heat load response are considered at the same time; and the total carbon emission of the system is reduced by 65.28 %, 20.63 %, 3.85 % and 18.03 %, respectively. Considering carbon trading costs and demand-side flexible electric and thermal load response can improve the reliability of the system. The overall reliability of the system is improved by 10 %, 15 %, 16.67 % and 20 % for the four scenarios, respectively.

Research and application of low-carbon economic dispatch of urban building integrated energy system

Abstract Global warming is a serious threat common to all countries. The technological revolution and sustainable development of energy have become the international consensus. While ensuring economic benefits, promoting low carbon environmental protection of comprehensive energy system has become a top priority. Therefore, this paper designs a low-carbon economic scheduling system based on consistency theory. First, the importance of user demand response should be considered in the integrated energy system to ensure the economy of scheduling. Secondly, the improved step-step carbon trading mechanism is used in the algorithm to improve the low carbon performance of scheduling. Then, the distributed consistency algorithm is used to schedule the integrated energy system to realize the most energy output of each unit. In addition, a noise function is added to the algorithm for encryption and desensitization to prevent user privacy leakage. Finally, the effectiveness of the proposed economic scheduling method.

ECONOMIC ANALYSIS OF POWER PLANT USING UNIT COMMITMENT SCHEDULING

Unit commitment is the scheduling of disconnected generation of generating units in an electric power system within a certain period, with the aim of meeting load demand. This scheduling is usually determined based on human load demand which always changes over time. This research aims to determine the production costs of thermal plants using the Second Order Gradient method to obtain economical operating costs, to determine fuel costs based on plant loading, and to determine the most economical scheduling using unit commitment. This is to determine the most optimal on/off schedule for generating units to be operated to meet the estimated load to achieve minimum fuel costs in rupiah.

A Master–Slave Game Model of Electric Vehicle Participation in Electricity Markets under Multiple Incentives

In order to achieve low carbon emissions in the power grid, the impact of new energy grid connections on the power grid should be reduced, as well as the peak-to-valley load difference caused by large-scale electric vehicle grid connections. This paper proposes a two-tier, low-carbon optimal dispatch master–slave game model involving virtual power plant operators as well as electric vehicle operators. Firstly, the carbon flow is tracked based on the proportional sharing principle, and the carbon emission factor during the charging and discharging process of electric vehicles is calculated. Secondly, the node carbon potential and time-sharing tariff are used to guide and change the charging behaviour of electric vehicles and to construct a master–slave game model for low-carbon optimal scheduling with the participation of multiple subjects, with economic scheduling at the upper level of the model and demand response scheduling at the lower level. Finally, the IEEE30 node system is used as an example to verify that the method adopted in this paper can effectively reduce the peak-to-valley difference of loads, reduce the carbon emissions of the grid, and reduce the cost of each participating entity.

Economic Scheduling Strategy for Multi-Energy-Integrated Highway Service Centers Considering Carbon Trading and Critical Peak Pricing Mechanism

This study proposes an optimized economic scheduling strategy for multi-energy-integrated highway service centers (MEIHSCs) within a 24 h operational timeframe. With the imperative of carbon peaking and carbon neutrality, highway areas are increasingly incorporating renewable energy systems, such as photovoltaic arrays, to capitalize on abundant resources along highways. Considering the diverse load demands of new energy vehicles and the mismatch between energy supply and demand on the highway, MEIHSCs must adapt to these trends by establishing integrated networks for electricity, natural gas, and hydrogen refueling. However, there is a lack of coordination between equipment switching and the phases of low electricity prices and peak renewable energy periods. To address this challenge and improve economic efficiency, this study proposes an economic dispatch strategy that combines economic incentives based on carbon trading and critical peak pricing mechanisms. This strategy aims to maximize economic benefits while fully meeting the load demands of new energy vehicles. Case studies indicate that operating costs are reduced by 28.04% compared to strategies without new energy installations, and by 47.85% compared to strategies without optimization. The results demonstrate that this integrated and optimized strategy significantly reduces energy costs and enhances economic benefits in highway service centers.

Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions

Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions

Uncertain Scheduling of the Power System Based on Wasserstein Distributionally Robust Optimization and Improved Differential Evolution Algorithm

The rapid development of renewable energy presents challenges to the security and stability of power systems. Aiming at addressing the power system scheduling problem with load demand and wind power uncertainty, this paper proposes the establishment of different error fuzzy sets based on the Wasserstein probability distance to describe the uncertainties of load and wind power separately. Based on these Wasserstein fuzzy sets, a distributed robust chance-constrained scheduling model was established. In addition, the scheduling model was transformed into a linear programming problem through affine transformation and CVaR approximation. The simplex method and an improved differential evolution algorithm were used to solve the model. Finally, the model and algorithm proposed in this paper were applied to model and solve the economic scheduling problem for the IEEE 6-node system with a wind farm. The results show that the proposed method has better optimization performance than the traditional method.

Optimized scheduling of integrated energy systems for low carbon economy considering carbon transaction costs

With the introduction of the “dual carbon” goal and the continuous promotion of low-carbon development, the integrated energy system (IES) has gradually become an effective way to save energy and reduce emissions. This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs. With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads, an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established. The role of flexible loads in improving the economy of an energy system was investigated using examples, and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios. The results showed that the total cost of the system in different scenarios was reduced by 18.04%, 9.1%, 3.35%, and 7.03%, respectively, whereas the total carbon emissions of the system were reduced by 65.28%, 20.63%, 3.85%, and 18.03%, respectively, when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously. Flexible electrical and thermal loads did not have the same impact on the system performance. In the analyzed case, the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account. Photovoltaics have an excess of carbon trading credits and can profit from selling them, whereas other devices have an excess of carbon trading and need to buy carbon credits.

Low‐carbon economic schedule of the H2DRI‐EAF steel plant integrated with a power‐to‐hydrogen system driven by blue hydrogen and green hydrogen

AbstractHydrogen direct reduction iron coupled with electronic arc furnace (H2DRI‐EAF) technology, as an important technology for decarbonisation in the iron and steel industry, has the advantages of high electrification and low carbon emissions. However, the large demand for hydrogen in this technology relies significantly on the production of electrolytic hydrogen, leading to a substantial increase in power consumption in the steel production process. Moreover, the use of an unclean power source in electrolytic hydrogen production leads to increases in indirect carbon emissions, reducing the low‐carbon attributes of the technology. This study investigates the integrated flexible operation mode of a steel plant. An illustrating method is utilised for modelling the entire steel production process and power to hydrogen (PtH2) process in detail for the H2DRI‐EAF steel plant, which includes natural gas, photovoltaic, wind power self‐provided power plants, and carbon capture and storage (CCS) systems. A mixed integer linear programming (MILP) model is developed for the comprehensive scheduling of the steel mill. The results of the case studies indicate that by reliably integrating the production of renewable energy and natural gas power plants, the PtH2 system can fully consume the renewable energy output while ensuring the smooth progress of steel production and maximising the reduction of carbon emissions from hydrogen production and the total cost of steel production.

Economic and risk based optimal energy management of a multi-carrier energy system with water electrolyzing and steam methane reform technologies for hydrogen production

This paper proposes stochastic programming to optimal economic scheduling problems of a sustainable integrated energy system with renewable resources including power, natural gas, and hydrogen carriers. Hydrogen is considered a byproduct of electrical power and natural gas carriers using water electrolysis and steam methane reform process technologies. The main objective is to minimize the operation and emission costs while addressing uncertainties of electric/heating/cooling demands, electricity price, and renewable generation using scenario-based stochastic programming. To achieve a risk-hedging strategy, downside risk constraints are involved to minimize the risky scenarios portfolio. The whole problem is modeled as mixed-integer linear programming in GAMS optimization software. The analysis revealed that the expected operation cost without DRC is $ 577.52 and the expected risk-in-cost value for risky scenarios is $ 470.7. However, a conservative decision for reaching zero risk is costly and increases the expected cost up to $ 2901.1, which is not economically viable. A modest decision-making strategy compromises the cost and risk values where for decreasing the risk-in-cost to $ 288.7 (36.8%), the expected cost increased by to $1010.39 (75.95%). Moreover, the risk-averse decision-maker lowers the power sold to the grid, increases the use of water electrolyzing instead of the SMR process to reduce the environmental charges, and tries to increase flexibility with the help of hydrogen and battery storage.

Economic optimization scheduling of virtual power plants considering an incentive based tiered carbon price

Against the backdrop of China's carbon peak and carbon neutrality goals, the use of virtual power plants for electricity economic dispatch has gradually become a research hotspot. The virtual power plant includes both supply and demand sides. If the inherent conflicts of interest between both parties cannot be effectively addressed, there are significant hidden dangers in the sustainable operation of the virtual power plant. On the basis of existing research, this article constructs a regional virtual power plant. With the goal of maximizing supply and demand utility, a two-stage Stackelberg dynamic game model was established between virtual power plant operators and users in the electricity market and carbon trading market mechanisms, and an incentive based hierarchical carbon price constraint was designed in the optimization model. In the model constructed in this article, the operator of a virtual power plant will consider environmental benefits based on expected returns, adjust the output of controllable units to change the electricity supply strategy, and formulate a pricing strategy. At the same time, users will adjust their electricity consumption strategy based on their own electricity consumption characteristics, and both will achieve a game equilibrium. Based on the optimization results, this article conducted a sensitivity analysis on the incentive coefficient of graded carbon prices and obtained the optimal incentive coefficient under the case conditions. The research results indicate that the model can coordinate and meet the interests and needs of both the power generation and consumption sides, and the implementation of incentive based hierarchical carbon pricing strategies has certain advantages compared to traditional carbon pricing strategies in different user types scenarios. The research conclusion of this article has good reference value for the sustainable green operation of virtual power plants.

Renewable energy utilization and stability through dynamic grid connection strategy and AI-driven solution approach

The connection of renewable energy sources such as wind and solar power into the power grid can significantly reduce both costs and pollution emissions. However, the variability, volatility, and anti-peak regulation characteristics of renewable energy pose significant challenges for power system dispatch. This paper proposes a hybrid economic emission dispatch model (HDEED) for wind–solar–thermal-storage systems, with operational cost and pollution emission as objective functions. The study aims to develop optimal grid-connection strategies for clean energy by utilizing the energy-shifting capability of energy storage systems. This includes strategies based on optimal load fluctuation and optimal operation income for new energy stations. A generalized load fluctuation coefficient is proposed to assess load fluctuations after wind and solar energy integration, comparing and analyzing the performance of energy storage power stations with varying capacities. In terms of algorithm development, the paper proposes the Pelican optimization algorithm with a clustering strategy (POA-CS), specifically tailored to address the complexities of economic emission scheduling. The effectiveness of the proposed strategy and algorithm is validated using an enhanced IEEE-39 bus test system. Results indicate that the generalized load fluctuation coefficient under the optimal grid-connected strategy based on load fluctuation is 21% lower than that of direct grid connection of wind power and photovoltaic, leading to a significant reduction in net load fluctuation. Furthermore, under the optimal grid-connected strategy based on the operation income of new energy stations, the revenue of these plants increased by 22.40% compared to direct grid connections of wind power and photovoltaic systems. The POA-CS algorithm demonstrates superior performance, continuity, and smoothness in obtaining the Pareto optimal boundary under consistent testing conditions.

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.

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.

Economic Energy Scheduling of electrical micro grid considering optimal participation of the electric vehicles

This research presents a strategy for managing energy scheduling within an electrical microgrid, with a specific focus on enhancing the integration of electric vehicles (EVs). By incorporating Monte Carlo simulation to address uncertainties related to EV charging power and demand-side variables, the study aims to ensure precise outcomes. The economic energy scheduling is conducted on a day-ahead basis, taking these uncertainties into consideration to assess the efficiency of the recommended approach. The primary objective is to reduce the overall system costs, encompassing operational expenditures and EV charging power. To tackle the intricacies of the operational framework, the study utilizes the modified sunflower optimization (MSFO) algorithm to resolve the outlined issue. The simulation findings highlight the superior performance of the proposed optimization algorithms compared to others. The proposed approach leads to minimize cost of microgrid by 4.31%, 3.82% and 1.87% than genetic algorithm (GA), Particle swarm optimization (PSO) algorithm and Teaching learning-based optimization (TLBO) algorithm, respectively.

Optimization and scheduling scheme of park-integrated energy system based on multi-objective Beluga Whale Algorithm

To improve the consumption rate of renewable energy and ensure the stability of power and heat supply within the industrial park, a park-integrated energy system (PIES), which incorporates electric heating equipment, energy storage devices, and multiple micro sources, is established. Based on the established PIES, a price-based demand response (PDR) mechanism is introduced to establish a multi-objective optimization and scheduling model with two time scales: day-ahead scheduling and intra-day scheduling, aiming to minimize the impact on the main grid and ensure the flexibility of system operation and real-time scheduling. With the objectives of minimizing operating costs and maximizing environmental benefits for PIES, a hybrid algorithm named Non-Dominated Sorting Beluga Whale Optimization (NSBWO), which combines the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Beluga Whale Optimization (BWO) algorithm is proposed to solve the optimization problem of the model. Simultaneously, an adaptive penalty function is introduced in the algorithm to handle constraint problems in optimization and additional penalty term for wind and solar curtailment is included within the constraints to improve the consumption rate of sustainable energy. The proposed NSBWO algorithm exhibits more robust search capabilities and faster convergence speed than the NSGA-II and MOPSO algorithm. Simulation results show that the proposed NSBWO algorithm can achieve low-carbon economic scheduling of the system in both long-term and short-term time scales and improve renewable energy consumption. Due to its flexibility, intra-day scheduling has lower operating costs than day-ahead scheduling.