Economic Scheduling Model Research Articles

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.

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.

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

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

Optimal Scheduling of Integrated Energy System Considering Hydrogen Blending Gas and Demand Response

In the context of carbon neutrality and carbon peaking, in order to achieve low carbon emissions and promote the efficient utilization of wind energy, hydrogen energy as an important energy carrier is proposed to mix hydrogen and natural gas to form hydrogen-enriched compressed natural gas (HCNG). It is also injected into the natural gas pipeline network to achieve the transmission and utilization of hydrogen energy. At the same time, the participation of demand response is considered, the load’s peak and trough periods are adjusted, and the large-scale consumption of renewable energy and the reduction in carbon emissions are achieved. First of all, a fine model of hydrogen production and hydrogen use equipment is established to analyze the impact of adding hydrogen mixing on the economy and the low-carbon property of the system. With green certificates and demand response, the utilization rate of hydrogen energy is improved to further explore the energy utilization rate and emission reduction capacity of the system. Secondly, on the basis of modeling, the optimal scheduling strategy is proposed with the sum of energy purchase cost, equipment operation cost, carbon emission cost, wind curtailment cost, and green certificate income as the lowest objective function. Considering the constraints such as hydrogen blending ratio and flexible load ratio of the pipeline network, a low-carbon economic scheduling model of hydrogen mixed natural gas was established. The model was linearized and solved by using MATLAB 2021a and CPLEX solver. By comparing different scenarios, the superiority of the model and the effectiveness of the strategy are verified.

Low-carbon optimal operation of the integrated energy system considering integrated demand response and oxygen-rich combustion capture technology

In view of the current operating constraints and environmental pollution problems of traditional units, in this article, oxygen-rich combustion capture technology is introduced to transform gas-fired units, demand response technology is used on the load side, the energy conversion equipment such as power-to-gas equipment is combined to form an integrated energy system, and then, a low-carbon optimization approach of the integrated energy system is proposed. First, the system architecture is constructed, and a model with an oxygen-rich combustion unit and integrated demand response is established. Second, a power-to-gas equipment model considering reaction waste heat utilization and oxygen recovery is established. Finally, a stepped carbon trading mechanism is introduced to establish a low-carbon economic scheduling model for the integrated energy system with the goal of minimizing the operating cost of the integrated energy system. The simulation results show that the total cost and carbon emissions of the integrated energy system are reduced by 6.44% and 44.24%, respectively, under this model. At the same time, the operation adjustment capability and the oxygen production efficiency of the internal units of the system are improved.

Research on optimization of multi-energy coupling source-network-load-storage based on micro energy network

Based on the architecture of the micro energy network system, a “source network load storage” multi-energy coupling hub system for regional energy internet with electricity gas interconnection is proposed. The lowest operating is set as the optimization goal for the operation of the regional energy internet. A dynamic economic scheduling model for regional energy interconnection based on the sky and an optimization scheduling model for regional energy internet are established. A mixed integer linear programming program based on Matlab is written and solved using CPLex. The results show the operation of the “source grid load storage” regional energy internet with multiple energy coupling hubs as the core is lower than that of traditional micro-grids When adding an energy storage system, the daily operating of the micro-grid system is 16.7% less than that of Control Scenario 1 and 14.3% less than that of Control Scenario 2 The proposal of a regional energy internet “source network load storage” multi-energy coupling hub system based on the micro energy network system architecture provides a feasible basis for the planning and construction research of the regional energy internet.

Low-carbon environment-friendly economic optimal scheduling of multi-energy microgrid with integrated demand response considering waste heat utilization

- The integration of multi-energy sources in microgrids offers a promising approach to address the challenges of energy efficiency and bolster environmental stewardship. This paper presents a novel optimization scheduling model for multi-energy microgrids (MEMG) with carbon capture and storage (CCS) technology in various renewable energy scenarios. The model effectively coordinates integrated demand response (IDR) and the flexible operation of waste heat utilization, enhancing energy utilization efficiency while ensuring user satisfaction. Furthermore, introducing flexible operation combined with ladder-type carbon trading minimizes operating costs and carbon emissions. The improved particle swarm optimization (PSO) algorithm is employed to solve the eco-economic economic scheduling model. Simulation analysis shows that integrating CCS into MEMG can reduce carbon emissions by 74.4%, while lowering the overall cost by 37.9%. These findings not only improve MEMG's low-carbon and economic efficiency but also pave the way for environmentally responsible energy management practices.

Low carbon economic scheduling model for a park integrated energy system considering integrated demand response, ladder-type carbon trading and fine utilization of hydrogen

With the intensification of energy crisis and the aggravation of greenhouse effect, It is particularly essential to develop a sustainable energy system. For this reason, a low-carbon economic dispatch model for a park integrated energy system considering integrated demand response and ladder-type carbon trading is designed. First, a refined model of hydrogen utilization is developed, and hydrogen-enriched natural gas as hybrid energy to extend the scenario of hydrogen utilization. Second, in order to investigate the effect of demand response mechanism and carbon trading mechanism on the system, an integrated demand response model for electricity-heat-gas load and a ladder-type carbon trading model that takes into account the carbon emissions of the whole process are constructed respectively. Finally, based on above models, a low-carbon economic scheduling model is established to optimize the total cost of the system. The simulation results show that the refined modeling and hydrogen-doped operation of the equipment can help reduce the system's carbon emissions. When the integrated demand response mechanism and the ladder-type carbon trading mechanism are considered together, the carbon emissions of the system are greatly reduced, while its total cost can be kept low.

Low-Carbon Optimal Scheduling of Integrated Energy System Considering Multiple Uncertainties and Electricity–Heat Integrated Demand Response

To realize the low-carbon operation of integrated energy systems (IESs), this paper proposes a low-carbon optimal scheduling method. First of all, considering the integrated demand response of price-based electricity and heating, an economic scheduling model of the IES integrated demand response based on chance-constrained programming is proposed to minimize the integrated operating cost in an uncertain environment. Through the comprehensive demand response model, the impact of the demand response ratio on the operating economy of the IES is explored. Afterward, the carbon emission index is introduced, and gas turbines and energy storage devices are used as the actuators of multi-energy coupling to further explore the potential interactions between the coupling capacities of various heterogeneous energy sources and carbon emissions. Finally, the original uncertainty model is transformed into a mixed-integer linear-programming model and solved using sequence operation theory and the linearization method. The results show that the operating economy of the IES is improved by coordinating the uncertainty of the integrated demand response and renewable energy. In addition, the tradeoff between the working economy and reliability of the EIS can be balanced via the setting of an appropriate confidence level for the opportunity constraints.

Economic dispatch of community-integrated energy system considering demand-side coordinated response

There are a large number of potential schedulable resources in the integrated energy system of electricity, heat, cold, and gas. However, most of these energy sources are currently operated separately, with low system flexibility, low energy utilization rate, and serious abandonment of wind and solar energy. In order to improve the flexibility of integrated energy systems and the capacity of renewable energy consumption, an economic dispatch of community-integrated energy systems considering demand-side coordinated response is proposed. Firstly, according to various energy characteristics, mathematical models of various energy forms are established, including wind energy, photovoltaic, gas turbine, gas boiler, and other component characteristics modeling. Secondly, an economic optimal scheduling model of community-integrated energy system considering demand side response is established, including the constraints and objective functions of the optimization model, and the optimization model is solved based on the Yalmip toolbox and Cplex solver in Matlab software. Finally, the effectiveness of the proposed strategy is verified by a simulation example.

Dynamic Stepwise Carbon Trading Game Scheduling with Accounting and Demand-Side Response

Abstract With the help of cooperative game theory, this study constructs an integrated demand response economic game scheduling model for carbon trading, aiming to maximize collective and individual benefits. The model effectively reduces the system operating cost by adjusting the electricity price to incentivize PDR-VGU output. The impacts of price demand and integrated flexible operation demand on the coordination degree of carbon trading and the optimized dispatch results under different scenarios are analyzed in depth. It is found that when considering the carbon trading mechanism, the system operating cost and carbon emissions are reduced by RMB 2,129 and 9.63 tons, and RMB 2,350 and 11.96 tons, respectively, showing a win-win situation in terms of economy and environmental protection. In addition, the energy time-shift strategy implemented in the carbon capture power plant system effectively balances the peak-to-valley difference of thermal power output, further reducing the cost.

A fully distributed algorithm for dynamic economic dispatch of cross regional power systems based on alternating direction multiplier method

n this study, a novel approach to dynamic economic dispatch for multi-region power systems is introduced, leveraging the alternating direction multiplier method (ADMM) for computational efficiency. The method begins by dividing an interconnected power system into coherent groups, assigning local processors to each area to estimate black-box transfer function models using Lagrange multipliers. These processors then collaborate to form a global transfer function model through a consensus algorithm, leading to the derivation of a transfer function residue for the optimal wide-area control loop. A wide-area damping controller is designed from this residue, and its effectiveness is validated on two area and IEEE-39 bus test systems using RTDS/RSCAD and MATLAB co-simulation platforms. Simultaneously, the study proposes an economic scheduling model that aims to minimize operational costs while meeting various operational constraints. By severing inter-regional connections, the ADMM enables distributed resolution of the optimization problem, breaking it down into regional sub-problems that are iteratively solved to reach the global optimum. This process eliminates the need for a centralized data repository for multiplier updates, supporting a fully distributed scheduling approach. Additionally, a multi-period optimization technique is integrated to address the power system’s temporal dependencies. The approach's validity is demonstrated through empirical analysis of a tri-regional interconnected system based on the IEEE standard test system, confirming the strategy's effectiveness in economic dispatch.

A bi-objective low-carbon economic scheduling method for cogeneration system considering carbon capture and demand response

Carbon capture and storage (CCS), energy storage (ES), and demand response (DR) mechanisms are introduced into a cogeneration system to enhance their ability to absorb wind energy, reduce carbon emissions, and improve operational efficiency. First, a bi-objective low-carbon economic scheduling model of a cogeneration system considering CCS, ES, and DR was developed. In this model, the ES and CCS remove the coupling between power generation and heating. The DR mechanism, which is based on the time-of-use electricity price and heating comfort, further enhanced the flexibility of the system. In addition, an improved bare-bones multi-objective particle swarm optimisation (IBBMOPSO) was designed to directly obtain the Pareto front of the low-carbon economy scheduling model. The particle position update mode was improved to balance global and local search capabilities in various search stages. The Taguchi method was used to calibrate the algorithm parameters. The inverse generational distance (IGD), hypervolume (HV), and maximum spread (MS) were used to evaluate the distribution and convergence performance of the algorithm. The improved technique for order preference by similarity to an ideal solution (TOPSIS) method was utilised to obtain the optimal compromise solution. Finally, the proposed method was tested on a cogeneration system in Northeast China. According to the comparison results, the average economic cost of the cogeneration system considering CCS, ES, and DR was reduced by approximately 1.13%, and carbon emissions were reduced by 6.79%. The IBBMOPSO is more competitive than the NSGA-II, MOWDO, MOMA, MOPSO, and BBMOPSO in low-carbon economic scheduling for the cogeneration system.

Optimal operation strategy of peak regulation combined thermal power units and concentrating solar power plant with energy conversion and spinning reserve

In recent years, the high percentage of wind power accessibility in Northwest China has worsened the dilemma of peak regulation and spinning reserve in the power system, frequently resulting in wind abandonment. Therefore, a concentrated solar power (CSP) plant equipped with an electric heater (EH) is implemented to join the peak regulation, and the joint peak regulation strategy between thermal power units (TPUs) and a CSP plant is proposed. Firstly, the peak regulation principle of a CSP plant with EH is analyzed in detail. The CSP plant is divided into load mode and power source mode of peak regulation, and mathematical models of the two modes are established. Secondly, the effectiveness of joint peak regulation of TPUs and CSP plants with EH is analyzed, and the principle of low-carbon power supply during peak and off-peak periods is analyzed in the proposed strategy. On this basis, the ladder carbon trading mechanism is introduced to quantify the low-carbon level of the system, and an economic scheduling model considering the operation cost of the system is established to determine the optimal schedule. The results suggest that the proposed scheme can improve the operational efficiency of the system and create more consumptive space for wind power by utilizing the flexible operating potential of a CSP plant.

Optimal Scheduling of Virtual Power Plant Considering Reconfiguration of District Heating Network

A combined heat and power virtual power plant (CHP-VPP) can effectively control the distributed resources in an electric–thermal coupling system and solve the problem of lack of flexibility caused by large-scale renewable energy grid connection. Similar to the optimal reconfiguration of distribution network topology by operating switches, the district heating system is also equipped with tie and sectionalizing valves to realize the optimal adjustment of district heating network (DHN) topology, which provides an economical and effective method for improving the power system’s flexibility. Based on this, this paper proposes a CHP-VPP economic scheduling model considering reconfigurable DHN. Firstly, the energy flow model is introduced to reduce the computational complexity. Secondly, adaptive robust optimization solved by the column-and-constraint generation algorithm is used to settle the randomness of wind power to ensure that the results are feasible in all worst scenarios. Finally, the feasibility of the proposed model is illustrated by case studies based on an actual CHP-VPP. The results show that compared with the reference case, considering the reconfigurability of DHN in the CHP-VPP optimization scheduling process can reduce the cost by about 2.78%.

Data-driven source-load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling

A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed. The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems (IESs) in the operation scheduling problem of integrated energy production units (IEPUs). First, to solve the problem of inaccurate prediction of renewable energy output, an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction. Subsequently, to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs, a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established. The system considers the further utilization of energy using hydrogen energy coupling equipment (such as hydrogen storage devices and fuel cells) and the comprehensive demand response of load-side schedulable resources. The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions, improve the source-load interaction of the IES, realize the efficient use of hydrogen energy, and improve system robustness.

Two-stage stochastic decentralized low-carbon economic dispatch of integrated electricity-gas networks

To achieve low-carbon operation optimization of integrated electricity gas network (IEGN) system with uncertainty, a two-stage stochastic decentralized low-carbon economic scheduling model and its solution method for multi-region interconnection is proposed. Firstly, an IEGN operation cost model considering the stepped carbon emission cost and the benefits of P2G's participation in carbon market transactions is constructed, considering demand-side management (DSM) with user satisfaction and degree of regulation. Secondly, multi-region interconnection is served to overcome single-region space-time limitations with the multi-energy flow, regional decoupling is realized by copying multi-region IEGN boundary nodes, and the scenario method is used to quantify the output uncertainty of IEGN. Hence, a two-stage stochastic optimal scheduling model of multi-region IEGN is established. Then, an improved successive linearization method is proposed for the non-convex transformation. Considering the decentralized autonomy and information privacy of different decision-makers, a decentralized optimization model is established, and the synchronous alternating direction multiplier method (synchronous-ADMM) based on nested improved successive linearization is introduced for the iterative solution. Finally, the feasibility and superiority of the proposed model and algorithm are verified by comparing and analyzing the simulation results of multi-region IEGN under different scenarios and scheduling strategies.

Low-carbon economic scheduling strategy for active distribution network considering carbon emissions trading and source-load side uncertainty

The double uncertainty of the source-load side poses a new challenge to the reliability of the low carbon scheduling in the active distribution network (ADN) system. In this study, a low-carbon economic scheduling model of the ADN system is proposed, which considers step-type carbon emissions trading (CET) mechanism and source-load side uncertainty. The multi-dimensional discrete probability sequences of source-load side are converted into the global equivalent load (EL) by the proposed discretized step transformation (DST) and the convolutional sequence operation (CSO) method, and the predicted value of EL is expressed by the expected value of the EL probability sequence. By setting the probability constraint of the spinning reserve capacity, the influence of power prediction error on the ADN system is fully considered. On this basis, the chance constraint problem (CCP) is further converted to a deterministic mixed-integer linear programming (MILP) problem by linearizing power flow constraints. The proposed strategy is verified based on the IEEE 33-bus ADN system. The experimental results indicate that the system can further realize the synergy of economic benefits and environmental benefits. In addition, compared with other intelligent algorithms, the solution time of the proposed strategy is significantly reduced, and the optimization effect is greatly improved.